Title
Two New Hyperspectral Indices For Comparing Vegetation Chlorophyll Content
Abstract
The red edge region of a hyperspectral vegetation reflectance curve provides important information regarding the biochemical and biophysical parameters of plants such as stress, senescence, and chlorophyll capacity. However, shifts of the red edge position (REP) to longer or shorter wavelengths have also been correlated with other factors such as water content, nitrogen, and salinity. These other factors can confuse the effect of chlorophyll on REP. The objective of this study is to define two new hyperspectral curve indices, the red valley width (RVW) and the chlorophyll absorption region (CAR) that are designed to provide less-sensitive characterizations of the chlorophyll content of vegetation in order to allow better comparisons among spatially or temporally distant populations of vegetation. The RVW and the CAR are both located in the visible near-infrared portion of the light spectrum and are derived from multiple hyperspectral curve features that have been found to be correlated with chlorophyll content, thus making them less sensitive to other biophysical and biochemical factors that can affect the REP independently. The robustness of the two new features is tested using the Leaf Optical Properties Experiment database, and the findings are used to compare two populations of saltcedar (Tamarix spp.) from a native habitat in China and an invasive habitat in the USA. Saltcedar is a highly invasive plant species in the USA but does not pose the same ecological and economic threats in its native habitat throughout Eurasia. The findings are interpreted in the context of the environmental characteristics of each region.
Year
DOI
Venue
2014
10.1080/10095020.2014.889264
GEO-SPATIAL INFORMATION SCIENCE
Keywords
Field
DocType
remote sensing, spectral comparison, non-native, exotic species
Vegetation,Remote sensing,Hyperspectral imaging,Chlorophyll content,Salinity,Water content,Mathematics,Wavelength,Chlorophyll,Red edge
Journal
Volume
Issue
ISSN
17
1
1009-5020
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Amy E. Frazier152.34
Le Wang2142.70
Jin Chen325931.87