ABSTRACT
Narrowband Data ....................................................................................................................24 1.8.1 Class Separability Using Unique Hyperspectral Narrowbands ..................................24 1.8.2 Class Separability Using Statistical Methods .............................................................25 1.8.3 Accuracy Assessments of Vegetation and Crop Classiœcation Using
Hyperspectral Narrowbands .......................................................................................26 1.9 Optimal Hyperspectral Narrowbands in Study of Vegetation and Agricultural Crops .........27 1.10 Conclusions .............................................................................................................................30 Acknowledgments ............................................................................................................................ 31 References ........................................................................................................................................ 31
Recent advances in hyperspectral remote sensing (or imaging spectroscopy) demonstrate a great utility for a variety of land monitoring applications. It is now possible to be diagnostic in sensing species and plant communities using remotely sensed data and to do so in a direct and informed manner using modern tools and analyses. Hyperspectral data analyses are superior to traditional broadband analyses in spectral information. Many investigations explore and document remote sensing of vegetation and agricultural croplands. Some examples include (a) detecting plant stress [1], (b) measuring chlorophyll content of plants [2], (c) identifying small differences in percent of green vegetation cover [3], (d) extracting biochemical variables such as nitrogen and lignin [2,4-6], (e) discriminating land-cover types [7], (f) detecting crop moisture variations [8], (g) sensing subtle variations in leaf pigment concentrations [2,9,10], (h) modeling biophysical and yield characteristics of agricultural crops [6,11,12], (i) improving the detection of changes in sparse vegetation [13], and (j) assessing absolute water content in plant leaves [14]. This is a fairly detailed list but not exhaustive, meant to provide the reader with a measure of the current, proven experimental capabilities, and operational applications, and stimulate investigations of new, ambitious applications.
