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Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands

文献类型：期刊论文

作者Han, Fusong1; Fu, Gang1; Yu, Chengqun1; Wang, Shaohua2,3
刊名REMOTE SENSING
出版日期2022-07-01
卷号14期号:14页码:20
关键词random-forest model multiple linear regression support-vector machines recursive-regression trees
DOI10.3390/rs14143410
通讯作者Fu, Gang(fugang@igsnrr.ac.cn)
英文摘要Quantifying forage nutritional quality and pool at various spatial and temporal scales are major challenges in quantifying global nitrogen and phosphorus cycles, and the carrying capacity of grasslands. In this study, we modeled forage nutrition quality and storage using climate data under fencing conditions, and using climate data and a growing-season maximum normalized-difference vegetation index under grazing conditions based on four different methods (i.e., multiple linear regression, random-forest models, support-vector machines and recursive-regression trees) in the alpine grasslands of Tibet. Our results implied that random-forest models can have greater potential ability in modeling forage nutrition quality and storage than the other three methods. The relative biases between simulated nutritional quality using random-forest models and the observed nutritional quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were lower than 2.00% and 6.00%, respectively. The RMSE between simulated nutrition quality using random-forest models and the observed nutrition quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were no more than 0.99% and 4.50 g m(-2), respectively. Therefore, random-forest models based on climate data and/or the normalized-difference vegetation index can be used to model forage nutrition quality and storage in the alpine grasslands of Tibet.
WOS关键词PLANT-PRODUCTION ; CRUDE PROTEIN ; MEADOW ; QUANTITY ; IMAGERY ; STEPPE
资助项目Youth Innovation Promotion Association of the Chinese Academy of Sciences[2020054] ; Second Comprehensive Scientific Expedition on the Tibetan Plateau[2019QZKK0302] ; National Natural Science Foundation of China[31600432] ; Bingwei Outstanding Young Talents Program of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[2018RC202] ; Tibet Science and Technology Major Projects of the Pratacultural Industry[XZ202101ZD0003N] ; Science and Technology Project of the Tibet Autonomous Region[XZ202101ZD0007G] ; Science and Technology Project of the Tibet Autonomous Region[XZ202201ZY0003N] ; STS Project of the Chinese Academy of Sciences[KFJ-STS-QYZD-2021-22-003] ; construction of the fixed Observation and Experimental Station of the first Support System for Agricultural Green Development in Zhongba County ; Central Government Guides Local Science and Technology Development Program[XZ202202YD0009C]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000831978600001
出版者MDPI
资助机构Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Second Comprehensive Scientific Expedition on the Tibetan Plateau ; National Natural Science Foundation of China ; Bingwei Outstanding Young Talents Program of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; Tibet Science and Technology Major Projects of the Pratacultural Industry ; Science and Technology Project of the Tibet Autonomous Region ; STS Project of the Chinese Academy of Sciences ; construction of the fixed Observation and Experimental Station of the first Support System for Agricultural Green Development in Zhongba County ; Central Government Guides Local Science and Technology Development Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/181339]  
专题中国科学院地理科学与资源研究所
通讯作者Fu, Gang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Lhasa Plateau Ecosyst Res Stn, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China
推荐引用方式
GB/T 7714		 Han, Fusong,Fu, Gang,Yu, Chengqun,et al. Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands[J]. REMOTE SENSING,2022,14(14):20.
APA Han, Fusong,Fu, Gang,Yu, Chengqun,&Wang, Shaohua.(2022).Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands.REMOTE SENSING,14(14),20.
MLA Han, Fusong,et al."Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands".REMOTE SENSING 14.14(2022):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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