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NASA NTRS1993

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Mcgwire, K.↗

NASA NTRS1993

Design of a global soil moisture initialization procedure for the simple biosphere model

Global soil moisture and land-surface evapotranspiration fields are computed using an analysis scheme based on the Simple Biosphere (SiB) soil-vegetation-atmosphere interaction model. The scheme is driven with observed precipitation, and potential evapotranspiration, where the potential evapotranspiration is computed following the surface air temperature-potential evapotranspiration regression of Thomthwaite (1948). The observed surface air temperature is corrected to reflect potential (zero soil moisture stress) conditions by letting the ratio of actual transpiration to potential transpiration be a function of normalized difference vegetation index (NDVI). Soil moisture, evapotranspiration, and runoff data are generated on a daily basis for a 10-year period, January 1979 through December 1988, using observed precipitation gridded at a 4 deg by 5 deg resolution.

Liston, G. E.↗

NASA NTRS1993

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.

Rignot, Eric↗

NASA NTRS1993

Session on coupled land surface/hydrological/atmospheric models

The current model capabilities in the context of land surface interactions with the atmosphere include only one-dimensional characteristics of vegetation and soil surface heat, moisture, momentum, and selected other trace gas fluxes (e.g., CO2). The influence of spatially coherent fluxes that result from landscape heterogeneity were not included. Valuable representations of several aspects of the landscape pattern currently exist. These include digital elevation data and measures of the leaf area index (i.e., Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) data). A major deficiency, however, is the lack of an ability to sample spatially representative shallow and (especially) deep soil moisture. Numerous mesoscale modeling and observed studies demonstrated the sensitivity of planetary boundary layer structure and deep convection to the magnitude of the surface moisture flux.

Pielke, Roger↗

NASA NTRS1994

Trace gas emissions to the atmosphere by biomass burning in the west African savannas

Savanna fires and atmospheric carbon dioxide (CO2) detection and estimating burned area using Advanced Very High Resolution Radiometer_(AVHRR) reflectance data are investigated in this two part research project. The first part involves carbon dioxide flux estimates and a three-dimensional transport model to quantify the effect of north African savanna fires on atmospheric CO2 concentration, including CO2 spatial and temporal variability patterns and their significance to global emissions. The second article describes two methods used to determine burned area from AVHRR data. The article discusses the relationship between the percentage of burned area and AVHRR channel 2 reflectance (the linear method) and Normalized Difference Vegetation Index (NDVI) (the nonlinear method). A comparative performance analysis of each method is described.

Frouin, Robert J.↗

NASA NTRS1994

Direct and indirect methods for correcting the aerosol effect on remote sensing

Aspects of aerosol studies and remote sensing are reviewed. Aerosol scatters solar radiation before it reaches the surface and scatters and absorbs it again after it is reflected from the surface and before it reaches the satellite sensor. The effect is spectrally and spatially dependent. Therefore atmospheric aerosol (dust, smoke and air pollution particles) has a significant effect on remote sensing. Correction for the aerosol effect was never achieved on an operational basis though several case studies were demonstrated. Correction can be done in a direct way by deriving the aerosol loading from the image itself and correcting for it using the appropriate radiative transfer model or by an indirect way, by defining remote sensing functions that are less dependent on the aerosol loading. To some degree this was already achieved in global remote sensing of vegetation where a composite of several days of NDVI (Normalized Difference Vegetation Index) measurements, choosing the maximal value, was used instead of a single cloud screened value. The Atmospheric Resistant Vegetation Index (ARVI) introduced recently for the NASA Earth Observing System EOS-MODIS is the most appropriate example of indirect correction, where the index is defined in such a way that the atmospheric effect in the blue spectral channel cancels to a large degree the atmospheric in the red channel in computations of a vegetation index. Atmospheric corrections can also use aerosol climatology and ground based instrumentation.

