Processes driving land cover change are inherently complex and reflect spatio-temporal dependencies. Efforts at understanding land cover change therefore necessarily require the need for disentangling the underlying socio-political and environmental forcings. This is largely because factors influencing landscape dynamics at regional scales reflect outcomes of decisions made at the household-to-community scales. Landscape-scale changes in land cover patterns therefore reflect changes in economic activities that are, in turn, influenced by regional-scale land management policies. Recent land cover dynamics in Ghana offer a case study into how regional-scale policies can affect land cover change. Policymaking aimed at mitigating land degradation and resolving land utilization conflicts therefore needs data on land cover and socioeconomic dynamics at high spatiotemporal resolutions. These data are not currently available in a consistent manner, especially in developing countries. In this project, we aim to leverage recently developed methods of spatiotemporally downscaling socioeconomic data (funded by the NASA Land Use Land Cover Program), identifying objects using high-resolution imagery (funded by the National Science Foundation), and combining socioeconomic and geospatial datasets (funded by the NASA-USAID SERVIR Program) to generate a fine-scale framework for identifying precursors to land degradation and conflict resolution in the greater West African region. This proposal is aimed at SERVIR’s Land Cover and Land Use Change and Ecosystems focus area. This project will advance stakeholders’ skills in regional land cover mapping, land planning, deforestation precursor assessments, and assessments of forest degradation linked to charcoal production and illegal mining. In addition, this project will also directly contribute to the understanding of the causality between changing socioeconomic dynamics, land cover and land use change. The goal of the proposed project is to develop a land use conflict and degradation mitigation framework by integrating high-resolution socioeconomic data with localized information on drivers of land degradation. The framework will integrate remote sensing and GIS-based modeling, and use historical trends and causal linkages among deforestation, urbanization, and agricultural expansion. Specific objectives of the project are to (1) Develop an operational artificial intelligence (AI)-based method for the detection and monitoring of artisanal mines (Galamsey) and charcoal-production activities using a moderate and high-resolution data fusion strategy, (2) Extend and implement an existing deep learning-based AI model to downscale socio-economic data on livelihoods and poverty indicators across Ghana, (3) Link land degradation with socioeconomic indicators in a causal modeling framework: through the LUCIS-plus open-source GIS tools, and (4) build local capacity with collaborating institutions on the use of advanced remote sensing and deep-learning methods to address land degradation and conflict management. Primary outputs of the proposed project are 1) spatially downscaled estimates of key socioeconomic indicators, 2) operational techniques for utilizing high-resolution satellite data for monitoring and mapping land degradation, and 3) the generation of a GIS-based decision-making framework to help minimize conflict. Outcomes will include capacity building of local personnel in newer AI-based data analysis and model building techniques. Training of personnel in newer tools and techniques will allow the expansion of applications to research relevant to the larger region. The project will involve hands-on training delivery to collaborators and personnel from local agencies and will help disseminate information through a dedicated website and Github code repositories.

