Gregory P. Asner

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

This paper contrasts the natural and anthropogenic controls on the conversion of unreactive N2 to more reactive forms of nitrogen (Nr). A variety of data sets are used to construct global N budgets for 1860 and the early 1990s and to make projections for the global N budget in 2050. Regional N budgets for Asia, North America, and other major regions for the early 1990s, as well as the marine N budget, are presented to Highlight the dominant fluxes of nitrogen in each region. Important findings are that human activities increasingly dominate the N budget at the global and at most regional scales, the terrestrial and open ocean N budgets are essentially disconnected, and the fixed forms of N are accumulating in most environmental reservoirs. The largest uncertainties in our understanding of the N budget at most scales are the rates of natural biological nitrogen fixation, the amount of Nr storage in most environmental reservoirs, and the production rates of N2 by denitrification.

The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL, has been used for about sixteen years to study plant canopy spectral and directional reflectance in the solar domain. PROSAIL has also been used to develop new methods for retrieval of vegetation biophysical properties. It links the spectral variation of canopy reflectance, which is mainly related to leaf biochemical contents, with its directional variation, which is primarily related to canopy architecture and soil/vegetation contrast. This link is key to simultaneous estimation of canopy biophysical/structural variables for applications in agriculture, plant physiology, or ecology, at different scales. PROSAIL has become one of the most popular radiative transfer tools due to its ease of use, general robustness, and consistent validation by lab/field/space experiments over the years. However, PROSPECT and SAIL are still evolving: they have undergone recent improvements both at the leaf and the plant levels. This paper provides an extensive review of the PROSAIL developments in the context of canopy biophysics and radiative transfer modeling.

Abstract Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Analyses of various biophysical and biochemical factors affecting plant canopy reflectance have been carried out over the past few decades, yet the relative importance of these factors has not been adequately addressed. A combination of field and modeling techniques were used to quantify the relative contribution of leaf, stem, and litter optical properties (incorporating known variation in foliar biochemical properties) and canopy structural attributes to nadir-viewed vegetation reflectance data. Variability in tissue optical properties was wavelength-dependent. For green foliage, the lowest variation was in the visible (VIS) spectral region and the highest in the near-infrared (NIR). For standing litter material, minimum variation occurred in the VIS/NIR, while the largest differences were observed in the shortwave-IR (SWIR). Woody stem material showed opposite trends, with lowest variation in the SWIR and highest in the NIR. Leaf area index (LAI) and leaf angle distribution (LAD) were the dominant controls on canopy reflectance data with the exception of soil reflectance and vegetation cover in sparse canopies. Leaf optical properties (and thus foliar chemistry) were expressed most directly at the canopy level in the NIR, but LAI and LAD strongly controlled the relationship between leaf and canopy spectral characteristics. Stem material played a small but significant role in determining canopy reflectance in woody plant canopies, especially those with LAI<5.0. Standing litter significantly affected the reflectance characteristics of grassland canopies; small increases in the percentage of standing litter had a disproportionately large affect on canopy reflectance. The structural attributes of ecosystems determine the relative contribution of tissue, canopy, and landscape factors that drive variation in a reflectance signal. Deconvolution of these factors requires an understanding of the sources of variance at each scale (which is ecosystem dependent) as well as an adequate sampling (spectral, angular, and temporal) of the shortwave (400–2500 nm) spectrum.

▪ Abstract Managed grazing covers more than 25% of the global land surface and has a larger geographic extent than any other form of land use. Grazing systems persist under marginal bioclimatic and edaphic conditions of different biomes, leading to the emergence of three regional syndromes inherent to global grazing: desertification, woody encroachment, and deforestation. These syndromes have widespread but differential effects on the structure, biogeochemistry, hydrology, and biosphere-atmosphere exchange of grazed ecosystems. In combination, these three syndromes represent a major component of global environmental change.

Amazon deforestation has been measured by remote sensing for three decades. In comparison, selective logging has been mostly invisible to satellites. We developed a large-scale, high-resolution, automated remote-sensing analysis of selective logging in the top five timber-producing states of the Brazilian Amazon. Logged areas ranged from 12,075 to 19,823 square kilometers per year (+/-14%) between 1999 and 2002, equivalent to 60 to 123% of previously reported deforestation area. Up to 1200 square kilometers per year of logging were observed on conservation lands. Each year, 27 million to 50 million cubic meters of wood were extracted, and a gross flux of approximately 0.1 billion metric tons of carbon was destined for release to the atmosphere by logging.

The PROSPECT leaf optical model has, to date, combined the effects of photosynthetic pigments, but a finer discrimination among the key pigments is important for physiological and ecological applications of remote sensing. Here we present a new calibration and validation of PROSPECT that separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types. These data include leaf biochemical (chlorophyll a, chlorophyll b, carotenoids, water, and dry matter) and optical properties (directional–hemispherical reflectance and transmittance measured from 400 nm to 2450 nm). We first provide distinct in vivo specific absorption coefficients for each biochemical constituent and determine an average refractive index of the leaf interior. Then we invert the model on independent datasets to check the prediction of the biochemical content of intact leaves. The main result of this study is that the new chlorophyll and carotenoid specific absorption coefficients agree well with available in vitro absorption spectra, and that the new refractive index displays interesting spectral features in the visible, in accordance with physical principles. Moreover, we improve the chlorophyll estimation (RMSE = 9 µg/cm2) and obtain very encouraging results with carotenoids (RMSE = 3 µg/cm2). Reconstruction of reflectance and transmittance in the 400–2450 nm wavelength domain using PROSPECT is also excellent, with small errors and low to negligible biases. Improvements are particularly noticeable for leaves with low pigment content.

ABSTRACT Aim We present the first global synthesis of plant canopy leaf area index (LAI) measurements from more than 1000 published estimates representing ∼400 unique field sites. LAI is a key variable for regional and global models of biosphere‐atmosphere exchanges of energy, carbon dioxide, water vapour, and other materials. Location The location is global, geographically distributed. Results Biomes with LAI values well represented in the literature included croplands, forests and plantations. Biomes not well represented were deserts, shrublands, tundra and wetlands. Nearly 40% of the records in the database were published in the past 10 years (1991–2000), with a further 20% collected between 1981 and 1990. Mean (± SD) LAI, distributed between 15 biome classes, ranged from 1.3 ± 0.9 for deserts to 8.7 ± 4.3 for tree plantations, with temperate evergreen forests (needleleaf and broadleaf) displaying the highest average LAI (5.1–6.7) among the natural terrestrial vegetation classes. Following a statistical outlier analysis, the global mean (± SD) LAI decreased from 5.2 (4.1) to 4.5 (2.5), with a maximum LAI of 18. Biomes with the highest LAI values were plantations &gt; temperate evergreen forests &gt; wetlands. Those with the lowest LAI values were deserts &lt; grasslands &lt; tundra. Mean LAI values for all biomes did not differ statistically by the methodology employed. Direct and indirect measurement approaches produced similar LAI results. Mean LAI values for all biomes combined decreased significantly in the 1990s, a period of substantially more studies and improved methodologies. Main conclusions Applications of the LAI database span a wide range of ecological, biogeochemical, physical, and climate research areas. The data provide input to terrestrial ecosystem and land‐surface models, for evaluation of global remote sensing products, for comparisons to field studies, and other applications. Example uses of the database for global plant productivity, fractional energy absorption, and remote sensing studies are highlighted.

Conversion of land to grow crops, raise animals, obtain timber, and build cities is one of the foundations of human civilization. While land use provides these essential ecosystem goods, it alters a range of other ecosystem functions, such as the provisioning of freshwater, regulation of climate and biogeochemical cycles, and maintenance of soil fertility. It also alters habitat for biological diversity. Balancing the inherent trade-offs between satisfying immediate human needs and maintaining other ecosystem functions requires quantitative knowledge about ecosystem responses to land use. These responses vary according to the type of land-use change and the ecological setting, and have local, short-term as well as global, long-term effects. Land-use decisions ultimately weigh the need to satisfy human demands and the unintended ecosystem responses based on societal values, but ecological knowledge can provide a basis for assessing the trade-offs.

The movement of dissolved organic carbon (DOC) through soils is an important process for the transport of carbon within ecosystems and the formation of soil organic matter. In some cases, DOC fluxes may also contribute to the carbon balance of terrestrial ecosystems; in most ecosystems, they are an important source of energy, carbon, and nutrient transfers from terrestrial to aquatic ecosystems. Despite their importance for terrestrial and aquatic biogeochemistry, these fluxes are rarely represented in conceptual or numerical models of terrestrial biogeochemistry. In part, this is due to the lack of a comprehensive understanding of the suite of processes that control DOC dynamics in soils. In this article, we synthesize information on the geochemical and biological factors that control DOC fluxes through soils. We focus on conceptual issues and quantitative evaluations of key process rates to present a general numerical model of DOC dynamics. We then test the sensitivity of the model to variation in the controlling parameters to highlight both the significance of DOC fluxes to terrestrial carbon processes and the key uncertainties that require additional experiments and data. Simulation model results indicate the importance of representing both root carbon inputs and soluble carbon fluxes to predict the quantity and distribution of soil carbon in soil layers. For a test case in a temperate forest, DOC contributed 25% of the total soil profile carbon, whereas roots provided the remainder. The analysis also shows that physical factors—most notably, sorption dynamics and hydrology—play the dominant role in regulating DOC losses from terrestrial ecosystems but that interactions between hydrology and microbial–DOC relationships are important in regulating the fluxes of DOC in the litter and surface soil horizons. The model also indicates that DOC fluxes to deeper soil layers can support a large fraction (up to 30%) of microbial activity below 40 cm.

Life on Earth depends on photosynthesis. Photosynthetic systems evolved early in Earth history and have been stable for 2.5 billion years, providing prima facie evidence for the significance of pigments in plant functions. Photosynthetic pigments fill multiple roles from increasing the range of energy captured for photosynthesis to protective functions. Given the importance of pigments to leaf functioning, greater effort is needed to determine whether individual pigments can be identified and quantified in vivo using high fidelity spectroscopy. We review recent advances in detecting plant pigments at the leaf level and discuss successes and reasons why challenges remain for robust remote observation and quantification. New methods to identify and quantify individual pigments in the presence of overlapping absorption features would provide a major advance in understanding their biological functions, quantifying net carbon exchange, and identifying plant stresses.

Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.

The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent spectral components or spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative mixture analysis cycle, (ii) the selection of a subset of stable spectral features, (iii) the spectral weighting of bands, (iv) spectral signal transformations and (v) the use of radiative transfer models in a mixture analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA.

The Global Deal for Nature (GDN) is a time-bound, science-driven plan to save the diversity and abundance of life on Earth. Pairing the GDN and the Paris Climate Agreement would avoid catastrophic climate change, conserve species, and secure essential ecosystem services. New findings give urgency to this union: Less than half of the terrestrial realm is intact, yet conserving all native ecosystems-coupled with energy transition measures-will be required to remain below a 1.5°C rise in average global temperature. The GDN targets 30% of Earth to be formally protected and an additional 20% designated as climate stabilization areas, by 2030, to stay below 1.5°C. We highlight the 67% of terrestrial ecoregions that can meet 30% protection, thereby reducing extinction threats and carbon emissions from natural reservoirs. Freshwater and marine targets included here extend the GDN to all realms and provide a pathway to ensuring a more livable biosphere.

For two decades, remotely sensed data from imaging spectrometers have been used to estimate non-pigment biochemical constituents of vegetation, including water, nitrogen, cellulose, and lignin. This interest has been motivated by the important role that these substances play in physiological processes such as photosynthesis, their relationships with ecosystem processes such as litter decomposition and nutrient cycling, and their use in identifying key plant species and functional groups. This paper reviews three areas of research to improve the application of imaging spectrometers to quantify non-pigment biochemical constituents of plants. First, we examine recent empirical and modeling studies that have advanced our understanding of leaf and canopy reflectance spectra in relation to plant biochemistry. Next, we present recent examples of how spectroscopic remote sensing methods are applied to characterize vegetation canopies, communities and ecosystems. Third, we highlight the latest developments in using imaging spectrometer data to quantify net primary production (NPP) over large geographic areas. Finally, we discuss the major challenges in quantifying non-pigment biochemical constituents of plant canopies from remotely sensed spectra.

T he consequences of land use, disturbance, and climate change in the world's ecosystems have created increased demand for remote sensing data at all scales.A wide range of information is needed to predict the consequences of climate change and to monitor carbon, water, and nutrient cycles, from land cover, land-use history, and estimates of standing biomass to succession, biodiversity, and sustainability.Traditional field-based sampling methods are prohibitively expensive and time-consuming at large spatial scales, and such methods are inadequate for today's needs.Satellite observations provide the only practical means to obtain a synoptic view of Earth's ecosystems, including their spatial distribution, extent, and temporal dynamics (Cohen and Goward 2004).Accurate maps of the spatial distribution, percentage cover, and variability of global ecosystems are essential to improve ecosystem process models (Running et al. 2004, Turner et al. 2004).Current vegetation maps contain significant classification errors, and there is little understanding of how to scale mixed land cover, variable stand age, and density classes from local estimates.Normalized difference vegetation index (NDVI) data are used for estimating carbon fluxes, stores, and turnover rates, as well as other land-cover characteristics affecting the carbon budget.However, ecosystem and biogeochemical models could significantly benefit from independently derived information about terrestrial biomes.Several new types of data that could improve model results, including quantitative estimates of canopy and soil biochemistry, canopy structural information, and improved land-cover classifications, can be produced by imaging spectrometers.A wide range of new instrument capabilities are available or planned, based on airborne and satellite platforms that measure all parts of the electromagnetic spectrum, from ultraviolet to radar (Treuhaft et al. 2004).Imaging spectrometers are instruments that measure a detailed spectrum of reflected solar energy for each pixel.Spectroscopy data have already significantly improved theoretical understanding of the interactions of electromagnetic radiation with matter and are changing the way remotely sensed data are analyzed

The Amazon Basin is one of the world's most important bioregions, harboring a rich array of plant and animal species and offering a wealth of goods and services to society. For years, ecological science has shown how large-scale forest clearings cause declines in biodiversity and the availability of forest products. Yet some important changes in the rainforests, and in the ecosystem services they provide, have been underappreciated until recently. Emerging research indicates that land use in the Amazon goes far beyond clearing large areas of forest; selective logging and other canopy damage is much more pervasive than once believed. Deforestation causes collateral damage to the surrounding forests – through enhanced drying of the forest floor, increased frequency of fires, and lowered productivity. The loss of healthy forests can degrade key ecosystem services, such as carbon storage in biomass and soils, the regulation of water balance and river flow, the modulation of regional climate patterns, and the amelioration of infectious diseases. We review these newly revealed changes in the Amazon rainforests and the ecosystem services that they provide.

