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For: Sun Z, Qian W, Huang Q, Lv H, Yu D, Ou Q, Lu H, Tang X. Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics. Remote Sensing 2022;14:1066. [DOI: 10.3390/rs14051066] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Number Cited by Other Article(s)
1
Hojo A, Avtar R, Nakaji T, Tadono T, Takagi K. Modeling forest above-ground biomass using freely available satellite and multisource datasets. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.101973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
2
Carbon Sink under Different Carbon Density Levels of Forest and Shrub, a Case in Dongting Lake Basin, China. REMOTE SENSING 2022. [DOI: 10.3390/rs14112672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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