This proposed SARI synthesis project for South Asia is focused on understanding LCLUC patterns and processes related to agricultural landscapes of smallholder tree-based systems and their potential as natural climate solutions. The synthesis shall provide an observation-based evaluation of the degree to which these landscapes are increasing in terms of cover and biomass, and then evaluate what conditions lead to increases in tree and forest cover in South Asia, and under what conditions do improvements in tree and forest cover contribute to improving rural livelihoods. The objective of the proposed SARI South Asia Synthesis Consortium (SARI-SAS) is twofold: 1) synthesize current and recent NASA research on LCLUC to contribute to a fundamental understanding of their patterns and drivers and 2) translate fundamental science into evidence-based contributions to important climate mitigation and adaptation policy for the region. The team proposing this synthesis effort is comprised of all the current SARI projects in South Asia, 6 university teams with 12 regional counterparts and collaborators. The SARI-SAS Consortium will synthesize existing research to assess the current state and trends of land-use change in the SARI region and identify important emerging trends and themes relevant to global change science and climate change policy. This shall advance our understanding of the processes, drivers and impacts on carbon emissions and removals, with the ultimate goal of developing new understanding of the landscape-level drivers of biotic emissions and removals. To do this in a tractable and focused way that illuminates new and emerging issues, the SARI-SAS Consortium shall evaluate the importance of tree-based systems in non-forest landscapes outside the well-understood forest estate, with a focus on atmospheric emissions and removals of carbon and the processes that drive or mediate increasing woody cover and biomass at the landscape scale. The project deploys a synthesis framework around the concept of Sustainable Landscapes (SL), which is an emerging framework that combines evidence from empirical and process-based scientific research with policy and development oriented models that integrates biophysical and socio-economic analysis. The SL framework is adept at translational work that links evidence from empirical analysis to successful policy interventions; a central focal point being linking LCLUC observations to their social and economic drivers to support climate change mitigation and adaptation. The strategic flow of synthesis begins with assessments of the observational data from remote sensing. We synthesize all reporting on tree cover change, with an emphasis on where we see increases in trees outside of forest (TOF). We assess trends based on both medium resolution data analysis as well as very high resolution data analyses of individual trees. We extend beyond cover analysis to explicitly assess biomass and carbon increases. Next, we examine questions related to process and drivers of observed change. First, we review the relationship between TOF and a range of social and economic indicators. The proposed framework here benefits from the LCLUC work that integrates satellite remote sensing data with downscaled socioeconomic indicators to generate a broad view of causes of tree cover change. The second line of inquiry reviews what we can synthesize from LCLUC projects specifically related to income and livelihood drivers. The third line of inquiry seeks to understand how farmers internalize values of ecosystem services. The fourth line of inquiry seeks to understand how governance, farm-scale decision-making and policy influences tree cover. Lastly, we shall develop a knowledge base that informs more effective policies on natural climate solutions and interventions for climate change mitigation and adaptation in the AFOLU sector.

The resurgence in sustainable farming practices in recent years is driven mostly by interests in improving soil health, nutrient cycling, and carbon sequestration. However, most of the research has focused on utilizing annual cover crops, which are often terminated at the end of the season, and the benefits of alfalfa (Medicago sativa L.) in cropping systems have been largely overlooked. Due to its perennial nature, alfalfa can improve soil structure, decrease erosion, and increase carbon sequestration in soil. Increased utilization of alfalfa will not only help to reach ecological goals, but it will also help in improving wildlife habitat and biodiversity, while providing a highly nutritious feedstuff for livestock. In the United States, alfalfa is the fourth most valued crop behind corn, soybeans, and wheat, with an estimated value of $9 billion (USDA NASS, 2020). Non-dormant alfalfa cultivars are grown in the southeastern United States (Bouton, 2012). In Florida, nondormant cultivars were developed for improved adaptation to the state’s subtropical agroecosystem [‘Florida 66’ (Horner, 1970), ‘Florida 77’ (Horner and Ruelke, 1981), and ‘Florida 99’]; however, these cultivars are no longer commercially available due to seed unavailability. Breeding efforts are underway to develop new non-dormant cultivars with improved biomass yield and persistence (Acharya et al., 2020; Biswas et al., 2021). Both yield and persistence are highly influenced by environmental conditions (Figure 1) and understanding genotype by environment interaction (GxE) is critical to develop new cultivars. Imaging spectroscopy is an established, non-destructive, method for estimating alfalfa yields (Biswas et al., 2021) and monitoring crop nutrient status (Liu et al., 2021). While several studies have estimated nitrogen stress or crop nutrient status using spectral imaging systems (Cilia et al., 2014; Liu et al., 2021; Nigon et al., 2015), methods are not yet available for nutrient stress detection in alfalfa. The utilization of UAVs can speed selection by making available field and plot-scale estimates of foliar nitrogen and biomass and can help boost genomic selection programs (Mir et al., 2019). The main goal of this study is to utilize UAV-based hyperspectral sensors for germplasm screening of non-dormant alfalfa under two contrasting nitrogen levels (0 vs 30 kg/ha per harvest) for biomass yield and persistence in Florida. We will utilize ground-based and airborne sensors for high-throughput phenotyping and genomic prediction models by leveraging genomic resources in alfalfa developed by the breeding insight project. Our study and data analysis will help the breeding community and other plant science researchers interested in plant trait analysis using UAVs.

