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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Remote sensing-based vegetation and soil moisture constraints reduce irrigation estimation uncertainty

Understanding the human water footprint and its impact on the hydrological cycle is essential to inform water management under climate change. Despite efforts in estimating irrigation water withdrawals in earth system models, uncertainties and discrepancies exist within and across modeling systems conditioned by model structure, irrigation parameterization, and the choice of input datasets. Achieving model reliability could be much more challenging for data-sparse regions, given limited access to ground truth for parameterization and validation. Here, we demonstrate the potential of utilizing remotely sensed vegetation and soil moisture observations in constraining irrigation estimation in the Noah-MP land surface model. Results indicate that the two constraints together can effectively reduce model sensitivity to the choice of irrigation parameterization by 7%–43%. It also improves the characterization of the spatial patterns of irrigation and its impact on evapotranspiration and surface soil moisture by correcting for vegetation conditions and irrigation timing. This study highlights the importance of utilizing remotely sensed soil moisture and vegetation measurements in detecting irrigation signals and correcting for vegetation growth. Integrating the two remote sensing datasets into the model provides an effective and less feature engineered approach to constraining the uncertainty of irrigation modeling. Such strategies can be potentially transferred to other modeling systems and applied to regions across the globe.

Wanshu Nie↗
DOE OSTI2021

Calculated Roughness Changes due to Clear-Cutting At Savannah River Site

At the Savannah River Site (SRS), multiple facilities are routinely producing and working with radiological constituents which require safety basis modeling by the National Nuclear Safety Administration. The models used to determine the safety basis employ roughness length as an input parameter which describes the roughness of the ground surface which influences momentum exchange near the surface and the subsequent generation of mechanical turbulence. Because roughness length is dependent on surface characteristics, including vegetation, any changes to the surface would have a subsequent impact on the roughness length and require a new safety basis analysis to be performed. An analysis of measurements prior to and after the dear-cuts and thinning was performed and roughness lengths were calculated for each of sixteen wind sectors. Not all sectors contained dear-cuts, so this provides comparison of natural variations in roughness. Based on the results of the estimated changes, consideration will be given to how these changes may affect atmospheric dispersion modeling. It was found that clear-cutting activities generally led to a reduction in roughness. Reductions in roughness length also occurred in some of the wind sectors containing only undisturbed forest, but those in sectors with dear-cuts were found to be slightly greater in magnitude than natural variation. Only in one instance was the change in roughness length found to exceed a decrease of 10%, indicating that there is no significant safety basis to the site as a whole based on previous clear-cutting activities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan Civil War (1975-2002), human habitation in Iona National Park has grown exponentially, as has the livestock population. An ongoing drought beginning in 2017 has brought people, livestock, and wildlife into increasing competition for resources within the park. This study used Earth observation data, primarily Landsat and Sentinel imagery, to examine landscape trends to improve wildlife preservation approaches in Iona National Park, Angola. In collaboration with the NGO African Parks, we developed a robust land use and land cover (LULC) classification model using remote sensing data to augment sparse ground-based data in this arid land region. We used Google Earth Engine and a random forest classifier to map vegetation types, water bodies, and potential wildlife habitats. This analysis resulted in a high spatial resolution LULC time-series between 1984-2023, highlighting critical periods of socioecological change over the past 40 years. These results increased the partner’s ability to make scientifically grounded decisions about resource allocation and conservation priorities. This analysis supports the feasibility of applying remote sensing techniques coupled with machine learning models in dry regions, where standard survey methods are frequently limited by accessibility and resource availability. However, we identified limitations in ground-truth data and the difficulty of recognizing certain vegetation types in arid areas. Despite these limitations, the study demonstrated Earth observations' ability to transform wildlife management techniques in distant and data-scarce locations, providing a reproducible foundation for similar ecosystems around the world.

