Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model
Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rel...
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MDPI AG
2025-01-01
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| author | Zhenjiang Wu Fengmei Yao Adeel Ahmad Fan Deng Jun Fang |
| author_facet | Zhenjiang Wu Fengmei Yao Adeel Ahmad Fan Deng Jun Fang |
| author_sort | Zhenjiang Wu |
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| description | Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors’ influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation. |
| format | Article |
| id | doaj-art-483fd74e63414612af68d82d156b22ca |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-483fd74e63414612af68d82d156b22ca2025-01-24T13:48:03ZengMDPI AGRemote Sensing2072-42922025-01-0117229910.3390/rs17020299Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM ModelZhenjiang Wu0Fengmei Yao1Adeel Ahmad2Fan Deng3Jun Fang4College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Geoscience, Yangtze University, Wuhan 430100, ChinaHunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, ChinaSpatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors’ influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation.https://www.mdpi.com/2072-4292/17/2/299Yangtze River BasinkNDVIspatiotemporal evolutionPLSPMdriving mechanisms |
| spellingShingle | Zhenjiang Wu Fengmei Yao Adeel Ahmad Fan Deng Jun Fang Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model Remote Sensing Yangtze River Basin kNDVI spatiotemporal evolution PLSPM driving mechanisms |
| title | Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model |
| title_full | Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model |
| title_fullStr | Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model |
| title_full_unstemmed | Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model |
| title_short | Spatiotemporal Evolution and Driving Mechanisms of kNDVI in Different Sections of the Yangtze River Basin Using Multiple Statistical Methods and the PLSPM Model |
| title_sort | spatiotemporal evolution and driving mechanisms of kndvi in different sections of the yangtze river basin using multiple statistical methods and the plspm model |
| topic | Yangtze River Basin kNDVI spatiotemporal evolution PLSPM driving mechanisms |
| url | https://www.mdpi.com/2072-4292/17/2/299 |
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