Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia

Kura, Abiyot Legesse; Beyene, Dereje

doi:10.1016/j.rsase.2020.100321

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…If this assumption does not lie on the base of the dynamics of the modelled system, these models will probably fail when modelling the intended dynamics. Nevertheless, a wide range of land dynamics have shown to align with this CA behaviour, especially urban sprawl and deforestation (Barredo et al 2003;White et al 2015;Kura and Beyene 2020).…”

Section: Structural Uncertaintymentioning

confidence: 98%

Structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages. A comparison

García-Álvarez¹,

Olmedo²,

Delden³

et al. 2022

Preprint

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Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management.The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not prove to be more successful that user-driven approaches. The available options for uncertainty management vary depending on the model. No model pays enough attention to the communication of uncertainties.

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Section: Structural Uncertaintymentioning

confidence: 98%

Structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages. A comparison

García-Álvarez¹,

Olmedo²,

Delden³

et al. 2022

Preprint

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“…One of the most popular ANN approaches is the Cellular Automata (CA) which estimates the future pixel state in accordance with the initial state of the pixel alongwith considerations on the neighbourhood and some transitional probability rules (Liu et al, 2017). The advantage of this technique lies in the fact that the nonlinear stochastic land category transition processes can be represented efficiently (Batty et al, 1997;Liu et al, 2017).CA-based ANN models have been widely used in the urban landuse simulations (Clarke and Gaydos, 1998;Li et al, 2011;Liu et al, 2017).Some studies have also focussed on simulating deforestation induced by human activities (Kok and Winograd, 2002;Kura and Beyene, 2020). However, although the process of soil erosion is temporally dynamic and prone to human alterations of the landscape, the application of ANN models islimited (Gholami et al, 2021).In this paper, the RUSLE-based erosion scenario for the year 2011 has been used as the input layer.…”

Section: Temporal Change In Soil Loss and Future Predictionmentioning

confidence: 99%

Current Status and Future Projection of Soil Erosion Based on RUSLE, Landscape Ecology and Deep Learning Technologies: Observations from a Tropical River Basin in Eastern India

Roy,

Das,

Chatterjee

et al. 2023

Preprint

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Soil erosion has contributed to loss of enormous amounts of top soil worldwide. Since the exact quantification of soil erosion is impossible, numerous researchers across the world have used prediction-based models (such as Revised Universal Soil Loss Equation, RUSLE) for assessing the temporal context of soil erosion at the catchment-scale. This paper has tried to integrate the RUSLE-based empirical soil erosion model and landscape ecology for the soils of a tropical river basin in Eastern India. It is observed that more than 60% of the areas in the studied basin are presently witnessing erosion greater than 11.2 tons/ha/year, which is above the tolerable limit as proposed by Food and Agricultural Organization (FAO). The process was applied for 2011 and 2021 and it was observed that soil erosion was augmented by about 6% during this period. Landscape ecological metrices reveal that the patches of high erosion are getting clustered and coalesced and becoming larger in areal extent, especially in the upper and middle domains of the studied basin. This paper, with the help of the soil erosion status of 2011 and 2021, has tried to predict the future scenario of soil erosion in the next five decades (2021 – 2071) with the help of the Artificial Neural Network, a popular deep learning technology. It is found that if erosion continues at the present rate, the patches may increase in extent by about 50% in the next five decades, which is detrimental. Finally, it is recognized that due to the lower clay content (< 30%) in the upper and middle domains of the basin, the study suggests the use of plot-scale mulching technique as an efficient measure to combat soil erosion in the region.

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“…Menurut Kura & Beyene, (2020), pertambahan penduduk yang tidak terkelola dengan baik dapat menyebabkan masalah seperti pengurangan lahan terbuka, hilangnya lahan pertanian, penurunan kualitas lingkungan, dan kemacetan lalu lintas. Oleh karena itu, perlu adanya perencanaan tata ruang yang baik dan kebijakan pengelolaan lahan yang berkelanjutan untuk mengatasi dampak pertambahan penduduk terhadap luasan lahan terbangun (Rakuasa & Somae, 2022).…”

Section: Metodologiunclassified

Analisis Spasial Temporal Perubahan Tutupan Lahan di Pulau Ternate Provinsi Maluku Utara Citra Satelit Resolusi Tinggi

Latue

2023

Buana j. Geografi Ekologi dan Kebencanaan

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Pertambahan jumlah penduduk yang semakin meningkat, tentunya berpengaruh terhadap kebutuhan dan ketersedian lahan di Pulau Ternate. Dengan menggunakan citra satelit resolusi tinggi dapat mengidentifikasi dan menganalsis perubahan tutupan lahan di Pulau Ternate dengan detail. Penelitian ini menggunakan data Worldview -2 tahun 2013 dan 2023 untuk analisisi tutupan lahan tahun 2013 dan 2023 dengan mengacu pada standar klasifikasi tutupan lahan berdasarkan SNI-2010 yang dilakukan dengan interpertasi dan digitasi yang dilakukakn secara visiual di software Arc GIS 10.8. Hasil penelitian menunjukan bahwa lahan terbangun di Pulau Ternate mengalami pertambahan luasan pada periode 2013-2023. Pada tahun 2013 presentase luasan lahan terbangyn di Pulau Ternate sebesar 15.19 % dan terus mengalami pertambahan luasan di tahun 2023 sebesar 19.69% dari total luasan Pulau Ternate. Berbeda dengan kelas tutupan lahan lainnya yang mengalami penurunan luasan. Hasil penelitian diharapkan dapat menjadi informasi bagi pemerintah dan masyarakat di Pulau Ternate dalam upaya penataan ruang kedepannya di kedepannya.

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Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia

Cited by 11 publications

References 29 publications

Structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages. A comparison

Structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages. A comparison

Current Status and Future Projection of Soil Erosion Based on RUSLE, Landscape Ecology and Deep Learning Technologies: Observations from a Tropical River Basin in Eastern India

Analisis Spasial Temporal Perubahan Tutupan Lahan di Pulau Ternate Provinsi Maluku Utara Citra Satelit Resolusi Tinggi

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