Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China.
Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region.
Yue
Yuemin
Y
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China. hnyym829@163.com
Wang
Kelin
K
Zhang
Bing
B
Chen
Zhengchao
Z
Jiao
Quanjun
Q
Liu
Bo
B
Chen
Hongsong
H
D002681:China; D017753:Ecosystem; D004784:Environmental Monitoring; D010944:Plants