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

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Covariance of greenness and terrain variables over the Konza Prairie

An analysis is made of time-dependent covariance of the greenness vegetation index with mapped terrain variables over the Konza Prarie (Kansas) during the 1987 growing season. The analysis was part of an ongoing project to establish appopriate ground-sampling and data-integration strategies for satellite-based monitoring of land surface climate conditions. Greenness images for six dates between May and October were derived from atmospherically corrected thematic mapper (TM) data and coregistered with maps of woody vegetation, fire, and soils. Local variance in greenness peaked in mid-June, falling rapidly until mid-August, and declining gradually thereafter. Greenness images exhibited positive autocorrelation up to distances of 180-210 m, but the dominant scale of pattern occurred at a block size of 60 m by 60 m throughout the growing season. 40-44 percent of total scene variance in July and August was accounted for by the effects of woody vegetation (8.9 percent of the area), prairie burning, and soil type. The effect of these terrain variables was fairly consistent between June and late August and was manifested as additional high-frequency spatial variation in imagery from that period.

Davis, Frank W.↗
NASA NTRS1992

Mapping the spectral variability in photosynthetic and non-photosynthetic vegetation, soils, and shade using AVIRIS

The primary objective of this research was to map as many spectrally distinct types of green vegetation (GV), non-photosynthetic vegetation (NPV), shade, and soil (endmembers) in an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene as is warranted by the spectral variability of the data. Once determined, a secondary objective was to interpret these endmembers and their abundances spatially and spectrally in an ecological context.

Roberts, Dar A.↗
NASA NTRS2012

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Case, Jonathan L.↗
NASA NTRS2021

Moisture and Temperature Influences on Nonlinear Vegetation Trends in Serengeti National Park

While long-term vegetation greening trends have appeared across large land areas over the late 20th century, uncertainty remains in identifying and attributing finer-scale vegetation changes and trends, particularly across protected areas. Serengeti National Park (SNP) is a critical East African protected area, where seasonal vegetation cycles support vast populations of grazing herbivores and a host of ecosystem dynamics. Previous work has shown how non-climate drivers (e.g. land use) shape the SNP ecosystem, but it is still unclear to what extent changing climate conditions influence SNP vegetation, particularly at finer spatial and temporal scales. We fill this research gap by evaluating long-term (1982–2016) changes in SNP leaf area index (LAI) in relation to both temperature and moisture availability using Ensemble Empirical Mode Decomposition and Principal Component Analysis with regression techniques. We find that SNP LAI trends are nonlinear, display high sub-seasonal variation, and are influenced by lagged changes in both moisture and temperature variables and their interactions. LAI during the long rains (e.g. March) exhibits a greening-to-browning trend reversal starting in the early 2000s, partly due to antecedent precipitation declines. In contrast, LAI during the short rains (e.g. November, December) displays browning-to-greening alongside increasing moisture availability. Rising temperature trends also have important, secondary interactions with moisture variables to shape these SNP vegetation trends. Our findings show complex vegetation-climate interactions occurring at important temporal and spatial scales of the SNP, and our rigorous statistical approaches detect these complex climate-vegetation trends and interactions, while guarding against spurious vegetation signals.

Moisture↗
NASA NTRS1984

Effects of atmosphere and view and illumination geometry on visible and near infrared radiance data from the advanced very high resolution radiometer (AVHR)

The use of Dave's models to evaluate satellite off-nadir remote sensing of green vegetation cover types by simulating the visible and near-infrared advanced very high resolution radiometer (AVHRR) NOAA-6 and NOAA-7 radiances for three green-leaf biomass levels and bare soil. Ground measurements of surface reflectances were used. The simulations were done along a scan line at 30 deg latitude during the summer solstice, equinox, and winter solstice. The simulation models are described and the effect of atmosphere over moderately vegetated surfaces is discussed. The results show that sensor response to atmospheric path length can be substantial for the AVHRR visible and near-infrared channels and normalized difference values, but they can be minimized by high sun and clear atmospheric viewing. The results indicate that AVHRR data would be most useful for monitoring low green leaf biomas canopies.

Holben, B. N.↗
NASA NTRS1977

Effect of vegetation on rock and soil type discrimination

The effect of naturally occurring vegetation on the spectral reflectance of earth materials in the wavelength region of 0.45 to 2.4 microns is determined by computer averaging of in situ acquired spectral data. The amount and type of vegetation and the spectral reflectance of the ground are considered. Low albedo materials may be altered beyond recognition with only ten per cent green vegetation cover. Dead or dry vegetation does not greatly alter the shape of the spectral reflectance curve and only changes the albedo with minimum wavelength dependency. With increasing amounts of vegetation the Landsat MSS band ratios 4/6, 4/7, 5/6, and 5/7 are significantly decreased whereas MSS ratios 4/5 and 6/7 remain entirely constant.

