Smoothness membership in Besov spaces B/sub q//sup /spl alpha//(I) is used to compare the spatial coherence of satellite images. Smoothness is given by a complexity index computed as the rate of decay of the approximation error /spl epsiv/(M) when the image is approximated by its M-largest quantized wavelet coefficient. The technique was applied to a set of nine normalized difference vegetation index (NDVI) time series data as a quantitative quality measure of spatial coherence. The NDVI data set comprises different compositing and atmospheric correction techniques. The estimates of the complexity index give a quantitative measure of the performance of these techniques that agrees well with visual evaluation and with the physics of the image collection process. The authors demonstrate the maximum value NDVI composites with Rayleigh, ozone, and water vapor correction consistently provide the highest spatial coherence among the compositing and atmospheric correction techniques evaluated. They also show the complexity index is regionally dependent and is higher in dry periods than in wet periods where residual cloud interference is more likely to appear.
Meteorological satellite data from 1982 to 1990 were used to identify areas of significant association between tropical Pacific sea surface temperature (SST) and remotely sensed normalized difference vegetation index (NDVI) anomalies, here taken as a surrogate for rainfall anomalies. During this period, large areas of arid and semi‐arid Africa, Australia and South America experienced NDVI anomalies directly correlated to tropical Pacific SST anomalies. The results are limited by the relatively short time period of analysis. However, they confirm the disruptive effects of large‐scale tropical Pacific SST variations on arid and semiarid continental rainfall patterns in Africa, Australia, and South America, as reported previously.
Normalized difference vegetation index data obtained from polar-orbiting meteorological satellites were used to compare the growing or rainy seasons of 1984 and 1985 for the Sahelian zone of Africa. A substantial difference was found between these two years, with 1985 generally having higher normalized difference vegetation index values indicating higher levels of primary production in 1985 than in 1984. 1 km data were compared for Senegal, Mali, Niger and Sudan, and 7 km data were compared for sub-Saharan Africa. The qualitative comparison of these data suggests the use of similar data to assist in centralized monitoring of rangeland conditions, to identify areas of deficiencies in primary production and provide synoptic information in support of regional drought monitoring
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The evidence for global warming is very conclusive for the past 400-500 years. Prior to the 16th century, proxy surface temperature data are regionally good but lack a global distribution. The speaker will review surface temperature reconstruction based upon ice cores, coral cores, tree rings, deep sea sediments, and bore holes and discuss the controversy surrounding global warming. This will be contrasted with the excellent data we have from the satellite era of earth observations the past 30+ years that enables the quantitative study of climate across earth science disciplines.
Images are presented that show the mean and coefficient of variation of nine years (1981-1989) of NOAA AVHRR normalized difference vegetation index (NDVI) data for the growing season (July-October) in Africa, north of the equator. The variation in the growing season NDVI is represented by the coefficient of variation image that shows the large variation in the Sahelian growing season between years. It is concluded that these images illustrate some aspects of the perspective being brought to regional and continental scale processes by coarse resolution satellite sensors and the potential of these sensors to provide consistent, long-term datasets.
A stochastic leaf radiation model was used to predict leaf spectral reflectance as a function of leaf water content for a dicot leaf. Simulated spectral reflectances were analyzed to quantify reflectance differences between different equivalent water thicknesses. Simulated results coupled with consideration of atmoshperic transmission properties and the incident solar spectral irradiance at the earth's surface resulted in the conclusion that the 1.55–1.75 μm region was the best-suited wavelength interval for satellite—platform remote sensing of plant canopy water status in the 0.7–2.5 μm region of the spectrum.
