Spatial Heterogeneity of Combined Factors Affecting Vegetation Greenness Change in the Yangtze River Economic Belt from 2000 to 2020

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-12, Vol.15 (24), p.5693
• Main Authors: Peng, Chuanjing, Du, Lin, Ren, Hangxing, Li, Xiong, Li, Xiangyuan
• Format: Article
• Language: English
• Subjects: Algorithms; Anthropogenic factors; Belts; Carbon dioxide; Carbon dioxide emissions; Climate change; Economics; Emissions; geographical detector; Global temperature changes; Growing season; Heterogeneity; Human influences; Impact factors; Influence; Land cover; Land use; Machine learning; Population density; recursive feature elimination; Regression analysis; Rivers; Spatial heterogeneity; stable factors; Statistical methods; Vegetation; Vegetation effects; vegetation greenness change; Wind; Wind speed
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15245693

Vegetation greenness change is the result of the combination of natural and anthropogenic factors. Understanding how these factors individually and collectively affect vegetation dynamics and whether their spatial heterogeneity has any effect on vegetation greenness change is the crucial investigation area. Previous studies revealed the distinct characteristics of spatial and temporal heterogeneity in the impact factors influencing vegetation greenness change across various regions, often assuming a linear contribution mechanism between vegetation greenness change and these drivers. However, such a simplistic assumption fails to adequately capture the real-world dynamics of vegetation greenness change. Thus, this study firstly used geographical detector (Geodetector) to quantitatively measure the contribution of each factor to vegetation greenness change considering spatial heterogeneity in the Yangtze River Economic Belt (YREB) during the growing season from 2000 to 2020, then selecting significant factors from numerous drivers with the recursive feature elimination algorithm combined with a random forest model (RFE-RF), which is able to reduce redundant features in the data and prevent overfitting. Finally, four stable impact factors and the spatial heterogeneity of some factors contributing to vegetation greenness change were identified. The results show that approximately 83% of the regional vegetation has shown an overall increasing trend, while areas undergoing rapid development predominantly experienced a decline in greenness. Single factor screened by Geodetector with the explanatory power greater than 10% for vegetation greenness change included temperature (Tem), population density (PD), the land-use/land-cover (LULC), DEM, wind speed, and slope. The RFE-RF method identified precipitation (Pre) and CO2 emissions as additional influential factors for vegetation greenness change, in addition to the first four factors mentioned previously. These findings suggest that the four stable factors consistently influence vegetation greenness change. Combined with the principles of the algorithms and the above results, it was found that the spatial heterogeneity of wind speed and slope has an effect on vegetation greenness change, whereas the spatial heterogeneity of Pre and CO2 emissions has minimal effect.