Spatial assessment of land degradation sensitive areas in southwestern Romania using modified MEDALUS method

Prăvălie, Remus; Săvulescu, Ionuț; Patriche, Cristian Valeriu; Dumitrașcu, Monica; Bandoc, Georgeta

doi:10.1016/j.catena.2017.02.011

Cited by 57 publications

(44 citation statements)

References 45 publications

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“…The Environmental Sensitivity Assessment (ESA) methodology [79], in particular, concerns the assessment of desertification risk on the basis of selected indicators of climate, soil, vegetation and land management at lower levels (landscape/watershed, regional, national). It is continuously being used widely up to the present [90][91][92][93][94][95][96] and it has been refined through the application of Remote Sensing and GIS techniques [97][98][99][100].…”

Section: Problem Description and Analysis; Specification Of Planning mentioning

confidence: 99%

Combating Land Degradation and Desertification: The Land-Use Planning Quandary

Briassoulis

2019

Land

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Land-use planning (LUP), an instrument of land governance, is often employed to protect land and humans against natural and human-induced hazards, strengthen the resilience of land systems, and secure their sustainability. The United Nations Convention to Combat Desertification (UNCCD) underlines the critical role of appropriate local action to address the global threat of land degradation and desertification (LDD) and calls for the use of local and regional LUP to combat LDD and achieve land degradation neutrality. The paper explores the challenges of putting this call into practice. After presenting desertification and the pertinent institutional context, the paper examines whether and how LDD concerns enter the stages of the LUP process and the issues arising at each stage. LDD problem complexity, the prevailing mode of governance, and the planning style endorsed, combined with LDD awareness, knowledge and perception, value priorities, geographic particularities and historical circumstances, underlie the main challenges confronting LUP; namely, adequate representation of LDD at each stage of LUP, conflict resolution between LDD-related and development goals, need for cooperation, collaboration and coordination of numerous and diverse actors, sectors, institutions and policy domains from multiple spatial/organizational levels and uncertainty regarding present and future environmental and socio-economic change. In order to realize the integrative potential of LUP and foster its effectiveness in combating LDD at the local and regional levels, the provision of an enabling, higher-level institutional environment should be prioritized to support phrοnetic-strategic integrated LUP at lower levels, which future research should explore theoretically, methodologically and empirically.

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Section: Problem Description and Analysis; Specification Of Planning mentioning

confidence: 99%

Combating Land Degradation and Desertification: The Land-Use Planning Quandary

Briassoulis

2019

Land

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“…A number of human-induced conditioning factors of LD have been identified in previous studies (Huber-Sannwald et al 2006;Lu et al 2007;Prăvălie et al 2017;Mekonnen et al 2018;Speranza et al 2019). We selected five of these for use in LD risk mapping in the study watershed.…”

Section: Human-induced Variablesmentioning

confidence: 99%

“…To our knowledge, most previous studies assessing LD conditions have used geographic information system (GIS) and remote sensing techniques in spatial assessments of LD risk based on the environmental conditioning variables (Prăvălie et al 2017;Mariano et al 2018;Cerretelli et al 2018). Spatiotemporal patterns of land use change (anthropogenic factors) are the main factor in land degradation (Bewket and Sterk 2005;Gebremicael et al 2018).…”

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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“…The results are generally based on the land use/cover of Katsina state with the assumption that other factors are held constant. Such factors include climate, rainfall, soil quality and other socioeconomic parameters [42].…”

Section: Vegetation Quality Index (Vqi)mentioning

confidence: 99%

Assessment of Environmental Sensitivity to Desertification in Katsina State, Nigeria

James¹,

Jega²,

Halilu³

et al. 2018

eer

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Land degradation and Desertification (LDD) are critical issues facing most states in northern Nigeria, with severe consequences for national food security. Land degradation refers to total loss of the productive capacity of the soils for present and future use while desertification is seen as a process whereby the productivity of arid or semi-arid land falls by 10% or more. The Environmental Sensitivity to Desertification Index (ESDI) is the most frequently applied indicator-based system for accessing Land Degradation and Desertification. The research reported here adopted the MEDALUS (Mediterranean Desertification and Land Use) approach for identifying environmentally sensitive areas (ESA) to desertification in Katsina State, Nigeria based on Soil Quality Index (SQI), Climate Quality Index (CQI) and Vegetation Quality Index (VQI). The findings from each of these indicators show the variability between the northern and southern parts of Katsina State. The northern part of Katsina state was found to be the most vulnerable area to desertification and is covered by soil having the highest sand depth while the southern part of the state was found to be the least affected by desertification. This result specifically highlights areas in the State that requires urgent attention and remediation. Findings from this research can be used to support informed decision-making and policy development to focus on sustaining the environment, as well as the livelihood of the communities in the affected local government areas in the northern part of the State, given the high sensitivity of the area to desert conditions.

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Spatial assessment of land degradation sensitive areas in southwestern Romania using modified MEDALUS method

Cited by 57 publications

References 45 publications

Combating Land Degradation and Desertification: The Land-Use Planning Quandary

Combating Land Degradation and Desertification: The Land-Use Planning Quandary

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

Assessment of Environmental Sensitivity to Desertification in Katsina State, Nigeria

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