Directional reflectance measurements spanning the entire exitance hemisphere for a number of vegetation and bare soil sites were measured from the ground in NOAA-7 AVHRR bands 1 (0-58-0-68 μm) and 2 (0-73-1-1 μm) for various solar zenith angles. The normalized difference and ratio transformations of these bands were calculated. The effects of the atmosphere on the directional radiance above the atmosphere was explored by applying the average mid-latitude atmospheric model of Dave to a series of ground measurements. These data and simulations were analysed for the optimal directional view angles with respect to two strategies. The first strategy views the utility of off-nadir measurements as extending spatial and temporal coverage while the second strategy views the utility of off-nadir measurements as increasing information content about the physical characteristics of the target(s). The optimum view angles for the first strategy are view angles which minimize the change in sensor response of target(s) relative to the sensor response to the nadir direction. The optimum view angles for the second strategy are view angles which, relative to a nadir view, provide superior and/or additional information about the physical characteristics of the target(s). For the first strategy the results indicate that, from the ground level, the optimum view angles are in the azimuth direction perpendicular to the principal plane of the Sun for all targets, bands and Sun angles. The differences in reflectance between the nadir and off-nadir view angles in this azimuth plane decreased significantly with decreasing solar zenith angle for both bands and all targets. The normalized difference transformation of bands 1 and 2 is significantly less sensitive to variations of view angles than the individual bands for all Sun angles and targets. The results for the second strategy from the ground level showed that off-nadir view angles with an aximuth in the principal plane of the Sun were optimum for all targets and both bands. Finally, the results of the atmospheric simulation study indicated that a mid-latitude summer atmosphere does not significantly change the optimum directional view angles for either the first strategy or the second strategy. However, the atmosphere increased off-nadir sensor responses relative to the ground-level responses particularly in the forwardscattering direction. The normalized difference transformation of bands 1 and 2 was a good transformation for the first strategy for off-nadir viewing angles of less than 45°. The study provides valuable information for interpreting present remotely sensed off-nadir data and in designing future systems with off-nadir capabilities.
The spectral methods of vegetation analysis not only measure herbage biomass on a nondestructive basis but also can be adapted to aircraft and satellite devices to map the spatial distribution over an area in an efficient and economical fashion. This study reviews the ground-based in situ field spectrometry in the 0.350-0.800 micron region of the spectrum. A statistical analysis of in situ spectroreflectance data from sample plots of the shortgrass prairie shows that green biomass, chlorophyll concentration, and leaf water content are directly interrelated to that composite property of the plot which is called functioning green biomass. Spectrocorrelation data indicate the spectral regions of optimum sensitivity for a remote estimation of the green biomass, chlorophyll, and leaf water content. The near-infrared region of the spectrum shows a high positive spectrocorrelation to these three sample parameters, regardless of the amount of standing dead vegetation.
. Measurements of green‐leaf vegetation dynamics recorded by the advanced very high resolution radiometer instruments onboard National Oceanic and Atmospheric Administration satellites 7 and 9 were used to derive ground moisture and rainfall patterns in Kenya and monitor resultant flooding of mosquito larval habitats (dambos) likely to support Rift Valley Fever virus vector mosquitoes (Aedes and Culex spp.). Satellite‐derived data from mid‐1981 to December 1988 have been analysed with corresponding rainfall, flooding and vector population data as they relate to Rift Valley Fever virus ecology. Single (7times7 km) and multiple grid‐cell image analysis (200times300 km) in small localized areas and large geographical regions, respectively, of vegetation data were used to quantify the potential for flooding of mosquito breeding habitats. The ability to detect accurately parameters, such as ground moisture, that determine flooding could provide local officials with sufficient warning to allow for implementation of specific mosquito control measures before a disease outbreak.
The first four Landsat-D thematic mapper sensors were evaluated and compared to the RBV and MSS sensors from Landsats-1, 2, and 3, Colvocoresses' proposed 'operational Landsat' three band system, and the French SPOT three band system using simulation/integration techniques and in situ collected spectral reflectance data. Sensors were evaluated by their ability to discriminate vegetation biomass, chlorophyll concentration, and leaf water content. The thematic mapper and SPOT bands were superior in a spectral resolution context to the other three sensor systems for vegetational applications. Significant improvements are expected for vegetational analyses from Landsat-D thematic mapper and SPOT imagery over MSS and RBV imagery.
In this paper, we estimate the year‐to‐year variations in northern vegetation greenness as they relate to the dominant modes of climate variability. In particular, we analyze spatial data of Northern Hemisphere satellite‐sensed vegetation greenness, surface temperature, precipitation, and upper air data for the 1982–1998 period to isolate well correlated modes of variability between temperature and greenness and to assess their relationship to large‐scale circulation anomalies. It is found that during spring, large‐scale modes of interannual vegetation variability are strongly correlated with spatiotemporal modes of variability in the overlying temperature field. In addition, the results indicate that the two predominant hemispheric‐scale modes of covariability are related to teleconnections associated with the El Niño‐Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The warm event ENSO signal is manifested as warmer and greener conditions in North America, Far East Asia, and to some extent central Europe, while the features of the positive phase AO signal include enhanced warm and green conditions over large regions in Europe and Asian Russia, with opposite anomalies in the eastern half of North America. Whether observed trends in vegetation activity over the past 20 years are also related to systematic changes in these two modes of variability is still unclear.
