Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model

Abstract

Abstract Predicting vegetation distribution strengthens ecosystem management, protection, and restoration in arid and degraded areas. However, data quality and incomplete data coverage limit prediction accuracy for Picea crassifolia Kom. (Qinghai spruce) forest in the Qilian Mountains of China. Here, we used a logistic regression model combined with high-resolution vegetation distribution data for different sampling scales and digital elevation models (DEMs) to determine the potential distribution of P. crassifolia forest in the Dayekou catchment in the Qilian Mountains. We found that the model with the best simulation accuracy was based on data with a DEM scale of 30 m and a sampling accuracy of 90 m (Nagelkerke’s R2 = 0.48 and total prediction accuracy = 83.89%). The main factors affecting the distribution of P. crassifolia forest were elevation and potential solar radiation. We conclude that it is feasible to calculate the distribution of arid mountain forests based on terrain and that terrain data at 30 m spatial resolution can fully support the simulation of P. crassifolia forest distribution.