Remote sensing and GIS-enabled research on precipitation and land cover

The geographical system is a complex giant system formed by the interaction of natural and human elements, and its dynamics and complexity are the core of geographical research. Traditional studies have provided support for ecological protection and economic development by qualitatively analyzing macro-characteristics through geographical synthesis (e.g., the three major terrain steps, Qinling-Huaihe Line, and Hu Huanyong Line). However, against the background of global warming, these studies can hardly meet the demand for predicting extreme disasters such as rainstorms—such disasters are affected by dynamic factors like precipitation, terrain, and temperature, and urbanization further increases the risks. Using remote sensing and GIS technologies, this study adopted linear regression, PW-MK method, and complex Morlet wavelet transform to analyze the spatiotemporal evolution characteristics of precipitation and land cover. Then, the CAMarkov model was used to predict the area and distribution of land cover in 2020 to verify the model's effectiveness, aiming to provide references for disaster prevention, mitigation, and sustainable land use in China.