Emmanuel Aklie↗
NASA NTRS1993

Direction angle sensitivity of agricultural field backscatter with AIRSAR data

This paper presents a study of the direction angle sensitivity of radar backscatter from agricultural fields. The direction angle is defined as the angle between the incident plane and the perpendicular to the field row direction. Previous studies have concentrated on comparing the backscatter of agricultural fields imaged with 0, 45, and 90 degree direction angles. In contrast, this study concentrates on the backscatter changes occurring when the direction angle is modified by a few degrees or even by fractions of a degree. This is possible by using the output of the NASA/JPL AIRSAR processor, in which sixteen independent frames are formed, each one corresponding to the same radar scene imaged with a slightly different squint angle. The studied data set is an agricultural area in La Mancha, Spain acquired in June and July 1991 during the EFEDA experiment. This paper describes the observed backscatter variations of the agricultural fields with direction angle measured at P, L, and C bands. As expected, the backscatter is maximum for a 0 degree direction angle. For several fields, the backscatter at P and L bands drops by more than 10 dB for a 5 degree change in direction angle. Furthermore, the sensitivity to the direction angle decreases with increasing vegetation. The variations in backscatter are compared with model predictions. One model, which agrees with scatterometer data, underestimates the observed backscatter variations with direction angle by more than 10 dB. It does not take into account the possible coherent component of the radar signal. We believe the strong direction sensitivity of agricultural field backscatter obtained with SAR data is due to a Bragg resonant effect, resulting in a strong coherent return when the direction angle is zero. The observations are then projected to the case of spaceborne SAR data.

Dubois, Pascale C.↗
DOE OSTI2021

Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance

Abstract Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil–plant–atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Improved assessment of mangrove forests in Sundarbans East Wildlife Sanctuary using WorldView 2 and TanDEM-X high resolution imagery

Recent developments of remote sensing techniques which can capture both the structure and function of the ecosystem provide a more representative view of the landscape. These unique Earth observations were used to help improve traditional forestry surveys by providing species-specific land cover classes for mangrove forests in the Sundarbans East Wildlife Sanctuary. By combining optical data from WorldView2 (WV2; 2 m pixel) and a canopy height model derived using radar data from TanDEM-X (TDX; 12 m pixel), we identified nine mangrove and five non-mangrove classes by following an Iterative Self-Organizing Data Analysis Algorithm. Three dominant mangrove species accounted for nearly 50% of the sanctuary. Heritieria fomes disproportionately covered the largest area at 43%, overturning previous field-based estimates of Excoecaria agallocha dominance. E. agallocha and Sonneratia apetala, covered 3% and 1.47% of the sanctuary, respectively. Four mixed species classes were also identified with clear vegetation zonation patterns that trended toward species homogeneity with increasing distance from shore. The overall land cover accuracy (WV2: 89.33%; WV2-TDX: 89.89%), the Kappa Coefficient (WV2:0.88; WV2-TDX: 0.89) and change statistics between WV2 and WV2-TDX landcover classifications indicate that the WV2 imagery can separate mangrove community types without structural data. The combination of the land cover classifications and the canopy height model indicated that H. fomes were not only the most dominant forest but also, on average, the tallest (12.3 m) among the other eight mangrove types. Our large-scale mapping with high resolution optical and radar platforms can capture subtle changes in mangrove vegetation and canopy structural gradients more accurately and be used to monitor biodiversity changes and Aichi Biodiversity Targets and Indicators, which would contribute to biodiversity policy updating.

Md Mizanur Rahman↗
DOE OSTI2023

Catching up with the trees: empirical advancements to improve herbaceous representation in models

Ecosystems dominated by herbaceous vegetation (i.e. nonwoody species) represent c. 40% of the terrestrial land surface on Earth (White et al., 2000), facilitate large quantities of carbon sequestration (reviewed by Bai & Cotrufo, 2022), and provide much of the food production for humans (O'Mara, 2012). Many of these ecosystems are currently in danger of being lost due to global change drivers, shrub encroachment, and calls for afforestation (Bond et al., 2019). Furthermore, our predictive understanding of these systems is limited, which can be seen from high levels of uncertainty in modeled future carbon (C) storage of these systems (e.g. fig. 3 in McGuire et al., 2018).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Constraining long‐term model predictions for woody growth using tropical tree rings

The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO 2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO 2 (C a ). When forced with historical C a , ED2.2-hydro reproduced the magnitude of increases in intercellular CO 2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing C a based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO 2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to C a and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing C a . Finally, more accurate parameterization of WBPE will likely reduce the stimulation of woody growth by C a rise predicted by biosphere models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

Zobler, L.↗
NASA NTRS2019

Integrating Remote Sensing Observations with NASA's GEOS-5 Modeling Framework in Support of Retrospective Analyses and Seasonal Prediction of Biosphere-Atmosphere CO2 Flux

