Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
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Forested stand structure is an important target variable within the fields of wildlife ecology. Remote sensing has often been suggested as a viable alternative to time consuming field and aerial investigations to determine forest structural attributes. In this study, 44 stands of recently harvested, regenerating, and old growth forest within the Foothills Model Forest in west-central Alberta were selected to test the ability of pan-sharpened SPOT-5 spectral response to classify stand structure. For each stand, a Structural Complexity Index (SCI) was calculated from field data using principal components analysis. To complement the spectral response data set and further increase accuracy, the normalized difference moisture index (NDMI) and three window sizes (5×5, 11×11, and 25×25) of first-(mean and standard deviation) and second-order (homogeneity, entropy, contrast, and correlation) textural measures were calculated over the pan-sharpened image. Stepwise multivariate regression analysis was used to determine the best explanatory model of the SCI using the spectral and textural data. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (r2 = 0.79). The most appropriate window size for the texture measures was 5×5 indicating that this is a measure only detectable at a very high spatial resolution. The resulting classified SCI values were comparable to the actual field level SCI (r2 = 0.74, p = 0.01) and were limited by the strong variability within stands. Future research may find this measure useful either as a separate parameter or as an indicator of forest age for use in wildlife habitat modelling.