Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and Climate Data in Xizang's Grasslands
文献类型：期刊论文
| 作者 | Han, Fusong2; Ding, Rang3,4; Deng, Yujie5; Zha, Xinjie1; Fu, Gang4 |
| 刊名 | AGRONOMY-BASEL
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 14期号:7页码:1515 |
| 关键词 | data mining random forest global change Tibetan Plateau alpine region |
| DOI | 10.3390/agronomy14071515 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | In grassland ecosystems, aboveground biomass (AGB) is critical for energy flow, biodiversity maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on the climate-sensitive Tibetan Plateau, accurate AGB monitoring is crucial for assessing large-scale grassland livestock capacity. Previous studies focused on predicting AGB mainly at the plant community level and from the perspective of dry weight (AGBd). This study aims to predict grassland AGB in Xizang at both the plant taxonomic group (sedge, graminoid, forb) and community levels, from both an AGBd and a fresh weight (AGBf) perspective. Three to four independent variables (growing mean temperature, total precipitation, total radiation and NDVImax, maximum normalized difference vegetation index) were used for AGB prediction using nine models in Xizang grasslands. The random forest model (RFM) showed the greatest potential in simulating AGB (training R2 >= 0.62, validation R2 >= 0.87). This could be due to the nonlinear relationships between AGB, meteorological factors, and NDVImax. The RFM exhibited robustness against outliers and zero values resulting from taxonomic groups that were absent from the quadrats. The accuracies of the RFM were different between fresh and dry weight, and among the three taxonomic groups. The RFM's use of fewer variables can reduce complexity and costs compared to previous studies. Therefore, the RFM emerged as the optimal model among the nine models, offering potential for large-scale investigations into grassland AGB, especially for analyzing spatiotemporal patterns of plant taxonomic groups. |
| WOS关键词 | RANDOM FOREST ; IMAGES ; WATER |
| WOS研究方向 | Agriculture ; Plant Sciences |
| WOS记录号 | WOS:001276643600001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206943]  |
| 专题 | 拉萨站高原生态系统研究中心_外文论文 |
| 通讯作者 | Deng, Yujie; Fu, Gang |
| 作者单位 | 1.Xian Univ Finance & Econ, Xian 710100, Peoples R China 2.Hunan Univ Technol, Coll Urban & Environm Sci, Zhuzhou 412007, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.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 5.Inst Sci & Technol Informat Res Xizang Autonomous, Lhasa 850000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Fusong,Ding, Rang,Deng, Yujie,et al. Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and Climate Data in Xizang's Grasslands[J]. AGRONOMY-BASEL,2024,14(7):1515. |
| APA | Han, Fusong,Ding, Rang,Deng, Yujie,Zha, Xinjie,&Fu, Gang.(2024).Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and Climate Data in Xizang's Grasslands.AGRONOMY-BASEL,14(7),1515. |
| MLA | Han, Fusong,et al."Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and Climate Data in Xizang's Grasslands".AGRONOMY-BASEL 14.7(2024):1515. |
入库方式： OAI收割
来源：地理科学与资源研究所
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