Exploring the Roles of Ancient Trees in Disturbance and Recovery Processes Using Monthly Landsat Time Series Analysis

Quantifying forest patch dynamics is essential for understanding how forest patch characteristics vary in relation to ancient tree locations. This study developed a satellite-based framework to analyze the differences among forest patches associated with natural and planted ancient trees across the Sichuan–Chongqing region, China. Using monthly LandTrendr on Google Earth Engine, we analyzed long-term (1990–2024) and high-frequency observations of forest dynamics at a 180 m × 180 m (6 × 6 pixels) spatial scale. Disturbance and recovery events were characterized by their magnitude, rate, timing, and duration. Patches were classified into six categories based on ancient tree type and proximity and further subdivided by land use type. The results show that in natural forests, patches with natural ancient trees are associated with more stable change signatures, whereas in planted forests, patches containing planted ancient trees are associated with stronger recovery-related change patterns. Over 60% of detected changes were short-lived (≤5 years), indicating that most disturbances and recovery processes were transient rather than persistent. These findings show that the presence and spatial context of ancient trees are associated with differences in patch change patterns. The proposed workflow provides a scalable approach for integrating multi-temporal remote sensing into large-scale monitoring and management of ancient trees and their associated forest patches.