Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China
文献类型：期刊论文
| 作者 | Qiu, Yue2,3; Zhao, Xuesheng2; Fan, Deqin2; Zheng, Zhoutao1; Zhang, Yuhan2; Zhang, Jinyu2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 16期号:13页码:24 |
| 关键词 | SDG indicator 15.3.1 NPP Northeast China population spatial distribution climate change |
| DOI | 10.3390/rs16132455 |
| 英文摘要 | Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location. |
| WOS关键词 | ECOLOGICAL RESTORATION PROJECTS ; CLIMATE-CHANGE ; NPP ; PRECIPITATION ; TERRESTRIAL ; TEMPERATURE ; RESPONSES ; PROGRESS ; IMPACTS |
| 资助项目 | National Natural Science Foundation of China[41930650] ; National Natural Science Foundation of China[42371412] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001269693800001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207405]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhao, Xuesheng |
| 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.China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China 3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiu, Yue,Zhao, Xuesheng,Fan, Deqin,et al. Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China[J]. REMOTE SENSING,2024,16(13):24. |
| APA | Qiu, Yue,Zhao, Xuesheng,Fan, Deqin,Zheng, Zhoutao,Zhang, Yuhan,&Zhang, Jinyu.(2024).Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China.REMOTE SENSING,16(13),24. |
| MLA | Qiu, Yue,et al."Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China".REMOTE SENSING 16.13(2024):24. |
入库方式： OAI收割
来源：地理科学与资源研究所
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