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NASA NTRS1993

A comparison of spectral mixture analysis an NDVI for ascertaining ecological variables

In this study, we compare the performance of spectral mixture analysis to the Normalized Difference Vegetation Index (NDVI) in detecting change in a grassland across topographically-induced nutrient gradients and different management schemes. The Konza Prairie Research Natural Area, Kansas, is a relatively homogeneous tallgrass prairie in which change in vegetation productivity occurs with respect to topographic positions in each watershed. The area is the site of long-term studies of the influence of fire and grazing on tallgrass production and was the site of the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) from 1987 to 1989. Vegetation indices such as NDVI are commonly used with imagery collected in few (less than 10) spectral bands. However, the use of only two bands (e.g. NDVI) does not adequately account for the complex of signals making up most surface reflectance. Influences from background spectral variation and spatial heterogeneity may confound the direct relationship with biological or biophysical variables. High dimensional multispectral data allows for the application position of techniques such as derivative analysis and spectral curve fitting, thereby increasing the probability of successfully modeling the reflectance from mixed surfaces. The higher number of bands permits unmixing of a greater number of surface components, separating the vegetation signal for further analyses relevant to biological variables.

Wessman, Carol A.↗

NASA NTRS1986

Early warning and crop condition assessment research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

Boatwright, G. O.↗

NASA NTRS1972

Determining density of maize canopy. 3: Temporal considerations

Multispectral scanner data were collected in two flights over ground cover plots at an altitude of 305 m. Eight ground reflectance panels in close proximity to the ground cover plots were used to normalize the scanner data obtained on different dates. Separate prediction equations were obtained for both flight dates for all eleven reflective wavelength bands of the multispectral scanner. Ratios of normalized scanner data were related to leaf area index over time. Normalized scanner data were used to plot relative reflectance versus wavelength for the ground cover plots. Spectral response curves were similar to those for bare soil and green vegetation as determined by laboratory measurements. The spectral response curves from the normalized scanner data indicated that reflectance in the 0.72 to 1.3 micron wavelength range increased as leaf area index increased. A decrease in reflectance was observed in the 0.65 micron chlorophyll absorption band as leaf area index increased.

Stoner, E. R.↗

NASA NTRS1983

Vegetation monitoring and classification using NOAA/AVHRR satellite data

A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.

Greegor, D. H., Jr.↗

NASA NTRS1984

Thematic Mapper simulator research for forest resource mapping in the Clearwater National Forest, Idaho

Per-pixel maximum likelihood digital classification and photo interpretation of Thematic Mapper Simulator (TMS) composited images for a managed conifer forest were used to evaluate both land cover and forest structure characteristics. TMS channels 4, 7, 5 and 3, which were found to be optimal for forest vegetation analysis, used the full range of the Thematic Mapper's spectral capability. Photo interpretation results indicate that a false color composite from TMS channels 4, 7, and 2 provided the highest accuracies; the combination of improved spatial, spectral and radiometric resolution of the Thematic Mapper yielded greater sensitivity to forest structural characteristics.

Brass, J. A.↗

NASA NTRS2008

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors: Theory - Part 1

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRRmode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.

Ganguly, Sangram↗

NASA NTRS1973

Textural signatures for wetland vegetation

This investigation indicates that unique textural signatures do exist for specific wetland communities at certain times in the growing season. When photographs with the proper resolution are obtained, the textural features can identify the spectral features of the vegetation community seen with lower resolution mapping data. The development of a matrix of optimum textural signatures is the goal of this research. Seasonal variations of spectral and textural features are particularly important when performing a vegetations analysis of fresh water marshes. This matrix will aid in flight planning, since expected seasonal variations and resolution requirements can be established prior to a given flight mission.

Whitman, R. I.↗

NASA NTRS1976

The nature of spectral signatures in native arid plant communities

Radiometric data in ERTS bands 5 and 7 of spectral signature components were compared to the overall signatures obtained from an airborne radiometric data collection system flown at low altitude. Results indicate that due to the low density and low vigor of the vegetation, vegetation has little effect on the overall signature, thus making differentiation of desert plant communities on the basis of spectral signature extremely difficult.

Conn, J. S.↗

NASA NTRS1982

Diurnal patterns of wheat spectral reflectances and their importance in the assessment of canopy parameters from remotely sensed observations

Spectral reflectances of Produra wheat were measured at 13 different times of the day at Phoenix, Arizona, during April 1979 using a nadir-oriented hand-held 4-band radiometer which had bandpass characteristics similar to those on LANDSAT satellites. Different Sun altitude and azimuth angles caused significant diurnal changes in radiant return in both visible and near-IR regions of the spectrum and in several vegetation indices derived from them. The magnitude of these changes were related to different canopy architecture, percent cover and green leaf area conditions. Spectral measurements taken at each time period were well correlated with green leaf area index but the nature of the relationship changed significantly with time of day. Thus, a significant bias in the estimation of the green leaf area index from remotely sensed spectral data could occur if sun angles are not properly accounted for.

