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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

Description: 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 wit… more
Date: February 24, 2023
Creator: Huang, Pei; Zhao, Xiaoqing; Pu, Junwei; Gu, Zexian; Feng, Yan; Zhou, Shijie et al.
Item Type: Refine your search to only Article
Partner: UNT College of Science
open access

Linking random forest and auxiliary factors for extracting the major economic forests in the mountainous areas of southwestern Yunnan Province, China

Description: 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.
Item Type: Refine your search to only Article
Partner: UNT College of Science
open access

Urban Expansion Monitoring Based on the Digital Surface Model—A Case Study of the Beijing–Tianjin–Hebei Plain

Description: This article presents a study of urban expansion in the Beijing–Tianjin–Hebei plain based on ALOS Global Digital Surface Model “ALOS World 3D-30 m” (AW3D30 DSM), Shuttle Radar Topography Mission (SRTM) DSM, and Landsat 7 ETM+ images. The method proposed in this paper can provide rapid and large-scale statistics to study urban construction expansion in the world.
Date: May 24, 2022
Creator: Wang, Yanping; Dong, Pinliang; Liao, Shunbao; Zhu, Yueqin; Zhang, Da & Yin, Na
Item Type: Refine your search to only Article
Partner: UNT College of Liberal Arts & Social Sciences
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