466 publications from this institution
Red and photographic infrared data were collected with a hand-held radiometer under a variety of conditions at 4- to 12-day intervals throughout the growing season and were used to monitor corn and soybean growth and development. The normalized difference transformation was used to effectively compensate for the variation in irradiational conditions. With these data, plotted against time, green-leaf biomass dynamics were compared between the crops. By this approach, based entirely upon spectral inputs, the crop canopies were nondestructively monitored. Five spectral stages were defined and were related to crop development for corn and soybeans.
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The global parameter fields used in the revised Simple Biosphere Model (SiB2) of Sellers et al. are reviewed. The most important innovation over the earlier SiB1 parameter set of Dorman and Sellers is the use of satellite data to specify the time-varying phonological properties of FPAR, leaf area index. and canopy greenness fraction. This was done by processing a monthly 1° by 1° normalized difference vegetation index (NDVI) dataset obtained farm Advanced Very High Resolution Radiometer red and near-infrared data. Corrections were applied to the source NDVI dataset to account for (i) obvious anomalies in the data time series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminated cold land surface points, and (iv) persistent cloud cover in the Tropics. An outline of the procedures for calculating the land surface parameters from the corrected NDVI dataset is given, and a brief description is provided of source material, mainly derived from in situ observations, that was used in addition to the NDVI data. The datasets summarized in this paper should he superior to prescriptions currently used in most land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular those related to vegetation, are obtained directly from a consistent set of global-scale observations instead of being inferred from a variety of survey-based land-cover classifications.
A newly discovered assemblage of predominantly small tracks from the Cretaceous Patuxent Formation at NASA's Goddard Space Flight Center, Maryland, reveals one of the highest track densities and diversities ever reported (~70 tracks, representing at least eight morphotypes from an area of only ~2 m2). The assemblage is dominated by small mammal tracks including the new ichnotxon Sederipes goddardensis, indicating sitting postures. Small crow-sized theropod trackways, the first from this unit, indicate social trackmakers and suggest slow-paced foraging behavior. Tracks of pterosaurs, and other small vertebrates suggest activity on an organic-rich substrate. Large well-preserved sauropod and nodosaurs tracks indicate the presence of large dinosaurs. The Patuxent Formation together with the recently reported Angolan assemblage comprise the world's two largest Mesozoic mammal footprint assemblages. The high density of footprint registration at the NASA site indicates special preservational and taphonomic conditions. These include early, penecontemporaneous deposition of siderite in organic rich, reducing wetland settings where even the flesh of body fossils can be mummified. Thus, the track-rich ironstone substrates of the Patuxent Formation, appear to preserve a unique vertebrate ichnofacies, with associated, exceptionally-preserved body fossil remains for which there are currently no other similar examples preserved in the fossil record.
Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.
We examine seasonal variability in snow‐cover, normalized difference vegetation index (NDVI), and temperature in a broad region of northern Eurasia, and the spatial and temporal correspondence among trends in these variables between 1982 and 1999. Our results support the contention that the previously reported springtime “greening” trend in northern Eurasian land areas arises from a combination of: (1) the direct effects of declining snow‐cover on surface spectral reflectance and NDVI, and (2) enhanced vegetation growth and green biomass stimulated by warmer air temperatures and potentially greater vegetation absorption of photosynthetically active radiation (PAR) during the period of annual peak solar irradiance.
[1] We examine seasonal variability in snow-cover, normalized difference vegetation index (NDVI), and temperature in a broad region of northern Eurasia, and the spatial and temporal correspondence among trends in these variables between 1982 and 1999. Our results support the contention that the previously reported springtime “greening” trend in northern Eurasian land areas arises from a combination of: (1) the direct effects of declining snow-cover on surface spectral reflectance and NDVI, and (2) enhanced vegetation growth and green biomass stimulated by warmer air temperatures and potentially greater vegetation absorption of photosynthetically active radiation (PAR) during the period of annual peak solar irradiance.
Three-band hand-held spectral radiometer measures electromagnetic energy reflected from plant canopies in the visible and infrared portions of the spectrum. It is mobile and easy to use for rapid, repeated measurements. Radiometer probe is held level over plant canopy, readout range switches are set and measurements are recorded. Chlorophyll content, leaf area index and leaf water content can be quickly and easily measured.
The African real time environmental monitoring using imaging satellites (Artemis) system, which should monitor precipitation and vegetation conditions on a continental scale, is presented. The hardware and software characteristics of the system are illustrated and the Artemis databases are outlined. Plans for the system include the use of hourly digital Meteosat data and daily NOAA/AVHRR data to study environmental conditions. Planned mapping activities include monthly rainfall anomaly maps, normalized difference vegetation index maps for ten day and monthly periods with a spatial resolution of 7.6 km, ten day crop/rangeland moisture availability maps, and desert locust potential breeding activity factor maps for a plague prevention program.
The number of radiometric quantizing levels required for satellite monitoring of vegetation resources was evaluated by using in situ collected spectral reflectance data, an atmospheric radiative transfer simulation model and a satellite sensor simulation model. Reflectance data were converted to radiance data; passed through a model atmosphere to an altitude of 706 km; and subsequently quantized at 16,32,64,128,256 and 512 digital count levels for Thematic Mapper bands TM3 (0·63-0·69 μm) and TM4 (0·76-0·90 μm), The simulated digital count data were regressed against in situ biological data to quantify the relationship(s) between quantizing levels. Results of the analysis demonstrated that solar zenith angle had an effect on QEΔρ, that 256 quantizing levels gave a 1-3 per cent improvement per channel over 64 quantizing levels, and that 256 quantizing levels gave a 1 per cent improvement per channel over 128 quantizing levels. No improvements were found for 256 versus 512 quantizing levels.