Pinter, P. J.↗

NASA NTRS1993

Relationships of soil, grass, and bedrock over the Kaweah Serpentinite Melange through spectral mixture analysis of AVIRIS data

Mustard, John F.↗

NASA NTRSDate not supplied

Quantitative Assessment of the HLS Surface Reflectance Consistency

The Harmonized Landsat and Sentinel-2 (HLS) project produces compatible surface reflectance (SR) from observations acquired by Landsat-8/9 OLI and Sentinel-2A/2B MSI. The HLS harmonization procedures include atmosphere correction, cloud masking, view angle normalization, and bandpass adjustment. The objective of this study is to quantitatively assess the reflectance consistency between Landsat and Sentinel-2 within the Version 2 HLS data. We collected 545 pairs of same-day Landsat/Sentinel-2 images across the globe to represent a wide range of vegetation types and climate regimes. The mean absolute difference (MAD) in reflectance between Landat and Sentinel-2 was calculated as a consistency indicator for each harmonization step. The MAD generally increased after the atmosphere correction, and then greatly decreased after the BRDF and bandpass adjustments, to smaller than the top-of-atmosphere MAD values. The MAD ranged from 0.0048 to 0.0093 for the six common spectral bands (blue, green, red, NIR, SWIR1, and SWIR2) in the final products, only slightly greater than the difference between Landsat and Sentinel-2 calibrations.. An evaluation on a few commonly used vegetation indices also showed good agreement between Landsat and Sentinel-2 reflectance. All these evaluations demonstrate that the HLS project produces a consistent SR dataset from Landsat-OLI and Sentinel-MSI, which will be a valuable resource for a wide range of remote sensing applications.

Qiang Zhou↗

NASA NTRS1991

Relationships of soil, grass, and bedrock over the Kaweah serpentine melange through spectral mixture analysis of AVIRIS data

A linear mixing model is used to model the spectral variability of an AVIRIS scene from the western foothills of the Sierra Nevada and calibrate these radiance data to reflectance. Five spectral endmembers from the AVIRIS data, plus an ideal 'shade' endmember were required to model the continuum reflectance of each pixel in the image. Three of the endmembers were interpreted to model the surface constituents green vegetation, dry grass, and illumination. These are the main transient surface constituents that are expected to change with shifts in land use or climatic influences and viewing conditions ('shade' only). The spectral distinction between the other three endmembers is very small, yet the spatial distributions are coherent and interpretable. These distributions cross anthropogenic and vegetation boundaries and are best interpreted as different soil types. Comparison of the fraction images to the bedrock geology maps indicates that substrate composition must be a factor contributing to the spectral properties of these endmembers. Detailed examination of the reflectance spectra of the three soil endmembers reveals that differences in the amount of ferric and ferrous iron and/or organic constituents in the soils is largely responsible for the differences in spectral properties of these endmembers.

Mustard, John F.↗

DOE OSTI2026

Unraveling plant phenotype to genotype associations with daily hyperspectral traits in Populus trichocarpa

Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (spearman rho = 0.3, p < 1x10 −8 ) between individual wavebands or vegetative indices and growth rate, assessed as the relative change of tree height over the growing season. The growth rate prediction was substantially improved by a regularization multivariate model (spearman rho>0.5, p < 1x10 −16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.

09 BIOMASS FUELS↗

NASA NTRS1986

Discrimination of natural and cultivated vegetation using Thematic Mapper spectral data

The availability of high quality spectral data from the current suite of earth observation satellite systems offers significant improvements in the ability to survey and monitor food and fiber production on both a local and global basis. Current research results indicate that Landsat TM data when used in either digital or analog formats achieve higher land-cover classification accuracies than MSS data using either comparable or improved spectral bands and spatial resolution. A review of these quantitative results is presented for both natural and cultivated vegetation.

Degloria, Stephen D.↗

NASA NTRS1992

Air-surface exchange of H2O, CO2, and O3 at a tallgrass prairie in relation to remotely sensed vegetation indices

Parameters derived from eddy correlation measurements of the air-surface exchange rates of H2O, CO2, and O3 over a tallgrass prairie are examined in terms of their relationships with spectral reflectance data remotely sensed from aircraft and satellites during the four 1987 intensive field campaigns of the First ISLSCP Field Experiment (FIFE). The surface conductances were strongly modulated by photosynthetically active radiation received at the surface when the grass was green and well watered; mesophyll resistances were large for CO2 but negligible for H2O and O3.

Gao, W.↗

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 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.↗

NASA NTRS1978

Post senescent grass canopy remote sensing

Analysis of in situ collected spectral reflectance data from a dormant or senescent grass canopy showed a direct relationship existed between spectral reflectance and biomass for the 0.50-0.80 micron spectral region. The data, collected four weeks after the end of the growing season, indicated that post senescent remote sensing of grass canopy biomass is possible and helps to elucidate the spectral contribution of recently dead vegetation in mixed live/dead canopy situations.

Tucker, C. J.↗