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Results for “vegetation modeling”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Interpretation of surface and planetary directional albedos for vegetated regions

An atmospheric solar radiation model has been coupled with surface reflectance measurements for two vegetation types, pasture land and savannah, in order to address several issues associated with understanding the directional planetary albedo; i.e., the dependence of planetary albedo upon solar zenith angle. These include an elucidation of processes that influence the variation of planetary albedo with solar zenith angle, as well as emphasizing potential problems associated with converting narrowband planetary albedo measurements to broadband quantities. It is suggested that, for vegetated surfaces, this latter task could be somewhat formidable, since the model simulations indicate that narrowband to broadband conversions strongly depend upon vegetation type. A further aspect of this paper is to illustrate a procedure by which reciprocity inconsistencies within a bidirectional reflectance dataset, if they are not too severe, can be circumvented.

Cess, Robert D.↗
NASA NTRS1998

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

Kimes, Daniel S.↗
NASA NTRS2019

Retrieval of Vegetation Water Content Using Brightness Temperatures from the Soil Moisture Active Passive (SMAP) Mission

In this paper, we explore a time series approach to using the tau-omega (τ-ω) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor α and an offset factor β. By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, α and β can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of α and β can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) ofSMAP brightness temperature observations.

radiative transfer↗
NASA NTRS1985

Reflectance Modeling

The overall goal of this work has been to develop a set of computational tools and media abstractions for the terrain bidirectional reflectance problem. The modeling of soil and vegetation surfaces has been emphasized with a gradual increase in the complexity of the media geometries treated. Pragmatic problems involved in the combined modeling of soil, vegetation, and atmospheric effects have been of interest and one of the objectives has been to describe the canopy reflectance problem in a classical radiative transfer sense permitting easier inclusion of our work by other workers in the radiative transfer field.

Smith, J. A.↗
DOE OSTI2024

Impact of Vegetation Assimilation on Flash Drought Characteristics across the Continental United States

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

Drought↗
NASA NTRS2002

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗
NASA NTRS1985

Satellite measurements of surface albedo and temperatures in semi-desert

Measurements of surface parameters in an arid steppe (the semi-desert of the northern Sinai) were made from the NOAA-6 satellite to assess the effects of the vegetation recovery in a fenced-off area. The radiances measured in the solar wavelengths over the vegetated area were about 25 percent lower than those measured over the surrounding bare sandy soil (where the surface albedo measured from Landsat is about 0.42). This implies a reduction in the albedo by the vegetation also by about 25 percent if both surfaces are regarded as Lambertian, but by as much as 42 percent if the vegetated area is modeled as a plane of soil with vertically protruding plants. The radiation temperatures in the 11 micron channel at approximately 0730 LST measured over the vegetated area were by as much as 2.5 K higher than over the surrounding sands.

Otterman, J.↗
NASA NTRS1999

Canopy Cover Predictions using Ground Observations and Remotely Sensed Data

Maps of vegetation status are needed at many scales, from the field level to monitor ecosystem condition to the global level to understand the carbon cycle. Status is quantified by such variables as leaf area index, biomass, and fraction of canopy cover. Current methods of predicting vegetation variables use remote sensing data to provide a spatially exhaustive data source. In a study in western Montana, several hundred ground observations made by the US Forest Service on tenth-acre conifer plots were used to develop aspatial regression and geostatistical prediction models. Normalized Difference Vegetation Index (NDVI) values from Landsat Thematic Mapper images were used as ancillary data. These models were then used to predict canopy cover at unsampled locations in a 97 square kilometer region on the boundary of the Flathead National Forest and the Bob Marshall Wilderness. Independent data from two dates six years apart were used for validation. Given the assumption that actual canopy cover remained relatively unchanged within this time period, partial validation can be achieved by measuring the correspondence of the two maps. This criterion results in ranking the aspatial regression maps as less accurate than the geostatistically generated maps. The geostatistical approach emphasizes ground measurements more heavily than does aspatid regression. Geostatistical simulations of canopy cover also provide a means of describing uncertainty about the patterns of canopy cover.

Dungan, Jennifer L.↗
DOE OSTI2021

Statistical upscaling of ecosystem CO 2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties

Abstract The regional variability in tundra and boreal carbon dioxide (CO 2 ) fluxes can be high, complicating efforts to quantify sink‐source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO 2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO 2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high‐latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO 2 fluxes and test the accuracy and uncertainty of different statistical models. CO 2 fluxes were upscaled at relatively high spatial resolution (1 km 2 ) across the high‐latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE‐focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO 2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m −2 yr −1 , respectively) compared to tundra (average annual NEE +10 and −2 g C m −2 yr −1 ). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO 2 budget, estimated using the annual NEE ensemble prediction, suggests the high‐latitude region was on average an annual CO 2 sink during 1990–2015, although uncertainty remains high.

