Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R² between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system.

中文翻译：

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1991-2019 年亚美尼亚 Dilijan 国家公园森林和土地覆盖变化特征的基于地球观测的机器学习方法的开发和应用

迪利然国家公园是亚美尼亚最重要的国家公园之一，成立于 2002 年，旨在保护其丰富的动植物生物多样性并防止非法采伐。本研究的目的是首先使用 Sentinel-2 和 Landsat 时间提供 1991 年至 2019 年 28 年监测周期中每 5 年一次的森林退化和森林砍伐地图系列和机器学习方法。极高空间分辨率图像用于校准和验证森林密度建模和相关变化。相关系数 R ²森林密度图和参考值之间的范围从最早时期的 0.70 到最新时期的 0.90。土地覆盖/土地利用分类产生了良好的结果，大多数类别的用户和生产者的准确度都在 80% 以上。尽管由于采取了保护措施，大约 30 年前开始的森林退化和森林砍伐得到了遏制，但随着定居点、旅游业或农业的增加，人为压力仍然是一个威胁。该案例研究可用作亚美尼亚政府可持续森林管理和政策的决策支持工具，并可作为未来全国森林监测系统的模型。