Jun Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jun Chen has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Hongwen Yao
Forest Resources Monitoring Center for Zhejiang Province, 71 Fengqi East Road, Hangzhou 310020, China
1 shared publication - Ke Wang
Forest Resources Monitoring Center for Zhejiang Province, 71 Fengqi East Road, Hangzhou 310020, China
1 shared publication - Nan Li
Forest Resources Monitoring Center for Zhejiang Province, 71 Fengqi East Road, Hangzhou 310020, China
1 shared publication - Wei Jin
Forest Resources Monitoring Center for Zhejiang Province, 71 Fengqi East Road, Hangzhou 310020, China
1 shared publication
Latest Publications
- Open Access
- 9 May 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (10) , 4737
Vegetation Dynamics and Influencing Mechanisms in Zhejiang Province, a Typical Subtropical Region of China
Vegetation cover plays a fundamental role in maintaining ecosystem structure and function. Understanding its spatial and temporal variability, along with its driving factors, is critical for advancing environmental studies. This research targets the subtropical Zhejiang region in southeastern China, utilizing MODIS-derived NDVI data covering 2001 to 2020. By integrating Sen’s slope estimator, Mann–Kendall trend analysis, spatial autocorrelation (Moran’s I), and the Geodetector framework, we assessed trends, patterns, and primary influencing factors of vegetation change. Our findings include: (1) a statistically significant upward trend in NDVI across 59.4% of the study area (Sen’s slope = 0.0025, p < 0.01), corresponding to an approximate annual increase of 0.44%; (2) notable spatial clustering of NDVI values, with high NDVI zones located in southwestern forested areas and low NDVI zones in expanding urban regions, indicating a clear spatial differentiation between natural and human-dominated landscapes; (3) elevation (Q = 0.64), nighttime lights (Q = 0.63), and slope (Q = 0.57) showed relatively higher explanatory power, and the interaction between nighttime lights and land use (NTL × LULC) exhibited the strongest explanatory power (Q = 0.72); (4) high-risk zones, associated with dense populations and intense urban development, coincided with lower NDVI values. These results deepen our understanding of vegetation dynamics in subtropical zones and provide insights for sustainable ecosystem and land management.
- 9 May 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (10) , 4737
Open AccessVegetation Dynamics and Influencing Mechanisms in Zhejiang Province, a Typical Subtropical Region of China
Vegetation cover plays a fundamental role in maintaining ecosystem structure and function. Understanding its spatial and temporal variability, along with its driving factors, is critical for advancing environmental studies. This research targets the subtropical Zhejiang region in southeastern China, utilizing MODIS-derived NDVI data covering 2001 to 2020. By integrating Sen’s slope estimator, Mann–Kendall trend analysis, spatial autocorrelation (Moran’s I), and the Geodetector framework, we assessed trends, patterns, and primary influencing factors of vegetation change. Our findings include: (1) a statistically significant upward trend in NDVI across 59.4% of the study area (Sen’s slope = 0.0025, p < 0.01), corresponding to an approximate annual increase of 0.44%; (2) notable spatial clustering of NDVI values, with high NDVI zones located in southwestern forested areas and low NDVI zones in expanding urban regions, indicating a clear spatial differentiation between natural and human-dominated landscapes; (3) elevation (Q = 0.64), nighttime lights (Q = 0.63), and slope (Q = 0.57) showed relatively higher explanatory power, and the interaction between nighttime lights and land use (NTL × LULC) exhibited the strongest explanatory power (Q = 0.72); (4) high-risk zones, associated with dense populations and intense urban development, coincided with lower NDVI values. These results deepen our understanding of vegetation dynamics in subtropical zones and provide insights for sustainable ecosystem and land management.