Siegal, B. S.↗
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 NTRS1997

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate-vegetation greenness relationship for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes If the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980's in order to refine our understanding of intra-annual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global 1o gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80 percent of the geographic variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from lo grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.

Potter, C. S.↗
NASA NTRS1997

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

This study describes the use of satellite data to calibrate a new climate-vegetation greenness function for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our empirical understanding of intraannual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global l(sup o) gridded data sets suggest that three climate indexes: growing degree days, annual precipitation total, and an annual moisture index together can account to 70-80 percent of the variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same climate index values from the previous year explained no significant additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes was closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from l(sup o) grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes were not accurately predicted are mainly high latitude ecosystems and other remote locations where climate station data are sparse.

Potter, C. S.↗
NASA NTRS1984

Monitoring and mapping global vegetation cover using data from meteorological satellites

The role of coarse resolution meteorological satellite data for monitoring and mapping of vegetation for global, continental and regional scales is outlined. In the NOAA products used the effects of cloud cover are reduced by the generation of temporal composites of images of the normalized difference vegetation index. Different land cover types are shown to have characteristic spectral phenological curves. Such data have the disadvantage of effectively increasing the apparent areal extent of small areas of green vegetation.

Townshend, J. R. G.↗
NASA NTRS1985

Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape

An investigation is conducted, for a complex vegetated land area, into the statistical relationship between remotely sensed thermal emissions and reflected spectral radiance. The Kauth-Thomas Tasseled Cap transformation is employed to infer the albedo and amount of green vegetation present from Landsat multispectral scanner (MSS) observations. Reflective data and thermal infrared data were acquired from the Heat Capacity Mapping Mission (HCMM) satellite along with reflective data from the Landsat 3 MSS for a site near Hartford, CT on a single date. Results are presented which indicate that thermal emissions had the greatest association with the amount of vegetation as indicated by a multispectral index, while albedo did not exhibit any substantial relationship with these emissions. These findings are explained in terms of the enhanced latent heat flux to the atmosphere associated with actively transpiring vegetation.

Goward, S. N.↗
NASA NTRS1993

Surface-induced brightness temperature variations and their effects on detecting thin cirrus clouds using IR emission channels in the 8-12 micrometer region

A method for detecting cirrus clouds in terms of brightness temperature differences between narrow bands at 8, 11, and 12 mu m has been proposed by Ackerman et al. (1990). In this method, the variation of emissivity with wavelength for different surface targets was not taken into consideration. Based on state-of-the-art laboratory measurements of reflectance spectra of terrestrial materials by Salisbury and D'Aria (1992), we have found that the brightness temperature differences between the 8 and 11 mu m bands for soils, rocks and minerals, and dry vegetation can vary between approximately -8 K and +8 K due solely to surface emissivity variations. We conclude that although the method of Ackerman et al. is useful for detecting cirrus clouds over areas covered by green vegetation, water, and ice, it is less effective for detecting cirrus clouds over areas covered by bare soils, rocks and minerals, and dry vegetation. In addition, we recommend that in future the variation of surface emissivity with wavelength should be taken into account in algorithms for retrieving surface temperatures and low-level atmospheric temperature and water vapor profiles.

Gao, Bo-Cai↗
NASA NTRS2012

Synegies Between Visible/Near-Infrared Imaging Spectrometry and the Thermal Infrared in an Urban Environment: An Evaluation of the Hyperspectral Infrared Imager (HYSPIRI) Mission

A majority of the human population lives in urban areas and as such, the quality of urban environments is becoming increasingly important to the human population. Furthermore, these areas are major sources of environmental contaminants and sinks of energy and materials. Remote sensing provides an improved understanding of urban areas and their impacts by mapping urban extent, urban composition (vegetation and impervious cover fractions), and urban radiation balance through measures of albedo, emissivity and land surface temperature (LST). Recently, the National Research Council (NRC) completed an assessment of remote sensing needs for the next decade (NRC, 2007), proposing several missions suitable for urban studies, including a visible, near-infrared and shortwave infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) instrument called the Hyperspectral Infrared Imagery (HyspIRI). In this talk, we introduce the HyspIRI mission, focusing on potential synergies between VSWIR and TIR data in an urban area. We evaluate potential synergies using an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS-ASTER (MASTER) image pair acquired over Santa Barbara, United States. AVIRIS data were analyzed at their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60 m spatial resolution similar to HyspIRI. Surface reflectance was calculated using ACORN and a ground reflectance target to remove atmospheric and sensor artifacts. MASTER data were processed to generate estimates of spectral emissivity and LST using Modtran radiative transfer code and the ASTER Temperature Emissivity Separation algorithm. A spectral library of common urban materials, including urban vegetation, roofs and roads was assembled from combined AVIRIS and field-measured reflectance spectra. LST and emissivity were also retrieved from MASTER and reflectance/emissivity spectra for a subset of urban materials were retrieved from co-located MASTER and AVIRIS pixels. Fractions of Impervious, Soil, Green Vegetation (GV) and Non-photosynthetic Vegetation (NPV), were estimated using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to AVIRIS data at 7.5, 15 and 60 m spatial scales. Surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and LST were also determined for urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography.

