Forest community mapping using hyperspectral (CHRIS/PROBA) and Sentinel-2 multispectral images

Abstract

The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer's accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.