Increased availability of low-cost and quality feedstocks is critical for a thriving U.S. bioeconomy. Perennial crops are a major source of land-based feedstocks, but challenges remain in harnessing and transforming this diffuse and low-value resource into marketable bioenergy and bioproducts. An optimized feedstock production system is needed to make bioenergy and bioproducts cost-competitive without impacting our capabilities to meet other societal needs of the growing population. The key is to produce more biomass over the status quo with less land, using cost-effective and regionally-fit management practices, while providing environmental benefits. The key goals of this project are to: 1) assess field-scale yield, quality, and ecosystem services of newly available energycane (EC) cultivar on marginal croplands and fallow lands and the effects on soil biodiversity and avian population; 2) test sensor (e.g multispectral, hyperspectral, and LiDAR sensors) for estimating nutrient loading, water quantity and quality, and agronomic attributes of EC; 3) ground-truth information management platforms; 4) develop ML model for predicting EC’s agronomic attributes; 5) use field data to generate baseline (ES market values excluded) and enhanced (ES market values included) techno-economic analyses (TEA) ; 6) develop a life-cycle analysis (LCA), and 7) develop a market transformation plan. The critical success factors include:1) securing adequate quantities of the planting materials (billets) for the target EC cultivar for planting field-scale trials at the EREC and IREC sites; and 2) achieving successful establishment of EC in the planting year and maintaining adequate stands over the three successive years of harvesting for sustainable biomass production. This project will enable creation of field-scale demonstration on how to sustainably embed bioenergy crops in marginally productive croplands of the USSCP, another region in the U.S. with high potential to supply large biomass quantities for the bioeconomy. Field-scale production and demonstration will allow us to generate data of quality and quantity not achieved before for the proposed unique production system using one of the most productive bioenergy crops suitable for sub-tropical growing conditions. The expected multiple project outputs directly support the growing U.S. bioeconomy.

Pontederia crassipes (Mart. Solms), commonly known as water hyacinth, is an aquatic plant in the family Pontederiaceae. It is native to South America and is considered one of the most invasive aquatic weeds worldwide. It was first introduced into the United States in 1884. Water hyacinth causes extensive damage by covering large water bodies, altering aquatic habitats by reducing dissolved oxygen and light penetration, and blocking access to agricultural and recreational activities. Two weevils, Neochetina spp (Coleoptera: Curculionidae), and a planthopper, Megamelus scutellaris (Hemiptera: Delphacidae) were released in the 1970s and 2010 respectively, to control water hyacinth and mitigate its effect on freshwater ecosystems. Assessing the impact of biocontrol on water hyacinth is essential to maintain support and demonstrate efficacy. Accessing aquatic habitats for surveillance or impact assessment is cumbersome, costly, and dangerous. For example, accessing the interior of the Everglades ecosystem in Florida requires airboats or helicopters. Other methods rely on ground-based visual surveys such as physical searches via transect, grids, or points. These methods are subjective, often restrictive, challenging, and time-consuming. Remote sensing can overcome most of these limitations in surveillance and assessment methods. Unmanned Aerial Systems (UAS) based remote sensing can provide a safe, quick, and efficient survey in areas where access is difficult and dangerous for traditional survey methods. This study aims to develop methods that accurately detect the impact of biocontrol on water hyacinth using UAS. Hyperspectral and photosynthetic data were collected in the lab on water hyacinth plants with varying levels of biocontrol agent pressure. These data will be used to quantify herbivore densities and impact. Applying UAS-based remote sensing could potentially provide mapping and monitoring solutions to detect biocontrol damage and better inform management decisions.