Over the past 50 years, human agents of deforestation have changed in ways that have potentially important implications for conservation efforts. We characterized these changes through a meta-analysis of case studies of land-cover change in the tropics. From the 1960s to the 1980s, small-scale farmers, with state assistance, deforested large areas of tropical forest in Southeast Asia and Latin America. As globalization and urbanization increased during the 1980s, the agents of deforestation changed in two important parts of the tropical biome, the lowland rainforests in Brazil and Indonesia. Well-capitalized ranchers, farmers, and loggers producing for consumers in distant markets became more prominent in these places and this globalization weakened the historically strong relationship between local population growth and forest cover. At the same time, forests have begun to regrow in some tropical uplands. These changing circumstances, we believe, suggest two new and differing strategies for biodiversity conservation in the tropics, one focused on conserving uplands and the other on promoting environmental stewardship in lowlands and other areas conducive to industrial agriculture.

Forest fragmentation results from deforestation and disturbance, with subsequent edge effects extending deep into remaining forest areas. No study has quantified the effects of both deforestation and selective logging, separately and combined, on forest fragmentation and edge effects over large regions. The main objectives of this study were to: (1) quantify the rates and extent of forest fragmentation from deforestation and logging within the Brazilian Amazon, and (2) contextualize the spatio-temporal dynamics of this forest fragmentation through a literature review of potential ecological repercussions of edge creation. Using GIS and remote sensing, we quantified forest fragmentation – defined as both increases in the forest edge-to-area ratio and number of forest fragments – and edge-effected forest occurring from these activities across more than 1.1 million km2 of the Brazilian Amazon from 1999 to 2002. Annually, deforestation and logging generated ∼32,000 and 38,000 km of new forest edge while increasing the edge-to-area ratio of remaining forest by 0.14 and 0.15, respectively. Combined deforestation and logging increased the edge-to-area ratio of remaining forest by 65% over our study period, while generating 5539 and 3383 new forest fragments, respectively. Although we found that 90% of individual forest fragments were smaller than 4 km2, we also found that 50% of the remaining intact forests were located in contiguous forest areas greater than 35,000 km2. We then conducted a literature review documenting 146 edge effects and found that these penetrated to a median distance of 100 m, a distance encompassing 6.4% of all remaining forests in our study region in the year 2002, while 53% of forests were located within two km of an edge. Annually deforestation and logging increased the proportion of edge-forest by 0.8% and 3.1%, respectively. As a result of both activities, the total proportion of edge-forest increased by 2.6% per year, while the proportion within 100-m increased by 0.5%. Over our study period, deforestation resulted in an additional ∼3000 km2 of edge-forest, whereas logging generated ∼20,000 km2, as it extended deep into intact forest areas. These results show the large extent and rapid expansion of previously unquantified soft-edges throughout the Amazon and highlight the need for greater research into their ecological impacts.

In recent decades the rate and geographic extent of land-use and land-cover change has increased throughout the world's humid tropical forests. The pan-tropical geography of forest change is a challenge to assess, and improved estimates of the human footprint in the tropics are critical to understanding potential changes in biodiversity. We combined recently published and new satellite observations, along with images from Google Earth and a literature review, to estimate the contemporary global extent of deforestation, selective logging, and secondary regrowth in humid tropical forests. Roughly 1.4% of the biome was deforested between 2000 and 2005. As of 2005, about half of the humid tropical forest biome contained 50% or less tree cover. Although not directly comparable to deforestation, geographic estimates of selective logging indicate that at least 20% of the humid tropical forest biome was undergoing some level of timber harvesting between 2000 and 2005. Forest recovery estimates are even less certain, but a compilation of available reports suggests that at least 1.2% of the humid tropical forest biome was in some stage of long-term secondary regrowth in 2000. Nearly 70% of the regrowth reports indicate forest regeneration in hilly, upland, and mountainous environments considered marginal for large-scale agriculture and ranching. Our estimates of the human footprint are conservative because they do not resolve very small-scale deforestation, low-intensity logging, and unreported secondary regrowth, nor do they incorporate other impacts on tropical forest ecosystems, such as fire and hunting. Our results highlight the enormous geographic extent of forest change throughout the humid tropics and the considerable limitations of the science and technology available for such a synthesis.

High spatial resolution Landsat imagery is employed in efforts to understand the impact of human activities on ecological, biogeochemical and atmospheric processes in the Amazon basin. The utility of Landsat multi-spectral data depends both on the degree to which surface properties can be estimated from the radiometric measurements and on the ability to observe the surface through the atmosphere. Clouds are a major obstacle to optical remote sensing of humid tropical regions, therefore cloud cover probability analysis is a fundamental prerequisite to land-cover change and Earth system process studies in these regions. This paper reports the results of a spatially explicit analysis of cloud cover in the Landsat archive of Brazilian Amazonia from 1984 to 1997. Monthly observations of any part of the basin are highly improbable using Landsat-like optical imagers. Annual observations are possible for most of the basin, but are improbable in northern parts of the region. These results quantify the limitations imposed by cloud cover to current Amazon land-cover change assessments using Landsat data. They emphasize the need for improved radar and alternative optical data fusion techniques to provide time-series analyses of biogeophysical properties for regional modelling efforts.

Correlations between foliar nutrient concentrations and soil nutrient availability have been found in multiple ecosystems. These relationships have led to the use of foliar nutrients as an index of nutrient status and to the prediction of broadscale patterns in ecosystem processes. More recently, a growing interest in ecological stoichiometry has fueled multiple analyses of foliar nitrogen : phosphorus (N:P) ratios within and across ecosystems. These studies have observed that N:P values are generally elevated in tropical forests when compared to higher latitude ecosystems, adding weight to a common belief that tropical forests are generally N rich and P poor. However, while these broad generalizations may have merit, their simplicity masks the enormous environmental heterogeneity that exists within the tropics; such variation includes large ranges in soil fertility and climate, as well as the highest plant species diversity of any biome. Here we present original data on foliar N and P concentrations from 150 mature canopy tree species in Costa Rica and Brazil, and combine those data with a comprehensive new literature synthesis to explore the major sources of variation in foliar N:P values within the tropics. We found no relationship between N:P ratios and either latitude or mean annual precipitation within the tropics alone. There is, however, evidence of seasonal controls; in our Costa Rica sites, foliar N:P values differed by 25% between wet and dry seasons. The N:P ratios do vary with soil P availability and/or soil order, but there is substantial overlap across coarse divisions in soil type, and perhaps the most striking feature of the data set is variation at the species level. Taken as a whole, our results imply that the dominant influence on foliar N:P ratios in the tropics is species variability and that, unlike marine systems and perhaps many other terrestrial biomes, the N:P stoichiometry of tropical forests is not well constrained. Thus any use of N:P ratios in the tropics to infer larger-scale ecosystem processes must comprehensively account for the diversity of any given site and recognize the broad range in nutrient requirements, even at the local scale.

Variation in the foliar chemistry of humid tropical forests is poorly understood, and airborne imaging spectroscopy could provide useful information at leaf and canopy scales. However, variation in canopy structure affects our ability to estimate foliar properties from airborne spectrometer data, yet these structural affects remain poorly quantified. Using leaf spectral (400–2500 nm) and chemical data collected from 162 Australian tropical forest species, along with partial least squares (PLS) analysis and canopy radiative transfer modeling, we determined the strength of the relationship between canopy reflectance and foliar properties under conditions of varying canopy structure. At the leaf level, chlorophylls, carotenoids and specific leaf area (SLA) were highly correlated with leaf spectral reflectance (r = 0.90–0.91). Foliar nutrients and water were also well represented by the leaf spectra (r = 0.79–0.85). When the leaf spectra were incorporated into the canopy radiative transfer simulations with an idealistic leaf area index (LAI) = 5.0, correlations between canopy reflectance spectra and leaf properties increased in strength by 4–18%. The effects of random LAI (= 3.0–6.5) variation on the retrieval of leaf properties remained minimal, particularly for pigments and SLA (r = 0.92–0.93). In contrast, correlations between leaf nitrogen (N) and canopy reflectance estimates decreased from r = 0.87 at constant LAI = 5 to r = 0.65 with randomly varying LAI = 3.0–6.5. Progressive increases in the structural variability among simulated tree crowns had relatively little effect on pigment, SLA and water predictions. However, N and phosphorus (P) were more sensitive to canopy structural variability. Our modeling results suggest that multiple leaf chemicals and SLA can be estimated from leaf and canopy reflectance spectroscopy, and that the high-LAI canopies found in tropical forests enhance the signal via multiple scattering. Finally, the two factors we found to most negatively impact leaf chemical predictions from canopy reflectance were variation in LAI and viewing geometry, which can be managed with new airborne technologies and analytical methods.

Industrial agricultural plantations are a rapidly increasing yet largely unmeasured source of tropical land cover change. Here, we evaluate impacts of oil palm plantation development on land cover, carbon flux, and agrarian community lands in West Kalimantan, Indonesian Borneo. With a spatially explicit land change/carbon bookkeeping model, parameterized using high-resolution satellite time series and informed by socioeconomic surveys, we assess previous and project future plantation expansion under five scenarios. Although fire was the primary proximate cause of 1989-2008 deforestation (93%) and net carbon emissions (69%), by 2007-2008, oil palm directly caused 27% of total and 40% of peatland deforestation. Plantation land sources exhibited distinctive temporal dynamics, comprising 81% forests on mineral soils (1994-2001), shifting to 69% peatlands (2008-2011). Plantation leases reveal vast development potential. In 2008, leases spanned ∼65% of the region, including 62% on peatlands and 59% of community-managed lands, yet <10% of lease area was planted. Projecting business as usual (BAU), by 2020 ∼40% of regional and 35% of community lands are cleared for oil palm, generating 26% of net carbon emissions. Intact forest cover declines to 4%, and the proportion of emissions sourced from peatlands increases 38%. Prohibiting intact and logged forest and peatland conversion to oil palm reduces emissions only 4% below BAU, because of continued uncontrolled fire. Protecting logged forests achieves greater carbon emissions reductions (21%) than protecting intact forests alone (9%) and is critical for mitigating carbon emissions. Extensive allocated leases constrain land management options, requiring trade-offs among oil palm production, carbon emissions mitigation, and maintaining community landholdings.

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.

Accuracy of vegetation cover fractions, computed with spectral mixture analysis, may be compromised by variation in canopy structure and biochemistry when a single endmember represents top-of-canopy reflectance. In this article, endmember variability is incorporated into mixture analysis by representing each endmember by a set or bundle of spectra, each of which could reasonably be the reflectance of an instance of the endmember. Endmember bundles are constructed from the data itself by an extension to a previously described method of manually deriving endmembers from remotely sensed data. Applied to remotely sensed images, bundle unmixing produces maximum and minimum fraction images bounding the correct cover fractions and specifying error due to endmember variability. In this article, endmember bundles and bounding fraction images were created for an airborne visible/infrared imaging spectrometer (AVIRIS) subscene simulated with a canopy radiative transfer/geometric-optical model. Variation in endmember reflectance was achieved using ranges of parameter values including leaf area index (LAI) and tissue optical properties observed In a North Texas savanna. The subscene's spatial pattern was based on a 1992 Landsat Thematic Mapper image of the study region. Bounding fraction images bracketed the cover fractions of the simulated data for 98% of the pixels for soil, 97% for senescent grass and 93% for trees. Averages of bounding images estimated fractional coverage used in the simulation with an average error of /spl les/0.05, a significant improvement over previous methods with important implications for regional-scale research on vegetation extent and dynamics.

Tree canopies play an enormous role in the maintenance of tropical forest diversity and ecosystem function, and are therefore central to conservation, management, and resource policy development in tropical regions. However, high‐resolution mapping of tropical forest canopies is very difficult, because traditional field, airborne, and satellite measurements cannot resolve the number of canopy species, or particular species of interest, over the large regional scales commensurate with conservation goals and strategies. Newer technologies, such as imaging spectroscopy and light detection and ranging (lidar), are just now reaching performance levels that will allow monitoring of tropical forest diversity from the air, but the methods for applying these technologies are not yet ready. Here, we present concepts that combine chemical and spectral remote sensing perspectives to facilitate canopy diversity mapping. Using examples from our ongoing work in the Hawaiian Islands, we demonstrate how a new “airborne spectranomics” approach could revolutionize tropical forest monitoring in the future.

The long-term viability of a forest industry in the Amazon region of Brazil depends on the maintenance of adequate timber volume and growth in healthy forests. Using extensive high-resolution satellite analyses, we studied the forest damage caused by recent logging operations and the likelihood that logged forests would be cleared within 4 years after timber harvest. Across 2,030,637 km2 of the Brazilian Amazon from 1999 to 2004, at least 76% of all harvest practices resulted in high levels of canopy damage sufficient to leave forests susceptible to drought and fire. We found that 16+/-1% of selectively logged areas were deforested within 1 year of logging, with a subsequent annual deforestation rate of 5.4% for 4 years after timber harvests. Nearly all logging occurred within 25 km of main roads, and within that area, the probability of deforestation for a logged forest was up to four times greater than for unlogged forests. In combination, our results show that logging in the Brazilian Amazon is dominated by highly damaging operations, often followed rapidly by deforestation decades before forests can recover sufficiently to produce timber for a second harvest. Under the management regimes in effect at the time of our study in the Brazilian Amazon, selective logging would not be sustained.

Disturbance and deforestation have profound ecological and socioeconomic effects on tropical forests, but their diffuse patterns are difficult to detect and quantify at regional scales. We expanded the Carnegie forest damage detection system to show that, between 1999 and 2005, disturbance and deforestation rates throughout the Peruvian Amazon averaged 632 square kilometers per year and 645 square kilometers per year, respectively. However, only 1 to 2% occurred within natural protected areas, indigenous territories contained only 11% of the forest disturbances and 9% of the deforestation, and recent forest concessions effectively protected against clear-cutting. Although the region shows recent increases in disturbance and deforestation rates and leakage into forests surrounding concession areas, land-use policy and remoteness are serving to protect the Peruvian Amazon.

