Ecological restoration success on the Loess Plateau of China: A qualitative and quantitative exploration based on rephotography

恢复生态学 归一化差异植被指数 植被(病理学) 中国 环境资源管理 黄土高原 生态学 地理 自然地理学 环境科学 遥感 气候变化 考古 土壤科学 生物 医学 病理
作者
Lanmin Liu,Yanchen Gao,Junru Chen,Francis Zvomuya,Hailong He
出处
期刊:Earth Surface Processes and Landforms [Wiley]
卷期号:49 (3): 1081-1098 被引量:1
标识
DOI:10.1002/esp.5752
摘要

Abstract Ecological restoration programs such as the “Grain for Green Project” (GFGP) have significantly reduced soil erosion and increased vegetation cover on the Loess Plateau (LP) of China over the last decades. The LP has become the paragon of ecological restoration and soil and water conservation across the world and has been highlighted by numerous reports in the literature. However, there is a lack of “seeing is believing” evidence (that is, a picture is worth a thousand words) depicting the effectiveness of the ecological restoration on the LP. Rephotography (repeat photography) was used in this study to compare historical and current photographs of the same locations to explore the landscape changes associated with ecological restoration. Twenty‐nine photo pairs between 1925 and 2021 were compiled and combined with satellite imagery and the deduced Normalized Difference Vegetation Index (NDVI) between 1986 and 2021. This allowed the establishment of a rephotography library reflecting the achievements of ecological remediation and reconstruction on the LP. This in turn provides intuitive and detailed evidence for the ecological environment construction of the LP and resonates with the notion of “lucid waters and lush mountains being invaluable assets”. In addition to presenting a great opportunity to investigate landscape change over time, which is difficult to discern using other approaches, the study provides a scientific reference and a basis for government decision‐making. The potential use of rephotography for calibrating remote sensed data was also discussed. Good agreement was found between the NDVI derived from rephotography of large areas and NDVI derived from satellite imageries, which may facilitate the accurate reconstruction of time series NDVI.

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