Welcome to Bulletin of Botanical Research! Today is Share:

Bulletin of Botanical Research ›› 2016, Vol. 36 ›› Issue (4): 613-619.doi: 10.7525/j.issn.1673-5102.2016.04.018

Previous Articles     Next Articles

Forest Type Classification Based on Multi-temporal SAR and SPOT Remote Sensing Data in Pangu Forest Farm

LI Ming-Ze, FU Yu, YU Ying, FAN Wen-Yi   

  1. Northeast Forestry University, Harbin 150040
  • Received:2016-03-10 Online:2016-07-15 Published:2016-06-15
  • Supported by:
    National Natural Science Foundation of China(NSFC)(31470640,31500518,31500519)

Abstract: Information extraction of forest type is difficult in remote sensing image classification. Daxing'an Mountains is an important forestry area in China mainly covered with natural forests, rich with a wide range of plants resources, which makes it difficult to accurately identify the forest types in this region. In order to compare and improve the accuracy of classification result, taking Pangu Forest Farm in Daxing'an Mountains as the study area, we proposed three methods to classify forest types by the maximum likelihood method combining with SPOT-5 and two different temporal RADARSAT-2 fully polarimetric SAR remote sensing data. We designed three schemes to classify the forest types and compared the accuracy. In the three schemes, SPOT image was only used to distinguish forest types, some descriptive parameters extracted from SAR polarimetry(POLSAR) images and the SPOT data were used for classification, and the integration of parameters extracted from multi-temporal of full polarimetric SAR(PolSAR) images with SPOT data was used for classification. The most effective method to identify white birch, larch, Pinus sylvestris and spruce among three proposed schemes was the third using multi-temporal SAR and SPOT remote sensing image. The classification accuracy and the Kappa coefficient were 84.64% and 0.79, respectively. However, the accuracy of forest type classification by using SPOT data individually was the lowest of 76.66% with the Kappa coefficient of 0.70.

Key words: forest type classification, multi-temporal, multi-sources Remote Sensing information, polarization decomposition

CLC Number: