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 Douglasfir 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 foliar level.

Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data

“…Because we also wish to gauge the importance of the sill ratio variables when used in combination with other spatial and spectral information for modeling forest structure, we employ partial least squares (PLS) regression. While PLS has been used extensively with hyperspectral data (Ourcival et al, 1999;Smith et al, 2002Smith et al, , 2003Townsend et al, 2003;Coops et al, 2003;McDonald et al, 2003), Wolter et al (2008) demonstrated the capability of this approach to estimate forest BA and species composition using broad band, satellite sensor data. PLS regression is convenient in this regard as it allows simultaneous modeling of multiple continuous predictor variables, does not make unrealistic assumptions about spectral or ground measurement error, as in ordinary least-squares regression (Curran & Hay, 1986;Cohen et al, 2003), and addresses the problem of collinearity (dependence) among multiple independent and dependent variables (Helland, 1988 Semivariance and other texture measures applied to remote sensing data have been used extensively to identify unique forest structure (Woodcock & Strahler, 1987;Woodcock et al (1988), Cohen et al, 1990;Franklin et al, 2001;Song & Woodcock, 2002;Coburn & Roberts, 2004).…”

Forest chemistry mapping with hyperspectral data

“…Then the obtained gains and offsets were applied to other pixels of the image. This data pre-processing was identical to that used in [6]. Instead of relating the sample foliar chemical and moisture concentration measurements directly to the corresponding pixel spectral features by Partial Least Squares (PLS) regression as we did in [6], in this paper we used another approach by adding more data processing steps to improve the accuracy of chemical prediction and mapping.…”