Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021
Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground moni...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/4005 |
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| author | Mingxin Yang Ang Chen Wenqiang Cao Shouxin Wang Mingyuan Xu Qiang Gu Yanhe Wang Xiuchun Yang |
| author_facet | Mingxin Yang Ang Chen Wenqiang Cao Shouxin Wang Mingyuan Xu Qiang Gu Yanhe Wang Xiuchun Yang |
| author_sort | Mingxin Yang |
| collection | DOAJ |
| description | Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R<sup>2</sup> of 0.44 and an RMSE of 2.56 n/m<sup>2</sup> on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management. |
| format | Article |
| id | doaj-art-57ee9d6bd8dd4697aa4f22961e89e52f |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-57ee9d6bd8dd4697aa4f22961e89e52f2025-08-20T02:14:23ZengMDPI AGRemote Sensing2072-42922024-10-011621400510.3390/rs16214005Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021Mingxin Yang0Ang Chen1Wenqiang Cao2Shouxin Wang3Mingyuan Xu4Qiang Gu5Yanhe Wang6Xiuchun Yang7Xining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810021, ChinaSchool of Grassland Science, Beijing Forestry University, Beijing 100081, ChinaXining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810021, ChinaXining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810021, ChinaXining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810021, ChinaXining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810021, ChinaXining Natural Resources Comprehensive Survey Center, China Geological Survey, Xining 810021, ChinaSchool of Grassland Science, Beijing Forestry University, Beijing 100081, ChinaBiodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R<sup>2</sup> of 0.44 and an RMSE of 2.56 n/m<sup>2</sup> on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management.https://www.mdpi.com/2072-4292/16/21/4005species diversitymachine learningspatial and temporal changesremote sensingdriving factorsthe Three Rivers Headwater Region |
| spellingShingle | Mingxin Yang Ang Chen Wenqiang Cao Shouxin Wang Mingyuan Xu Qiang Gu Yanhe Wang Xiuchun Yang Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 Remote Sensing species diversity machine learning spatial and temporal changes remote sensing driving factors the Three Rivers Headwater Region |
| title | Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 |
| title_full | Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 |
| title_fullStr | Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 |
| title_full_unstemmed | Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 |
| title_short | Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 |
| title_sort | spatial and temporal patterns of grassland species diversity and their driving factors in the three rivers headwater region of china from 2000 to 2021 |
| topic | species diversity machine learning spatial and temporal changes remote sensing driving factors the Three Rivers Headwater Region |
| url | https://www.mdpi.com/2072-4292/16/21/4005 |
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