Application of decision tree classification to rubber plantations extraction with remote sensing

Abstract: The border region of China, Laos and Myanmar (BRCLM) has attracted much international attention due to the special geo-economic and geo-political characteristics, as well as being the hinterland of the world's famous "Golden Triangle," and the optimal rubber planting areas for Chinese investment. Monitoring the spatial-temporal pattern of the rubber plantations is significant for regional land resource development, eco-environment protection, and maintaining border security. Based on Landsat remote sensing image data and MODIS-NDVI data, rubber plantations were extracted by the decision tree classification method in BRCLM using spectral features and texture characteristics. The results showed that: (1) On account of spectral differences between rubber forests at different growth stages, we were able to extract rubber plantations according to young rubber forest (<10 a) and mature rubber forest (≥10 a) respectively. The optimum temporal window to discriminate rubber plantations was from early January to late March, which is especially appropriate for mature rubber forest. Mature rubber forest, dry land with high vegetation cover, and forest land were prone to misclassification. Meanwhile, young rubber forest, tea plantation, shrubland and grassland were confused with each type in spectral characteristics according to the index of NDVI. (2) Based on the original spectral characteristics, normalized indices, K-T transform indices, and texture features, we established young rubber forest and mature rubber forest decision tree classification models respectively. The overall accuracy of the mature rubber forest went beyond 90%, and the young rubber forest beyond 75%, which meant that the decision tree method was better for mature rubber forest extraction. The rubber plantation distribution maps were obtained using the established decision tree models in 1980, 1990, and 2000 with high classification accuracy, which indicated that the models were simple and efficient for extracting rubber plantations in the tropical areas. This is an effective method for perennial vegetation extraction and classification accuracy verification. (3) From 1980 to 2010, the size of rubber plantations in BRCLM increased nearly nine times, from 705 km2 to 6 014 km2, and the expansion rate of the young rubber forest was faster than that of the mature rubber forest. National differences of rubber plantations in BRCLM were significant, and the cross-border planting developed quickly in the recent 10 years. Rubber plantations in BRCLM will definitely expand across the borders of China to the territories of Laos and Myanmar.