The role of remote imaging for mountain soil loss and sports image detection

As people continue to study soil loss, remote sensing and GIS technology will also be widely used in soil loss estimation models. In the current research results, it is found that the factors of vegetation coverage can be obtained through remote sensing image data, such as vegetation index, soil utilization classification map, and the component image between soil and vegetation. GIS can store multi-source data, perform operational management, etc., from these perspectives, estimate soil loss, and make corresponding charts. For example, GIS can transform the weather station data into a continuous image through regression or interpolation to estimate the necessary precipitation when the soil is lost. Another essential factor in the soil loss model is soil. GIS can present the data in the form of a raster through soil sample data or a vector format map of soil types. To enrich the content of soil loss assessment, GIS has introduced DEM data, making terrain indicators quantified for better research. Scholars at home and abroad have done a lot of research and analysis on this algorithm, but in real life, the environment where the moving target is located is more complicated. Due to various interference factors, the accuracy of sports image detection and target tracking is difficult to determine. The current algorithm cannot accurately detect different changing scenes. For the sports image detection algorithm’s problems, this article combines the remote sensing image technology to study the mountain soil loss phenomenon and focuses on the sports image detection algorithm and remote sensing image technology, improving existing shortcomings in remote sensing image technology.