The Carnegie Airborne Observatory (CAO) was developed to address a need for macroscale measurements that reveal the structural, functional and organismic composition of Earth's ecosystems. In 2011, we completed and launched the CAO-2 next generation Airborne Taxonomic Mapping Systems (AToMS), which includes a high-fidelity visible-to-shortwave infrared (VSWIR) imaging spectrometer (380–2510 nm), dual-laser waveform light detection and ranging (LiDAR) scanner, and high spatial resolution visible-to-near infrared (VNIR) imaging spectrometer (365–1052 nm). Here, we describe how multiple data streams from these sensors can be fused using hardware and software co-alignment and processing techniques. With these data streams, we quantitatively demonstrate that precision data fusion greatly increases the dimensionality of the ecological information derived from remote sensing. We compare the data dimensionality of two contrasting scenes — a built environment at Stanford University and a lowland tropical forest in Amazonia. Principal components analysis revealed 336 dimensions (degrees of freedom) in the Stanford case, and 218 dimensions in the Amazon. The Amazon case presents what could be the highest level of remotely sensed data dimensionality ever reported for a forested ecosystem. Simulated misalignment of data streams reduced the effective information content by up to 48%, highlighting the critical role of achieving high precision when undertaking multi-sensor fusion. The instrumentation and methods described here are a pathfinder for future airborne applications undertaken by the National Ecological Observatory Network (NEON) and other organizations.

Airborne remote sensing could play a more integrative role in regional ecosystem studies if the information derived from airborne observations could be readily converted to physical and chemical quantities representative of ecosystem processes and properties. We have undertaken an effort to specify, deploy, and apply a new system - the Carnegie Airborne Observatory (CAO) - to remotely measure a suite of ecosystem structural and biochemical properties in a way that can rapidly advance regional ecological research for conservation, management and resource policy development. The CAO "Alpha System" provides in-flight fusion of high-fidelity visible/near-infrared imaging spectrometer data with scanning, waveform light detection and ranging (wLiDAR) data, along with an integrated navigation and data processing approach, that results in geo-orthorectified products for vegetation structure, biochemistry, and physiology as well as the underlying topography. Here we present the scientific rationale for developing the system, and provide sample data fusion results demonstrating the potential breakthroughs that hybrid hyperspectral-wLiDAR systems might bring to the scientific community.

Tropical forests are renowned for their biological diversity, but also harbor variable combinations of soil age, chemistry and susceptibility to erosion or tectonic uplift. Here we contend that the combined effects of this biotic and abiotic diversity promote exceptional biogeochemical heterogeneity at multiple scales. At local levels, high plant diversity creates variation in chemical and structural traits that affect plant production, decomposition and nutrient cycling. At regional levels, myriad combinations of soil age, soil chemistry and landscape dynamics create variation and uncertainty in limiting nutrients that do not exist at higher latitudes. The effects of such heterogeneity are not well captured in large-scale estimates of tropical ecosystem function, but we suggest new developments in remote sensing can help bridge the gap.

Spatial and temporal information on plant functional traits are lacking in ecology, which limits our understanding of how plant communities and ecosystems are changing. This problem is acute in remote tropical regions, where information on plant functional traits is difficult to ascertain. We used Carnegie Airborne Observatory visible-to-shortwave infrared (VSWIR) imaging spectroscopy with light detection and ranging (LiDAR) to assess the foliar traits of Amazonian and Andean tropical forest canopies. We calibrated and validated the retrieval of 15 canopy foliar chemicals and leaf mass per area (LMA) across a network of 79 1-hectare field plots using a new VSWIR-LiDAR fusion approach designed to accommodate the enormous scale mismatch between field and remote sensing studies. The results indicate that sparse and highly variable field sampling can be integrated with VSWIR-LiDAR data to yield demonstrably accurate estimates of canopy foliar chemical traits. This new airborne approach addresses the inherent limitations and sampling biases associated with field-based studies of forest functional traits, particularly in structurally and floristically complex tropical canopies.

Ecological studies in tropical forests have long been plagued by difficulties associated with sampling the crowns of large canopy trees and large inaccessible regions, such as the Amazon basin. Recent advances in remote sensing have overcome some of these obstacles, enabling progress towards tackling difficult ecological problems. Breakthroughs have helped transform the dialog between ecology and remote sensing, generating new regional perspectives on key environmental gradients and species assemblages with ecologically relevant measures such as canopy nutrient and moisture content, crown area, leaf-level drought responses, woody tissue and surface litter abundance, phenological patterns, and land-cover transitions. Issues that we address here include forest response to altered precipitation regimes, regional disturbance and land-use patterns, invasive species and landscape carbon balance.

We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.

Mapping aboveground carbon density (ACD) in tropical forests can enhance large-scale ecological studies and support CO2 emissions monitoring. Light Detection and Ranging (LiDAR) has proven useful for estimating carbon density patterns outside of field plot inventory networks. However, the accuracy and generality of calibrations between LiDAR-assisted ACD predictions (EACDLiDAR) and estimated ACD based on field inventory techniques (EACDfield) must be increased in order to make tropical forest carbon mapping more widely available. Using a network of 804 field inventory plots distributed across a wide range of tropical vegetation types, climates and successional states, we present a general conceptual and technical approach for linking tropical forest EACDfield to LiDAR top-of-canopy height (TCH) using regional-scale inputs of basal area and wood density. With this approach, we show that EACDLiDAR and EACDfield reach nearly 90% agreement at 1-ha resolution for a wide array of tropical vegetation types. We also show that Lorey's Height – a common metric used to calibrate LiDAR measurements to biomass – is severely flawed in open canopy forests that are common to the tropics. Our proposed approach can advance the use of airborne and space-based LiDAR measurements for estimation of tropical forest carbon stocks.

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.

African savannas are undergoing management intensification, and decision makers are increasingly challenged to balance the needs of large herbivore populations with the maintenance of vegetation and ecosystem diversity. Ensuring the sustainability of Africa's natural protected areas requires information on the efficacy of management decisions at large spatial scales, but often neither experimental treatments nor large-scale responses are available for analysis. Using a new airborne remote sensing system, we mapped the three-dimensional (3-D) structure of vegetation at a spatial resolution of 56 cm throughout 1640 ha of savanna after 6-, 22-, 35-, and 41-year exclusions of herbivores, as well as in unprotected areas, across Kruger National Park in South Africa. Areas in which herbivores were excluded over the short term (6 years) contained 38%–80% less bare ground compared with those that were exposed to mammalian herbivory. In the longer-term (&gt; 22 years), the 3-D structure of woody vegetation differed significantly between protected and accessible landscapes, with up to 11-fold greater woody canopy cover in the areas without herbivores. Our maps revealed 2 scales of ecosystem response to herbivore consumption, one broadly mediated by geologic substrate and the other mediated by hillslope-scale variation in soil nutrient availability and moisture conditions. Our results are the first to quantitatively illustrate the extent to which herbivores can affect the 3-D structural diversity of vegetation across large savanna landscapes.

•Structural heterogeneity is more influential for animal diversity than is simple canopy cover. •Taxonomic groups respond to different components of 3D ecosystem structure. •Biases in the literature preclude syntheses for some taxonomic groups and regions. •LiDAR has tremendous potential to further advance understanding of animal ecology. The advent and recent advances of Light Detection and Ranging (LiDAR) have enabled accurate measurement of 3D ecosystem structure. Here, we review insights gained through the application of LiDAR to animal ecology studies, revealing the fundamental importance of structure for animals. Structural heterogeneity is most conducive to increased animal richness and abundance, and increased complexity of vertical vegetation structure is more positively influential compared with traditionally measured canopy cover, which produces mixed results. However, different taxonomic groups interact with a variety of 3D canopy traits and some groups with 3D topography. To develop a better understanding of animal dynamics, future studies will benefit from considering 3D habitat effects in a wider variety of ecosystems and with more taxa. The advent and recent advances of Light Detection and Ranging (LiDAR) have enabled accurate measurement of 3D ecosystem structure. Here, we review insights gained through the application of LiDAR to animal ecology studies, revealing the fundamental importance of structure for animals. Structural heterogeneity is most conducive to increased animal richness and abundance, and increased complexity of vertical vegetation structure is more positively influential compared with traditionally measured canopy cover, which produces mixed results. However, different taxonomic groups interact with a variety of 3D canopy traits and some groups with 3D topography. To develop a better understanding of animal dynamics, future studies will benefit from considering 3D habitat effects in a wider variety of ecosystems and with more taxa. the extent of the canopy in 2D (x and y) horizontal space. the vertical (z dimension) height of the canopy above the ground. the amount of detail and number of components present in the canopy layer. Greater structural complexity is characterised by more branches and greater connectedness of tree canopies. variation in the vegetation canopy layer, encompassing vertical (canopy height and vertical distribution) and horizontal (canopy cover and extent) variation. the vertical spread of canopy components and tissues (e.g., tree branches and leaves). structure in the x and y dimensions, including canopy cover and vegetation extent. an active remote sensing technology that emits and receives its own near-infrared light to measure 3D structure (Box 2). the physical arrangement of individual components into a complex whole. In relation to 3D ecosystem structure, it refers to the physical arrangement of vegetation and ground elements. any vegetation layer that is beneath the uppermost canopy.

Monitoring deforestation and forest degradation is central to assessing changes in carbon storage, biodiversity, and many other ecological processes in tropical regions. Satellite remote sensing is the most accurate and cost-effective way to monitor changes in forest cover and degradation over large geographic areas, but the tools and methods have been highly manual and time consuming, often requiring expert knowledge. We present a new user-friendly, fully automated system called CLASlite, which provides desktop mapping of forest cover, deforestation and forest disturbance using advanced atmospheric correction and spectral signal processing approaches with Landsat, SPOT, and many other satellite sensors. CLASlite runs on a standard Windows-based computer, and can map more than 10,000 km2, at 30 m spatial resolution, of forest area per hour of processing time. Outputs from CLASlite include maps of the percentage of live and dead vegetation cover, bare soils and other substrates, along with quantitative measures of uncertainty in each image pixel. These maps are then interpreted in terms of forest cover, deforestation and forest disturbance using automated decision trees. CLASlite output images can be directly input to other remote sensing programs, geographic information systems (GIS), Google EarthTM serif}, or other visualization systems. Here we provide a detailed description of the CLASlite approach with example results for deforestation and forest degradation scenarios in Brazil, Peru, and other tropical forest sites worldwide.

The accurate classification and mapping of individual trees at species level in the savanna ecosystem can provide numerous benefits for the managerial authorities. Such benefits include the mapping of economically useful tree species, which are a key source of food production and fuel wood for the local communities, and of problematic alien invasive and bush encroaching species, which can threaten the integrity of the environment and livelihoods of the local communities. Species level mapping is particularly challenging in African savannas which are complex, heterogeneous, and open environments with high intra-species spectral variability due to differences in geology, topography, rainfall, herbivory and human impacts within relatively short distances. Savanna vegetation are also highly irregular in canopy and crown shape, height and other structural dimensions with a combination of open grassland patches and dense woody thicket – a stark contrast to the more homogeneous forest vegetation. This study classified eight common savanna tree species in the Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. The most important predictors, which were found to play an important role in the different classification models and contributed to the success of the hybrid dataset model when combined, were species tree height; NDVI; the chlorophyll b wavelength (466 nm) and a selection of raw, continuum removed and Spectral Angle Mapper (SAM) bands. It was also concluded that the hybrid predictor dataset Random Forest model yielded the highest classification accuracy and prediction success for the eight savanna tree species with an overall classification accuracy of 87.68% and KHAT value of 0.843.

Biological invasions contribute to global environmental change, but the dynamics and consequences of most invasions are difficult to assess at regional scales. We deployed an airborne remote sensing system that mapped the location and impacts of five highly invasive plant species across 221,875 ha of Hawaiian ecosystems, identifying four distinct ways that these species transform the three-dimensional (3D) structure of native rain forests. In lowland to montane forests, three invasive tree species replace native midcanopy and understory plants, whereas one understory invader excludes native species at the ground level. A fifth invasive nitrogen-fixing tree, in combination with a midcanopy alien tree, replaces native plants at all canopy levels in lowland forests. We conclude that this diverse array of alien plant species, each representing a different growth form or functional type, is changing the fundamental 3D structure of native Hawaiian rain forests. Our work also demonstrates how an airborne mapping strategy can identify and track the spread of certain invasive plant species, determine ecological consequences of their proliferation, and provide detailed geographic information to conservation and management efforts.

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions.

Drought varies spatially and temporally throughout the Amazon basin, challenging efforts to assess ecological impacts via field measurements alone. Remote sensing offers a range of regional insights into drought-mediated changes in cloud cover and rainfall, canopy physiology, and fire. Here, we summarize remote sensing studies of Amazônia which indicate that: fires and burn scars are more common during drought years; hydrological function including floodplain area is significantly affected by drought; and land use affects the sensitivity of the forest to dry conditions and increases fire susceptibility during drought. We highlight two controversial areas of research centering on canopy physiological responses to drought and changes in subcanopy fires during drought. By comparing findings from field and satellite studies, we contend that current remote sensing observations and techniques cannot resolve these controversies using current satellite observations. We conclude that studies integrating multiple lines of evidence from physiological, disturbance-fire, and hydrological remote sensing, as well as field measurements, are critically needed to narrow our uncertainty of basin-level responses to drought and climate change.

Airborne LiDAR is increasingly used to map carbon stocks in tropical forests, but our understanding of mapping errors is constrained by the spatial resolution (i.e., plot size) used to calibrate LiDAR with field data (typically 0.1–0.36 ha). Reported LiDAR errors range from 17 to 40 Mg C ha− 1, but should be lower at coarser resolutions because relative errors are expected to scale with (plot area)–1/2. We tested this prediction empirically using a 50-ha plot with mapped trees, allowing an assessment of LiDAR prediction errors at multiple spatial resolutions. We found that errors scaled approximately as expected, declining by 38% (compared to 40% predicted from theory) from 0.36- to 1-ha resolution. We further reduced errors at all spatial resolutions by accounting for tree crowns that are bisected by plot edges (not typically done in forestry), and collectively show that airborne LiDAR can map carbon stocks with 10% error at 1-ha resolution — a level comparable to the use of field plots alone.