Virkkala, Anna‐Maria↗
NASA NTRS2015

The 2010 Russian Drought Impact on Satellite Measurements of Solar-Induced Chlorophyll Fluorescence: Insights from Modeling and Comparisons with the Normalized Differential Vegetation Index (NDVI)

We examine satellite-based measurements of chlorophyll solar-induced fluorescence (SIF) over the region impacted by the Russian drought and heat wave of 2010. Like the popular Normalized Difference Vegetation Index (NDVI) that has been used for decades to measure photosynthetic capacity, SIF measurements are sensitive to the fraction of absorbed photosynthetically-active radiation (fPAR). However, in addition, SIF is sensitive to the fluorescence yield that is related to the photosynthetic yield. Both SIF and NDVI from satellite data show drought-related declines early in the growing season in 2010 as compared to other years between 2007 and 2013 for areas dominated by crops and grasslands. This suggests an early manifestation of the dry conditions on fPAR. We also simulated SIF using a global land surface model driven by observation-based meteorological fields. The model provides a reasonable simulation of the drought and heat impacts on SIF in terms of the timing and spatial extents of anomalies, but there are some differences between modeled and observed SIF. The model may potentially be improved through data assimilation or parameter estimation using satellite observations of SIF (as well as NDVI). The model simulations also offer the opportunity to examine separately the different components of the SIF signal and relationships with Gross Primary Productivity (GPP).

vegetation↗
DOE OSTI2022

Fine resolution remote sensing spectra improves estimates of gross primary production of croplands

Gross primary production (GPP) is a fundamental measure of the terrestrial carbon cycle critical to our understanding of ecosystem function under the changing climate and land use. Remote sensing enables access to continuous spatial coverage, but remains challenged in heterogeneous croplands. Coarse resolution products, like MOD17A (500 m), may aggregate fragmented land cover types commonly found in heavily managed landscapes and misrepresent their respective contribution to carbon production. Consequently, this study demonstrates the capability of fine-resolution imagery (20-30 m) and available red-edge vegetation indices to characterize GPP across seven Midwest cropping systems. Four sites were established on a 22-year-old USDA Conservation Reserve Program (CRP); and the other three on land conventionally farmed with corn-soybean-wheat rotation (AGR). We compare in situ GPP estimates from eddy-covariance towers with ten satellite models: eight variants of the vegetation photosynthesis models (VPM), of which five include a red-edge vegetation index, as well as conventional products Landsat CONUS GPP (30 m) and MOD17A2H V6 (500 m). Daily and cumulative fine-resolution imagery integrated within VPM generally agreed with tower-based GPP in heterogeneous landscapes more than those from MODIS 500 m VPM or conventional GPP products from MOD17AH V6 or Landsat 8 CONUS. Here, replacing EVI2 with red-edge indices NDRE2, NDRE1, and MTCI in Sentinel 2 VPMs notably improved explanation of variance and estimation of cumulative GPP. While existing methods using MODIS- and Landsat-derived GPP are important baselines for regional and global studies, future research may benefit from the higher spatial, temporal, and radiometric resolution.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Satellite-derived leaf-area-index and vegetation maps as input to global carbon cycle models - A hierarchical approach

A hierarchical procedure for developing a leaf area index (LAI) map of deciduous boreal forests is studied. The collection of spectral reflectance data from the Boundary Waters Canoe area in Minnesota using helicopter-, high-altitude aircraft-, and Landsat-mounted spectral sensors is described. The relationship between LAI and biomass and the reflectance ratio is analyzed. The sensitivity of canopy reflectance in the visible and infrared to the LAI of the canopy for various boreal forest species is evaluated. The data reveal that Landsat data are useful for producing LAI maps of deciduous forest areas and the maps provide data which clarifies the function of vegetation in the global carbon cycle models.

Badhwar, G. D.↗
NASA NTRS1993

Linear mixing model applied to AVHRR LAC data

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.

Holben, Brent N.↗
NASA NTRS1984

Measured microwave emission and scattering in vegetation canopies

Reflecting metal screens were placed beneath vegetation of three types to allow measurement of the brightness temperature of the vegetation cover alone. A two-frequency radiometer (wavelengths: 11.1 and 5.88 cm) was used to measure the emission from the vegetation, and a theoretical model involving a loss term and a scattering albedo was fitted to the measured temperatures. Passive and active microwave measurements were also made on fields immediately adjacent to the 'screened' fields. Emission and backscattering were measured for these fields under two conditions: with full vegetation cover, and with vegetation removed. Using the same theoretical model and the calculated values for absorption and scattering derived from the screened fields, the effect of the vegetation cover on the soil emission was predicted. The predicted full-canopy temperatures were then compared to those measured. The values for loss and scattering in the vegetation canopy were used in another model to predict the effect of vegetation on backscattering from the soil surface. Again, the predicted backscattering from the vegetation canopy was compared with the measured values of the backscattering coefficient.

