Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019
Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and c...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/12/12/2121 |
| _version_ | 1797380324528750592 |
|---|---|
| author | Xiuping Hao Xueliu Wang Jianqin Ma Yang Chen Shiyi Luo |
| author_facet | Xiuping Hao Xueliu Wang Jianqin Ma Yang Chen Shiyi Luo |
| author_sort | Xiuping Hao |
| collection | DOAJ |
| description | Unveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world. |
| first_indexed | 2024-03-08T20:35:40Z |
| format | Article |
| id | doaj.art-3f981e999e07493ab0e91dae1393aa01 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-08T20:35:40Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-3f981e999e07493ab0e91dae1393aa012023-12-22T14:20:32ZengMDPI AGLand2073-445X2023-11-011212212110.3390/land12122121Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019Xiuping Hao0Xueliu Wang1Jianqin Ma2Yang Chen3Shiyi Luo4School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaSchool of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaSchool of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaSchool of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaSchool of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaUnveiling the variation mechanism of vegetation net primary productivity (NPP) and elucidating the underlying drivers of these changes is highly necessitated for terrestrial carbon cycle research and global carbon emission control. Taking Henan Province, renowned as the anciently central China and current China’s foremost grain producer, as an example, this study employed the Theil–Sen Median Trend Analysis to evaluate the spatiotemporal characteristics and trends of NPP. Correlation Analysis and Residual Analysis were used to explain the drivers of NPP dynamics. To deepen the inquiry, the Geodetector method was employed to scrutinize the multifaceted effects and interplay among diverse variables influencing NPP. The result showed demonstrated that approximately 85.72% of the area showed an increase in NPP, covering a broad geographical distribution. Notably, 89.31% of the province has witnessed a positive human-driven NPP change. It means human activities emerged as a driving force with a positive effect on vegetation NPP, consequently fostering an increasing trend of NPP. Among climatic factors, the correlation between NPP and precipitation was stronger than that between the temperature and NPP, the determined power of factors in Henan Province was population density, (0.341) > GDP (0.326) > precipitation (0.255) > elevation (0.167) > slope (0.136) > temperature (0.109), and a single factor had a lesser interaction effect than two factors. The implications of these findings extend beyond the realms of research, potentially offering valuable insights into the formulation of targeted ecosystem restoration measures tailored to the distinct context of Henan Province, and also expect to provide crucial references for carbon emission control in China and across the world.https://www.mdpi.com/2073-445X/12/12/2121NPPresidual analysisGeodetectorhuman-driven NPPclimate change |
| spellingShingle | Xiuping Hao Xueliu Wang Jianqin Ma Yang Chen Shiyi Luo Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019 Land NPP residual analysis Geodetector human-driven NPP climate change |
| title | Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019 |
| title_full | Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019 |
| title_fullStr | Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019 |
| title_full_unstemmed | Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019 |
| title_short | Spatiotemporal Characteristic Prediction and Driving Factor Analysis of Vegetation Net Primary Productivity in Central China Covering the Period of 2001–2019 |
| title_sort | spatiotemporal characteristic prediction and driving factor analysis of vegetation net primary productivity in central china covering the period of 2001 2019 |
| topic | NPP residual analysis Geodetector human-driven NPP climate change |
| url | https://www.mdpi.com/2073-445X/12/12/2121 |
| work_keys_str_mv | AT xiupinghao spatiotemporalcharacteristicpredictionanddrivingfactoranalysisofvegetationnetprimaryproductivityincentralchinacoveringtheperiodof20012019 AT xueliuwang spatiotemporalcharacteristicpredictionanddrivingfactoranalysisofvegetationnetprimaryproductivityincentralchinacoveringtheperiodof20012019 AT jianqinma spatiotemporalcharacteristicpredictionanddrivingfactoranalysisofvegetationnetprimaryproductivityincentralchinacoveringtheperiodof20012019 AT yangchen spatiotemporalcharacteristicpredictionanddrivingfactoranalysisofvegetationnetprimaryproductivityincentralchinacoveringtheperiodof20012019 AT shiyiluo spatiotemporalcharacteristicpredictionanddrivingfactoranalysisofvegetationnetprimaryproductivityincentralchinacoveringtheperiodof20012019 |