Quantitative analysis of fractional vegetation cover in southern Sichuan urban agglomeration using optimal parameter geographic detector model, China

Vegetation is a main part of ecosystems and an essential indicator for monitoring changes in terrestrial ecosystems. It is crucial for us to discover the temporal and spatial features and potential drivers of vegetation change to promote regional ecological environment protection and management. However, it can be difficult to pinpoint the causes of vegetation change when considering both human activity and climate change. We used trend and stability method to study the temporal and spatial patterns of vegetation evolution in the South Sichuan Urban Agglomeration (SSUA) from 2001 to 2021 with the Google Earth Engine (GEE) platform. An optimal parameter-based geographical detector (OPGD) model was applied to optimize the spatial scale and zoning effect of geographic data, effectively solving the problem of spatial data heterogeneity. It compensates for the inadequacies of conventional approaches that neglect the modifiable areal units problem (MAUP), and improving the science and accuracy of quantitative analysis and identification of vegetation drivers. We studied demonstrate that (1) During the last 21 years, Fractional Vegetation Cover (FVC) has generally been in good condition, with a multi-year average FVC greater than 0.4 of 71.74 %, and the vegetation is significantly characterized by a low fluctuation of 78.16 %. However, there is a significant trend of vegetation degradation, accounting for 8.89 %, mainly in the main urban areas of Neijiang and Zizhong County, Lu County of Luzhou City, Gao County of Yibin City, and other areas with rapid urbanization. In general, FVC is low in the built-up areas of towns and along transportation roads, while the mountainous and agricultural areas have a high level of vegetation cover. (2) The OPGD model detection showed the optimal spatial scale of vegetation cover in this study region was 2 km. Optimal discrete parameter combinations for slope, elevation, temperature and GDP are quantile breaks with 9 intervals, which contribute to improved scientific accuracy and precision in studies of vegetation change and its drivers. (3) The explanatory power of urbanization rate, land use type, slope, GDP, population density, and average annual precipitation were all above 20 % and were the main drivers of vegetation change. Moreover, any two factors interacted in a nonlinear enhancement and a bi-variable enhancement, increasing the impact on vegetation spatial variation. When the slope is 26.9°∼87.4°, the elevation is 967 m ∼ 4207 m, the average annual temperature is 0.18 °C ∼ 13.6 °C, the average annual precipitation is 328 mm ∼ 439 mm, the GDP is 4.07 ∼ 5.23 million yuan km−2, the population density is 12.7 ∼ 21.1 people/km2, the urbanization rate is 33.4 %∼37.7 %, and the land-use type is forest land, the FVC value is the highest and suitable for vegetation growth. The study showed that using the OPGD model to detect the zoning effects and spatial scale of the explanatory variables solves for the shortcomings of the previous methods for variable regional units and discrete methods, may more precisely explore the features of temporal and spatial changes in the vegetation and the driving mechanisms, offers scientific references for environmental conservation and long-term economic growth in the region.