Remote sensing of canopy chemistry could greatly advance the study and monitoring of functional processes and biological diversity in humid tropical forests. Imaging spectroscopy has contributed to canopy chemical remote sensing, but efforts to develop general, globally-applicable approaches have been limited by sparse and inconsistent field and laboratory data, and lacking analytical methods. We analyzed leaf hemispherical reflectance and transmittance spectra, along with a 21-chemical portfolio, taken from 6136 fully sunlit humid tropical forest canopies, and developed an up-scaling method using a combination of canopy radiative transfer, chemometric and high-frequency noise modeling. By integrating these steps, we found that the accuracy and precision of multi-chemical remote sensing of tropical forest canopies varies by leaf constituent and wavelength range. Under conditions of varying canopy structure and spectral noise, photosynthetic pigments, water, nitrogen, cellulose, lignin, phenols and leaf mass per area (LMA) are accurately estimated using visible-to-shortwave infrared spectroscopy (VSWIR; 400–2500 nm). Phosphorus and base cations are retrieved with lower yet significant accuracy. We also find that leaf chemical properties are estimated far more consistently, and with much higher precision and accuracy, using the VSWIR range rather than the more common and limited visible to near-infrared range (400–1050 nm; VNIR). While VNIR spectroscopy proved accurate for predicting foliar LMA, photosynthetic pigments and water, VSWIR spectra provided accurate estimates for three times the number of canopy traits. These global results proved to be independent of site conditions, taxonomic composition and phylogenetic history, and thus they should be broadly applicable to multi-chemical mapping of humid tropical forest canopies. The approach developed and tested here paves the way for studies of canopy chemical properties in humid tropical forests using the next generation of airborne and space-based high-fidelity imaging spectrometers.

Abstract The functional role of herbivores in tropical rainforests remains poorly understood. We quantified the magnitude of, and underlying controls on, carbon, nitrogen and phosphorus cycled by invertebrate herbivory along a 2800 m elevational gradient in the tropical Andes spanning 12°C mean annual temperature. We find, firstly, that leaf area loss is greater at warmer sites with lower foliar phosphorus, and secondly, that the estimated herbivore‐mediated flux of foliar nitrogen and phosphorus from plants to soil via leaf area loss is similar to, or greater than, other major sources of these nutrients in tropical forests. Finally, we estimate that herbivores consume a significant portion of plant carbon, potentially causing major shifts in the pattern of plant and soil carbon cycling. We conclude that future shifts in herbivore abundance and activity as a result of environmental change could have major impacts on soil fertility and ecosystem carbon sequestration in tropical forests.

The Malaysian states of Sabah and Sarawak are global hotspots of forest loss and degradation due to timber and oil palm industries; however, the rates and patterns of change have remained poorly measured by conventional field or satellite approaches. Using 30 m resolution optical imagery acquired since 1990, forest cover and logging roads were mapped throughout Malaysian Borneo and Brunei using the Carnegie Landsat Analysis System. We uncovered ∼364,000 km of roads constructed through the forests of this region. We estimated that in 2009 there were at most 45,400 km(2) of intact forest ecosystems in Malaysian Borneo and Brunei. Critically, we found that nearly 80% of the land surface of Sabah and Sarawak was impacted by previously undocumented, high-impact logging or clearing operations from 1990 to 2009. This contrasted strongly with neighbouring Brunei, where 54% of the land area remained covered by unlogged forest. Overall, only 8% and 3% of land area in Sabah and Sarawak, respectively, was covered by intact forests under designated protected areas. Our assessment shows that very few forest ecosystems remain intact in Sabah or Sarawak, but that Brunei, by largely excluding industrial logging from its borders, has been comparatively successful in protecting its forests.

Mapping savanna tree species is of broad interest for savanna ecology and rural resource inventory. We investigated the utility of (i) the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and (ii) a combined spectral + tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa. We convolved the 72 spectral bands of the CAO imagery to eight and four multispectral channels available in the WorldView-2 and Quickbird satellite sensors, respectively. A combination of the simulated WorldView-2 data and LiDAR tree height imagery was also assessed for species classification. First, the simulated WorldView-2 imagery provided a higher classification accuracy (77% ± 3.1 (mean ± standard deviation)) when compared to the simulated Quickbird (65% ± 1.9) and CAO (65% ± 1.2) data. Secondly, the combined spectral + height dataset provided a slightly higher overall classification accuracy (79% ± 1.8) when compared to the WorldView-2 spectral only dataset. The difference was however, statistically significant (p < 0.001; one-way analysis of variance for 30 bootstrapped replicates (n = 100) of the independent validation dataset). Higher classification accuracies were observed for trees with large crowns such as S. birrea, S. africana and A. nigrescens as compared to trees with small crowns. Species composition and diversity maps of trees with large crowns were consistent with established knowledge in the area. For example, the results showed higher tree diversity (number of different species per ha) in the Sabi Sands game reserve than in the communal areas. This study highlights the feasibility of remote sensing of tree species at the landscape scale in African savannas and the potential applicability of WorldView-2 sensor in mapping savanna tree species with a large crown.

We identify canopy species in a Hawaiian tropical forest using supervised classification applied to airborne hyperspectral imagery acquired with the Carnegie Airborne Observatory-Alpha system. Nonparametric methods (linear and radial basis function support vector machine, artificial neural network, and k-nearest neighbor) and parametric methods (linear, quadratic, and regularized discriminant analysis) are compared for a range of species richness values and training sample sizes. We find a clear advantage in using regularized discriminant analysis, linear discriminant analysis, and support vector machines. No unique optimal classifier was found for all conditions tested, but we highlight the possibility of improving support vector machine classification with a better optimization of its free parameters. We also confirm that a combination of spectral and spatial information increases accuracy of species classification: we combine segmentation and species classification from regularized discriminant analysis to produce a map of the 17 discriminated species. Finally, we compare different methods to assess spectral separability and find a better ability of Bhattacharyya distance to assess separability within and among species. The results indicate that species mapping is tractable in tropical forests when using high-fidelity imaging spectroscopy.

The “Global Safety Net” shows where nature could be conserved and connected to reverse biodiversity loss and stabilize climate.

Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.7 Pg C y(-1) over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.2 Pg C y(-1), and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.004 Pg C y(-1). Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink.

Mapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500 m - 1000 m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAO's Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100 m) biomass map generated for a portion of the Colombian Amazon.We find substantial differences between the two maps, in particular in central Amazonia, the Congo basin, the south of Papua New Guinea, the Miombo woodlands of Africa, and the dry forests and savannas of South America. There is little consistency in the direction of the difference. However, when the maps are aggregated to the country or biome scale there is greater agreement, with differences cancelling out to a certain extent. When comparing country level biomass stocks, the two maps agree with each other to a much greater extent than to the FRA 2010 estimates. In the Colombian Amazon, both pantropical maps estimate higher biomass than the independent high resolution map, but show a similar spatial distribution of this biomass.Biomass mapping has progressed enormously over the past decade, to the stage where we can produce globally consistent maps of aboveground biomass. We show that there are still large uncertainties in these maps, in particular in areas with little field data. However, when used at a regional scale, different maps appear to converge, suggesting we can provide reasonable stock estimates when aggregated over large regions. Therefore we believe the largest uncertainties for REDD+ activities relate to the spatial distribution of biomass and to the spatial pattern of forest cover change, rather than to total globally or nationally summed carbon density.

Reduced soil-carbon storage in response to warming is a potential reinforcing feedback that could enhance climate change. A study now shows that for tropical montane wet forest, long-term warming (represented by an altitudinal gradient) accelerates below-ground carbon processes but has no apparent impact on soil-organic-carbon storage. The universally observed exponential increase in soil-surface CO2 efflux ('soil respiration'; FS) with increasing temperature has led to speculation that global warming will accelerate soil-organic-carbon (SOC) decomposition1, reduce SOC storage, and drive a positive feedback to future warming2. However, interpreting temperature–FS relationships, and so modelling terrestrial carbon balance in a warmer world, is complicated by the many sources of respired carbon that contribute to FS (ref. 3) and a poor understanding of how temperature influences SOC decomposition rates4. Here we quantified FS, litterfall, bulk SOC and SOC fraction size and turnover, and total below-ground carbon flux (TBCF) across a highly constrained 5.2 °C mean annual temperature (MAT) gradient in tropical montane wet forest5. From these, we determined that: increases in TBCF and litterfall explain >90% of the increase in FS with MAT; bulk SOC and SOC fraction size and turnover rate do not vary with MAT; and increases in TBCF and litterfall do not influence SOC storage or turnover on century to millennial timescales. This gradient study shows that for tropical montane wet forest, long-term and whole-ecosystem warming accelerates below-ground carbon processes with no apparent impact on SOC storage.

There is a growing need for operational biodiversity mapping methods to quantify and to assess the impact of climate change, habitat alteration, and human activity on ecosystem composition and function. Here, we present an original method for the estimation of α‐ and β‐diversity of tropical forests based on high‐fidelity imaging spectroscopy. We acquired imagery over high‐diversity Amazonian tropical forest landscapes in Peru with the Carnegie Airborne Observatory and developed an unsupervised method to estimate the Shannon index ( H ′) and variations in species composition using Bray‐Curtis dissimilarity (BC) and nonmetric multidimensional scaling (NMDS). An extensive field plot network was used for the validation of remotely sensed α‐ and β‐diversity. Airborne maps of H ′ were highly correlated with field α‐diversity estimates ( r = 0.86), and BC was estimated with demonstrable accuracy ( r = 0.61–0.76). Our findings are the first direct and spatially explicit remotely sensed estimates of α‐ and β‐diversity of humid tropical forests, paving the way for new applications using airborne and space‐based imaging spectroscopy.

Arid and semiarid ecosystems endure strong spatial and temporal variation of climate and land use that results in uniquely dynamic vegetation phenology, cover, and leaf area characteristics. Previous remote sensing efforts have not fully captured the spatial heterogeneity of vegetation properties required for functional analyses of these ecosystems, or have done so only with manually intensive algorithms of spectral mixture analysis that have limited operational use. These limitations motivated the development of an automated spectral unmixing approach based on a comprehensive analysis of vegetation and soil spectral variability resulting from biogeophysical variation in arid and semiarid regions. A field spectroscopic database of bare soils, green canopies, and litter canopies was compiled for 17 arid and semiarid sites in North and South America, representing a wide array of plant growth forms and species, vegetation conditions, and soil mineralogical-hydrological properties. Spectral reflectance of dominant cover types (green vegetation, litter, and bare soil) varied widely within and between sites, but the reflectance derivatives in the shortwave-infrared (SWIR2: 2,100–2,400 nm) were similar within and separable between each cover type. Using this result, an automated SWIR2 spectral unmixing algorithm was developed that includes a Monte Carlo approach for estimating errors in derived subpixel cover fractions resulting from endmember variability. The algorithm was applied to SWIR2 spectral data collected by the Airborne Visible and Infrared Imaging Spectrometer instrument over the Sevilleta and Jornada Long-Term Ecological Research sites. Subsequent comparisons to field data and geographical information system (GIS) maps were deemed successful. The SWIR2 region of the reflected solar spectrum provides a robust means to estimate the extent of bare soil and vegetation covers in arid and semiarid regions. The computationally efficient method developed here could be extended globally using SWIR2 spectrometer data to be collected from platforms such as the NASA Earth Observing-1 satellite.

Abstract Although local increases in woody plant cover have been documented in arid and semiarid ecosystems worldwide, there have been few long‐term, large‐scale analyses of changes in woody plant cover and aboveground carbon (C) stocks. We used historical aerial photography, contemporary Landsat satellite data, field observations, and image analysis techniques to assess spatially specific changes in woody vegetation cover and aboveground C stocks between 1937 and 1999 in a 400‐km 2 region of northern Texas, USA. Changes in land cover were then related to topo‐edaphic setting and historical land‐use practices. Mechanical or chemical brush management occurred over much of the region in the 1940–1950s. Rangelands not targeted for brush management experienced woody cover increases of up to 500% in 63 years. Areas managed with herbicides, mechanical treatments or fire exhibited a wide range of woody cover changes relative to 1937 (−75% to + 280%), depending on soil type and time since last management action. At the integrated regional scale, there was a net 30% increase in woody plant cover over the 63‐year period. Regional increases were greatest in riparian corridors (33%) and shallow clay uplands (26%) and least on upland clay loams (15%). Allometric relationships between canopy cover and aboveground biomass were used to estimate net aboveground C storage changes in upland (nonriparian) portions of regional landscapes. Carbon stocks increased from 380 g C m −2 in 1937 to 500 g C m −2 in 1999, a 32% net increase across the 400 km 2 region over the 63‐year period. These plant C storage change estimates are highly conservative in that they did not include the substantial increases in woody plant cover observed within riparian landscape elements. Results are discussed in terms of implications for ‘carbon accounting’ and the global C cycle.

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R2 ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km2. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.

Quantifying crop production at regional scales is critical for a wide range of applications, including management and carbon cycle modeling. Remote sensing offers great potential for monitoring regional production, yet the uncertainties associated with large-scale yield estimates are rarely addressed. In this study, we estimated crop area, yield, and planting dates for 2 years of Landsat imagery in an intensive agricultural region in northwest Mexico. Knowledge of crop phenology was combined with multi-temporal imagery to estimate crop rotations throughout the region. Remotely sensed estimates of the fraction of absorbed photosynthetically active radiation (fAPAR) were then incorporated into a simple model based on crop light-use efficiency to predict yield and planting dates for wheat. Uncertainty analysis revealed that regional yield predictions varied up to 20% with the method used to determine fAPAR from satellite, but were relatively insensitive to modeled variability in crop phenology, light-use efficiency, and harvest index (the ratio of grain mass to aboveground biomass). Comparisons of satellite-based and field-based estimates indicated errors in regional wheat yields of less than 4% for both years of data. In contrast, planting date calculations exhibited uncertainties of up to 50% using a sparse, three-date sampling from satellite-based sensors. A simplified model was also developed to explore yield predictions using only one date of imagery, demonstrating high accuracies depending on the date of image acquisition. The spatial and temporal distributions of crop production derived here offer valuable information for agricultural management and biogeochemical modeling efforts, provided that their uncertainties are well understood.

Amazônia contains vast stores of carbon in high-diversity ecosystems, yet this region undergoes major changes in precipitation affecting land use, carbon dynamics, and climate. The extent and structural complexity of Amazon forests impedes ground studies of ecosystem functions such as net primary production (NPP), water cycling, and carbon sequestration. Traditional modeling and remote-sensing approaches are not well suited to tropical forest studies, because ( i ) biophysical mechanisms determining drought effects on canopy water and carbon dynamics are poorly known, and ( ii ) remote-sensing metrics of canopy greenness may be insensitive to small changes in leaf area accompanying drought. New spaceborne imaging spectroscopy may detect drought stress in tropical forests, helping to monitor forest physiology and constrain carbon models. We combined a forest drought experiment in Amazônia with spaceborne imaging spectrometer measurements of this area. With field data on rainfall, soil water, and leaf and canopy responses, we tested whether spaceborne hyperspectral observations quantify differences in canopy water and NPP resulting from drought stress. We found that hyperspectral metrics of canopy water content and light-use efficiency are highly sensitive to drought. Using these observations, forest NPP was estimated with greater sensitivity to drought conditions than with traditional combinations of modeling, remote-sensing, and field measurements. Spaceborne imaging spectroscopy will increase the accuracy of ecological studies in humid tropical forests.

