High-resolution map of the upper range limit of trees over the cold and arid region, a case study in the Three-River-Source National Park, Tibetan Plateau

The upper range limit of trees is the most conspicuous boundary on the Earth. However, the publicly available forest extent or forest cover datasets systematically underestimated sparse tree cover, which hindered our recognition of tree limit distribution and its drivers over cold and arid regions. Here, we built a three-step upscaling strategy, that integrates in situ measured vegetation types with spaceborne Light Detection and Ranging (LiDAR), microwave, and Landsat images in a Convolutional Neural Networks (CNN) classification algorithm, to develop a new map of the upper range limit of trees over the Three-River-Source National Park circa 2020 at 30 m resolution. The multi-satellite-based new products consider vertical structure information that could better detect sparse trees and better distinguish between the shrub, grass, and forest. Validation shows our result reveals high consistency with manual interpretations from Google Earth high-resolution images (R² = 0.97, slope = 0.99, ME = 18 m). Our proposed method provides a fast and effective tree limit mapping solution at the global scale.