International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 367–373, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-367-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 367–373, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-367-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

KARST ROCKY DESERTIFICATION INFORMATION EXTRACTION BASED ON THE DECISION TREE

C. J. Su1,2, T. Yue1, L. Jiang2, X. M. Li2, and W. G. Wang2 C. J. Su et al.
  • 1Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, Guangxi, 541004, China
  • 2Department of Civil Engineering, Guangxi Polytechnic of Construction, Nanning,Guangxi, 530007, China

Keywords: karst rocky, decision tree, vegetation cover, slope

Abstract. Rocky desertification is a common geo-ecological disasters in China are mainly distributed in southwest karst region, and a wide range of further deterioration. Based on the theory of decision tree Guangxi rocky information extraction, selection of experimental data of Guangxi Zhuang Autonomous Region in 2005 TM image. First of remote sensing images after geometric correction image registration and other pretreatment. Secondly based on binary model of pixel, the Guangxi Zhuang Autonomous Region NDVI values and vegetation cover and slope analysis combining the results of Guangxi Zhuang Autonomous Region, the use of decision tree classification of remote sensing images, and finally get different levels of Guangxi Zhuang Autonomous Region rocky area and spatial distribution. The experimental results showed that: 2005 Guangxi rocky area of about 22,000 km2, accounting for 9% of the total land area in Guangxi, accounting for 24.30% of the karst area the overall classification accuracy of 89.03%, Kappa coefficient was 0.8417. From the classification results and the accuracy evaluation shows that the use of the information extracted rocky achieve better results.