Wheat yields in Mexico, which represent an important measure of breeding and management progress in developing world wheat production, have increased by 25% over the past two decades. Using a combination of mechanistic and statistical models, we show that much of this increase can be attributed to climatic trends in Northwest states, in particular cooling of growing season nighttime temperatures. This finding suggests that short-term prospects for yield progress are smaller than suggested by recent yield increases, and that future gains will require an intensification of research and extension efforts aimed at raising wheat yields.

We used airborne imaging spectroscopy and photon transport modeling to determine how biological invasion altered the chemistry of forest canopies across a Hawaiian montane rain forest landscape. The nitrogen-fixing tree Myrica faya doubled canopy nitrogen concentrations and water content as it replaced native forest, whereas the understory herb Hedychium gardnerianum reduced nitrogen concentrations in the forest overstory and substantially increased aboveground water content. This remote sensing approach indicates the geographic extent, intensity, and biogeochemical impacts of two distinct invaders; its wider application could enhance the role of remote sensing in ecosystem analysis and management.

Large-scale carbon mapping is needed to support the UNFCCC program to reduce deforestation and forest degradation (REDD). Managers of forested land can potentially increase their carbon credits via detailed monitoring of forest cover, loss and gain (hectares), and periodic estimates of changes in forest carbon density (tons ha−1). Satellites provide an opportunity to monitor changes in forest carbon caused by deforestation and degradation, but only after initial carbon densities have been assessed. New airborne approaches, especially light detection and ranging (LiDAR), provide a means to estimate forest carbon density over large areas, which greatly assists in the development of practical baselines. Here I present an integrated satellite–airborne mapping approach that supports high-resolution carbon stock assessment and monitoring in tropical forest regions. The approach yields a spatially resolved, regional state-of-the-forest carbon baseline, followed by high-resolution monitoring of forest cover and disturbance to estimate carbon emissions. Rapid advances and decreasing costs in the satellite and airborne mapping sectors are already making high-resolution carbon stock and emissions assessments viable anywhere in the world.

Abstract Remote sensing of net primary production (NPP) is a critical tool for assessing spatial and temporal patterns of carbon exchange between the atmosphere and biosphere. However, satellite estimates suffer from a lack of large‐scale field data needed for validation, as well as the need to parameterize plant light‐use efficiencies (LUEs). In this study, we estimated cropland NPP with the Carnegie‐Ames‐Stanford‐Approach (CASA), a biogeochemical model driven by satellite observations, and then compared these results with field estimates based on harvest data from United States Department of Agriculture National Agriculture Statistics Service (NASS) county statistics. Observed interannual variations in NPP over a 17‐year period were well modelled by CASA, with exceptions mainly due to occasional difficulties in estimating NPP from harvest yields. The role of environmental stressors in agriculture was investigated by running CASA with and without temperature and moisture down‐regulators, which are used in the model to simulate climate impacts on plant LUE. In most cases, correlations with NASS data were highest with modelled stresses, while the opposite was true for irrigated and temperature resistant crops. Analysis of the spatial variability in computed LUE revealed significantly higher values for corn than for other crops, suggesting a simple parameterization of LUE for future studies based on the fraction of area with corn. Absolute values of LUE were much lower than those reported in field trials, due to uncommonly high yields in most field trials, as well as overestimates of absorbed radiation in CASA attributed to bias from temporal compositing of satellite data. Total NPP for US croplands, excluding Alaska and Hawaii, was estimated as 0.62 Pg C year −1 , representing ∼20% of total US NPP, and exhibited a positive trend of 3.7 Tg C year −2 . These results have several implications for large‐scale carbon cycle research that are discussed, and are especially relevant for studies of the role of agriculture in the global carbon balance.

We used airborne imaging spectroscopy and scanning light detection and ranging (LiDAR), along with bioacoustic recordings, to determine how a plant species invasion affects avian abundance and community composition across a range of Hawaiian submontane ecosystems. Total avian abundance and the ratio of native to exotic avifauna were highest in habitats with the highest canopy cover and height. Comparing biophysically equivalent sites, stands dominated by native Metrosideros polymorpha trees hosted larger native avian communities than did mixed stands of Metrosideros and the invasive tree Morella faya. A multi‐trophic analysis strongly suggests that native avifauna provide biotic resistance against the invasion of Morella trees and exotic birds, thus slowing invasion “meltdowns” that disrupt the functioning of native Hawaiian ecosystems.

Detection and mapping of invasive species is an important component of conservation and management efforts in Hawai'i, but the spectral separability of native, introduced, and invasive species has not been established. We used high spatial resolution airborne imaging spectroscopy to analyze the canopy hyperspectral reflectance properties of 37 distinct species or phenotypes, 7 common native and 24 introduced tree species, the latter group containing 12 highly invasive species. Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) reflectance and derivative-reflectance signatures of Hawaiian native trees were generically unique from those of introduced trees. Nitrogen-fixing trees were also spectrally unique from other groups of non-fixing trees. There were subtle but significant differences in the spectral properties of highly invasive tree species in comparison to introduced species that do not proliferate across Hawaiian ecosystems. The observed differences in canopy spectral signatures were linked to relative differences in measured leaf pigment (chlorophyll, carotenoids), nutrient (N, P), and structural (specific leaf area; SLA) properties, as well as to canopy leaf area index. These leaf and canopy properties contributed variably to the spectral separability of the trees, with wavelength-specific reflectance and absorption features that overlapped, but which were unique from one another. A combination of canopy reflectance from 1125–2500 nm associated with leaf and canopy water content, along with pigment-related absorption features (reflectance derivatives) in the 400–700 nm range, was best for delineating native, introduced, and invasive species. There was no single spectral region that always defined the separability of the species groups, and thus the full-range (400–2500 nm) spectrum was highly advantageous in differentiating these groups. These results provide a basis for more detailed studies of invasive species in Hawai'i, along with more explicit treatment of the biochemical properties of the canopies and their prediction using imaging spectroscopy.

Gully erosion removes comparatively large volumes of soil from small areas. It is often difficult to quantify the loss of soil because the footprint of individual gullies is too small to be captured by most generally available digital elevation models (DEMs), such as the USGS National Elevation Dataset. Airborne LiDAR (Light Detection and Ranging) has the potential to provide the required data density, but an even newer class of ground-based sensors may provide better local resolution at lower cost. In this study, we compared digital elevation models produced by airborne and ground-based LiDAR systems with ground-based geomorphic and geodetic survey data to determine their utility in quantifying volumetric soil loss due to gully erosion in a heavily degraded watershed (7.55 × 10− 2 km2), on southwestern Santa Cruz Island in southern California. Volumetric estimates of the eroded sediment were produced by comparing the LiDAR-derived DEMs of the gully system to a modeled pre-erosion surface. Average point densities were significantly higher for the ground-based LiDAR system and provided more detailed information; however, its limited scanning footprint and side-looking orientation presented serious challenges in collecting continuous data from deeply incised gullies, making the airborne system preferable for this type of investigation and likely for most applications where heavy topographic shadowing is prevalent.

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km2 of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia–Mailliard area) and (2) 58 km2 of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia–Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha− 1 (old-growth coast redwood) and 510 ± 120 Mg ha− 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70–97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha− 1 in Garcia–Mailliard and 140 ± 0.9 Mg ha− 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon.

We combined a detailed field study of forest canopy damage with calibrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) reflectance data and texture analysis to assess the sensitivity of basic broadband optical remote sensing to selective logging in Amazonia. Our field study encompassed measurements of ground damage and canopy gap fractions along a chronosequence of postharvest regrowth of 0.5–3.5 years. We found that canopy damage and regrowth rates varied according to the logging method used, either conventional logging or reduced impact logging. Areas used to stage felled trees prior to transport, log decks, had the largest gap fractions immediately following cutting. Log decks were quickly colonized by early successional plant species, resulting in significant gap fraction decreases within 1.5 years after site abandonment. Although log decks were the most obvious damage areas on the ground and in satellite imagery, they accounted for only 1–2% of the total harvested area of the blocks studied. Other forest damage features such as tree-fall gaps, skid trails, and roads were difficult to recognize in Landsat reflectance data or through textural analysis. These landscape features could be only crudely resolved in the most intensively logged forests and within about 0.5 years following harvest. We found that forest damage within any of the landscape strata (decks, roads, skids, tree falls) could not be resolved with Landsat reflectance or texture data when the canopy gap fraction was <50%. The basic Landsat ETM+ imagery lacks the resolution of forest structural features required for quantitative studies of logging damage. Landsat textural analyses may be useful for broad delineation of logged forests, but detailed ecological and biogeochemical studies will probably need to rely on other remote sensing approaches. Until spatial gradients of canopy damage and regrowth resulting from selective logging operations in tropical forests in the Amazon region are resolved, the impacts of this land use on a continental scale will remain poorly understood.

We investigated ground and canopy damage and recovery following conventional logging and reduced-impact logging (RIL) of moist tropical forest in the eastern Amazon of Brazil. Paired conventional and RIL blocks were selectively logged with a harvest intensity of approximately 23 m3 ha−1 (geometric volume) in the dry seasons (July–December) of 1996 and 1998. Ground damage (roads+skid trails+log decks) in the conventional logging treatments occupied 8.9–11.2% of total operational area. In contrast, ground damage in RIL treatments ranged from 4.6 to 4.8% of the total area. Forest canopy damage was assessed using gap fraction measurements collected with an automated optical canopy analyzer (LAI-2000; Licor Inc.) in March 1999. Canopy opening varied by time since logging. The recently logged (1998) blocks had integrated canopy gap fractions of 21.6 and 10.9% of total area for conventional and RIL blocks, respectively. The blocks logged in 1996 had more closed canopies with 16.5 and 4.9% gap fraction for conventional and RIL blocks, respectively. For comparison, undisturbed forest had a canopy gap fraction of 3.1%. Measurements of ground disturbance and gap fraction using the Licor LAI-2000 generally agree with other field evaluations of RIL and conventional logging. Detailed understanding of canopy structural changes resulting from different logging intensities are critical to the prospect of logging damage estimation using current and future remote sensing products.

Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems.This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery.Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale.Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree.An overall prediction accuracy of 76% was achieved for 15 species.We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data.We find that confronting these three challenges-reflectance anisotropy, integration of pixel-and crown-level data, and crown delineation over large areas-enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.

Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote sensing information. During the May 1997 NASA Earth Observing System Jornada Prototype Validation Exercise, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover with data from four instruments: (1) a Dycam Agricultural Digital Camera (ADC), (2) a LI-COR LAI-2000 plant canopy analyzer, (3) a Decagon sunfleck Ceptometer, and (4) a laser altimeter. Estimates from the LAI-2000 and Ceptometer were very similar (plant area index 0.3, leaf area index 0.22, total fractional cover 0.19, green fractional cover 0.14), while the ADC produced values 5% to 10% higher. Laser altimeter values, depending on the height cutoff used to establish total fractional cover, were either higher or lower than the other instruments' values: a 10-cm cutoff produced values

Abstract Coarse woody debris (CWD) is an important component of the carbon cycle in tropical forests. We measured the volume and density of fallen CWD at two sites, Cauaxi and Tapajós in the Eastern Amazon. At both sites we studied undisturbed forests (UFs) and logged forests 1 year after harvest. Conventional logging (CL) and reduced impact logging (RIL) were used for management on areas where the geometric volumes of logs harvested was about 25–30 m 3 ha −1 . Density for five classes of fallen CWD for large material (&gt;10 cm diameter) ranged from 0.71 to 0.28 Mg m −3 depending upon the degree of decomposition. Density of wood within large fallen logs varied with position relative to the ground and with distance from the center of the log. Densities for materials with diameters from 2 to 5 and 5 to 10 cm were 0.36 and 0.45 Mg m −3 , respectively. The average mass (±SE) of fallen CWD at Cauaxi was 55.2 (4.7), 74.7 (0.6), and 107.8 (10.5) Mg ha −1 for duplicate UF, RIL, and CL sites, respectively. At Tapajós, the average mass of fallen CWD was 50.7 (1.1) Mg ha −1 for UF and 76.2 (10.2) Mg ha −1 for RIL for duplicate sites compared with 282 Mg ha −1 for live aboveground biomass. Small‐ and medium‐sized material (&lt;10 cm dia.) accounted for 8–18% of the total fallen CWD mass. The large amount of fallen CWD at these UF sites relative to standing aboveground biomass suggests either that the forests have recently been subjected to a pulse of high mortality or that they normally suffer a high mortality rate in the range of 0.03 per year. Accounting for background CWD in UF, CL management produced 2.7 times as much CWD as RIL management. Excess CWD at logging sites would generate a substantial CO 2 emission given the high rates of decay in moist tropical forests.

