Detecting and mapping Gonipterus scutellatus induced vegetation defoliation using WorldView-2 pan-sharpened image texture combinations and an artificial neural network

Defoliation induced by the weevil Gonipterus scutellatus is causing significant damage to South Africa's eucalyptus plantations. Therefore, the ability of remote sensing to detect and map G. scutellatus defoliation is essential for monitoring the spread of the weevil so that precautionary measures are set in place. In our study, an integrated approach using image texture in various processing combinations and an artificial neural network (ANN) were developed to detect and map G. scutellatus induced vegetation defoliation. A 0.5-m WorldView-2 pansharpened image was used to compute texture parameters from the gray-level occurrence matrix and gray-level co-occurrence matrix, using optimal moving windows for specific levels of G. scutellatus induced vegetation defoliation. In order to improve the accuracy of detecting and mapping G. scutellatus induced vegetation defoliation, a method involving a three-band texture processing combination was proposed and tested. Using a sequential forward selection algorithm allowed for the selection of optimal texture combinations, which were subsequently input into a backpropagation ANN. The results showed an improvement in detecting vegetation defoliation using single texture bands [R-2 = 0.82, root mean square error (RMSE) = 0.95 (1.82% of the mean measured defoliation)] when compared to single spectral reflectance bands [R-2 = 0.60, RMSE = 1.79 (3.43% of the mean measured defoliation)], two-band spectral reflectance combination model [R-2 = 0.74, RMSE = 1.48 (2.83% of the mean measured defoliation)], and the three-band spectral reflectance combination model [R-2 = 0.80, RMSE = 1.35 (2.59% of the mean measured defoliation)]. Further improvements were obtained using the two-band texture combination model [R-2 = 0.85, RMSE = 1.05 (2.01% of the mean measured defoliation)] and the most promising result was obtained using the proposed three-band texture combination model [R-2 = 0.90, RMSE = 0.85 (1.63% of the mean measured defoliation)]. Overall, our study highlights the potential of image texture combinations in improving the detection and mapping of vegetation defoliation. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)