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Forest diebacks pose a major threat to global ecosystems. Identifying and mapping both living and dead trees is crucial for understanding the causes and implementing effective management strategies. This study explores the efficacy of Mask R–CNN for automated forest dieback monitoring. The method detects individual trees, delineates their crowns, and classifies them as alive or dead. We evaluated the approach using aerial imagery and canopy height models in the Harz Mountains, Germany, a region severely affected by forest dieback. To assess the model's ability to track changes over time, we applied it to images from three separate flight campaigns (2009, 2016, and 2022). This evaluation considered variations in acquisition dates, cameras, post-processing techniques, and image tilting. Forest changes were analyzed based on the detected trees' number, spatial distribution, and height. A comprehensive accuracy assessment demonstrated the Mask R–CNN's robust performance, with precision scores ranging from 0.80 to 0.88 and F1-scores from 0.88 to 0.91. These results confirm the model's ability to generalize across diverse image acquisition conditions. While minor changes were observed between 2009 and 2016, the period between 2016 and 2022 witnessed substantial dieback, with a 64.57% loss of living trees. Notably, taller trees appeared to be particularly affected. This study highlights Mask R–CNN's potential as a valuable tool for automated forest dieback monitoring. It enables efficient detection, delineation, and classification of both living and dead trees, providing crucial data for informed forest management practices.

Rubber (Hevea brasiliensis) has been introduced to Southeast Asia for over a century. In various regions throughout Southeast Asia, over the past two decades, natural tropical forests have rapidly been converted into rubber monoculture. To encourage the reconversion of rubber plantations back to close-to-nature rainforests, we developed a theoretical combined market and government payment system. To evaluate the potential impacts of the system, we carried out a simulation study from 2020 to 2050 in Xishuangbanna, Southwest China. The results suggest that the payment system may encourage a total reconversion area of 198,490 ha with a total government payment of US$0.377 billion in net present value (NPV) by 2050. The total carbon sequestration benefit resulting from the replacement of rubber plantations would be 14.83 million tons of carbon over the modelled period. Sensitivity analyses revealed that rubber price was the most sensitive factor on the total NPV of compensatory payments, and followed by the discount rate. The rubber price increased by 96.8% could enhance the compensatory payments in NPV by 272%, whereas a 10% decrease in the discount rate resulted in a 18.9% increase of the compensatory payments in NPV. This new integrated payment system holds the potential of stimulating restoration of cash crop monoculture-dominated landscapes to multifunctional forest ecosystems.