The absorption of nearly half of all anthropogenic carbon dioxide (CO2) emissions by terrestrial and marine ecosystems has played a critical role in mitigating climate change. However, a persistent lack of understanding of these uptake processes impedes attribution, and thereby introduces large uncertainties into projections of the trajectory of carbon and climate in coming decades. A better understanding and quantification of the seasonal and interannual variability of carbon flux between the atmosphere and ocean and terrestrial biospheres is critically needed to improve predictions from Earth system models. We propose to bring together a diverse suite of remote sensing observations, mechanistic models, and NASA's Goddard Earth Observing System, version 5 (GEOS-5) model to address two objective 1) quantify the processes controlling the temporal variability of biosphere-atmosphere CO2 flux on seasonal and interannual timescales from 1989 to 2018 and 2) evaluating the predictability of the components of carbon flux on seasonal timescales. Because GEOS-5 has been developed to incorporate a variety of Earth system observations and includes a complete and physically consistent depiction of the atmosphere, land, and ocean carbon cycles, it represents a unique and ideal synthesis framework for this effort. By expanding the realism of processes that could be included in GEOS-5 seasonal forecasts, this effort supports NASA's goals toward an integrated Earth system modeling and prediction framework.We propose to integrate a number of observations into the GEOS-5 modeling system to refine retrospective estimates of both terrestrial and marine carbon flux and to test hypotheses regarding the effects of climate drivers on terrestrial carbon flux. This will include assimilating several satellite ocean color products in the NASA Ocean Biogeochemistry Model (NOBM) to improve the representation of ocean biology. We will also utilize remote sensing datasets to increase the temporal resolution of global land-use change estimates and fire emissions to better constrain the temporal variability of the terrestrial carbon cycle. This information will be incorporated into two NASA terrestrial biosphere models Catchment-CN, a dynamic vegetation model that is integrated within GEOS-5 and the Ecosystem Demography (ED) model, a core modeling resource supporting the Global Ecosystem Dynamics Investigation (GEDI) mission to map biomass. Fluxes will be evaluated through 1) comparison to top-down flux estimates inferred from surface CO2 observations and 2) comparison against available aircraft and satellite observations.By combining predictions of net ecosystem exchange and air-sea CO2 flux from land and ocean models with statistical predictions of land use change, fire, and fossil fuel emissions, we propose to create the first dynamical CO2 forecast on seasonal timescales. Because of the experimental nature of this endeavor, an important project objective will be to evaluate the limits to the predictability of different carbon cycle processes over lead times of 1-12 months using different satellite and surface data streams.

Ott, Lesley↗
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

This white paper responds to Focal Area 2. We seek to build predictive models of leaf and surface conductance of water by implementing deep learning (DL) data assimilation techniques. These new models would then be implemented in existing Land Surface Models (LSMs) and Earth System models (ESMs), generating novel water cycle feedbacks. In doing so, we would improve predictability of expected changes in land precipitation, soil moisture, and vegetation dynamics in the long-term, and the role of land cover on the impacts and feedbacks of extreme weather events in the short-term

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

A four-layer model for the heat budget of homogeneous land surfaces

The present model envisions the heat balance of a homogeneous land surface in terms of available energy, a set of driving potentials, and parameters for the physical state of the soil and vegetation. Two unique features of the model are: (1) the expression of the interaction of evaporation from the soil and from foliage by changes in the value of the saturation vapor pressure deficit of air in the canopy (the conclusions of this interaction being consistent with field observations); and (2) the treatment of sensible and latent heat exchange between the atmosphere and a soil consisting of two discrete layers.

Choudhury, B. J.↗
NASA NTRS2012

L-Band H Polarized Microwave Emission During the Corn Growth Cycle

Hourly L-band (1.4 GHz) horizontally (H) polarized brightness temperatures (T(sub B))'s measured during five episodes (more than two days of continuous measurements) of the 2002 corn growth cycle are analyzed. These T(sub B)'s measurements were acquired as a part of a combined active/passive microwave field campaign, and were obtained at five incidence and three azimuth angles relative to the row direction. In support of this microwave data collection, intensive ground sampling took place once a week. Moreover, the interpretation of the hourly T(sub B)'s could also rely on the data obtained using the various automated instruments installed in the same field. In this paper, the soil moisture and temperature measured at fixed time intervals have been employed as input for the tau-omega model to reproduce the hourly T(sub B). Through the calibration of the vegetation and surface roughness parameterizations, the impact of the vegetation morphological changes on the microwave emission and the dependence of the soil surface roughness parameter, h(sub r), on soil moisture are investigated. This analysis demonstrates that the b parameter, appearing in the representation of the canopy opacity, has an angular dependence that varies throughout the growing period and also that the parameter hr increases as the soil dries in a portion of the dry-down cycle. The angular dependence of the b parameter imposes the largest uncertainty on T(sub B) simulations near senescence as the response of b to the incidence is also affected by the crop row orientation. On the other hand, the incorporation of a soil moisture dependent h(sub r) parameterization was responsible for the largest error reduction of T(sub B) simulations in the early growth cycle.

