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Linking random forest and auxiliary factors for extracting the major economic forests in the mountainous areas of southwestern Yunnan Province, China
Article describes how forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County, which is a mountainous area with rich biodiversity and is witnessing rapid development of economic forests in Yunnan province of China.
Date:
February 24, 2023
Creator:
Huang, Pei; Zhao, Xiaoqing; Pu, Junwei; Gu, Zexian; Feng, Yan; Zhou, Shijie et al.
System:
The UNT Digital Library
Linking random forest and auxiliary factors for extracting the major economic forests in the mountainous areas of southwestern Yunnan Province, China
Article describes how forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County.
Date:
February 24, 2023
Creator:
Huang, Pei; Zhao, Xiaoqing; Pu, Junwei; Gu, Zexian; Feng, Yan; Zhou, Shijie et al.
System:
The UNT Digital Library