Kaufman, Yoram J.↗

NASA NTRS1994

The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynthetically Active Radiation (A Par)

Most remote sensing estimations of vegetation variables such as Leaf Area Index (LAI), Absorbed Photosynthetically Active Radiation (APAR), and phytomass are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. A study directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of APAR in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter is presented. A high spectral resolution method defined as the Chlorophyll Absorption Ratio Index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of APAR. CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of LAI were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI obtained from the simulated canopy reflectance was compared with the broad band vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR)). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy APAR more effectively than broad band vegetation indices.

Kim, Moon S.↗

NASA NTRS1994

Remote sensing of forest ecosystem dynamics: Measurements and modeling

The development of an integrated approach to the modeling of forest dynamics encompassing submodels of forest growth and succession, soil processes and radiation interactions, is reported. Remote sensing technology is a key element of this study in that it provides data for developing, initializing, updating, and validating the models. The objectives are reviewed, the data collected and models in use are discussed, and a framework for studying interactions between the forest growth, soil process and energy interaction components, is described. Remote sensing technology used in the study includes optical and microwave field, aircraft and satellite borne instruments. The types of data collected during intensive field and aircraft campaigns included bidirectional reflectance, thermal emittance and multifrequency, multipolarization synthetic aperture radar backscatter. Synthetic imagery of derived products such as forest biomass and NDVI (Normalized Difference Vegetative Index), and collections of ground data are being assembled in a georeferenced data base. These data are used to drive or test multidiscipline simulations of forested ecosystems. Enhancements to the modeling environment permit considerable flexibility in configuring simulations and selecting results for reporting and graphical display.

Williams, Darrel L.↗

NASA NTRS1993

The red edge in arid region vegetation: 340-1060 nm spectra

The remote sensing study of vegetated regions of the world has typically been focused on the use of broad-band vegetation indices such as NDVI. Various modifications of these indices have been developed in attempts to minimize the effect of soil background, e.g., SAVI, or to reduce the effect of the atmosphere, e.g., ARVI. Most of these indices depend on the so-called 'red edge,' the sharp transition between the strong absorption of chlorophyll pigment in visible wavelengths and the strong scattering in the near-infrared from the cellular structure of leaves. These broadband indices tend to become highly inaccurate as the green canopy cover becomes sparse. The advent of high spectral resolution remote sensing instrument such as the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) has allowed the detection of narrow spectral features in vegetation and there are reports of detection of the red edge even for pixels with very low levels of green vegetation cover by Vane et al. and Elvidge et al., and to characterize algal biomass in coastal areas. Spectral mixing approaches similar to those of Smith et al. can be extended into the high spectral resolution domain allowing for the analysis of more endmembers, and potentially, discrimination between material with narrow spectral differences. Vegetation in arid regions tends to be sparse, often with small leaves such as the creosote bush. Many types of arid region vegetation spend much of the year with their leaves in a senescent state, i.e., yellow, with lowered chlorophyll pigmentation. The sparseness of the leaves of many arid region plants has the dual effect of lowering the green leaf area which can be observed and of allowing more of the sub-shrub soil to be visible which further complicates the spectrum of a region covered with arid region vegetation. Elvidge examined the spectral characteristics of dry plant materials showing significant differences in the region of the red edge and the diagnostic ligno-cellulose absorptions at 2090 nm and 2300 nm. Ray et al. detected absorption at 2100 nm in AVIRIS spectra of an abandoned field known to be covered by a great deal of dead plant litter. In order to better study arid region vegetation remote sensing data, it is necessary to better characterize the reflectance spectra of in situ, living, arid region plants.