Abstract

Florida’s potato industry has a vital influence on its economy ($117 million annually) (NASS, USDA, 2016), with over 29,000 acres of potatoes, and is ranked 11th in the nation for potato production. Critical challenges for potato growers include rainfall variations, nutrient leaching, and poor water quality. Potato yields per unit of land area have been relatively stable over the last 20 years, while input costs have increased. This puts potato growers under intense pressure to reduce input costs and improve yields, which ultimately leads to a decrease in potato acres. In Florida, the average potato yield (chip and fresh market) is around 220 hundredweight/acre. This is significantly low despite the inclusion of irrigation, compared to other potato-growing states like Maine (short growth window of 90 days) where the average yield/acre is 260 hundredweight without irrigation. A reduction in environmental pollution as regulated by the Environmental Protection Agency (EPA) has resulted in a decrease of natural S deposition. Figures 1 and 2 demonstrate the S soil deposition value in different parts of the US, both of which show a decrease in S deposition in Florida. Sharma et al., 2017 explain the need for S in potatoes, especially to improve nitrogen and phosphorus efficiency; however, in Florida, there is a need to reduce the S application rate so that N and P are available to the potato plant. Understanding S behavior in the soil and the potato plant is necessary to establish S recommendations and is vital for sustainable potato production. The University of Florida - IFAS will develop the sulfur (S) recommendation to improve nitrogen (N) and phosphorus (P) efficiency for Florida potato growers, which will result in improved yield and water quality. Presently, Florida potato growers are applying excessive amounts of S, ~300 pounds/acre due to the lack of recommendation. The higher the S application, the lower N of N and P uptake by the plant. This negatively impacts crop yield and increases leaching and erosion of N and P. The objectives of this study are to develop S recommendations with different combinations of N and P rates on multiple sites to find the best combination for optimum yield. Water quality (leached water analysis); crop yield and quality; soil moisture; weather; S, N and P uptake; tissue sampling; and soil physical, chemical, and biological data will be used for this study. An economic optimum N rate (EONR) and maximum return to N rate (MRTN) will be compared for recommendations. A multispectral sensor will be used to monitor plant health and adjust fertilizer applications, which will help in developing yield prediction models. Significant outcomes of this study include an independent S recommendation for fresh market and processing potatoes, an optimum recommendation rate of N and P with S application. An online mobile application where growers will be able to access required S rates for their field conditions using maximum yield potential with ideal N and P rates. It will help in reducing N and P leaching and erosion.

West Africa has experienced significant deforestation over the last several decades (CILLS, 2016). About 30% of the Upper Guinean forest (UGF) has been lost since 1975. Ghana consists of 18% of the remaining. Recently, satellite remote sensing has shown that the decline in woody cover is associated with similar increase in herbaceous cover, indicating increase in agriculture. Even though the deforestation rates have slowed in recent years, the region is still experiencing significant anthropogenic pressures from logging, rapid urbanization, and agricultural expansion. Liu et al. (2017) found that human land use and not climate trends/anomalies was the primary driver of vegetation dynamics in the region. Many studies have used satellite remote sensing for monitoring of land cover change (LCC) in the region (for example, Liu et al., 2017; Enaruvbe et al., 2016; Malhl et al., 2013). However, land use in the region remain largely unplanned (Anderson et al., 2013; Buhaug & Urdal, 2013). In 2003, the Ministry of Lands and Natural Resources (MLNR) under the Government of Ghana (GoG) initiated a 15-25-year Land Administration Project funded by World Bank with the goal of strengthening and consolidating land administration for sustainable land management and effective land tenure. Phase 1 of the project lasted for 8 years with the aim of consolidating land policies, reforming institutional land administrative processes, and capacity building (LAP, 2011). In 2011, the Phase 2 was launched with the aim of further improvement in land use planning bills. One of the components (Component 3) of Phase 2 includes “Improved maps and spatial data for land administration” (www.ghanalap.gov.gh). In 2011, Ghana Strategic Investment Framework was produced by GoG through the Ministry of Environment, Science, and Technology in partnership with TerrAfrica Global Partnership Program, primarily, to address land degradation and “to enable land users maximize the economic and social benefits from land whilst enhancing and sustaining ecological support function” through strengthening policies and regulations. Sub-Component 2 of the framework includes Community Land Use Planning. These recent policy efforts at regional levels highlight an urgent need in operational land use planning tools that account for and balance linkages among various land-uses in the region. Complex interactions among human and natural systems make it challenging to identify precursors/drivers (Kleemann et al., 2017 a & b) and develop scenarios and opportunities of effective land use planning. The goal of the proposed project is to provide an effective land use planning framework to land managers in Ghana. The framework will integrate remote sensing and GIS-based modeling, and use historical trends and causal linkages among deforestation, urbanization, and agricultural expansion (as shown in Figure 1). The project leverages and enhances ongoing research in forest dynamics in the region in relation to climate change (e.g. Liu et al., 2017; Dwomoh & Wimberly, 2017) and incorporates analyses of anthropogenic interactions for land use planning. Specific objectives of the project are to (1) define causal linkages among deforestation, urbanization, and agricultural expansion in Western, Ashanti, Eastern, Central, Brong-Ahafo, and Volta regions of Ghana based upon multiple satellite-based earth observing systems (EOS), (Should we have a map of which regions (with color/shading) we are considering?) (2) implement a GIS-based, open-source Land-Use Conflict Identification Strategy (LUCIS) model in the region for land use planning. The model will use trends and linkages from objective (1), along with existing biophysical, socio-economic, and infrastructural datasets, and (3) build local capacity in the region regarding remote sensing, GIS, and LUCIS, and transfer the land use planning framework for continued planning.

