Modelling and Assessing the Spatiotemporal Changes to Future Land Use Change Scenarios Using Remote Sensing and CA-Markov Model in the Mellegue Catchment

Weslati, Okba; Bouaziz, Samir; Sarbeji, Mohamed Moncef

doi:10.1007/s12524-022-01618-4

Cited by 28 publications

(10 citation statements)

References 72 publications

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“…It ows in Tunisia, through the Tunisian Atlas, until nally unclogs into the river of Mejerdah, near Jendouba. The overall direction of the river match the southwestern-northeastern direction of the famous Algero-Tunisian Atlas [30,31]. The watershed is characterized by an elongated overall shape, oriented NE-SW and explicitly large in the mid.…”

Section: Study Areamentioning

confidence: 84%

“…Many causes can lead to the intensi cation of soil loss. The area has witnessed urban extension taking place during the period (2002-2020) which took place rapidly at the expense of several classes of land use, in particular forest cover [30]. The arti cial soils of the urban environment will prevent the in ltration of rainwater and therefore the volumes of water to be evacuated will be enormous.…”

Section: Soil Loss Assessmentmentioning

confidence: 99%

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Spatial Assessment of Soil Erosion Using RUSLE Model, Remote Sensing and GIS: A Case Study of Mellegue Watershed, Algeria- Tunisia

Weslati

Serbaji

2023

Preprint

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Soil erosion is an important global phenomenon that can cause many impacts, like shape and hydrology alteration, land degradation, and landslides. Moreover, soil loss has a significant effect on agricultural production by removing most valuable and productive top soil’s profile, leading to reduction in yields which requires high production budget. Soil erosion impact has been intensified by the magnification of global warning and drought, especially in north African regions, such as Tunisia and Algeria, where arid climate is the dominant weather on these regions. In addition, these regions are suffering from soil erosion by water which had cost a serious soil loss regionally and globally. Accordingly, this study aimed to compare soil losses, between 2002 and 2020, of the catchment of Mellegue which is a large cross-border basin commonly shared between Tunisian-Algerian countries. Soil erosion risk was assessed and mapped via the Revised Universal Soil Loss Equation (RUSLE). It requires diverse factors that are integrated in the erosion process such are topography, soil erodibility, rainfall erosivity, anti-erosion cultivation practice and vegetation cover. The computation of the various equation factors was applied in a GIS environment, using ArcGIS desktop 10.4. The results shows that the catchment has undergone significant soil water erosion where it exhibits the appearance of approximate 14000 new areas vulnerable to erosion by water in 2020 compared to 2002. Average erosion risk has also increased from 1.58 t/ha/year in 2002 to 1.78 in 2020 leading to an increase in total estimated soil loss by reaching 54000 t/ha in 2020 compared to around 25500 t/ha in 2002. Mapps of erosion risk shows that highly eroded areas are more frequent in downstream of the basin. These maps can be helpful for decision-makers to make better sustainable management plan and for land use preservation.

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Section: Study Areamentioning

confidence: 84%

Section: Soil Loss Assessmentmentioning

confidence: 99%

Spatial Assessment of Soil Erosion Using RUSLE Model, Remote Sensing and GIS: A Case Study of Mellegue Watershed, Algeria- Tunisia

Weslati

Serbaji

2023

Preprint

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“…Despite their benefits, the application of CA-Markov models in urban studies has several gaps. Firstly, more research on the calibration and validation of these models is needed, particularly in areas with limited data [20]. Secondly, the impact of local policy interventions on urban growth patterns and land-use changes needs to be investigated further [21].…”

Section: Introductionmentioning

confidence: 99%

Simulating Urban Growth Using the Cellular Automata Markov Chain Model in the Context of Spatiotemporal Influences for Salem and Its Peripherals, India

Theres

Radhakrishnan

Rahaman

2023

Earth

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Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring.

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“…The geospatial analysis utilizes a variety of statistical and rule-based modeling approaches to detect and predict land use changes (Overmars, et al, 2003). Among the models commonly used in lulcchange studies are; statistical models (Allbed, et al, 2014;Başaran, Matcı, and Avdan 2022), evolutionary models (Aitkenhead and Aalders 2009), cellular models (Kafy et al 2021), Markov models (Mubea, et al, 2011), hybrid models (Weslati, et al, 2023, Küçük Matcı, et al, 2022.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Urban Area Change in Eskişehir Province Using Remote Sensing Data and Machine Learning Algorithms

Matcı

2023

International Journal of Environment and Geoinformatics

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Rapid population growth, natural events, and increasing industrialization are among the factors affecting land use. To keep this change under control and to make sound plans, it is necessary to control the changes. In this study, the spatial use change in the Eskişehir region between the years 1990-2018 was examined with CORINE data. Based on this determined change, an urban change model was created with the multivariate regression method. As a result of the evaluations, while an increase was observed in urban areas and pastures between 1990-2018, a decrease was determined in agricultural and forest areas. This change is defined as 43.74% in urban areas, 3.28% in agricultural areas, 7.78% in forest areas, and 60.10% in pasture areas. SMOReg, MLP Regressor, and M5P Model Tree methods were used for the estimation study to be carried out with the obtained spatial change data. Urban values for 2018 were estimated to find the best method. Finally, the areas of 2030 were estimated with the method that gave the best results. The results demonstrated the usability of modeling using CORINE data.

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Modelling and Assessing the Spatiotemporal Changes to Future Land Use Change Scenarios Using Remote Sensing and CA-Markov Model in the Mellegue Catchment

Cited by 28 publications

References 72 publications

Spatial Assessment of Soil Erosion Using RUSLE Model, Remote Sensing and GIS: A Case Study of Mellegue Watershed, Algeria- Tunisia

Spatial Assessment of Soil Erosion Using RUSLE Model, Remote Sensing and GIS: A Case Study of Mellegue Watershed, Algeria- Tunisia

Simulating Urban Growth Using the Cellular Automata Markov Chain Model in the Context of Spatiotemporal Influences for Salem and Its Peripherals, India

Estimation of Urban Area Change in Eskişehir Province Using Remote Sensing Data and Machine Learning Algorithms

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