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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Joint Characterization of Vegetation by Satellite Observations from Visible to Microwave Wavelengths - A Sensitivity Analysis

This study presents an evaluation and comparison of visible, near-infrared, passive, and active microwave observations for vegetation characterization on a global basis for a year, with spatial resolution compatible with climatological studies. Visible and near-infrared observations along with the Normalized Difference Vegetation Index come from the Advanced Very High Resolution Radiometer. An atlas of monthly mean microwave land surface emissivities from 19 to 85 GHz has been calculated from the Special Sensor Microwave/Imager for a year, suppressing the atmospheric problems encountered with the use of simple channel combinations. The active microwave measurements are provided by the ERS-1 scatterometer at 5.25 GHz. The capacity to discriminate between vegetation types and to detect the vegetation phenology is assessed in the context of a vegetation classification obtained from in situ observations. A clustering technique derived from the Kohonen topological maps is used to merge the three data sets and interpret their relative variations. NDVI varies with vegetation density but is not very sensitive in semiarid environments and in forested areas. Spurious seasonal cycles and large spatial variability in several areas suggest that atmospheric contamination and/or solar zenith angle drift still affect the NDVI.

NORMALIZED DIFFERENCE VEGETATIVE INDEX↗
NASA NTRS2013

Characterization of ASTER GDEM Elevation Data over Vegetated Area Compared with Lidar Data

Current researches based on areal or spaceborne stereo images with very high resolutions (less than 1 meter) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is a state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 meters) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that accurate co-registration between ASTER GDEM and the National Elevation Dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-seconds and further improved to 0.6 if only homogenous vegetated areas were considered.

vegetation canopy↗
NASA NTRS2020

Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

Vegetation↗
NASA NTRSDate not supplied

Wildfire Risk Support via Satellite-Derived Vegetation Health and Land Surface Model Soil Moisture

Land Surface Model (LSM) and evaporative demand products provide advanced lead time to wildfire conditions that complement traditional fire indices and represent short-term changes that add context to overall, long-term drought conditions. Established fire indices typically use weather indicators (i.e., precipitation, temperature) and estimated dead fuel moisture to indirectly obtain land surface and sub-surface characterization. The convergence of LSM shallow and deep-layer soil moisture output and satellite-derived vegetation health combine to provide a tool for stakeholders to examine trends in the state of land surface conditions that can help assess wildfire threat. Satellite remote sensing data can constrain near-real time vegetation characteristics within the LSM and/or provide a derived stress index in order to help characterize the wildfire risk. In addition, a percentile product of soil moisture is derived from a comparison of current LSM conditions to the historical record of the LSM in order to put the current conditions in perspective relative to the season and geographic region. The 2015 season as well as the 2018 Camp Fire Complex in California were examined in terms of the changes in LSM soil moisture and vegetation states. Satellite vegetation health consistently showed decreases a month prior to wildfire initiation. Additionally, maximum changes in total column soil moisture corresponded with the greatest concentration of fire locations. While soil moisture deficits occurred in the shallow layers across northern California in 2018, significant deficits at all sub-surface levels were seen ahead of the Camp Fire event. This presentation will demonstrate the complementary value of LSM output and satellite measured vegetation health to diagnose short-term deficits in sub-surface soil moisture and the rapid decline in vegetation health which precedes large wildfire events.

Wildfire↗
NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗
NASA NTRS1973

Vegetation analysis in the Laramie Basin, Wyoming from ERTS-1 imagery

The author has identified the following significant results. The application of ERTS-1 imagery to vegetation mapping and identification was tested and confirmed by field checking. ERTS-1 imagery interpretation and density contour mapping allows definition of minute vegetation features and estimation of vegetative biomass and species composition. Large- and small-scale vegetation maps were constructed for test areas in the Laramie Basin and Laramie mountains of Wyoming. Vegetative features reflecting grazing intensity, moisture availability, changes within the growing season, cutting of hay crops, and plant community constituents in forest and grassland are discussed and illustrated. Theoretical considerations of scattering, sun angle, slope, and instrument aperture upon image and map resolution were investigated. Future suggestions for applications of ERTS-1 data to vegetative analysis are included.