Abstract

Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees. Together, the remote sensing and field data will be used to understand what influences the number of trees, their size, where different species occur, and how this changes from spatial scales of local parks to the entire United States. This project will also make it easier for other scientists to study trees over large areas by developing software, producing data products, and providing training and collaboration opportunities for working with these novel datasets. This will help drive rapid advances in the cross-scale understanding of tree ecology with broad applications in forestry, management, and fundamental scientific understanding. This project combines National Ecological Observatory Network (NEON) data from airborne remote sensing and field data collection. These data will be used to develop machine learning based approaches to identify, measure, and characterize to species all of the canopy trees located within each forested NEON site. This will yield data on approximately 50 million individual trees at about 40 sites across the United States. These data from NEON will be combined with data from the US Forest Service Forest Inventory and Analysis Project, which samples millions of trees at over 100,000 locations across the United States. These combined data will be used to develop joint models of the distribution, abundance, and structural traits of trees, that explicitly incorporate the concept of scale. These models will be used to understand how the processes influencing tree distribution and traits change across scales by comparing the importance of different factors at scales ranging from a few meters, where individual trees directly interact, to the entire United States, where large gradients in climate and land use are important. This research will address three broad questions in ecology: 1) what processes govern species distribution and abundance at different scales and how do they interact? 2) how are landscape and regional process of species coexistence connected to local biodiversity? 3) how do changes in the processes influencing tree traits across scales impact estimates of biomass and carbon storage?

Abstract

Imaging spectroscopy exhibits great potential for mapping foliar functional traits that are impractical or expensive to regularly measure on the ground, and are essentially impossible to characterize comprehensively across space. Knowledge of the variability in such traits is critical to understanding vegetation productivity, as well as responses to climatic variability, disturbances, pests and pathogens. In places where ground-measured trait data are sparse - such as India - imaging spectroscopy offer the capacity to fill gaps in our knowledge of global variation in foliar traits between and within biomes. In India, such data are important to understanding environmental drivers of variation in vegetation function, especially in biodiversity hotspots threatened by a range of change agents. Application of imaging spectroscopy algorithms to map foliar traits globally requires the development of "universal" algorithms that work across phenology, vegetation types, locations and years. Existing models that meet these requirements - such as those of Singh et al. (2015) for temperate forests - need to be tested in new regions and vegetation communities to ensure that the models are stable and transferable, as well as to identify gaps in coverage needed to improve those models. Such testing will help ensure that relatively robust models that operate well globally within or among physiognomic types (e.g. forests) are available for future global hyperspectral missions, such as HyspIRI. Here we propose the application of models developed by Singh et al. (2015) and new ones in development in the Townsend Lab in support of NEON's cross-site mapping activities to the imagery collected over Indian forests as part of the 2015-2016 joint NASA-ISRO AVIRIS-NG campaign in India. By linking to other trait-mapping work, this will enhance ongoing efforts to develop cross-biome trait retrieval models for future spaceborne imaging spectrometers such as EnMap or HyspIRI. We will partner with Dr. N.S.R. Krishnayya at The Maharaja Sayajirao (M.S.) University of Baroda, whose team collected foliar samples for validation at three of the forest sites imaged during the AVIRIS-NG campaign. We will collect additional foliar samples in ~January 2018 (approximately 2-year anniversary of the original acquisition) at these three sites and another one site having matching vegetation for further evaluation. Data will be used for both validation and chemometric (partial least-squares) model refinement. Our proposed sites fall along a series of climatic (rainfall, temperature) and phenological gradients in the Western Ghats of India, identified by multiple organizations as one of the world's premier biodiversity hotspots. We will use the results of our functional trait mapping to assess how forests across these regions vary functionally with well-characterized environmental gradients, both through empirical analyses and using coupled biophysical-radiative transfer model. The proposed project leverages a number of related efforts underway by the project participants.

