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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

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Title
Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments
Author
Im, JunghoJensen, J.R.Coleman, M.Nelson, E.
Keywords
Biomass; Hyperspectral analysis; Leaf area index; Leaf nutrients; Machine-learning regression trees; NDVI; Partial least squares regression; Remote sensing
Issue Date
2009-08
Publisher
Geocarto International Centre
Citation
GEOCARTO IMNTERNATIONAL, v.24, no.4, pp.293 - 312
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/8240
DOI
10.1080/10106040802556207
ISSN
1010-6049
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UEE_Journal Papers
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