Multi-Dimensional Evaluation of Ecosystem Health in China's Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm.

China's Loess Plateau (CLP) is a typical semi-arid region and is very sensitive to climate and human activity. Under the ecological restoration project, vegetation coverage increased significantly, but the limitation of climate and other factors has meant that vegetation mortality was relatively high. Therefore, it is of great significance to evaluate the ecosystem health in the CLP in terms of the sustainability of ecological restoration projects. The aim of this study is to propose a multi-dimensional assessment method to investigate vegetation health changes in the CLP based on BFAST and BFAST01 algorithms. To achieve this, we constructed local dimension health indexes (the number of disturbances and recovery rate) and overall dimension health indexes (trend types) based on the gross primary productivity (GPP) and vegetation evapotranspiration (Ec) data of the study area from 2001 to 2020 which was collected from the Google Earth Engine (GEE) platform. The result revealed the following. More than 90% of disturbance pixels of GPP and Ec in the short-term change only once and more than 60% of pixels recover after disturbance. However, the recovery rate after disturbance is slow, and the interval with the largest proportion is 0-0.00015. The long-term trend mostly exhibited a monotonic increasing trend. These results indicate that the function of the ecosystem on the CLP has been improved, but the resilience of vegetation is weak. In conclusion, the combination of the local dimension and overall dimension analysis can comprehensively reveal information about the CLP's vegetation health in the past two decades, and that the method will open new perspectives and generate new knowledge about vegetation health in the CLP.