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GIScience & Remote Sensing Volume 60, 2023 - Issue 1
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Research Article

A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing

Yali Zhanga Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China;b School of Geographic Information and Tourism, Chuzhou University, Chuzhou, China;c Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou University, Chuzhou, ChinaView further author information
,
Ni Wangb School of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaView further author information
,
Yuliang Wangb School of Geographic Information and Tourism, Chuzhou University, Chuzhou, ChinaView further author information
&
Mingshi Lia Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China;d College of Forestry, Nanjing Forestry University, Nanjing, ChinaCorrespondence[email protected]
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Article: 2163574 | Received 04 Aug 2022, Accepted 23 Dec 2022, Published online: 03 Jan 2023

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Xingguang Yan, Jing Li, Andrew R. Smith, Di Yang, Tianyue Ma, YiTing Su & Jiahao Shao. (2023) Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models. International Journal of Digital Earth 16:2, pages 4471-4491.
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Articles from other publishers (2)

Tianbao Huang, Guanglong Ou, Hui Xu, Xiaoli Zhang, Yong Wu, Zihao Liu, Fuyan Zou, Chen Zhang & Can Xu. (2023) Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis. Forests 14:9, pages 1742.
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Gengsheng Fang, Hangyuan Yu, Luming Fang & Xinyu Zheng. (2023) Synergistic Use of Sentinel-1 and Sentinel-2 Based on Different Preprocessing for Predicting Forest Aboveground Biomass. Forests 14:8, pages 1615.
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