Evans, M. A.↗
NASA NTRS1979

Effects of vegetation canopy structure on remotely sensed canopy temperatures

The effects of vegetation canopy structure on thermal infrared sensor response must be understood before vegetation surface temperatures of canopies with low percent ground cover can be accurately inferred. The response of a sensor is a function of vegetation geometric structure, the vertical surface temperature distribution of the canopy components, and sensor view angle. Large deviations between the nadir sensor effective radiant temperature (ERT) and vegetation ERT for a soybean canopy were observed throughout the growing season. The nadir sensor ERT of a soybean canopy with 35 percent ground cover deviated from the vegetation ERT by as much as 11 C during the mid-day. These deviations were quantitatively explained as a function of canopy structure and soil temperature. Remote sensing techniques which determine the vegetation canopy temperature(s) from the sensor response need to be studied.

Kimes, D. S.↗
NASA NTRS1981

A gradient model of vegetation and climate utilizing NOAA satellite imagery. Phase 1: Texas transect

A climatological model/variable termed the sponge (a measure of moisture availability based on daily temperature maxima and minima, and precipitation) was tested for potential biogeograhic, ecological, and agro-climatological applications. Results, depicted in tabular and graphic form, suggest that, as generalized climatic index, sponge is particularly appropriate for large-area and global vegetation monitoring. The feasibility of utilizing NOAA/AVHRR data for vegetation classification was investigated and a vegetation gradient model that utilizes sponge and AVHRR data was initiated. Along an east-west Texas gradient, vegetation, sponge, and AVHRR pixel data (channels 1 and 2) were obtained for 12 locations. The normalized difference values for the AVHRR data when plotted against vegetation characteristics (biomass, net productivity, leaf area) and sponge values along the Texas gradient suggest that a multivariate gradient model incorporating AVHRR and sponge data may indeed be useful in global vegetation stratification and monitoring.

Greegor, D.↗
NASA NTRS1983

Vegetation classification based on Advanced Very High Resolution Radiometer /AVHRR/ satellite imagery

Data from the NOAA-6 spacecraft Advanced Very High Resolution Radiometer (AVHRR) were tested for effectiveness for vegetation classification. Vegetation, climatological, and meteorological data were gathered for three days over 12 locations, and the normalized differences between the AVHRR bands 1 and 2 were determined. A vegetative greenness index was compared with a hydrologic factor and vegetation characteristics as measured by ground truth. A multivariate vegetation gradient model was formulated, incorporating AVHRR and climatological data. The hydrologic factor was calculated in terms of the precipitation, evaporation, maximum and minimum temperatures, and the hydrologic capacity. The observations were taken over Texas, which has a wide range of climates. A high correlation was found in the vegetation-HF index. The AVHRR data are concluded to be an effective tool for analysis of vegetation/climate relationships.

Norwine, J.↗
NASA NTRS1984

Comprehensive Understanding for Vegetated Scene Radiance Relationships

The improvement of our fundamental understanding of the dynamics of directional scattering properties of vegetation canopies through analysis of field data and model simulation data is discussed. Directional reflectance distributions spanning the entire existance hemisphere were measured in two field studies; one using a Mark III 3-band radiometer and one using rapid scanning bidirectional field instrument called PARABOLA. Surfaces measured included corn, soybeans, bare soils, grass lawn, orchard grass, alfalfa, cotton row crops, plowed field, annual grassland, stipa grass, hard wheat, salt plain shrubland, and irrigated wheat. Some structural and optical measurements were taken. Field data show unique reflectance distributions ranging from bare soil to complete vegetation canopies. Physical mechanisms causing these trends are proposed based on scattering properties of soil and vegetation. Soil exhibited a strong backscattering peak toward the Sun. Complete vegetation exhibited a bowl distribution with the minimum reflectance near nadir. Incomplete vegetation canopies show shifting of the minimum reflectance off of nadir in the forward scattering direction because both the scattering properties or the vegetation and soil are observed.