New PhytologistVolume 189, Issue 4 p. 999-1012 Full paperFree Access Canopy phylogenetic, chemical and spectral assembly in a lowland Amazonian forest Gregory P. Asner, Gregory P. Asner Department of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USASearch for more papers by this authorRoberta E. Martin, Roberta E. Martin Department of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USASearch for more papers by this author Gregory P. Asner, Gregory P. Asner Department of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USASearch for more papers by this authorRoberta E. Martin, Roberta E. Martin Department of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USASearch for more papers by this author First published: 30 November 2010 https://doi.org/10.1111/j.1469-8137.2010.03549.xCitations: 133 Author for correspondence:Gregory P. AsnerTel: +1 650 462 1047Email: gpa@stanford.edu AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary • Canopy chemistry and spectroscopy offer insight into community assembly and ecosystem processes in high-diversity tropical forests, but phylogenetic and environmental factors controlling chemical traits underpinning spectral signatures remain poorly understood. • We measured 21 leaf chemical traits and spectroscopic signatures of 594 canopy individuals on high-fertility Inceptisols and low-fertility Ultisols in a lowland Amazonian forest. The spectranomics approach, which explicitly connects phylogenetic, chemical and spectral patterns in tropical canopies, provided the basis for analysis. • Intracrown and intraspecific variation in chemical traits varied from 1.4 to 36.7% (median 9.3%), depending upon the chemical constituent. Principal components analysis showed that 14 orthogonal combinations were required to explain 95% of the variation among 21 traits, indicating the high dimensionality of canopy chemical signatures among taxa. Inceptisols and lianas were associated with high leaf nutrient concentrations and low concentrations of defense compounds. Independent of soils or plant habit, an average 70% (maximum 89%) of chemical trait variation was explained by taxonomy. At least 10 traits were quantitatively linked to remotely sensed signatures, which provided highly accurate species classification. • The results suggest that taxa found on fertile soils carry chemical portfolios with a deep evolutionary history, whereas taxa found on low-fertility soils have undergone trait evolution at the species level. Spectranomics provides a new connection between remote sensing and community assembly theory in high-diversity tropical canopies. Introduction High species diversity in tropical forest canopies results from environmental filtering, biotic interactions, and neutral processes, played out over timescales of evolution and biogeographic migration. However, the functional variation among tropical forest canopies is often expressed in plant traits (Wright et al., 2006), which may or may not track patterns in species composition and diversity (Kraft et al., 2009). Among these traits, leaf chemicals regulate light capture, growth, respiration, longevity and defense. Leaf chemicals are also key mediators of ecosystem processes such as decomposition and nutrient cycling (Vitousek, 1984). But the relationship between phylogeny and leaf chemistry is only starting to be explored in humid tropical forests (Fyllas et al., 2009), and the relevance of high canopy diversity to canopy chemistry or ecosystem processes remains unclear (Townsend et al., 2008). Leaf chemical variation occurs among many important elements and molecular compounds. Nitrogen (N), phosphorus (P), base cations (calcium (Ca), potassium (K), and magnesium (Mg)), and micronutrients (manganese (Mn), zinc (Zn), boron (B), and iron (Fe)) vary in concentration based on investments in processes ranging from CO2 fixation to protection against toxic metals (Johnson & Todd, 1983; Aber & Melillo, 1991). Polyphenols including tannins play a lead role in the defense against herbivores and other pests (Coley et al., 1993; Rothstein et al., 2004). Chlorophylls and carotenoids regulate light harvesting, while lightweight, soluble carbon fractions form the high-energy products of photosynthesis (Evans et al., 1988). Larger and heavier secondary metabolites including cellulose and lignin require greater energetic investment by plants, but yield increased leaf toughness, longevity, and defense capability (Hikosaka, 2004). This chemical portfolio is maintained in a structure of varying leaf mass per unit area (LMA) (Poorter et al., 2009). Traditionally, studies of leaf chemical variation have focused on soil fertility and climate controls, which differentially influence concentrations of N, P, base cations, and other foliar constituents (e.g. Vitousek & Sanford, 1986; Raich et al., 1996; Aerts, 1997). Others have focused on chemical differences among plant functional types (PFTs) as a means to generalize patterns at large biogeographic scales (Bonan et al., 2002; Wright et al., 2004). However, recent studies have highlighted the potential importance of species-level diversity and taxonomic organization of several leaf chemical properties in tropical forest canopies. Townsend et al. (2007) reported that species exert a dominating influence on variation in leaf N and P concentrations in Costa Rican and Brazilian lowland forests. Hattenschwiler et al. (2008) found pronounced inter-specific variation in leaf and litter chemical properties in Guyana, noting that such high chemical diversity weakens the role of general PFT-based rules in predicting canopy function or ecosystem processes. Fyllas et al. (2009) and Asner et al. (2009) documented taxonomic organization among leaf chemicals, against a backdrop of varying climate and soils, in Amazonian and Australian tropical forest canopies, respectively. In a lowland Borneo rainforest, Paoli (2006) found a differential effect of environment and phylogeny on leaf chemical variation: within the genus Shorea, variation in leaf P and specific leaf area (LMA−1) was influenced more by soil fertility than was leaf N, which more closely tracked phylogeny. These and other studies give us a sense that leaf chemical attributes are indeed strongly influenced by species composition. However, the role of soil fertility and climate in mediating the connection between phylogeny and leaf chemical traits is not well understood. A major barrier to linking canopy diversity and chemistry, and to understanding the role of this linkage to ecosystem processes, rests in measurement and tracking of the taxa at geographic scales commensurate with community dynamics and demographic change. Field measurements cannot easily resolve changes in forest canopy composition because the pertinent demographic dynamics occur at scales larger than most plots. In humid tropical forests, this limitation is evidenced by the fact that the spatial co-occurrence of species or even congeners is often relatively low and many singletons exist in a given plot (Condit et al., 2005). To understand the ecological importance of varying canopy composition, we need a way to observe and quantify plant traits that may indicate the presence of species and their functional role over relatively large areas. The potential observables are a challenge to identify, yet the regional perspective is proving critical to understanding ecological change for conservation and management decision-making. Recent work demonstrates that remotely sensed optical spectroscopy provides a window into the composition and diversity of tropical forests. In Hawaiian forests, Asner et al. (2008) developed airborne spectroscopic signatures to identify native and invasive species, while Carlson et al. (2007) employed the concept of spectral variance to map canopy species richness. Castro-Esau et al. (2004) and Sanchez-Azofeifa et al. (2009) used leaf-level spectroscopy to delineate liana and tree species in Panamanian forests. Clark et al. (2005) used airborne spectroscopy to classify several canopy tree species in a Costa Rican forest. These and other studies provide novel links between spectral data and species information, but none have developed the general approach required to broadly understand the interconnection between canopy composition and spectroscopy. We believe that this interconnection can be made robustly and generally via the chemical properties of the canopies. Asner & Martin (2009) introduced the concept of spectranomics to link a specific type of remote sensing – high-fidelity spectroscopy – of foliage to canopy taxa via their detailed chemical signatures. Variation in spectroscopic properties of canopies is determined by multiple molecular compounds ranging from pigments to secondary metabolites, along with variation in leaf area and volume, and canopy architecture (Curran, 1989; Asner, 1998). In highly foliated canopies of the humid tropics, leaf chemical traits are primary determinants over high-fidelity spectra (Asner, 2008; Asner & Martin, 2008). The spectranomics approach suggests that a spectral–chemical link would allow taxonomic analysis of tropical forest canopies from aircraft, yet community composition may be disconnected from the spectral-to-chemical linkages needed to indicate species presence and functional status. This disconnect may occur if intraspecific variation in chemical attributes is high and/or if phenotypic plasticity trumps phylogenetic patterns among chemical traits. Even if spectral measurements yield quantitative information on the chemical signatures of canopy foliage, closely related species may have similar chemical portfolios (phylogenetically conserved), making it difficult to differentiate taxa. To our knowledge, the link between community composition and spectral properties through chemical traits has not been broadly demonstrated. We sought to integrate phylogenetic, chemical and spectral properties of canopies in a lowland tropical forest spanning low- and high-fertility soils in the Peruvian Amazon. Our goals were to determine the degree to which canopy chemical traits, and combinations of traits termed 'chemical signatures', are phylogenetically organized within and across contrasting soil types, and to assess which chemical constituents can be quantitatively linked to canopy spectroscopy. This study is the first major test of the spectranomics concept, carried out in a very high diversity forest. Here we present data on 21 leaf chemical traits and high-fidelity spectroscopic signatures of 594 individuals of tree, palm, vine and liana growth habits. We carefully controlled for full-sunlight, upper canopy position to ensure that the light environment was relatively constant, thereby avoiding unwanted variation in chemical traits resulting from shade, and because upper canopy foliage plays a dominant role in determining the spectroscopic remote sensing signatures of tropical forests (Asner, 2008). Materials and Methods Site description The study was conducted in terra firme and floodplain forests along the Tambopata River in the southern Peruvian Amazon basin. Terra firme forests are located on flat to undulating Pleistocene surfaces c. 15–20 m above the floodplain forests and are classified as weathered, lower fertility haplic Ultisols (or Alisols in the Food and Agriculture Organization (FAO) World Reference Base) (Quesada et al., 2009). Floodplain forests are characterized by high-fertility alluvium originating in the Andes, and deposited throughout the Holocene, forming humic Inceptisols (or Cambisols) that do not currently experience frequent or extensive flooding. These soil types are immediately adjacent to one another and support large-statured forest canopies reaching 40 m in height. Mean annual precipitation and temperature are 2600 mm yr−1 and 24.0°C, respectively. The Holdridge Life Zone classification is moist lowland tropical forest. Canopy collections were undertaken over a total forest area of c. 1600 ha, with each soil type covering about half of this area. Our sampling was designed to capture the diversity of sunlit canopies throughout the site, while also maintaining statistical power for replication at the family, genus, species and branch (within crown) levels. In total, 594 individual canopies were randomly selected from 328 unique species spanning the two soil types and a range of growth habits. Full triplicate replication at the branch level within crowns was carried out for 450 of these individuals (n = 450 × 3 branches = 1350 samples) to assess the intracrown variability. In addition, 126 species (65 on Inceptisols and 61 on Ultisols) were selected for replication with two or more representatives. Of these 126, a total of 48 had three or more replicates. These different levels of replication were based on the requirements of the various statistical analyses employed in the study, described below and in the Methods S1 section of the online Supporting Information. Tree species comprised the majority of the individuals with 531 representatives, followed by lianas (40), hemi-epiphytes (11), palms (nine) and vines (three). The distribution was only slightly different on the two soil types. On the Inceptisols, the 294 individuals selected were comprised of 202 unique species with the following habits: tree (257), liana (20), hemi-epiphyte (eight), palm (seven), and vine (two). On the Ultisols, 300 individuals were selected consisting of 193 unique species (29 with three or more representatives; 103 individuals) with the following habits: tree (274), liana (20), hemi-epiphyte (three), palm (two), and vine (one). Taxonomically the 328 species were partitioned into 177 genera and 55 families. Samples were selected to control for full-sunlight canopies, providing comparable canopy position and illumination conditions among samples. Leaf collections were conducted using tree-climbing techniques to ensure that full-sunlight samples were taken. Samples were cryocooled (−80°C) and dried, with additional pre-processing, in the field and then transported to the laboratory for multi-chemical assays. Spectroscopic measurements were made in the field using fresh samples with a high-fidelity 400–2500-nm custom-built spectroradiometer, integrating sphere and light source. Statistical analyses included intraspecific coefficients of variation (with at least two replicates per species), general linear modeling (at least three replicates), nested random-effects modeling (at least three replicates), principal components analysis (all data used), and stepwise linear discriminant analysis (at least two replicates). Detailed methodologies are provided in the online Supporting Information Methods S1, and laboratory protocols are downloadable from the Carnegie spectranomics website (http://spectranomics.ciw.edu). Results Soil and habit effects Most leaf properties showed enormous variation on both soil types (Table 1). Nonetheless, chlorophylls, cellulose, lignin, phenols, and tannins were 7–23% lower in concentration on Inceptisols (Fig. 1). By contrast, P, B, Fe, and base cation contents were 10–52% higher on Inceptisols. Zn and Mn are 18 and 39% higher on Ultisols, respectively, but soluble C and hemi-cellulose fractions were lower in canopies found on these soils. LMA and N, water and carotenoid concentrations did not differ between Ultisols and Inceptisols. Table 1. Descriptive statistics for canopy leaf samples collected on high-fertility floodplain Inceptisols and low-fertility upland Ultisols Inceptisols Ultisols M SD CV Min Max M SD CV Min Max LMA (ns) 105.3 31.6 30.0 34.4 194.9 101.6 28.3 27.9 38.3 215.9 Water (ns) 57.8 6.7 11.6 44.3 79.0 57.7 7.0 12.1 40.7 83.6 Chla 5.22 1.77 33.95 1.60 13.26 5.57 1.69 30.42 1.64 11.44 Chlb 1.96 0.70 35.98 0.56 5.12 2.08 0.68 32.85 0.64 4.73 Carotenoids (ns) 1.56 0.48 30.63 0.53 3.67 1.62 0.48 29.67 0.51 4.32 Phenols 73.2 48.3 66.0 0.7 225.3 93.5 51.1 54.6 0.0 244.7 Tannins 36.0 20.9 58.1 0.0 115.8 46.5 22.8 48.9 0.0 119.2 N (ns) 2.19 0.68 30.84 1.04 4.66 2.20 0.58 26.41 1.17 4.62 C 46.8 3.3 7.0 34.7 53.5 49.0 3.4 6.9 34.0 56.0 P 0.17 0.08 46.55 0.06 0.51 0.13 0.05 38.02 0.06 0.36 Ca 1.29 0.91 70.38 0.03 4.40 0.85 0.69 81.66 0.06 3.97 K 1.11 0.59 53.28 0.20 4.40 0.88 0.43 48.93 0.29 2.75 Mg 0.31 0.13 43.20 0.09 0.85 0.28 0.15 51.92 0.09 0.91 B 28.7 21.6 75.5 4.0 173.0 22.8 15.2 66.7 4.4 84.7 Fe 75.4 43.2 57.3 28.3 391.2 61.6 33.7 54.8 19.7 296.1 Mn 289.2 664.5 230.4 10.6 6594.3 467.9 719.3 153.7 10.0 4843.5 Zn 17.7 12.5 70.3 4.6 88.03 22.0 13.1 59.3 4.8 118.9 Soluble C 45.8 10.4 22.7 18.7 71.8 43.1 11.0 25.6 22.9 76.4 Hemi-cellulose 15.6 5.1 32.3 3.3 35.2 12.3 4.4 35.3 1.9 34.7 Cellulose 18.1 4.9 27.5 6.5 41.8 19.9 5.4 27.4 7.7 35.7 Lignin 20.4 8.6 42.1 6.9 50.8 24.5 8.8 35.9 5.3 48.9 t-tests, performed on the loge-transformed data, indicated significant differences (P < 0.01) for all leaf properties except for leaf mass per unit area (LMA), N, water, and carotenoids (Car) as marked by 'ns'. M, mean chemical concentration; SD, standard deviation; CV, coefficient of variation; Min, minimum; Max, maximum. Figure 1Open in figure viewerPowerPoint Percentage differences in the ratio of each canopy chemical trait found on Inceptisols (I) and Ultisols (U), I : U (black bars); and in the ratio of each chemical trait for liana (L) and tree (T) habits, L : T (gray bars). LMA, leaf mass per unit area. We summarize differences between trees and lianas in Table 2, and provide additional data for less common growth habits in Table S2. All means (loge-transformed data) were statistically different with the exception of P, Fe, soluble C, and cellulose. Lianas had much higher concentrations of key growth-related attributes including chlorophylls, carotenoids, N, and base cations (Fig. 1). By contrast, trees had 20–40% higher concentrations of lignin, phenols and tannins. Finally, two-way ANOVA tests indicated no significant interaction between soil and growth habit for any leaf property, with the exception of Mn (F = 3.9, P = 0.05). Table 2. Descriptive statistics for canopy leaf samples collected from species with tree and liana growth habits Tree Liana M SD CV Min Max M SD CV Min Max LMA 104.3 29.2 28.0 38.3 215.9 78.3 23.4 29.9 34.4 140.9 Water 57.4 6.6 11.5 40.7 79.0 61.6 8.4 13.6 44.6 83.6 Chla 5.30 1.68 31.61 1.6 11.44 7.02 1.95 27.79 4.11 13.26 Chlb 1.97 0.67 33.77 0.56 4.73 2.70 0.78 28.76 1.66 5.12 Carotenoids 1.57 0.47 29.76 0.51 4.32 2.03 0.5 24.85 1.21 3.67 Phenols 87.2 50.5 57.9 0.0 244.7 53.4 47.1 88.3 0.7 184.4 Tannins 42.8 22.2 52.0 0.0 119.2 30.1 23.2 77.1 0.0 87.8 N 2.19 0.62 28.53 1.04 4.66 2.52 0.59 23.38 1.48 4.14 C 48.1 3.6 7.4 34.0 56.0 46.7 3.2 6.9 39.5 52.3 P (ns) 0.15 0.07 46.62 0.06 0.51 0.16 0.06 37.41 0.08 0.38 Ca 1.05 0.82 78.02 0.03 4.85 1.47 1.09 74.02 0.13 4.18 K 0.97 0.52 53.7 0.2 4.4 1.21 0.63 52.05 0.33 2.79 Mg 0.29 0.13 44.58 0.09 0.8 0.41 0.23 56.75 0.1 0.91 B 25.8 19.3 75.0 4.0 173.0 29.1 15.7 53.8 6.8 76.7 Fe (ns) 67.2 38.5 57.3 19.7 391.2 75.6 39.0 51.7 38.4 213.3 Mn 352.7 646.7 183.5 10.0 6594.3 834.1 1199.4 143.8 11.4 5034.3 Zn 19.4 11.6 59.8 4.6 81.2 27.6 23.8 86.3 7.9 118.9 Soluble C (ns) 44.6 10.8 24.1 22.9 76.4 45.6 9.9 21.8 25.2 68.3 Hemi-cellulose 13.7 4.9 36.1 1.9 34.7 17.1 5.2 30.4 8.1 28.2 Cellulose (ns) 18.7 5.0 26.8 6.5 35.7 18.8 4.9 26.2 9.7 31.9 Lignin 22.8 9.1 39.8 5.3 50.8 18.4 6.5 35.4 7.8 33.0 t-tests, performed on the loge-transformed data, indicated significant differences (P < 0.01) for all leaf properties except for P, Fe, soluble C and cellulose, as marked by 'ns'. LMA, leaf mass per unit area; M, mean chemical concentration; SD, standard deviation; CV, coefficient of variation; Min, minimum; Max, maximum. Statistics for less common growth habits including hemi-epiphytes (n = 11), palms (n = 9) and nonwoody vines (n = 3) are provided in Supporting Information Table S2. Within-crown and intraspecific variation Within-crown variation in leaf properties varied from medians of only 1.4 and 1.7% for C and water, respectively, to 27.7% for phenols (Fig. 2a). Median variation in macro- and micronutrients within crowns ranged from 4.0 to 9.8%, which was similar to the range measured for C fractions including lignin, cellulose, hemi-cellulose and soluble C (4.0–9.3%). Median variation in photosynthetic pigment concentrations ranged from 10.3 to 12.3%. Phenols and tannins showed occasional very high values for intracrown variation, reaching an absolute maximum of 123% in one case. Nonetheless, the upper quartile value for these leaf constituents was only 45 and 31% for phenols and tannins, respectively. Figure 2Open in figure viewerPowerPoint Coefficients of variation (CVs) for 20 chemical traits and leaf mass per unit area (LMA) for (a) samples collected on different branches within crowns and (b) samples collected from different crowns within species. Chemical data are mass-based; n = 126 species with two or more replicates per species. Intraspecific variation among leaf properties exceeded that of within-crown variation in most cases (Fig. 2b). Total C had the minimum median variation of 1.8%, and Mn had the maximum of 36.7%. Maximum intraspecific variation was about twice that of the maximum intracrown variation. We also analyzed intraspecific variation by soil type (Fig. S1), and this indicated no general differences in the variability of most chemicals within species. A few notable exceptions included 44–60% greater variation in Mn and Zn, and 10–17% less variation in photosynthetic pigments, in species found on Inceptisols. Phylogenetic patterns Above, we have reported wide-ranging values for all chemicals and LMA, independent of soil type or growth habit. However, we also showed that Inceptisols or lianas were associated with higher concentrations of nutrients and lower concentrations of defense-related compounds (lignin and phenols) compared with Ultisols or the tree growth habit. Yet despite these underlying substrate and habit effects, and the degree of intraspecific variation measured, an average 70% of the variation in the elemental and molecular composition of canopy foliage can be explained by phylogenetic groupings that incorporate families, genera, and species (Fig. 3a). Here we use the term 'taxonomy' as an expression of phylogeny; phylogeny resolves more detailed evolutionary relationships among taxa, which we did not assess. Factors other than taxonomy, including micro-environment, growth habit, sample selection and analytical error, contributed to the average remaining 30% residual in the nested ANOVA analyses. Family, genus within family, and species within genus within family groupings accounted for an average of 25, 15, and 30% of the chemical taxonomy, respectively. Chemical attributes with the highest degree of taxonomic organization were lignin (86%), total C (85%), phenols (84%), and N (82%). Taxonomy accounted for just 38–52% of the variation in chlorophylls and carotenoids, suggesting a more universal approach to light harvesting by tropical canopies. Figure 3Open in figure viewerPowerPoint Nested random-effects ANOVA results for canopy taxa from (a) combined sites, (b) Inceptisols and (c) Ultisols in the Tambopata National Forest, southern Peruvian Amazon basin. Results show the percentage variance of each chemical attributable to plant families, genera within families, species within genera within families, and the residual. The residual includes factors such as intraspecific variation, micro-site variability, canopy selection, and analytical error. Soil type imparted few effects on the degree and pattern of taxonomic organization for most chemicals (Fig. 3b,c). A few exceptions were the photosynthetic pigments: Chla, Chlb, and carotenoids showed 36, 39, and 18% less overall taxonomic organization on the higher fertility Inceptisols. Moreover, family- and genus-level organization of these pigments was 76–152% greater on Inceptisols, although species-level control was > 100% higher on the Ultisols. More generally, although families accounted for 39–423% more variation in all leaf chemicals on Inceptisols than on Ultisols, this heightened degree of family-level control was balanced by increased genus- and/or species-level control on the Ultisols. N is a good example: families accounted for 43% more variation in N on Inceptisols, whereas genus and species accounted for 15 and 34% more variation on Ultisols, respectively. This led to no net difference in overall phylogenetic control of leaf N on either soil. Regression analyses supported results from the nested ANOVA tests (Table S3). Family, genus, and species accounted for an average 25, 32, and 68% (adjusted r2; P < 0.01), respectively, of the variation in leaf chemicals and LMA. Maximum species-level regression (adjusted r2) values were 0.88 for C, 0.87 for lignin, 0.82 for phenols and 0.81 for N. The weakest species-level regressions were for chlorophylls and carotenoids (0.34–0.49). Families on Inceptisols had an average 60% higher adjusted r2 for leaf chemicals, and were nearly three times stronger determinants of Ca concentrations. By contrast, photosynthetic pigments showed 67–81% higher regression coefficients at the genus level on Inceptisols, but 48–64% higher at the species level on Ultisols. This is similar to the compensating effects at family, genus and species levels described earlier for N in the nested ANOVA analyses. Inter-relationships among leaf properties The diversity of chemical signatures among species is a function of stoichiometric relationships between constituents. Correlation among chemicals reduces the dimensionality of would-be chemical signatures, whereas orthogonal properties add to the chemical diversity of the signatures. Principal components analysis (PCA) showed that a single linear combination of traits (PC1) explained just 31% of the variation among 21 leaf properties, and 14 orthogonal combinations were required to explain 95% of the variation (Table 3). There was no effect of soil type on this pattern (data not shown). We also considered other leaf chemical trait combinations, starting with the photosynthetic pigments alone, for which PC1 accounted for 97% of the variation between chlorophylls and carotenoids. We analyzed three traits contributing to the growth-related leaf 'economics spectrum' (Wright et al., 2004) as a group, and PC1 accounted for 61% of the variation among N, P and LMA. Finally, the first PC accounted for 59% of the variation between C fractions. Table 3. Principal components analysis (PCA) results for different combinations of leaf properties Leaf properties % of variance explained (raw data) % of variance explained (loge-transformed data) (i) Chla, Chlb, and carotenoids 96.5 (96.2) 97.7 (96.5) (ii) LMA, N, and P 59.1 (58.7) 61.2 (60.8) (iii) N, P, Ca, K, and Mg 43.3 (49.1) 49.3 (52.4) (iv) Soluble C, cellulose, hemi-cellulose, and lignin 58.0 (59.0) 58.9 (59.6) (iv) i + ii+ iii + iv above 37.9 (36.7) 38.5 (37.4) (v) Remote sensing properties1 37.9 (37.1) 38.2 (37.6) (vi) All leaf properties 30.5 (30.1) 31.6 (31.2) The percentage variance explained by the first principal component is shown for raw and loge-transformed data. Data in parentheses are results for the tree habit only. 1Ten leaf properties selected for remote sensing analysis including water, C, N, leaf mass per unit area (LMA), chlorophylls, carotenoids, cellulose, soluble C, phenols and hemi-cellulose (see Table 5). Correlation analyses further supported results from the PCA work, indicating relatively weak correlation (e.g. r < 0.4 for 86% of trait pairs) among most chemicals (Table S4). Exceptions again included the photosynthetic pigments, for which inter-correlations are well understood (Sims & Gamon, 2002), but all other chemical relationships left 31–99% (mode = 80%) of the information uncorrelated. There was a highly variable effect of soil type on leaf chemical correlations (Tables S5, S6). About 68% of the trait pairs showed some level of increased correlation on the Inceptisols, with the majority of these increases observed among nutrients. About 32% of the trait pairs showed no difference or slightly lower correlation on Inceptisols than on Ultisols. Taxonomic classification using chemistry Stepwise linear discriminant analysis (LDA) indicated that 96.9% of the species were correctly classified using their full chemical signatures (Fig. 4). A combination of lignin, N, C and phenols alone classified 65.6% of the species correctly (Table 4); adding other elements and compounds yielded 1–10% more power per chemical to the classification until the signature was comprised of the full 21 constituents. The results of family- and genus-level analyses generally mirrored the species results (Fig. 4), although with lower overall classification accuracy because of the increased variation among leaf traits at these lower taxonomic

