Title
Hyperspectral Remote Sensing Of Conifer Chemistry And Moisture
Abstract
The chemical and moisture composition of conifer foliage in the Greater Victoria Watershed District (GVWD), Vancouver Island, Canada, was explored using hyperspectral remote sensing data. Imagery acquired from the airborne sensor Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) were evaluated along with sampled foliar chemical and moisture measurements to provide insight into ecological processes occurring within the watershed. Concentrations of nitrogen, total chlorophyll and moisture were used to provide an analysis of the forest canopy, comprised of Coastal Douglas-fir and Western Redcedar. The AVIRIS data were processed to correct for atmospheric and geometric distortion.The AVIRIS data were used to investigate the relationship between the hyperspectral imagery and the sampled chemical data. A total of 45 plots in the GVWD were sampled from a helicopter. These samples provided both organic and inorganic analysis of the forest canopy. A Partial Least Squares regression was used to analyze the relationship between the data sets in order to extract chemical constituents in the forest canopy. Results indicate that the regression equation explains 81%, 79% and 70% of the variation in nitrogen, total chlorophyll and moisture, respectively. An analysis of the chemical characteristics of the canopy can provide insight into factors controlling growth such as nutrient levels and water deficiencies at the follar level.
Year
DOI
Venue
2003
10.1109/IGARSS.2003.1293839
IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES
Keywords
DocType
ISSN
chemistry,data acquisition,hyperspectral sensors,chemical analysis,remote sensing,forest canopy,nitrogen,hyperspectral imagery,regression equation,inorganic analysis,partial least square regression,forestry,moisture,hyperspectral imaging
Conference
2153-6996
Citations 
PageRank 
References 
0
0.34
2
Authors
7
Name
Order
Citations
PageRank
sarah d mcdonald100.34
k o niemann2488.20
David G. Goodenough38419.70
a dyk48518.72
C. West5455.77
tian han63110.85
m murdoch700.34