Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS

Sandeep, P.; Reddy, G. P. Obi; Jegankumar, R.; Kumar, K. C. Arun

doi:10.1007/s10666-020-09739-1

Cited by 42 publications

(28 citation statements)

References 33 publications

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“…The VQI derived from the NDVI varied from 0.1 to 1.0, indicating a very high to very low quality [16]. The results in Figure 9 shows that the very low vegetation cover was the predominant class in the studied area and represented 92.74%.…”

Section: Vegetation Quality Index (Vqi)mentioning

confidence: 95%

“…It is well known that LDV is affected by a wide range of factors [16,17,19], and thus, a range of evaluation criteria have been identified based on literature (Table 1). Data were collected from RS imageries, field observations, laboratory analyses, and climatic conditions.…”

Section: Data Usedmentioning

confidence: 99%

“…Land degradation vulnerability (LDV) is the susceptibility of an area to loss of productivity due to various factors responsible, including climatic dryness, poor soil, and vegetation quality [16]. The assessment of LDV is the process of identification and quantification of pressure on land resources owing to several factors influencing the land system quality [17].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, GIS can gather, update, manipulate, store, and integrate spatially referenced datasets to be included in spatial modeling [30,31]. The combined use of geospatial technologies and AHP in zoning LDV areas enhances the decision-making process and provides better accuracy on a regional to local scale [3,16]. Previous case studies demonstrated that the integration of geospatial technologies and AHP proved successful in assessing LDV in many desert areas worldwide, such as in China [12], Iraq [32], and the Aral Sea basin in Central Asia [33].…”

Section: Introductionmentioning

confidence: 99%

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Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

et al. 2021

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Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

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Section: Vegetation Quality Index (Vqi)mentioning

confidence: 95%

Section: Data Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

et al. 2021

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“…As for the weight determination method of each evaluation index, it usually includes the experts grading method, analytic hierarchy process, principal component analysis, the entropy method, grey relational analysis, etc. (He et al 2008;Boori et al 2021;Sandeep et al 2021;Xia et al 2021), which are unsuitable for nonlinear analysis of complex systems. The city is a complex adaptive system with the characteristics of nonlinearity, interaction, and complexity (Nel et al 2018;Qiu 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial assessment of urban heatwave vulnerability of coupling adaptability based on BPNN model: a case study of Xiamen City, China

Yang

2021

Geomatics, Natural Hazards and Risk

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The continuous advancement of urbanization and the acceleration of global climate warming have severely aggravated the heatwave vulnerability of the urban complex human-land system. Therefore, the scientific assessment of urban heatwave vulnerability (UHV) is particularly critical. We used Xiamen City, one of the representative heatwave disaster-prone cities, as a case study area. We then constructed a UHV index system that coupled adaptability and selected 12 indicators from the three dimensions: exposure, susceptibility, and adaptability. The back propagation neural network (BPNN) model was used to composite each indicator layer and produce UHV results. Finally, we analyzed the spatial distribution characteristics of UHV. We found that the BPNN model had good training performance, with an overall accuracy of 0.92986. The value of UHV ranged from 0 to 1 and was divided into five grades, from low to high were 18.45%, 18.72%, 17.16%, 28.76%, and 16.91. In terms of spatial characteristics, high adaptability significantly improved UHV. The high value of UHV presented specific agglomeration characteristics, and the extremely vulnerable and disordered areas were mainly located in Huli District and the junction of Siming District and Huli District. The research will provide a new theoretical perspective and framework for urban heatwave assessment and help for disaster management and sustainable development in a high-risk area.

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Impact of coal mining on land use dynamics and soil quality: Assessment of land degradation vulnerability through conjunctive use of analytical hierarchy process and geospatial techniques

et al. 2022

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Coal is the major source of energy in India, where 66% of the thermal power is generated from different grades of coal. The surging energy demand for domestic and industrial sectors has accelerated coal production through intensive coal mining from existing mines and there is exploration for potential areas across the Country. More than 80% of the coal excavated from open cast mining, which is a damaging form of mining that is often associated with undesirable land use changes, soil degradation, and deforestation and is responsible for other environmental degradation. The present study has been conducted in Manendragarh coal mining area located in northern Chhattisgarh, Central India using the analytical hierarchy process (AHP) and geospatial tools. LANDSAT-TM and OLI satellite data from 1990 and 2020 were digitally analyzed applying a maximum likelihood supervised classification technique in ERDAS IMAGINE and Arc-GIS platforms. Spatial analysis of satellite data revealed that the area under forest, agriculture-fallow, and waterbodies fell by 14.5%, 12.9%, and 2.52%, respectively, due mainly to conversion into degraded land, while the mining area and built-up areas were proportionally enhanced by 3.2% and 0.59% between 1990 and 2020. The intense mining resulted in deterioration of soil health, where the organic C, pH, and soil depth were considerably decreased in mined sites and degraded lands, compared to agricultural and forest areas. The major determinant biophysical, topographic, and soil factors were integrated in a GIS platform to derive a land degradation vulnerability index (LDVI), which identified five vulnerability classes. The salient findings of study provides the critical inputs for the policy makers/environmentalists in prioritization and decision-making for implementing remedial measures for the eco-restoration of mining degraded landscapes and also the improvement of ecology and environment along with sustainable coal mining.

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Modeling and Assessment of Land Degradation Vulnerability in Semi-arid Ecosystem of Southern India Using Temporal Satellite Data, AHP and GIS

Cited by 42 publications

References 33 publications

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Spatial assessment of urban heatwave vulnerability of coupling adaptability based on BPNN model: a case study of Xiamen City, China

Impact of coal mining on land use dynamics and soil quality: Assessment of land degradation vulnerability through conjunctive use of analytical hierarchy process and geospatial techniques

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