Joseph, A. T.↗
DOE OSTI2021

Tradeoffs and Synergies in Tropical Forest Root Traits and Dynamics for Nutrient and Water Acquisition: Field and Modeling Advances

Vegetation processes are fundamentally limited by nutrient and water availability, the uptake of which is mediated by plant roots in terrestrial ecosystems. While tropical forests play a central role in global water, carbon, and nutrient cycling, we know very little about tradeoffs and synergies in root traits that respond to resource scarcity. Tropical trees face a unique set of resource limitations, with rock-derived nutrients and moisture seasonality governing many ecosystem functions, and nutrient versus water availability often separated spatially and temporally. Root traits that characterize biomass, depth distributions, production and phenology, morphology, physiology, chemistry, and symbiotic relationships can be predictive of plants’ capacities to access and acquire nutrients and water, with links to aboveground processes like transpiration, wood productivity, and leaf phenology. In this review, we identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils. We also identify interesting paradoxes in tropical forest root responses to changing resources that merit further exploration. For example, specific root length, which typically increases under resource scarcity to expand the volume of soil explored, instead can increase with greater base cation availability, both across natural tropical forest gradients and in fertilization experiments. Also, nutrient additions, rather than reducing mycorrhizal colonization of fine roots as might be expected, increased colonization rates under scenarios of water scarcity in some forests. Efforts to include fine root traits and functions in vegetation models have grown more sophisticated over time, yet there is a disconnect between the emphasis in models characterizing nutrient and water uptake rates and carbon costs versus the emphasis in field experiments on measuring root biomass, production, and morphology in response to changes in resource availability. Closer integration of field and modeling efforts could connect mechanistic investigation of fine-root dynamics to ecosystem-scale understanding of nutrient and water cycling, allowing us to better predict tropical forest-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Amplification of Heat Extremes by Plant CO2 Physiological Forcing

Plants influence extreme heat events by regulating land-atmosphere water and energy exchanges. The contribution of plants to changes in future heat extremes will depend on the responses of vegetation growth and physiology to the direct and indirect effects of elevated CO2. Here we use a suite of earth system models to disentangle the radiative versus vegetation effects of elevated CO2 on heat wave characteristics. Vegetation responses to a quadrupling of CO2 increase summer heat wave occurrence by 20 days or more-30-50% of the radiative response alone-across tropical and mid-to-high latitude forests. These increases are caused by CO2 physiological forcing, which diminishes transpiration and its associated cooling effect, and reduces clouds and precipitation. In contrast to recent suggestions, our results indicate CO2-driven vegetation changes enhance future heat wave frequency and intensity in most vegetated regions despite transpiration-driven soil moisture savings and increases in aboveground biomass from CO2 fertilization.

Skinner, Christopher B.↗
NASA NTRS1996

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

Price, Kevin P.↗
NASA NTRS2011

Applications of the BIOPHYS Algorithm for Physically-Based Retrieval of Biophysical, Structural and Forest Disturbance Information

Canopy reflectance model inversion using look-up table approaches provides powerful and flexible options for deriving improved forest biophysical structural information (BSI) compared with traditional statistical empirical methods. The BIOPHYS algorithm is an improved, physically-based inversion approach for deriving BSI for independent use and validation and for monitoring, inventory and quantifying forest disturbance as well as input to ecosystem, climate and carbon models. Based on the multiple-forward mode (MFM) inversion approach, BIOPHYS results were summarized from different studies (Minnesota/NASA COVER; Virginia/LEDAPS; Saskatchewan/BOREAS), sensors (airborne MMR; Landsat; MODIS) and models (GeoSail; GOMS). Applications output included forest density, height, crown dimension, branch and green leaf area, canopy cover, disturbance estimates based on multi-temporal chronosequences, and structural change following recovery from forest fires over the last century. Good correspondences with validation field data were obtained. Integrated analyses of multiple solar and view angle imagery further improved retrievals compared with single pass data. Quantifying ecosystem dynamics such as the area and percent of forest disturbance, early regrowth and succession provide essential inputs to process-driven models of carbon flux. BIOPHYS is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying biophysical structural dynamics and change. It is also suitable for integration with forest inventory, monitoring, updating, and other programs.

Peddle, Derek R.↗