Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments |
| |
| Authors: | Jungho Im John R. Jensen Mark Coleman Eric Nelson |
| |
| Affiliation: | 1. Department of Environmental Resources and Forest Engineering , State University of New York, College of Environmental Science and Forestry , Syracuse, New York, USA imj@esf.edu;3. Department of Geography , University of South Carolina , Columbia, South Carolina, USA;4. USDA Forest Service, Southern Research Station , Aiken, South Carolina, USA;5. Savannah River National Laboratory, Savannah River Site, US Department of Energy , Aiken, South Carolina, USA |
| |
| Abstract: | Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability. |
| |
| Keywords: | remote sensing hyperspectral analysis partial least squares regression machine-learning regression trees NDVI leaf nutrients leaf area index biomass |
|
|
