Hyperspectral Remote Sensing Combined with Ground Vegetation Surveys for the Study of the Age of Rodent Mounds

Background: Rodents severely damage the ecological environment of grasslands, and rodent mounds of different ages require distinct management strategies. Understanding the age of these mounds aids in formulating targeted restoration measures, which can enhance grassland productivity and biodiversity. Current surveys of rodent mounds rely on ground exposure and mound height to determine their age, which is time-consuming and labor-intensive. Remote sensing methods can quickly and easily identify the distribution of rodent mounds. Existing remote sensing images use ground exposure and mound height for identification but do not distinguish between mounds of different ages, such as one-year-old and two-year-old mounds. According to the existing literature, rodent mounds of different ages exhibit significant differences in vegetation structure, soil background, and plant diversity. Utilizing a combination of vegetation indices and hyperspectral data to determine the age of rodent mounds aims to provide a better method for extracting rodent hazard information. This experiment investigates and analyzes the age, distribution, and vegetation characteristics of rodent mounds, including total coverage, height, biomass, and diversity indices such as Patrick, Shannon–Wiener, and Pielou. Spectral data of rodent mounds of different ages were collected using an Analytical Spectral Devices field spectrometer. Correlation analysis was conducted between vegetation characteristics and spectral vegetation indices to select key indices, including NDVI 670 , NDVI 705 , EVI, TCARI, Ant, and SR. Multiple stepwise regression and Random Forest (RF) inversion models were established using vegetation indices, and the most suitable model was selected through comparison. Random Forest modeling was conducted to classify plateau zokor rat mounds of different ages, using both vegetation characteristic indicators and vegetation indices for comparison. The rodent mound classification models established using vegetation characteristic indicators and vegetation indices through Random Forest could distinguish rodent mounds of different ages, with out-of-bag error rates of 36.96% and 21.74%, respectively. The model using vegetation indices performed better. Conclusions: (1) Rodent mounds play a crucial ecological role in alpine meadow ecosystems by enhancing plant diversity, biomass, and the stability and vitality of the ecosystem. (2) The vegetation indices SR and TCARI are the most influential in classifying rodent mounds. (3) Incorporating vegetation indices into Random Forest modeling facilitates a precise and robust remote sensing interpretation of rodent mound ages, which is instrumental for devising targeted restoration strategies.