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Global Geology 2023, 26(2) 122-132 DOI: ISSN: 1673-9736 CN: 22-1371/P

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	PubMed
	Article by Yang Y
	Article by Xu M
	Article by Li S
	Article by Wang M
	Article by Liu ZAZS

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Classification of vegetative types in Changbai Mountain based on optical and microwave remote sensing data

YANG Ying1 , XU Mengxia2* , LI Sheng1 , WANG Mingchang2 , LIU Ziwei2 and ZHAO Shijun3

1. Shenzhen Data Management Center of Planning and Natural Resources, Shenzhen 518034, Guangdong, China;
2. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China;
3. China Water Northeastern Investigation, Design & Research Co., Ltd., Changchun 130021, China

Abstract: Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management. In order to improve the classification accuracy of forest types, Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected, and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity, texture, spectral, vegetation index and topography information and using random forest Gini index (GI) for optimization. The overall accuracy of classification was 94.60% and the Kappa coefficient was 0.933. Comparing the classification results before and after feature optimization, it shows that feature optimization has a greater impact on the classification accuracy. Comparing the classification results of random forest, maximum likelihood method and CART decision tree under the same conditions, it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.

Keywords: vegetative type classification random forest radar data optical data

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