Short term funding from UF/IFAS to conduct UAS surveys in areas affected by Hurricane Michael.

UF/IFAS instrument development funding to develop the Scanning Plant IoT platform (SPOT: see Products - Instrumentation - SPOT).

While there has been a considerable reduction in the number of undernourished people in the past two decades, India still has among the largest malnutrition rates in the world. Increasing pressures from population growth and urbanization have subsequently affected land use patterns in India, with as much as 36.6% of all land in India degraded. Climate change will likely exacerbate these issues through additional stresses on food production. Regionally, a range of socioeconomic factors also play a role, such as: lack of availability of and access to resources, land degradation, food insecurity and landlessness. Geographically differentiated strategies are needed to address these issues, but current strategies are severely hampered by the paucity of spatially-explicit information on food security and agriculture. This information is crucial for early detection of trends and to disentangle the complex relationships between food security and land use. Of critical need are spatially-explicit data on the factors that define dimensions of food security, and methods that allow the combination of these data into holistic synthetic indicators that explain causal factors in an integrated manner. Identifying, estimating and mapping spatial variations in the proportional strengths of these interrelationships will help identify the factors influencing food security and eventually land-use and land-cover change in rural as well as peri-urban areas. The specific objectives of this proposal are to: 1) generate spatially downscaled data of key demographic and socio-economic indicators that putatively define dimensions of food security in India, 2) use a hypothesis-driven approach to integrate economic, social, policy, infrastructural, and behavioral facets of food security into a holistic modeling framework, and, finally to 3) assess land cover change as an emergent outcome of patterns of socio-economic, demographic and policy instruments at local to regional scales. To do this, we will use small area estimation techniques to spatially disaggregate household-scale data on critical demographic, socioeconomic and food security indicators to the village scale. Subsequently, using a structural equation modeling framework, we will integrate indicators of food security with extant socioeconomic and demographic patterns, indicators of climate adaptability and metrics of infrastructural and policy instruments. Maps of latent vectors of the SEM will allow the first-ever spatialized representation of the combined effects of institutional support, accessibility to markets and extension services on regional indicators of poverty and malnutrition. Further, we will develop a generalized methodology for mapping land cover and producing probabilistic pixel-wise maps of classification uncertainties. We will eventually combine land cover change probabilities with indicators of food security derived from structural equation models to assess the influence of food security indicators on patterns of land cover change. These analyses will provide the first-ever assessments of the relative strengths of drivers of land cover change in the study regions at local to regional scales. The proposed research directly addresses NASAs high-priority science goals with a central focus on Land Cover Land Use Change science within the larger Carbon Cycle and Ecosystems program. In addition, the proposal directly addresses the influence of socio-economic drivers on land cover change. By developing a strong socio-ecological context to all our study sites and analyses, we will ensure the interdisciplinary application of space-borne technologies to help address issues of high societal relevance. The proposed research is therefore directly responsive to the NASA LCLUC program themes: detection and monitoring of change, predictive land use modeling, climate variability and change, and drivers of change and food security.