Kimes, D. S.↗
NASA NTRS1985

Geologic mapping using Landsat MSS and TM images - Removing vegetation by modeling spectral mixtures

Semi-arid regions of the island of Hawaii and of the Tucson Mountains, AZ, having less than 50 percent green vegetation cover were mapped into rock/soil types by first identifying spectral mixtures of vegetation and rock/soil, and then removing the vegetation information. Landsat MSS images were used for Hawaii, and a Landsat TM image was used for the Tucson Mountains. Following analysis of the images field transects were done in test areas to determine percent vegetation cover and vegetation community characteristics, and to observe rock and soil types. For both Hawaii and the Tucson Mountains 20 percent or more of green vegetation caused potential misidentification of the rock/soil units unless spectral mixing was taken into account.

Adams, J. B.↗
NASA NTRS1990

Bioclimatic distribution of vegetation for general circulation model studies

Four global bioclimatic schemes which qualify climates on the basis of the distribution of vegetation, including the Holdridge (1947), Thornthwaite (1948), Koeppen (1936), and Troll and Paffen (1964) schemes, were applied to two global climate data sets to produce maps of global vegetation distribution: the Rand set described by Schutz and Gates (1971, 1973, and 1974) and the Shea (1986) data set. The results show that only 38 to 40 percent of the observed land surface, mapped as 31 vegetation types, could be replicated by applying the four schemes to these data sets. The simulations were significantly improved by further subdividing and regrouping the climates defined by the schemes and by regrouping the observed vegetation types. With these alterations, 77 percent of the predicted vegetative landscape coresponded with the observed distribution of vegetation.

Prentice, Katharine Culbertson↗
NASA NTRS1992

Modeling the land surface boundary in climate models as a composite of independent vegetation stands

An efficient strategy for modeling the land surface boundary in general circulation models (GCMs) is presented which accounts for the effects of vegetation on surface energy fluxes and allows for an arbitrary number of vegetation types to coexist in a grid square. The GCM grid square is depicted as a 'mosaic' of vegetation 'tiles', with each tile consisting of a single vegetation type. The energy balance equation for each tile follows closely that of a single vegetation version of the simple biosphere (SiB) model of Sellers et al. (1986) but is simplified enough to be written in Penman-Monteith form. Each tile in the square is coupled independently to the GCM atmosphere, and tiles affect each other only through the atmosphere. This coupling strategy differs conceptually from that of models such as SiB that assume a homogeneous mixture of vegetation types within a GCM grid square. A quantitative comparison of the two strategies is presented.

Koster, Randal D.↗
NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗
NASA NTRS1993

Relationship of attenuation in a vegetation canopy to physical parameters of the canopy

A discrete scatter model is employed to compute the radiometric response (i.e. emissivity) of a layer of vegetation over a homogeneous ground. This was done to gain insight into empirical formulas for the emissivity which have recently appeared in the literature and which indicate that the attenuation through the canopy is proportional to the water content of the vegetation and inversely proportional to wavelength raised to a power around unity. The analytical result assumes that the vegetation can be modeled by a sparse layer of discrete, randomly oriented particles (leaves, stalks, etc.). The attenuation is given by the effective wave number of the layer obtained from the solution for the mean wave using the effective field approximation. By using the Ulaby-El Rayes formula to relate the dielectric constant of the vegetation to its water content, it can be shown that the attenuation is proportional to water content. The analytical form offers insight into the dependence of the empirical parameters on other variables of the canopy, including plant geometry (i.e. shape and orientation of the leaves and stalks of which the vegetation is comprised), frequency of the measurement and even the physical temperature of the vegetation. Solutions are presented for some special cases including layers consisting of cylinders (stalks) and disks (leaves).

Karam, M. A.↗
NASA NTRS1993

Temporal changes in endmember abundances, liquid water and water vapor over vegetation at Jasper Ridge