Roberts, Dar A.↗
NASA NTRS1992

Extraction of soil and vegetation parameters from high resolution bi-directional reflectance spectra

High spectral resolution reflectance spectra were collected over a semi-desert grassland at both dry and wet season periods. Spectral reflectance measurements were made from several viewing angles at both low and high solar zenith angles. A mixture model was used to separate and extract green vegetation from dry/dead vegetation and soil. The extracted vegetation signal varied greatly with view and sun angle variations such that off-nadir viewing and illuminating angles resulted in the highest vegetation loadings. These variations were normalized with cosine functions for both sun and view angle. These results offer a methodology for standardizing multi-temporal and multi-angular satellite measurements of vegetation activity.

Huete, A. R.↗
NASA NTRS1975

Evaluation of thermal X/5-detector Skylab S-192 data for estimating evapotranspiration and thermal properties of soils for irrigation management

An energy budget approach to evaluating the SKYLAB X/5-detector S-192 data for prediction of soil moisture and evapotranspiration rate was pursued. A test site which included both irrigated and dryland agriculture in Southern Texas was selected for the SL-4 SKYLAB mission. Both vegetated and fallow fields were included. Data for a multistage analysis including ground, NC-130B aircraft, RB-57F aircraft, and SKYLAB altitudes were collected. The ground data included such measurements as gravimetric soil moisture, percent of the ground covered by green vegetation, soil texture, net radiation, soil temperature gradients, surface emittance, soil heat flux, air temperature and humidity gradients, and cultural practices. Ground data were used to characterize energy budgets and to evaluate the utility of an energy budget approach for determining soil moisture differences among twelve specific agricultural fields.

Moore, D. G.↗
NASA NTRS2022

Diverse Responses of Multiple Satellite-Derived Vegetation Greenup Onsets to Dry Periods in the Amazon

Satellite-derived vegetation greenness and seasonal dynamics in the Amazon have generated considerable academic debate over the past two decades. Despite this, the phenological timing of Amazon forests and, in particular their responses to dry periods, remain poorly understood. Here we explicitly identify the diverse timing of vegetation canopy greenup onsets from 10-min geostationary satellite observations, and compute the timing of both the start and end of dry periods from daily precipitation data. We, for the first time, reveal that the Amazon vegetation canopy regularly experiences two cycles of greenup onsets during a year. The occurrence of greenup onset varies diversely from the start to end of the dry periods, but demonstrates regular shifts in local areas, although irregular shifts across the region. The multiple greenup onsets show complex spatial shifts, which closely follow the spatial movement of dry periods. The results provide a new insight into our understanding of the complexity of Amazonian vegetation canopy dynamics during dry periods, which could significantly improve the simulation of carbon and water cycles.

Satellite-Derived Vegetation↗
NASA NTRS2018

Monitoring Springs in the Mojave Desert Using Landsat Time Series Analysis

The purpose of this study, based on Landsat satellite data was to characterize variations and trends over 30 consecutive years (1985-2016) in perennial vegetation green cover at over 400 confirmed Mojave Desert spring locations. These springs were surveyed between in 2015 and 2016 on lands managed in California by the U.S. Bureau of Land Management (BLM) and on several land trusts within the Barstow, Needles, and Ridgecrest BLM Field Offices. The normalized difference vegetation index (NDVI) from July Landsat images was computed at each spring location and a trend model was first fit to the multi-year NDVI time series using least squares linear regression.Â

Time Series↗
NASA NTRS1984

Assessment of Landsat multispectral scanner spectral indexes for monitoring arid rangeland

Correlations between spectral indices and vegetation parameters in south-central New Mexico were used to determine the utility of Landsat Multispectral Scanner (MSS) spectral indices in arid rangeland monitoring. In addition, spectral index change for 1976-1980 was calculated from retrospective MSS data and compared with qualitative ground truth in order to evaluate vegetation change detection by means of spectral indices. Brightness index change consistently differentiated between cover increase and decrease, but index change appears to have been offset from true cover change; this may at least partly be attributed to the failure of the methods used to standardize MSS scenes for differences in sensor response. Green vegetation indices, by contrast to brightness indices, failed to consistently differentiate between cover increase and decrease.

Musick, H. B.↗