Applicability of the CA-Markov Model in Land-use/Land cover Change Prediction for Urban Sprawling in Batticaloa Municipal Council, Sri Lanka

Mathanraj, S.; Rusli, Noradila; Ling, Gabriel Hoh Teck

doi:10.1088/1755-1315/620/1/012015

Cited by 14 publications

(14 citation statements)

References 9 publications

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“…We used theland change modeler as a decision support land planning tool [6,7] to evaluate historical land-use changes (from 1990 to 2020) in the BMC, and the Markov chain stochastic simulation model (for example, [7,8,10,27,29,38,40]) to predict land-uses in 2030. Change detection is important for determining which land-use is changing, and how it is changing, for sustainable landuse planning [40][41][42][43]. The future forecast map of 2030 is useful for developing suitability modeling for homesteads, which were growing exponentially from 2010 to 2020.The landuses in the BMC have changed because of human-induced activities, such as illegal mining, plantations, sprawling development, encroachment, and natural events, such as storm surges and floods [31,41,42].…”

Section: Discussionmentioning

confidence: 99%

“…Change detection is important for determining which land-use is changing, and how it is changing, for sustainable landuse planning [40][41][42][43]. The future forecast map of 2030 is useful for developing suitability modeling for homesteads, which were growing exponentially from 2010 to 2020.The landuses in the BMC have changed because of human-induced activities, such as illegal mining, plantations, sprawling development, encroachment, and natural events, such as storm surges and floods [31,41,42]. An unpredictable sociopolitical system has caused a rapid change in land-use and land-cover in Batticaloa over the past twenty years [43].…”

Section: Discussionmentioning

confidence: 99%

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Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler

Sangasumana

2021

SSRN Journal

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler

Sangasumana

2021

SSRN Journal

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“…Modeling the spatial dynamics mostly depends on the LULCC (Sudhira et al, 2004). Consequently, it is crucial to address the relationship between urban sprawl and LULCC through the process of simulation since LULC dynamics are vital to a sustainable urban environment (Mathanraj et al, 2021;Aarthi & Gnanappazham, 2018). This helps urban planners, policy-makers as well as resource managers to obtain the most up-to-date data on this rapidly changing urban environment (Verburg et al, 2004).…”

Section: Study Areamentioning

confidence: 99%

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

Hasan

2024

Preprint

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With accelerated global urbanization, the expansion of cities has become a pressing concern for urban planners and researchers. The growth and dynamics of urban encroachment are closely tied to changes in land use, especially in urbanized areas. This study seeks to evaluate the accuracy of the CA-Markov (Cellular Automata) model in predicting land use/land cover changes (LULCC) in the Kurunegala district, Sri Lanka as a result of urban expansion. The investigation employs secondary data, including 2007, 2012, 2017, and 2022 Landsat 05, 07, 08, and 09 images respectively. Using software applications such as ArcGIS 10.8, IDRISI 17.0, and MS-Excel 2019, diverse techniques including supervised classification, Markovian transition estimation, and CA-Markov chain analysis, this study attempts to analyze the Urban Growth Modeling for 2027 and 2037 using CA-Markov Chain Multi-criteria Analysis (MCA) under Markov-Markovian Transition Estimator. The study used 32 spatial variables for determining the LULCC. As per the derived results, from 2022 to 2027, urban areas have increased quite markedly. The vegetation cover area has reduced and the areas of water bodies have increased spatially. From 2027 to 2037, the urban area increment is 72.552%. Also, vegetation cover and water body distribution have reduced by 8.051% and 39.91% respectively.

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“…However, cells, transition rules, cell size, time, and cell neighbourhood are the significant components that should be considered for the optimum prediction (Liping et al, 2018). The spatial and temporal state of neighbouring cells heavily relies on each cell's state (Mathanraj et al, 2021). The CA model can be mathematically expressed (Liping et al, 2018)…”

Section: Markov Chain Model and Cellular Automata-markovmentioning

confidence: 99%

Impact of Climate Change and Land Use Land Cover Change on Stream Flow of the Teesta River Basin under CMIP6 Climate Scenarios

Rahman,

Islam

2024

Preprint

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The Teesta basin, shared by Bangladesh and India, undergoes profound changes due to land use and land cover (LULC) dynamics combined with climate change, significantly impacting its water balance. This study intends to analyse the combined effects of future LULC changes and climate change on the stream flow of the Teesta River. Employing a cellular-automata model, we accurately predicted LULC for 2020 and projected scenarios for 2050 and 2080. A semi-distributed hydrological model, Soil Water Assessment Tool (SWAT) is used to generate flow for the base period (1995-2014), the near future (2035-2064), and the far future (2071-2100). SWAT model is forced by 8 General Circulation Models (GCMs) under two Socioeconomic Pathways (SSP245 and SSP585). Satellite image analysis showed notable LULC changes, with substantial increases in agricultural (0.49%), water bodies (4.24%) and settlement areas (1.67%), while a decrease in forest (1.45%) and sand bar (5.49%) decreased from 2000 to 2010. During 2010 and 2020, increases in agricultural (2.77%), water bodies (7.7%) and settlement areas (8.59%), while decreases in forest (1.36%) and sand bar (0.18%) were found. The CA-Markov prediction indicates significant future increases in settlements and agricultural land by 2050 and 2080, coupled with reductions in forest, water, and snow cover. The calibrated and validated SWAT model effectively simulated discharge, yielding satisfactory results. Projected changes in discharge during the monsoon and winter periods were examined under different scenarios. Monsoon discharge is predicted to increase by 26% to 38% in the near future and 30% to 45% in the far future. Conversely, the winter/dry period may experience a decrease of 46% to 49% in the near future and 38% in the far future for SSP245 and SSP585 scenarios, respectively. Considering LULC changes to account, additional discharge increases during the monsoon are projected, with slight increases in the dry period during the near future and decreases during the far future. This study anticipates an increasing trend in peak flow during the monsoon and a declining trend in mean flow during winter without further upstream water control. Understanding and managing the combined effects of LULC changes and climate change on the hydrological dynamics of the basin is of utmost importance for sustainable water resource management in the region.

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Applicability of the CA-Markov Model in Land-use/Land cover Change Prediction for Urban Sprawling in Batticaloa Municipal Council, Sri Lanka

Cited by 14 publications

References 9 publications

Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler

Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

Impact of Climate Change and Land Use Land Cover Change on Stream Flow of the Teesta River Basin under CMIP6 Climate Scenarios

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