Ray, Terrill W.↗

NASA NTRS1993

Synergy between optical and microwave remote sensing to derive soil and vegetation parameters from MAC Europe 1991 Experiment

The ability of remote sensing for monitoring vegetation density and soil moisture for agricultural applications is extensively studied. In optical bands, vegetation indices (NDVI, WDVI) in visible and near infrared reflectances are related to biophysical quantities as the leaf area index, the biomass. In active microwave bands, the quantitative assessment of crop parameters and soil moisture over agricultural areas by radar multiconfiguration algorithms remains prospective. Furthermore the main results are mostly validated on small test sites, but have still to be demonstrated in an operational way at a regional scale. In this study, a large data set of radar backscattering has been achieved at a regional scale on a French pilot watershed, the Orgeval, along two growing seasons in 1988 and 1989 (mainly wheat and corn). The radar backscattering was provided by the airborne scatterometer ERASME, designed at CRPE, (C and X bands and HH and VV polarizations). Empirical relationships to estimate water crop and soil moisture over wheat in CHH band under actual field conditions and at a watershed scale are investigated. Therefore, the algorithms developed in CHH band are applied for mapping the surface conditions over wheat fields using the AIRSAR and TMS images collected during the MAC EUROPE 1991 experiment. The synergy between optical and microwave bands is analyzed.

Taconet, O.↗

NASA NTRS1995

Determining successional stage of temperate coniferous forests with Landsat satellite data

Thematic Mapper (TM) digital imagery was used to map forest successional stages and to evaluate spectral differences between old-growth and mature forests in the central Cascade Range of Oregon. Relative sun incidence values were incorporated into the successional stage classification to compensate for topographic induced variation. Relative sun incidence improved the classification accuracy of young successional stages, but did not improve the classification accuracy of older, closed canopy forest classes or overall accuracy. TM bands 1, 2, and 4; the normalized difference vegetation index (NDVI); and TM 4/3, 4/5, and 4/7 band ratio values for old-growth forests were found to be significantly lower than the values of mature forests (P less than or equal to 0.010). Wetness and the TM 4/5 and 4/7 band ratios all had low correlations to relative sun incidence (r(exp 2) less than or equal to 0.16). The TM 4/5 band ratio was named the 'structural index' (SI) because of its ability to distinguish between mature and old-growth forests and its simplicity.

Fiorella, Maria↗

NASA NTRS1995

Determining coniferous forest cover and forest fragmentation with NOAA-9 advanced very high resolution radiometer data

NOAA-9 satellite data from the Advanced Very High Resolution Radiometer (AVHRR) were used in conjunction with Landsat Multispectral Scanner (MSS) data to determine the proportion of closed canopy conifer forest cover in the Cascade Range of Oregon. A closed canopy conifer map, as determined from the MSS, was registered with AVHRR pixels. Regression was used to relate closed canopy conifer forest cover to AVHRR spectral data. A two-variable (band) regression model accounted for more variance in conifer cover than the Normalized Difference Vegetation Index (NDVI). The spectral signatures of various conifer successional stages were also examined. A map of Oregon was produced showing the proportion of closed canopy conifer cover for each AVHRR pixel. The AVHRR was responsive to both the percentage of closed canopy conifer cover and the successional stage in these temperate coniferous forests in this experiment.

Ripple, William J.↗

NASA NTRS1995

Vegetation species composition and canopy architecture information expressed in leaf water absorption measured in the 1000 nm and 2200 spectral region by an imaging spectrometer

Plant species composition and plant architectural attributes are critical parameters required for the measuring, monitoring, and modeling of terrestrial ecosystems. Remote sensing is commonly cited as an important tool for deriving vegetation properties at an appropriate scale for ecosystem studies, ranging from local to regional and even synoptic scales. Classical approaches rely on vegetation indices such as the normalized difference vegetation index (NDVI) to estimate biophysical parameters such as leaf area index or intercepted photosynthetically active radiation (IPAR). Another approach is to apply a variety of classification schemes to map vegetation and thus extrapolate fine-scale information about specific sites to larger areas of similar composition. Imaging spectrometry provides additional information that is not obtainable through broad-band sensors and that may provide improved inputs both to direct biophysical estimates as well as classification schemes. Some of this capability has been demonstrated through improved discrimination of vegetation, estimates of canopy biochemistry, and liquid water estimates from vegetation. We investigate further the potential of leaf water absorption estimated from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data as a means for discriminating vegetation types and deriving canopy architectural information. We expand our analysis to incorporate liquid water estimates from two spectral regions, the 1000-nm region and the 2200-nm region. The study was conducted in the vicinity of Jasper Ridge, California, which is located on the San Francisco peninsula to the west of the Stanford University campus. AVIRIS data were acquired over Jasper Ridge, CA, on June 2, 1992, at 19:31 UTC. Spectra from three sites in this image were analyzed. These data are from an area of healthy grass, oak woodland, and redwood forest, respectively. For these analyses, the AVIRIS-measured upwelling radiance spectra for the entire Jasper Ridge scene were transformed to apparent surface reflectance using a radiative transfer code-based inversion algorithm.

