Remote sensing diagnosis of ecosystem resilience dynamics in agricultural heritage landscapes

This study combines time-series remote sensing data and analytical algorithms to quantify the dynamics of ecosystem disturbance and recovery within this agricultural heritage landscape. Stepwise regression and generalized additive modeling (GAM) were applied to identify the key environmental drivers of resilience. Agricultural heritage landscapes are complex socio-ecological systems increasingly challenged by human pressures and climate change, yet the mechanisms underlying their ecosystem resilience remain unclear.

Results reveal that resilience patterns are highly heterogeneous, with widespread rapid recovery coexisting alongside localized long-term stress. Major disturbance types within the agricultural heritage landscape were identified, and their associated environmental characteristics were characterized. The study further quantifies the influence of landscape configuration, water management, and human activities on ecosystem resilience. This work provides a transferable framework for monitoring and managing agricultural heritage landscapes.