Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin

Shao, Yaqi; Jiang, Qimeng; Wang, Chunli; Wang, Meilin; Xiao, Ling; Qi, Yongwen

doi:10.1016/j.scitotenv.2020.137082

Cited by 24 publications

(20 citation statements)

References 21 publications

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“…Under government guidance, land use changes using different kinds of measures for reducing poverty are beneficial for eco-economic relationship development. Considering human activities for eco-economic sustainability, Shao et al (2020) indicated that agricultural research investments could directly restrain farmland expansion to prevent land degradation, resulting in an increasing yield and improving ESs (Shao et al 2020). The Green for Grain Project (GFGP), REDD+ (reducing emissions from deforestation and forest degradation) and some payment for ecosystem services can not only improve human well-being, but also be conducive to environmental protection (Salzman et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Linking ecosystem services and economic development for optimizing land use change in the poverty areas

Wei

Li³

et al. 2021

Ecosyst Health Sustain

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For the sustainable development of human beings and society, it is important to conserve the ecosystems and alleviate poverty in the low agricultural productivity or ecologically vulnerable areas. To avoid the decrease in ecosystem services (ES) under the effects associated with reducing poverty, we need to carefully consider the relationship between land use changes and the economic development in economically poor areas. We selected the Lingqiu County as our study area, which is a national impoverished county in China, and analyzed the impact of poverty alleviation efforts on the ecological and economic sustainability in the region through ES evaluation. The results showed that land use change in the Lingqiu County has not changed the ES significantly during 2007-2016. The gross domestic product (GDP) growth rate (15.81%) during 2001-2016, indicates the effectiveness of the poverty alleviation efforts implemented. The rise in the index of ecological and economic relationship during 2001-2016 (from 0.2389 to 0.3629) maintains in a harmonious coordination situation. Presently, the value of ES has not been converted to market-based benefits, and therefore, it is indispensable to find a marketplace for ES (in an innovative way) to promote the high-quality sustainable development of a local economy.

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Section: Discussionmentioning

confidence: 99%

Linking ecosystem services and economic development for optimizing land use change in the poverty areas

Wei

Li³

et al. 2021

Ecosyst Health Sustain

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“…Protection and management of the environment need to carry out thoughtfully and consistently to create environmental quality for human life and other living creatures [8][9][10]. Some efforts should make to find the right handling solution without causing social, economic, and ecological impacts to the community [10][11][12]. One of them is to recover land affected by the mine [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Recovery of Agricultural Areas Affected by Traditional Gold Mining: Sustainable Food Supply Stability

Golar¹,

Basir-Cyio²,

Isrun³

et al. 2021

IJDNE

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This study aims to analyze the recovery of the agricultural area’s function affected by the Poboya traditional gold mining in supporting the stability of sustainable food supply. We began the research by examining the existing mining land conditions through spatial analysis (land cover and land use changes from 2010 to 2019). Apart from that, it also analyzed the land’s health was through the soil’s physical and chemical properties, especially mercury. The observation proved that changes in the land’s cover and uses lead to decreased land quality and degradation. The existing condition showed heavy metals, particularly mercury, mostly polluted agricultural land in the mining area. The model design produced by this study may 1) emphasize land arrangement; 2) revegetation design with forestry, plantation, and food crops; 3) domesticated plant; and 4) environmental monitoring, concerning monitoring of soil quality, monitoring of erosion and sedimentation, water quality, acid mine drainage, successful revegetation, and others. These four aspects expect to help suppress the rate of land degradation in agriculture located in ex-mining areas and reduce forest destruction in the Grand Forest Park area.

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“…Land degradation (LD) is now a critical environmental issue worldwide, posing a threat to food security and socio-economic development, and the problem will worsen without rapid remedial action (Jiang et al 2019;Shao et al 2020). Land degradation, defined as declining capability of the biological or economic productivity of land to provide ecosystem services, is closely connected to food security, human well-being, and development (Wieland et al 2019;Crossland et al 2018;Gichenje et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Spatiotemporal patterns of land use change (anthropogenic factors) are the main factor in land degradation (Bewket and Sterk 2005;Gebremicael et al 2018). Other researchers have reported that direct anthropogenic disturbances in environments and ecosystems can increase land degradation (Ahiablame and Shakya 2016;Davudirad et al 2016;Aladejana et al 2018;Schwieger and Mbidzo 2020;Shao et al 2020). Jaquet et al (2015) found that outmigration has led to land degradation in a western Nepal watershed.…”

Section: Introductionmentioning

confidence: 99%

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

et al. 2020

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Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

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Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin

Cited by 24 publications

References 21 publications

Linking ecosystem services and economic development for optimizing land use change in the poverty areas

Linking ecosystem services and economic development for optimizing land use change in the poverty areas

Recovery of Agricultural Areas Affected by Traditional Gold Mining: Sustainable Food Supply Stability

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

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