Performance of Multiple Models for Estimating Rodent Activity Intensity in Alpine Grassland Using Remote Sensing

Abstract

Rodents are a vital part of the natural succession chain of the alpine grassland ecosystem, and rodent activities have an important impact on alpine grassland ecology. Moderate rodent population activities positively improve soil permeability, promote nutrient cycling, and promote biodiversity. However, too much rodent population or excessive activity intensity will bring negative effects on the ecological environment. Therefore, it is of great significance to accurately grasp the rodent activity intensity (RAI) in alpine grassland to cope with the changes in rodent populations and maintain the stability of the alpine grassland ecosystem. The Zoige alpine grassland was used as the study area in this study. In addition, UAV was sent to sample the rodent activity area in the alpine grassland. With the aid of field survey data, the surface information of rodent activity in the experimental area was identified, and the RAI index in the sample plot was calculated. Then, based on Sentinel-2A satellite remote sensing multi-spectral data and spectral index, multiple linear regression (MLR), multi-layer perceptron neural networks (MPL neural nets), random forest (RF), and support vector regression (SVR) were used to construct four models for RAI and Sentinel-2 datasets. The accuracy of the four models was compared and analyzed. The results showed that the RF model had the highest prediction accuracy (R2 = 0.8263, RWI = 0.8210, LCCC = 0.8916, RMSE = 0.0840, MAE = 0.0549), followed by the SVR model, the MLP neural nets model, and the MLR model. Overall, the nonlinear relationship between rodent activity intensity and satellite remote sensing images is obvious. Machine learning with strong nonlinear fitting ability can better characterize the RAI in alpine grassland. The RF model, with the best accuracy, can quantitatively estimate RAI in the alpine grassland, providing theoretical and technical support for monitoring RAI and rodent control in the alpine grassland.

Keywords

• alpine grassland
• rodent activity intensity
• multiple linear regression
• MLP neural nets
• random forest
• support vector regression