An integrated GEE and machine learning framework for detecting ecological stability under land use/land cover changes

作者：Atiyeh Amindin, Narges Siamian, Narges Kariminejad, John J. Clague, Hamid Reza Pourghasemi · 发表于：Global Ecology and Conservation · 年份：2024 · DOI：10.1016/j.gecco.2024.e03010 · 被引用次数：23 · 研究领域：Land Use and Ecosystem Services、Remote Sensing in Agriculture、Species Distribution and Climate Change

Ecological stability (ES) is recognized as a crucial factor for sustainable development at global and regional scales. However, the importance of this factor was not considered significant. Hence, the main aim of this study was to introduce a new approach that focuses on detecting ES over the Maharloo watershed in Iran. To achieve this goal, we extracted land use and land cover (LULC) data from the Google Earth Engine (GEE) platform by applying the random forest (RF) machine learning method, which obtained Kappa statistics of 0.85, 0.86, and 0.87 for the years 2002, 2013, and 2023, respectively. We identified both stable and unstable regions based on LULC changes and employed them using machine learning to forecast the ES. The most important predictors of ecological stability were elevation, soil organic carbon index, precipitation, and salinity. The results of this research revealed that certain areas within the Maharloo watershed have experienced ecological instability in recent years, with gardens showing the highest percentage (60.65%) of instability among all land-use categories. The performance and validation of our model suggest that the study results are reliable (AUC = 0.86). This study offers detailed maps of ecological stability and trends, offering valuable insights for decision makers to support landscape conservation and restoration efforts. Overall, the findings contribute to a more comprehensive understanding of the ecological dynamics of the Maharloo watershe...