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests. The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated < 6.8% and < 18.6% error rates in the detection of invasive tree species at ∼ 7 m2 and ∼ 2 m2 minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.

Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to estimating the abundance of pure spectral constituents (endmembers) in each (possibly mixed) observation collected by the imaging instrument. In recent years, several endmember extraction algorithms (EEAs) have been proposed for automated endmember extraction from hyperspectral data sets. Traditionally, EEAs extract/select only one single standard endmember spectrum for each of the presented endmember classes or scene components. The use of fixed endmember spectra, however, is a simplification since in many cases the conditions of the scene components are spatially and temporally variable. As a result, variation in endmember spectral signatures is not always accounted for and, hence, spectral unmixing can lead to poor accuracy of the estimated endmember fractions. Here, we address this issue by developing a simple strategy to adapt available EEAs to select multiple endmembers (or bundles) per scene component. We run the EEAs in randomly selected subsets of the original hyperspectral image, and group the extracted samples of pure materials in a bundle using a clustering technique. The output is a spectral library of pure materials, extracted automatically from the input scene. The proposed technique is applied to several common EEAs and combined with an endmember variability reduction technique for unmixing purposes. Experiments with both simulated and real hyperspectral data sets indicate that the proposed strategy can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.

Abstract When woody plant abundance increases in grasslands and savannas, a phenomenon widely observed worldwide, there is considerable uncertainty as to whether aboveground net primary productivity (ANPP) and ecosystem carbon (C) and nitrogen (N) pools increase, decrease, or remain the same. We estimated ANPP and C and N pools in aboveground vegetation and surface soils on shallow clay and clay loam soils undergoing encroachment by Prosopis glandulosa in the Southern Great Plains of the United States. Aboveground Prosopis C and N mass increased linearly, and ANPP increased logarithmically, with stand age on clay loam soils; on shallow clays, Prosopis C and N mass and ANPP all increased linearly with stand age. We found no evidence of an asymptote in trajectories of C and N accumulation or ANPP on either soil type even following 68 years of stand development. Production and accumulation rates were lower on shallow clay sites relative to clay loam sites, suggesting strong edaphic control of C and N accumulation associated with woody plant encroachment. Response of herbaceous C mass to Prosopis stand development also differed between soil types. Herbaceous C declined with increasing aboveground Prosopis C on clay loams, but increased with increasing Prosopis C on shallow clays. Total ANPP ( Prosopis +herbaceous) of sites with the highest Prosopis basal area were 1.2 × and 4.0 × greater than those with the lowest Prosopis basal area on clay loam and shallow clay soils, respectively. Prosopis ANPP more than offset declines in herbaceous ANPP on clay loams and added to increased herbaceous ANPP on shallow clays. Although aboveground C and N pools increased substantially with Prosopis stand development, we found no corresponding change in surface soil C and N pools (0–10 cm). Overall, our findings indicate that Prosopis stand development significantly increases ecosystem C and N storage/cycling, and the magnitude of these impacts varied with stand age, soil type and functional plant traits

Abstract Fire is a major disturbance in the boreal forest, and has been shown to release significant amounts of carbon (C) to the atmosphere through combustion. However, less is known about the effects on ecosystems following fire, which include reduced productivity and changes in decomposition in the decade immediately following the disturbance. In this study, we assessed the impact of fire on net primary productivity (NPP) in the North American boreal forest using a 17‐year record of satellite NDVI observations at 8‐ km spatial resolution together with a light‐use efficiency model. We identified 61 fire scars in the satellite observations using digitized fire burn perimeters from a database of large fires. We studied the postfire response of NPP by analyzing the most impacted pixel within each burned area. NPP decreased in the year following the fire by 60–260 g C m −2 yr −1 (30–80%). By comparing pre‐ and postfire observations, we estimated a mean NPP recovery period for boreal forests of about 9 years, with substantial variability among fires. We incorporated this behavior into a carbon cycle model simulation to demonstrate these effects on net ecosystem production. The disturbance resulted in a release of C to the atmosphere during the first 8 years, followed by a small, but long‐lived, sink lasting 150 years. Postfire net emissions were three times as large as from a model run without changing NPP. However, only small differences in the C cycle occurred between runs after 8 years due to the rapid recovery of NPP. We conclude by discussing the effects of fire on the long‐term continental trends in satellite NDVI observed across boreal North America during the 1980s and 1990s.

