Impacts of land use change on ecosystem service value based on SDGs report--Taking Guangxi as an example

Ecosystem services provide a variety of benefits for mankind, and their sustainable use plays an important role in achieving the Sustainable Development Goals (SDGs). This study takes Guangxi as the research object and compares multiple intelligent classification algorithms for land cover based on multi-source data. Selects Random Forest (RF) classification algorithms for land cover classification, analyses their temporal and spatial changes, and the extracted results produce SDG 15.1.1, 15.1.2, 15.1.4 indicators, and assess the degree of completion of Guangxi's SDGs 15.2 indicators. On this basis, the equivalent factor method was used to evaluate the change of ecosystem value caused by land use change, and the direct impact of land use change on SDG15.9 was evaluated. The results indicate that:(1) From 1990 to 2020, forest was the most important land use mode in Guangxi, and the most serious wetland shrinkage was in Beihai and Nanning; The overall trend of land use changes is that wetlands, forests, and grasslands have decreased, dry land and construction land have increased, and bare land has remained basically remained unchanged. (2) From 1990 to 2020, the SDG 15.1.1 indicator showed a slow rise and then a rapid decline. The SDG 15.1.2 indicator showed a continuous downward trend. The SDG 15.1.4 indicator showed a steady upward trend. Guangxi failed to achieve the target of SDG15.2 by 2020. (3) From 1990 to 2020, the overall ecosystem service value in Guangxi showed a decreasing trend, and the one-way ecosystem service value in Guangxi was dominated by hydrological regulation and climate regulation. The ecosystem service value showed a spatial pattern of "low in the central region and high in the surrounding areas." (4) Grassland to forest and dryland to forest were the main types of ecological service income; Forest conversion to dryland is the most important type of ecological service loss. (5) As a whole, only dryland sensitivity index less than 0 during 2010–2015 had a negative impact on Ecosystem services values (ESV), while other time and land use type sensitivity index were all greater than 0 had a positive impact on ESV.