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

Abstract

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.

Landsat series images for evaluating ecological restoration effect from multi-time scale based on an ideal reference

“…Some studies have used change detection algorithms and models to evaluate the ecological restoration effects in different regions and identify the abrupt points in the time series of comprehensive ecological indices, e.g., continuous change detection and classification, continuous monitoring of land disturbance, Landsat-based detection of the trends in disturbance and recovery, breaks for additive seasonal and trend (BFAST), and vegetation change tracker (Huang et al, 2010;Verbesselt et al, 2010;Zhu and Woodcock, 2014;Zhang et al, 2018;Zhu et al, 2020). For example, short-term abrupt changes (disturbance time and recovery rate) in the gross primary productivity have been analyzed to describe and quantify vegetation health (Li et al, 2023). Significant progress has been made in the assessment of ecological restoration effects.…”

“…Significant progress has been made in the assessment of ecological restoration effects. However, the existing literature lacks comprehensive assessments that focus on the short-term changes and long-term trends in the ecological environment, resulting in uncertainties in the current knowledge of ecological restoration processes (Li et al, 2023;Wei et al, 2023). Further, it is difficult to provide an objective assessment of the effects of ecological restoration, owing to the non-consideration of the natural conditions (Li et al, 2017) of a region and the environmental change response at the global scale (Guo et al, 2022).…”