Vegetation is a basic component of urban-suburban landscapes, and turfgrass is a major vegetation type in U.S. cities. QuickBird images were acquired to map the spatial distribution of turfgrass, monitor vegetative conditions, and estimate carbon uptake. Shadows in the images were detected and removed by taking advantage of the high radiometric resolution of the QuickBird data. The results indicated that turfgrass accounted for significant proportions of land cover in the study area. Lawn grass generally has lower productivity and greater spatial variability than those of golf grass, but contributes more to carbon uptake because of the larger area coverage.

Landsat TM data have been used to map the percentage of impervious surface area of the seven-county Twin Cities Metropolitan Area in 1991, 1998 and 2000. Following classification of land cover types, a regression model relating percent impervious surface area to "tasseled cap" greenness was used to estimate the percent impervious surface area for pixels classified as urban or developed. Eighty to 90 percent of the variation in imperviousness is accounted for by greenness. Classification of the Landsat TM data provides a means to map and quantify the degree of impervious surface area, an indicator of environmental quality, over large areas and over time at modest cost.

Digital procedures to optimize the information content of multitemporal Landsat TM data sets for forest cover change detection are described. Imagery from three different years (1984, 1986, and 1990) were calibrated to exoatmospheric reflectance to minimize sensor calibration offsets and standardize data acquisition aspects. Geometric rectification was followed by atmospheric normalization and correction routines. The normalization consisted of a statistical regression over time based on spatially well-defined and spectrally stable landscape features spanning the entire reflectance range. Linear correlation coefficients for all bitemporal band pairs ranged from 0.9884 to 0.9998. The correction mechanism used a dark object subtraction technique incorporating published values of water reflectance. The association between digital data and forest cover was maximized and interpretability enhanced by converting band-specific reflectance values into vegetation indexes. Bitemporal vegetation index pairs for each time interval (two, four, and six years) were subjected to two change detection algorithms, standardized differencing and selective principal component analysis. Optimal feature selection was based on statistical divergence measures. Although limited to spectrally-radiometrically defined change classes, results show that the relationship between reflective TM data and forest canopy change is explicit enough to be of operational use in a forest cover change stratification phase prior to a more detailed assessment.>

This review discusses, from an agronomic perspective, contemporary remote sensing research on crop identification and condition assessment. The paper begins with a review of the basic relationships of reflectance and biophysical properties of crop canopies. Leaf area index is shown to be a key parameter linking multispectral reflectance and crop physiology. Major advancements in capability, particularly the development of spectral-temporal profile models, for crop identification have been made in the past decade. The same model form has been used for estimating crop development stage, leaf area index, and canopy light interception as inputs to crop growth and yield models. The paper concludes with a discussion of potential advancements in capability with respect to new sensors.