Green, Robert O.↗

NASA NTRS1995

Use of AIRSAR to identify woody shrub invasion and other indicators of desertification in the Jornada LTER

The replacement of semidesert grassland by woody shrubland is a widespread form of desertification. This change in physiognomy and species composition tends to sharply reduce the productivity of the land for grazing by domestic livestock, increase soil erosion and reduce soil fertility, and greatly alter many other aspects of ecosystem structure and functioning. Remote sensing methods are needed to assess and monitor shrubland encroachment. Detection of woody shrubs at low density would provide a particularly useful baseline on which to access changes, because an initially low shrub density often tends to increase even after cessation of the disturbance (e.g., overgrazing, drought, or fire suppression) responsible for triggering the initial stages of the invasion (Grover and Musick, 1990). Limited success has been achieved using optical remote sensing. In contrast to other forms of desertification, biomass does not consistently decrease with a shift from grassland to shrubland. Estimation of green vegetation amount (e.g., by NDVI) is thus of limited utility, unless the shrubs and herbaceous plants differ consistently in phenology and the area can be viewed during a season when only one of these is green. The objective of this study was to determine if the potential sensitivity of active microwave remote sensing to vegetation structure could be used to assess the degree of shrub invasion of grassland. Polarimetric Airborne Synthetic Aperture Radar (AIRSAR) data were acquired for a semiarid site containing varied mixtures of shrubs and herbaceous vegetation and compared with ground observations of vegetation type and other landsurface characteristics. In this preliminary report we examine the response of radar backscatter intensity to shrub density. The response of other multipolarization parameters will be examined in future work.

Musick, H. Brad↗

NASA NTRS1994

CO2 exchange in the Hudson Bay lowlands: Community characteristics and multispectral reflectance properties

Net ecosystem CO2 exchange was measured during the 1990 growing season (June to August) along a transect starting 10 km inland from James Bay and extending 100 km interior to Kinosheo Lake, Ontario. Sites were chosen in three distinct areas: a coastal fen, an interior fen, and a bog. For the most productive sites in the bog, net daily uptake rates reached a maximum of 2.5 g C-CO2 m(exp -2)/d with an area-weighted exchange of 0.3 g C-CO2 m(exp -2)/d near midsummer. This site was estimated to be a net carbon source of 9 g C-CO2 m(exp -2) to the atmosphere over a 153-day growing season. The interior fen was less productive on a daily basis with a net maximum uptake of 0.5 g C-CO2 m(exp -2)/d and with corresponding area-weighted uptake of 0.1 g C-CO2 m(exp -2)/d during midsummer. Early and late season release of carbon to the atmosphere resulted in a net loss of 21 g C-CO2 m(exp -2) over the growing season from this site. The coastal fen was the most productive site with uptake rates peaking near 1.7 g C-CO2 m(exp -2)/d which corresponded to an area-weighted uptake of 0.8 g C-CO2 m(exp -2)/d during midsummer and an estimated net uptake of 6 g C-CO2 m(exp -2) for the growing season. Associated with net CO2 exchange measurements, multispectral reflectance properties of the sites were measured over the growing season using portable radiometers. These properties were related to exchange rates with the goal of examining the potential for satellite remote sensing to monitor biosphere/atmosphere CO2 exchange in this biome. The normalized difference vegetation index (NDVI) computed from surface reflectance was correlated with net CO2 exchange for all sites with the exception of areas with large proportions of Sphagnum moss cover. These mosses have greater near-infrared reflectance than typical surrounding vegetation and may require special adjustment for regional exchange/remote sensing applications.

