Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

Abstract

We developed a forest type classification technology for the Daxing′an Mountains of northeast China using multisource remote sensing data. A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing′an Mountains. Forest types were identified using random forest (RF) classification with the following data combination types: SPOT-5 alone, SPOT-5 and SAR images in August or November, and SPOT-5 and two temporal SAR images. We identified many forest types using a combination of multitemporal SAR and SPOT-5 images, including Betula platyphylla, Larix gmelinii, Pinus sylvestris and Picea koraiensis forests. The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone. RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.