Brunfeldt, D. R.↗
NASA NTRS2019

SMAP L4 Assessment of the US Northern Plains 2017 Flash Drought

A rapidly developing "flash drought" occurred over the US Northern Plains in the summer of 2017, spurred by unusually high temperatures and strong evaporative demand. The impacts of the drought included widespread reductions in rangeland and agricultural productivity that cascaded into significant economic losses. Here, we used satellite information from the NASA Soil Moisture Active Passive (SMAP) mission to clarify the nature and impact of the drought on regional vegetation growth. The model enhanced SMAP Level 4 Soil Moisture (L4SM) and Carbon (L4C) products were used with other ancillary data to examine spatial and seasonal anomalies in surface to root zone soil moisture and vegetation productivity (GPP). We find that the flash drought was triggered by a mid-July heat wave, conditioned by exceptionally low spring rainfall. The drought resulted in anomalous low soil moisture levels and regional GPP collapse, coinciding with severe (D3) to exceptional (D4) drought conditions indicated from the US Drought Monitor. The SMAP L4C GPP anomalies closely tracked reported county-level crop production anomalies for the major regional crop types, indicating generally larger productivity decline in managed croplands than surrounding natural areas. The SMAP L4 global products provide an effective indicator of vegetation growth changes and moisture-related restrictions on ecosystem productivity that are complementary with more traditional drought assessment tools.

GPP↗
NASA NTRS1987

Monitoring biological impacts of space shuttle launches from Vandenberg Air Force Base: Establishment of baseline conditions

Space shuttle launches produce environmental impacts resulting from the formation of an exhaust cloud containing hydrogen chloride aerosols and aluminum oxide particulates. Studies have shown that most impacts occur near-field (within 1.5 km) of the launch site while deposition from launches occurs far-field (as distant as 22 km). In order to establish baseline conditions of vegetation and soils in the areas likely to be impacted by shuttle launches from Vandenberg Air Force Base (VAFB), vegetation and soils in the vicinity of Space Launch Complex-6 (SLC-6) were sampled and a vegetation map prepared. The areas likely to be impacted by launches were determined considering the structure of the launch complex, the prevailing winds, the terrain, and predictions of the Rocket Exhaust Effluent Diffusion Model (REEDM). Fifty vegetation transects were established and sampled in March 1986 and resampled in September 1986. A vegetation map was prepared for six Master Planning maps surrounding SLC-6 using LANDSAT Thematic Mapper imagery as well as color and color infrared aerial photography. Soil samples were collected form the 0 to 7.5 cm layer at all transects in the wet season and at a subsample of the transects in the dry season and analyzed for pH, organic matter, conductivity, cation exchange capacity, exchangeable Ca, Mg, Na, K, and Al, available NH3-N, PO4-P, Cu, Fe, Mn, Zn, and TKN.

Schmaizer, Paul A.↗
DOE OSTI2025

Leveraging High-resolution Molecular Composition of Soil Organic Matter to Enhance Carbon Cycling Modeling

Soils store more carbon than the atmosphere and vegetation combined, yet Earth system models still struggle to predict how this vast reservoir will respond to environmental change. A central limitation is that most soil biogeochemical models represent organic matter using bulk conceptual pools or chemically homogeneous fractions, preventing direct use of rapidly expanding molecular-scale datasets. Here we develop and test a new soil decomposition framework that explicitly integrates high-resolution information on organic matter composition. First, we construct a molecularly informed litter decomposition module in which plant inputs are partitioned into five functional compound classes—carbohydrates, proteins, lignin-like aromatics, lipids, and carbonyls—using a molecular mixing model calibrated to solid-state 13 C Nuclear Magnetic Resonance (NMR) spectra. Class-specific kinetics, lignin-dependent physical protection, and substrate-driven microbial carbon use efficiency allow the module to capture metabolic tradeoffs associated with enzyme production and nutrient limitation. We then embed this litter module within a microbially explicit whole-soil model that tracks the transformation of these compound classes through particulate organic matter, dissolved organic matter, mineral-associated organic matter, and microbial biomass. High-resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FTICR-MS) data are used to link internal pools to measurable soil organic matter fractions and to constrain key process parameters. Applications at soil-core and ecosystem scales demonstrate that the new model reproduces observed soil respiration dynamics while providing mechanistic attribution of CO 2 fluxes to specific chemical classes and pools. Compared to existing frameworks such as the Community Land Model soil biogeochemistry module and the Millennial model, our approach maintains competitive predictive skill while substantially improving interpretability and opportunities for data–model integration. This work illustrates a viable pathway for leveraging molecular-scale observations to reduce structural uncertainty in soil carbon–climate feedback projections.

54 ENVIRONMENTAL SCIENCES↗