Impacts of Ecological Migration on Land Use and Vegetation Restoration in Arid Zones

Abstract

Poverty and disasters are globally prevalent in ecologically fragile areas. Ecological migration is regarded as an effective way to address these issues. This paper investigates the spatial pattern of ecological migration and the corresponding spatiotemporal changes in land use and vegetation restoration in Gulang County, located in northwest China, between 2010 and 2018. For this purpose, we calculated three indicators: the transfer matrix of land use, normalized difference vegetation index (NDVI), and vegetation restoration degree (VRD). We found that ecological migrants in Gulang County moved from the Qilian Mountain National Park to the intersection between flat area and desert. The spatial patterns ranged from high-altitude to low-altitude, and the slopes became less steep. The distribution of the resettlements is more clustered and shaped by the traffic conditions and guided by the local governments. Unused land in the whole-village migration area and construction land in the resettlement area were dramatically impacted by ecological migration (625% and 279.3%, respectively). The cropland and construction land in the outmigration areas were mainly replaced with grassland and forest. In contrast, the grassland and unused land in the resettlement area were transferred to cropland and construction land. After ecological migration, the mean NDVI and VRD in Gulang County significantly increased, indicating that the vegetation in the outmigration areas quickly recovered. Moreover, the VRD in the whole-township migration areas was greater than that in the whole-village migration areas (121% > 68%). The main contribution to the increase in NDVI was the conversion of forest to grassland, accounting for 33%. In addition, the transition from other types of land to grassland made a larger contribution to the NDVI than conversions to forest.

Comparison of the effects of five long‐term land use and management practices on runoff, soil erosion, and nutrient loss under natural rainfall in the Mollisol region of Northeast China

“…Furthermore, the RD and SY in the NVR were 84.2% and 99.9% lower than in the BL, respectively. This result suggested that revegetation resulted in less soil erosion, demonstrating the negative effects of converting natural grassland to cropland, which was confirmed by many studies (e.g., Xia et al, 2014; Yan, Zhen, et al, 2021; Zhang et al, 2022). These results showed that the conversion of natural grassland/forest to farmland reduced soil quality and increased soil erodibility because of more frequent soil disturbances and less vegetation coverage, plant community diversity and root density (Manzoni & Porporato, 2009; Mohammad & Adam, 2010; Vityakon, 2007).…”

Impacts of Land Use and Land Cover Changes on Migration and Food Security of North Central Region, Nigeria

“…The results presented in Tables 3 and 4 indicate that LULC classification of the three states has a great significant alignment with ground observation of the various land cover classes. Changes in land use are a direct indication of ecological migration [38]. As seen in Table 5, Figure 2a,b and Appendix A, in Niger state between 1990 and 2020, the agricultural land experienced the largest increase with an annual rate of 7.44 units, while barren land and vegetation had the largest decrease of above 7 units each per year.…”

Study on suitability and spatial distribution of human settlement environment in arid areas based on GIS and analytic hierarchy process

“…At the same time, the high mountains and steep slopes hinder the development of production and life. Therefore, the quality of the humanistic environment is the lowest, which also highlights the necessity and urgency of implementing immigration (Zhang W et al, 2022). In addition, there are many towns and larger villages scattered along the main transportation roads.…”

“…The research data is transformed from survey data such as statistical yearbooks and questionnaire interviews into geographical data such as satellite images and land use (Wang et al, 2019); its evaluation index system covers physical geography, social economy, and public services. The research scale covers provinces (Yingying et al, 2022;, cities (Chen et al, 2022), villages (Zhao et al, 2019), communities (Aghamohammadi et al, 2021), and resettlement in arid areas (Gong et al, 2021;Zhang W et al, 2022).…”