Whiting, Gary J.↗

NASA NTRS1994

Radiative transfer in shrub savanna sites in Niger: Preliminary results from HAPEX-Sahel. Part 3: Optical dynamics and vegetation index sensitivity to biomass and plant cover

A shrub savannah landscape in Niger was optically characterized utilizing blue, green, red and near-infrared wavelengths. Selected vegetation indices were evaluated for their performance and sensitivity to describe the complex Sahelian soil/vegetation canopies. Bidirectional reflectance factors (BRF) of plants and soils were measured at several view angles, and used as input to various vegetation indices. Both soil and vegetation targets had strong anisotropic reflectance properties, rendering all vegetation index (6) responses to be a direct function of sun and view geometry. Soil background influences were shown to alter the response of most vegetation indices. N-space greenness had the smallest dynamic range in VI response, but the n-space brightness index provided additional useful information. The global environmental monitoring index (GEMI) showed a large 6 dynamic range for bare soils, which was undesirable for a vegetation index. The view angle response of the normalized difference vegetation index (NDVI), atmosphere resistant vegetation index (ARVI) and soil atmosphere resistant vegetation index (SARVI) were asymmetric about nadir for multiple view angles, and were, except for the SARVI, altered seriously by soil moisture and/or soil brightness effects. The soil adjusted vegetation index (SAVI) was least affected by surface soil moisture and was symmetric about nadir for grass vegetation covers. Overall the SAVI, SARVI and the n-space vegetation index performed best under all adverse conditions and were recommended to monitor vegetation growth in the sparsely vegetated Sahelian zone.

vanLeeuwen, W. J. D.↗

NASA NTRS1993

Assessing the Relationship Between Spectral Vegetation Indices and Shrub Cover in the Jornada Basin, New Mexico

We assessed the statistical relations between Spectral Vegetation Indices (SVI's) derived from SPOT multi-spectral data and semi-arid shrub cover at the Jornada LTER site in New Mexico. Despite a limited range of shrub cover in the sample the analyses resulted in r(sup 2) values as high as 0 central dot 77. Greenness SVI's (e.g., Simple Ratio, NDVI, SAVI, PVI and an orthogonal Greenness index) were shown to be more sensitive to shrub type and phenology than brightness SVis (e.g., green, red and near-infrared reflectances and a Brightness index). The results varied substantially with small-scale changes in plot size (60m by 60m to 100m by 100m) as a consequence of landscape heterogeneity. The results also indicated the potential for the spectral differentiation of shrub types, and shrubs from grass, using multi-temporal, multi-spectral analysis.

Duncan, Jeff↗

NASA NTRS1997

Predicting Photosynthetic Fluxes from Spectral Reflectance of Leaves and Canopies

The central hypothesis of this study has been that photosynthetic efficiency and capacity can be predicted from 'physiological reflectance indices' derived from spectral reflectance of leaves and canopies. I have approached this topic with a combination of laboratory and field experiments, and have also explored the potential of deriving a meaningful physiological index from imaging spectrometry (e.g. AVIRIS). A few highlights are presented below. The main emphasis has been on the 'Photochemical Reflectance Index' (PRI), derived from reflectance at 531 nm and 570 nm. Unlike most 'conventional' vegetation indices (e.g. NDVI), PRI changes rapidly both with illumination and physiological state, because it detects the interconversion of xanthophyll cycle pigments, which serve as photoregulatory pigments and control energy distribution for the photosynthetic system. This approach has differed dramatically from most remote sensing in that it has emphasized temporal variation in narrow-band spectral signatures, instead of spatial patterns of broadband indices. Our primary conclusion has been that PRI works well as an index of photosynthetic light-use efficiency at the leaf scale, much in the same way as the fluorescence index DeltaF/Fm. However, unlike DeltaF/Fm which must be measured at close scales, PRI can be sampled at a range of spatial scales, presenting the possibility of monitoring photosynthetic fluxes remotely.

Gamon, John A.↗