Mapping of Shorea kunstleri King in mixed dipterocarp forest using remote sensing technology

Abstract

The conventional method used in forest inventory is time-consuming, especially in field surveying and requires an expert’s knowledge and observation to do species identification. This standard method can be facilitated by advances in remote sensing technology. An airborne hyperspectral image mosaic was utilised in the research to evaluate the ability of this high spectral fidelity dataset that was incorporated into a feature extraction Support Vector Machine (SVM) classifier to successfully detect and map a tree species of interest (Shorea kunstleri King) within the mixed dipterocarp forest stand of the Lambir Hills National Park. In this study, two types of feature extraction Principal Component Analysis (PCA) and Spectral Derivative (SD) were applied to classifiers SVM. To demonstrate the effect, classification of the hyperspectral image with no feature extraction was done. The accuracies achieved from different feature extraction were compared statistically. Based on the results, Shorea kunstleri King was able to be discriminated against within 53 species of Shorea. SVM classifier is known for its ability to generalise well even with limited training samples and is commonly used in image classification. The Kernel parameter in SVM classifier alongside feature extraction significantly affects the classification accuracy. Therefore, feature extraction does affect the classification accuracy, but it depends on the morphology of the study area. It is concluded that the SVM method with SD feature extraction could detect tree crowns of Shorea kunstleri with a Kappa coefficient of 0.8126 which showed a great agreement with the observed samples and overall accuracy of 89.89% that shows the accuracy of the final map.