SEGSAMP: A Pixel Window Sampling Method Based on Image Segmentation

Abstract

Pixel windows are often used to average the image spectral response values that represent the area over which field measurements are collected to estimate biophysical parameters such as forest stand structure, aboveground biomass, and leaf area index. Averaging spectral values within a pixel window ensures the resulting spectral response is representative of the biophysical parameter, and reduces sampling error from spatial mis-registrations between the image and plot locations. These spectral values are related to field plot measurements through empirical models that may result in poor estimates if (a) the plot is located too close to a spectrally different land feature; and (b) natural and human-caused disturbance occurs between the field data collection and image acquisition. This paper introduces SEGSAMP (SEGmentation SAMPle), an image sampling method to extract pixel values within a segmented sampling window that would be more spectrally representative of biophysical parameters measured from field plots. Written in Arc Macro Language, the method uses a point and polygon shapefile from which to sample only those pixels that are within a defined pixel window size and the image segment the plot is contained in. The output forms a segmented pixel window grid, which can be used to extract pixel values from imagery. A case-study application demonstrates that segmented pixel windows resulted in models whose coefficient of determination values were higher for prediction of stand height and crown closure (height Radj 2 = 0.65 ; crown closure Radj 2 = 0.57) than from square windows (height Radj 2 = 0.57 ; crown closure Radj 2 = 0.48).