Accurately predicting plant species response to global change is difficult. Responses vary within and among species, with variation in the local and regional environment, and in response to external pressures such as insect infestations making extrapolation from individual plant to species-specific studies difficult. Plant leaf functional traits (e.g. leaf chemistry, plant pigments) have recently emerged as a potentially useful way to characterize and understand the variability in plant function (e.g. growth, stress, nutrient uptake), and plant responses to environmental change. Plant leaf traits or "foliar traits" have been shown to be strongly correlated with global variation in plant function and can be detected from remotely sensed imagery. Remote sensing offers the possibility to characterize and map the spatial variation in foliar functional traits to gain a better understanding of biological responses to global change. Remotely sensed and ground based measurements from the National Ecological Observatory will be used to develop a suite of foliar traits from 81 locations that encompass the range of ecosystems found in the United States. One component of the National Ecological Observatory infrastructure is its Aerial Observation Platform, which collects remote imagery annually over the locations using the latest generation imaging technologies. These imaging sensors have an unprecedented ability to map biological function, including plant chemistry and physiology, as well as the biomass and structure of plants. This award will result in the first comprehensive data set and methods for mapping plant biochemistry and physiology across the range of ecosystem types in the US and will enable characterization of how plant traits vary across space and time. The leaf traits, supporting data, maps and enabling equations and software will be made publically available via existing databases. Graduate students and post doctoral candidates will be engaged in the research. One of the great promises of imaging spectroscopy ? also known as hyperspectral remote sensing ? is the ability to map the spatial variation in foliar functional traits, such as nitrogen concentration, pigments, leaf structure, photosynthetic capacity and secondary biochemistry, that drive terrestrial ecosystem processes. Such foliar trait characterization offers an organizing principle that can be used to understand the occurrence and evolutionary differentiation of function across different taxonomic or phylogenetic levels and to detect functional differences across ecosystems. The National Ecological Observatory provides one of the first opportunities to characterize and compare these different ecosystem types, in terms of their biodiversity and ecosystem services such as maintaining air and water quality and sequestering and storing carbon. The imaging spectrometer on the National Ecological Observatory Airborne Observation Platform (AOP) will be used to regularly estimate trait variation across the major biomes of North America, providing high-resolution data suitable for scaling these traits continentally, as well as ecosystem modeling across domains. The award will enable hyperspectral mapping algorithms for a large number of plant foliar traits (such as nitrogen concentration, pigments, LMA, photosynthetic capacity and secondary metabolites) to be developed (or modified from existing algorithms), validated, applied and made publicly available. To accomplish this will entail fundamental research in remote sensing to understand how optical properties - detectable using imaging spectroscopy - permit mapping traits across biomes. Additionally the data will permit the evaluation of how trait retrieval algorithms differ across biomes and how they are affected by vegetation structure, physiognomy, or other ecosystem properties. Lidar data will be used to test and control for the influence of canopy vertical structure on trait mapping. The resulting maps of vegetation trait variation will be analyzed to determine the requisite geographic area that must be sampled to fully characterize trait variation across ecosystem types and, subsequently, compare to global variation. The ultimate objective is the synthesis, testing and validation of cross-biome trait retrieval models for hyperspectral imagery. By characterizing how multi-dimensional trait space is filled across landscapes and comparing the resulting trait maps with global data, this research will identify the extent to which hyperspectral imagery can be used to both extrapolate trait variation on Earth and fill geographical gaps in existing knowledge of biome- to continental- scale trait variation. Comprehensive trait information for the biomes represented in the Observatory is largely absent except for localized studies, and this work will enable better understanding and prediction of the response of global terrestrial ecosystems to disturbance, stress and change. This work will provide the scientific community with data products necessary to better understand local-, regional-, continental- and global-scale variation in ecosystem function, in part through linkage to a range of other data (e.g. flux tower estimates of GPP). The project will train students and postdoctoral scholars in cross-disciplinary research that merges ecology, remote sensing, and the quantitative analyses of dense data sets, creating a new generation of researchers that can address cross-cutting questions in global ecology.