PDF(Pubmed)
Abstract：
Reducing emissions from deforestation and forest degradation (REDD) is a specific strategy for combating deforestation and forest degradation to alleviate the effects of climate change. In this study, the potential greenhouse gas (GHG) emission reduction resulting from the implementation of a REDD project is estimated. Changes in forest cover throughout the years 1985, 1990, 1995, 2000, 2010, 2015, and 2020 were analyzed using time-series Landsat imagery (TM, ETM + , and OLI) and a random forest algorithm. Multilayer perceptron neural networks were used to model the transition potential of the forest cover, which were then predicted via Markov chain analysis. The change detection analysis revealed two discernible patterns in forest cover dynamics. Between 1985 and 2000, a notable decrease in forest cover was seen, whereas from 2000 to 2020, it significantly increased. The results suggested that the absence of REDD implementation would result in the deforestation of approximately 199,569 hectares of forest cover between 2020 and 2050, leading to the release of 1,995,695 tCO2e of emissions into the atmosphere. However, with the implementation of REDD, these emissions would be reduced to 405,512 tCO2e, effectively preventing the release of 1,590,183 tCO2e of emissions into the upper atmosphere. This study demonstrates that the implementation of REDD projects can be an effective strategy for reducing GHG emissions and mitigating climate change in the Hyrcanian forests.
摘要：
减少毁林和森林退化造成的排放（REDD）是打击毁林和森林退化以减轻气候变化影响的具体战略。在这项研究中,估计了由于实施REDD项目而导致的潜在温室气体（GHG）减排量。使用时间序列Landsat图像（TM，ETM+,和OLI)和随机森林算法。多层感知器神经网络用于模拟森林覆盖的过渡潜力，然后通过马尔可夫链分析进行预测。变化检测分析揭示了森林覆盖动态中的两种明显模式。在1985年至2000年期间，森林覆盖率显着下降，而从2000年到2020年，它显著增加。结果表明，如果没有实施REDD，将导致2020年至2050年期间约199,569公顷森林覆盖的森林砍伐，导致向大气中释放1,995,695tCO2的排放。然而,随着REDD的实施，这些排放量将减少到405,512tCO2e，有效防止1,590,183tCO2排放到高层大气中。这项研究表明，REDD项目的实施可以成为减少海尔卡尼亚森林温室气体排放和缓解气候变化的有效策略。