Imaging spectrometry offers a new way of deriving ecological information about vegetation communities from remote sensing. Applications include derivation of canopy chemistry, measurement of column atmospheric water vapor and liquid water, improved detectability of materials, more accurate estimation of green vegetation cover and discrimination of spectrally distinct green leaf, non-photosynthetic vegetation (NPV: litter, wood, bark, etc.) and shade spectra associated with different vegetation communities. Much of our emphasis has been on interpreting Airborne Visible/Infrared Imaging Spectrometry (AVIRIS) data spectral mixtures. Two approaches have been used, simple models, where the data are treated as a mixture of 3 to 4 laboratory/field measured spectra, known as reference endmembers (EM's), applied uniformly to the whole image, to more complex models where both the number of EM's and the types of EM's vary on a per-pixel basis. Where simple models are applied, materials, such as NPV, which are spectrally similar to soils, can be discriminated on the basis of residual spectra. One key aspect is that the data are calibrated to reflectance and modeled as mixtures of reference EM's, permitting temporal comparison of EM fractions, independent of scene location or data type. In previous studies the calibration was performed using a modified-empirical line calibration, assuming a uniform atmosphere across the scene. In this study, a Modtran-based calibration approach was used to map liquid water and atmospheric water vapor and retrieve surface reflectance from three AVIRIS scenes acquired in 1992 over the Jasper Ridge Biological Preserve. The data were acquired on June 2nd, September 4th and October 6th. Reflectance images were analyzed as spectral mixtures of reference EM's using a simple 4 EM model. Atmospheric water vapor derived from Modtran was compared to elevation, and community type. Liquid water was compare to the abundance of NPV, Shade and Green Vegetation (VG) for select sites to determine whether a relationship existed, and under what conditions the relationship broke down. Temporal trends in endmember fractions, liquid water and atmospheric water vapor were investigated also. The combination of spectral mixture analysis and the Modtran based atmospheric/liquid water models was used to develop a unique vegetation community description.

Roberts, Dar A.↗
NASA NTRS1994

Vegetation stress as a feedback mechanism in midlatitude drought

An atmospheric general circulation model with land surface properties represented by the Simplified Simple Biosphere Model is used to investigate the effect of soil moisture and vegetation stress on drought in the mid-latitudes. An idealized land-sea distribution with simple topography is used to remove as many external sources of climate variation as possible. The land consists of a single, flat, rectangular continent covered with prairie vegetation and centered on 44 deg N of an aqua planet. A control integration of 4 years is performed, and several sets of seasonal anomaly integrations are made to test the sensitivity of seasonal climate to low initial (1 April) soil moisture and dormant vegetation like what would occur during a severe drought. It is found that the inclusion of dormant vegetation during the spring and early summer greatly reduces evapotranspiration by eliminating transpiration. This affects local climate more strongly as summer progresses. Low initial soil moisture, combined with dormant vegetation, leads to a severe drought. The reduction in precipitation is much greater in magnitude than that due to low soil moisture alone, and greater than the sum of the effects computed separately. Although the short-term drought is more severe, the dormancy of the vegetation prevents further depletion of moisture in the root zone of the soil, so soil moisture begins to rebound toward the middle of summer.

Dirmeyer, Paul A.↗
NASA NTRS1999

Quantifying BRDF Effects in Comparing Landsat-7 and AVIRIS Near-Simultaneous Acquisitions for Studies of High Plains Vegetation Cover

Approximately 100,000 sq. km of the High Plains of the central United States are covered by sand dunes and sand sheets deposited during the Holocene. Soil-dating evidence shows that there were at least four periods of dune reactivation during major droughts in the last 10,000 years. The dunes in this region are anchored by vegetation. We have undertaken a study of land-use change in the High Plains from 1985 to the present using Landsat 5 TM and Landsat 7 ETM+ images to map variation in vegetation cover during wet and dry years. Mapping vegetation cover of less than 20% is important in modeling potential surface reactivation since at this level the vegetation no longer sufficiently shields sandy surfaces from movement by wind. Landsat TM data have both the spatial resolution and temporal coverage to facilitate vegetation cover analysis for model development and verification. However, there is still the question of how accurate TM data are for the measurement of both growing and senescent vegetation in and and semi-arid regions. AVIRIS provides both high spectral resolution as well as high signal-to-noise ratio and can be used to test the accuracy of Landsat TM and ETM+ data. We have analyzed data from AVIRIS flown nearly concurrently with a Landsat 7 overpass. The comparison between an AVIRIS image swath of 11 km width subtending a 30 deg. angle and the same area covered by a 0.8 deg. angle from Landsat required accounting for the BRDF. A normalization technique using the ratio of the reflectances from registered AVIRIS and Landsat data proved superior to the techniques of column averaging on AVIRIS data alone published previously by Kennedy et al. This technique can be applied to aircraft data covering a wider swath angle than AVIRIS to develop BRDF responses for a wide variety of surfaces more efficiently than from ground measurements.

Goetz, A. F. H.↗