JOURNAL OF NATURAL RESOURCES ›› 2017, Vol. 32 ›› Issue (12): 2136-2148.doi: 10.11849/zrzyxb.20161306

Special Issue: 气候变化与地表过程

• Research Method • Previous Articles     Next Articles

Land Cover Classification in Mountain Areas Based on Sentinel-1A Polarimetric SAR Data and Object Oriented Method

XIANG Hai-yan, LUO Hong-xia, LIU Guang-peng, YANG Ren-fei, LEI Xi, CHENG Yu-si, CHEN Jing-yi   

  1. School of Geographical Science/Chongqing Key Laboratory of Karst Environment, Southwest University, Chongqing 400715, China
  • Received:2016-11-28 Revised:2017-04-10 Online:2017-12-20 Published:2017-12-20
  • Supported by:
    National Natural Science Foundation of China, No.41201436; China Postdoctoral Science Foundation, No.2016M600714

Abstract: Land cover classification is fundamental and critical to the investigation and assessment of land resources and the global change. However, the cloud-prone and rainy weather in mountain areas make it difficult to obtain valid optical remote sensing images. In addition, the complexity of terrain also has a negative impact on the accuracy when classifying land covers using only optical remote sensing imagery. Synthetic aperture radar (SAR) can transmit energy at microwave frequencies that are unaffected by weather conditions. This advantage gives SAR all-day and all-weather imaging capability. In this paper, the research area is situated in the mountain areas of Southeast Chongqing. We obtained the backscattering coefficient through a series of preprocessing of the Sentinel-1A polarization data. Then, according to the statistical analyses of VV/VH polarization backscattering coefficients, textures, elevations and slopes of all kinds of land covers, we employed object oriented approach on single temporal and multi-temporal images to improve land cover classification accuracy by combing these features. Finally, we compared the classification results with the result of Landsat 8 OLI data. The research indicated that: 1) The object-oriented classification method with single temporal SAR data has about the same accuracy as the OLI Landsat 8 data, the object-oriented classification with multi-temporal SAR data has the best classification result with the total accuracy 85.65% and Kappa coefficient 0.829 9. 2) SAR data have the advantages in extracting broad-leaved forest and artificial building which improve the accuracy by more than 10%. The classification with multi-temporal data has the advantage in the extraction of coniferous and broad-leaved mixed forest and farmland whose accuracy is about 9% higher than that with single temporal data. 3) Forest is the main land cover type in the study area which accounts for 42.68% of the total area, followed by farmland and shrub, and the artificial construction, grassland and river are less.

Key words: land cover classification in mountain area, object oriented approach, Polarimetric synthetic aperture radar, Sentinel-1A

CLC Number: 

  • P237