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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Artificial Intelligence, 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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dc.contributor.author Im, Jungho ko
dc.contributor.author Jensen, J.R. ko
dc.contributor.author Coleman, M. ko
dc.contributor.author Nelson, E. ko
dc.date.available 2014-11-05T02:04:07Z -
dc.date.created 2014-11-05 ko
dc.date.issued 2009-08 -
dc.identifier.citation GEOCARTO IMNTERNATIONAL, v.24, no.4, pp.293 - 312 ko
dc.identifier.issn 1010-6049 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/8240 -
dc.description.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. ko
dc.description.statementofresponsibility close -
dc.language ENG ko
dc.publisher Geocarto International Centre ko
dc.subject Biomass ko
dc.subject Hyperspectral analysis ko
dc.subject Leaf area index ko
dc.subject Leaf nutrients ko
dc.subject Machine-learning regression trees ko
dc.subject NDVI ko
dc.subject Partial least squares regression ko
dc.subject Remote sensing ko
dc.title Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-70449377987 ko
dc.type.rims ART ko
dc.description.scopustc 12 *
dc.date.scptcdate 2014-11-05 *
dc.identifier.doi 10.1080/10106040802556207 ko
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