Abstract Herbivores cause treefalls in African savannas, but rates are unknown at large scales required to forecast changes in biodiversity and ecosystem processes. We combined landscape‐scale herbivore exclosures with repeat airborne Light Detection and Ranging of 58 429 trees in Kruger National Park, South Africa, to assess sources of savanna treefall across nested gradients of climate, topography, and soil fertility. Elephants were revealed as the primary agent of treefall across widely varying savanna conditions, and a large‐scale ‘elephant trap’ predominantly removes maturing savanna trees in the 5–9 m height range. Treefall rates averaged 6 times higher in areas accessible to elephants, but proportionally more treefall occurred on high‐nutrient basalts and in lowland catena areas. These patterns were superimposed on a climate‐mediated regime of increasing treefall with precipitation in the absence of herbivores. These landscape‐scale patterns reveal environmental controls underpinning herbivore‐mediated tree turnover, highlighting the need for context‐dependent science and management.

Remote sensing technologies can provide objective, practical and cost-effective solutions for developing and maintaining REDD+ monitoring systems. This paper reviews the potential and status of available remote sensing data sources with a focus on different forest information products and synergies among various approaches and evolving technologies. There is significant technical capability of remote sensing technologies but operational usefulness is constrained by lack of consistent and continuous coverage, data availability in developing countries, appropriate methodologies for national-scale use and available capacities in developing countries. Coordinated international efforts, regional cooperation and continued research efforts are essential to further develop national approaches and capacities to fully explore and use the potential remote sensing has to offer for REDD+ forest monitoring.

Leaf mass per area (LMA) is a trait of central importance to plant physiology and ecosystem function, but LMA patterns in the upper canopies of humid tropical forests have proved elusive due to tall species and high diversity. We collected top-of-canopy leaf samples from 2873 individuals in 57 sites spread across the Neotropics, Australasia, and Caribbean and Pacific Islands to quantify environmental and taxonomic drivers of LMA variation, and to advance remote-sensing measures of LMA. We uncovered strong taxonomic organization of LMA, with species accounting for 70% of the global variance and up to 62% of the variation within a forest stand. Climate, growth habit, and site conditions are secondary contributors (1–23%) to the observed LMA patterns. Intraspecific variation in LMA averages 16%, which is a fraction of the variation observed between species. We then used spectroscopic remote sensing (400–2500 nm) to estimate LMA with an absolute uncertainty of 14–15 g/m2 (r2 = 0.85), or ∼10% of the global mean. With radiative transfer modeling, we demonstrated the scalability of spectroscopic remote sensing of LMA to the canopy level. Our study indicates that remotely sensed patterns of LMA will be driven by taxonomic variation against a backdrop of environmental controls expressed at site and regional levels.

Despite the importance of fire in shaping savannas, it remains poorly understood how the frequency, seasonality, and intensity of fire interact to influence woody vegetation structure, which is a key determinant of savanna biodiversity. We provide a comprehensive analysis of vertical and horizontal woody vegetation structure across one of the oldest savanna fire experiments, using new airborne Light Detection and Ranging (LiDAR) technology. We developed and compared high‐resolution woody vegetation height surfaces for a series of large experimental burn plots in the Kruger National Park, South Africa. These 7‐ha plots (total area ∼1500 ha) have been subjected to fire in different seasons and at different frequencies, as well as no‐burn areas, for 54 years. Long‐term exposure to fire caused a reduction in woody vegetation up to the 5.0–7.5 m height class, although most reduction was observed up to 4 m. Average fire intensity was positively correlated with changes in woody vegetation structure. More frequent fires reduced woody vegetation cover more than less frequent fires, and dry‐season fires reduced woody vegetation more than wet‐season fires. Spring fires from the late dry season reduced woody vegetation cover the most, and summer fires from the wet season reduced it the least. Fire had a large effect on structure in the densely wooded granitic landscapes as compared to the more open basaltic landscapes, although proportionally, the woody vegetation was more reduced in the drier than in the wetter landscapes. We show that fire frequency and fire season influence patterns of vegetation three‐dimensional structure, which may have cascading consequences for biodiversity. Managers of savannas can therefore use fire frequency and season in concert to achieve specific vegetation structural objectives.

Patterns of tropical forest functional diversity express processes of ecological assembly at multiple geographic scales and aid in predicting ecological responses to environmental change. Tree canopy chemistry underpins forest functional diversity, but the interactive role of phylogeny and environment in determining the chemical traits of tropical trees is poorly known. Collecting and analyzing foliage in 2,420 canopy tree species across 19 forests in the western Amazon, we discovered (i) systematic, community-scale shifts in average canopy chemical traits along gradients of elevation and soil fertility; (ii) strong phylogenetic partitioning of structural and defense chemicals within communities independent of variation in environmental conditions; and (iii) strong environmental control on foliar phosphorus and calcium, the two rock-derived elements limiting CO2 uptake in tropical forests. These findings indicate that the chemical diversity of western Amazonian forests occurs in a regionally nested mosaic driven by long-term chemical trait adjustment of communities to large-scale environmental filters, particularly soils and climate, and is supported by phylogenetic divergence of traits essential to foliar survival under varying environmental conditions. Geographically nested patterns of forest canopy chemical traits will play a role in determining the response and functional rearrangement of western Amazonian ecosystems to changing land use and climate.

Leaf chemical and spectral properties of 162 canopy species were measured at 11 tropical forest sites along a 6024 mm precipitation/yr and 8.7 degrees C climate gradient in Queensland, Australia. We found that variations in foliar nitrogen, phosphorus, chlorophyll a and b, and carotenoid concentrations, as well as specific leaf area (SLA), were expressed more strongly among species within a site than along the entire climate gradient. Integrated chemical signatures consisting of all leaf properties did not aggregate well at the genus or family levels. Leaf chemical diversity was maximal in the lowland tropical forest sites with the highest temperatures and moderate precipitation levels. Cooler and wetter montane tropical forests contained species with measurably lower variation in their chemical signatures. Foliar optical properties measured from 400 to 2500 nm were also highly diverse at the species level, and were well correlated with an ensemble of leaf chemical properties and SLA (r2 = 0.54-0.83). A probabilistic diversity model amplified the leaf chemical differences among species, revealing that lowland tropical forests maintain a chemical diversity per unit richness far greater than that of higher elevation forests in Australia. Modeled patterns in spectral diversity and species richness paralleled those of chemical diversity, demonstrating a linkage between the taxonomic and remotely sensed properties of tropical forest canopies. We conclude that species are the taxonomic unit causing chemical variance in Australian tropical forest canopies, and thus ecological and remote sensing studies should consider the role that species play in defining the functional properties of these forests.

The three-dimensional spatial configuration of forest habitats affects the capacity of arboreal vertebrates to move, access food, and avoid predation. However, vegetation sampling over large areas from a sufficient density of field plots to quantify fine-grained heterogeneity in canopy structure is logistically difficult, labor-intensive, time-consuming and costly, particularly in remote areas of tropical forests. We used airborne waveform light detection and ranging (LiDAR) data acquired over the southeastern Peruvian Amazon in combination with detailed field data on a population of bald-faced saki monkeys (Pithecia irrorata) to assess the utility of LiDAR-derived indices of canopy structure in describing parameters of preferred forest types for this arboreal primate. Forest structure parameters represented by LiDAR measurements were significantly different between home range areas used by sakis and those that were not used. Home range areas used by sakis represented a predictable subset of available forest areas, generally those containing the tallest and most uniform canopies. Differences observed within a 335-ha focal area occupied by five previously habituated and systematically followed study groups were consistent across the wider study landscape (6,400 ha): sakis were missing from areas of low-statured, heterogeneous canopies, but they occupied adjacent areas dominated by taller and less variable canopies. These findings provide novel insights into the relationship between vegetation structure and habitat use by a tropical arboreal vertebrate and demonstrate that high-resolution, three-dimensional remote sensing measurements can be useful in predicting habitat occupancy and selection by forest canopy species.

Woody plant abundance is widely recognized to have increased in savannas and grasslands worldwide. The lack of information on the rates, dynamics, and extent of increases in shrub abundance is a major source of uncertainty in assessing how this vegetation change has influenced biogeochemical cycles. Projecting future consequences of woody cover change on ecosystem function will require knowledge of where shrub cover in present-day stands lies relative to the realizable maximum for a given soil type within a bioclimatic region. We used time-series aerial photography (1936, 1966, and 1996) and field studies to quantify cover and biomass of velvet mesquite (Prosopis velutina Woot.) following its proliferation in a semidesert grassland of Arizona. Mapping of individual shrubs indicated an encroachment phase characterized by high rates of bare patch colonization. Upon entering a stabilization phase, shrub cover increases associated with recruitment and canopy expansion were largely offset by contractions in canopy area of other shrub patches. Instances of shrub disappearance coincided with a period of below-average rainfall (1936-1966). Overall, shrub cover (mean +/- SE) on sandy uplands with few and widely scattered shrubs in 1902 was dynamically stable over the 1936-1996 period averaging approximately 35% +/- 5%. Shrub cover on clayey uplands in 1936 was 17% +/- 2% but subsequently increased twofold to levels comparable to those on sandy uplands by 1966 (36% +/- 7%). Cover on both soils then decreased slightly between 1966 and 1996 to 28% +/- 3%. Thus, soil properties influenced the rate at which landscapes reached a dynamic equilibrium, but not the apparent endpoint. Although sandy and clayey landscapes appear to have stabilized at comparable levels of cover, shrub biomass was 1.4 times greater on clayey soils. Declines in shrub cover between 1966 and 1996 were accompanied by a shift to smaller patch sizes on both sandy and clayey landscapes. Dynamics observed during the stabilization phase suggest that density-dependent regulation may be in play. If woody cover has transitioned from directional increases to a dynamic equilibrium, biomass projections will require monitoring and modeling patch dynamics and stand structure rather than simply changes in total cover.

With the goal of advancing remote sensing in biodiversity science, Spectranomics represents an emerging approach, and a suite of quantitative methods, intended to link plant canopy phylogeny and functional traits to their spectral-optical properties. The current Spectranomics database contains about one half of known tropical forest canopy tree species worldwide, and has become a forecasting asset for predicting aspects of plant functional and biological diversity to be remotely mapped and monitored with current and future spectral remote sensing technology. To mark ten years of Spectranomics, we review recent scientific outcomes to further stimulate engagement in the use of spectral remote sensing for biodiversity and functional ecology research. In doing so, we highlight three major emerging opportunities for the science and conservation communities based on Spectranomics.

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including—in the latter case—x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called “out-of-bag”), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha−1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

Remotely sensed data are the best and perhaps the only possible way for monitoring large‐scale, human‐induced land occupation and biosphere‐atmosphere processes in regions such as the Brazilian tropical savanna (Cerrado). Landsat imagery has been intensively employed for these studies because of their long‐term data coverage (>30 years), suitable spatial and temporal resolutions, and ability to discriminate different land‐use and land‐cover classes. However, cloud cover is the most obvious constraint for obtaining optical remote sensing data in tropical regions, and cloud cover analysis of remotely sensed data is a requisite step needed for any optical remote sensing studies. This study addresses the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat‐like sensors. Percent cloud cover from more than 35 500 Landsat quick‐looks were estimated by the K‐means unsupervised classification technique. The data were examined by month, season, and El Niño Southern Oscillation event. Monthly observations of any part of the biome are highly unlikely during the wet season (October–March), but very possible during the dry season, especially in July and August. Research involving seasonality is feasible in some parts of the Cerrado at the temporal satellite sampling frequency of Landsat sensors. There are several limitations at the northern limit of the Cerrado, especially in the transitional area with the Amazon. During the 1997 El Niño event, the cloudiness over the Cerrado decreased to a measurable but small degree (5% less, on average). These results set the framework and limitations of future studies of land use/land cover and ecological dynamics using Landsat‐like satellite sensors.

Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400–2510 nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data.

We evaluated the potential of a multi-temporal Multiple Endmember Spectral Mixture Analysis (MESMA) for invasive species mapping in Hawaiian rainforests. Earth Observing-1 Hyperion time series data were compiled in a single image cube and ingested into MESMA. While the temporal analysis provided a way to incorporate species phenology, a feature selection technique automatically identified the best time and best spectral feature set to optimize the separability among the native and invasive tree species in our study area. We initiated an alternative Separability Index (SI)-based feature selection approach in which a boundary condition reduced the amount of correlation in the selected spectral subset. We hypothesized that redundant spectral information could be avoided, and improved plant detection accuracy could be achieved, with reduced computational time due to the selection of fewer bands in the mixture analysis. Our analysis showed a systematic increase in the invasive species detection success when we compared the output of multi-temporal MESMA (Kappa = 0.78) with that of the traditional unitemporal approach (Kappa = 0.51–0.69). Even for unitemporal MESMA, in which only a single input image was used, the band selection strategy was beneficial both in plant detection accuracy and computational time. We could further demonstrate that, despite a lack of imagery covering all phenological events, a proper band selection strategy can emphasize subtle spectral and phenological differences between species and can thereby partly compensate for this lack of data. This creates opportunities for mapping in areas where cloud cover is a limiting factor for building extended spectral image time series. This approach is sufficiently general and inherently adaptive, thereby supporting species mapping using Hyperion and forthcoming space-borne imaging spectrometers.

Malaria is a significant public health threat in the Brazilian Amazon. Previous research has shown that deforestation creates breeding sites for the main malaria vector in Brazil, Anopheles darlingi, but the influence of selective logging, forest fires, and road construction on malaria risk has not been assessed. To understand these impacts, we constructed a negative binomial model of malaria counts at the municipality level controlling for human population and social and environmental risk factors. Both paved and unpaved roadways and fire zones in a municipality increased malaria risk. Within the timber production states where 90% of deforestation has occurred, compared with areas without selective logging, municipalities where 0-7% of the remaining forests were selectively logged had the highest malaria risk (1.72, 95% CI 1.18-2.51), and areas with higher rates of selective logging had the lowest risk (0.39, 95% CI 0.23-0.67). We show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon and highlight the need for regulation and monitoring of sub-canopy forest disturbance.

Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.