Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery

Abstract

There has been growing interest in Malaysia to increase the productivity of latex. This made accurate knowledge of rubber tree growth and age distribution a helpful decision making tool for the government, rubber plantation managers, and harvesters. Gathering this information using conventional methods is difficult, time consuming, and limited in spatial coverage. This paper presents hierarchical classification approach to obtain accurate map of rubber tree growth age distribution using SPOT-5 satellite imagery. The objective of the study is to evaluate the performance of pixel-based and object-oriented classifiers for rubber growth classification. At the first level, the general land cover was classified into eight land cover classes (soil, water body, rubber, mature oil palm, young oil palm, forest, urban area, and other vegetation) using Mahalanobis distance (MD), k-nearest neighbor (k-NN), and Support Vector Machine (SVM) classifiers. Thereafter, the best classification map, k-NN output, was used to select only pixels that belong to the rubber class from the SPOT-5 image. The extracted pixels served as input into the next classification hierarchy where four classifiers, MD, k-NN, SVM, and decision tree (DT), were implemented to map rubber trees into three intra-classes (mature, middle-aged, and young rubbers). The result produced overall accuracy of 97.48%, 96.90%, 96.25%, and 80.80% for k-NN, SVM, MD, and DT respectively. The result indicates that object-oriented classifiers are better than pixel-based methods mapping rubber tree growth.