Examining the efficacy of revegetation practices in ecosystem restoration programs: insights from a hotspot of sandstorm in northern China

Abstract

Retrospectively evaluating the efficacy of revegetation practices is helpful in planning and implementing future ecosystem restoration programs (ERP). Having a good understanding of how human activities can affect vegetation cover, both before and after ERP, is particularly important in sandstorm hotspot areas. The Beijing–Tianjin Sandstorm Source Region (BTSSR) is one such area. We conducted an investigation into vegetation dynamics within the BTSSR. This was done using remote sensing data in conjunction with climate data sets and land use data spanning the 1982–2014 period. The relationships between climatic factors (such as precipitation and temperature), and vegetative change were modeled using a neural network method. By a process of residual analysis, the proportions of human-induced vegetative change both before and after the ERP were established. Our results show that: 1) before the ERP (1982–2000), 40.96% of the study area exhibited significantly progressive vegetation changes (p<0.05). This proportion decreased to encompass only 20.23% of the study area in the period following the ERP (2001–2014). 2) 89.55% of the study area showed signs of human-induced vegetation degradation before the ERP. Between 2001 and 2014 however, following ERP, this figure fell to only 27.78%. 3) ERP implementation led to visible improvements in vegetative conditions within the BTSSR, especially in areas where ecological restoration measures were directly and anthropogenically applied. These results highlight the benefits that positive human action (i.e., revegetation initiatives implemented under the framework of an ERP) have brought to the BTSSR.

Keywords

vegetation dynamics / human activities / ERP / neural network model / Beijing–Tianjin sandstorm source region

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Acknowledgments

The authors are grateful to the editor and anonymous reviewers of this paper. This research received financial supports from the National Natural Science Foundation of China (Grant Nos. 41977412, U1810101, and 41871193) and the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (No. 2020L0014) and Higher Education Institution Project of Shanxi Province: Ecological Remediation of Soil Pollution Disciplines Group (No. 20181401).

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