Cellular Automata Markov Chain Model Research Articles

Prediction of land cover change in the Belawan watershed using the cellular automata-markov chain model

Belawan Watershed is one of the watersheds that cross Medan City, the capital of North Sumatra province. The spatial distribution of land cover affects the hydrological conditions in this watershed. The purpose of this study is to predict land cover in 2032 so that it can be used as information to mitigate changes in forest land cover. The method used is Cellular Automata-Markov Chain modeling with five change parameters (driving factors): distance from the river, distance from the road, altitude, population density, and slope. Land cover changes in the Belawan watershed used for this modeling include land cover in 2012, 2017, and 2022 and predicted land cover in 2032. The results showed that the predicted land cover is the water body 998.409 ha (1.36%), dryland forest 970.399 ha (1.32%), mangrove forest 3,351.496 ha (4.58%), settlement 11,600.105 ha (15.85%), plantation 38,787.924 ha (53.01%), agriculture 8,674.632 ha (11.85%), rice fields 5,568.183 ha (7.6%) and ponds 3,218.175 ha (4.3%). The area of forests and mangroves tends to continue to decline, so mitigation efforts are needed to reduce the reduction rate in forest and mangrove land cover.

Simulating urban expansion scenario by Logistic regression (LR) with CA-Markov chain Model for Bhanjanagar town (Odisha, India)

Simulating urban expansion scenario by Logistic regression (LR) with CA-Markov chain Model for Bhanjanagar town (Odisha, India)

Spatio-temporal analysis of land use land cover change and its impact on land surface temperature of Sialkot City, Pakistan

The dynamic interplay between urbanization and its impacts on climate is a subject of recent concern, particularly in rapidly urbanizing cities of Pakistan. This research investigated the spatio-temporal effects of urban growth in terms of Land Use Land Cover changes on the thermal environment (Land Surface Temperature) of the Sialkot city, Pakistan using satellite data spanning four distinct time periods (1989, 2000, 2009 and 2020) and predicted changes for year 2030 by employing Cellular Automata Markov Chain Model. Satellite imagery (Landsat 5, 7 and 8) was processed, and maximum likelihood supervised classification was done to generate LULC maps for each of the aforementioned years. In addition to LULC classification, thermal bands of satellite data (for summer and winter) were processed to compute Land Surface Temperature (LST) of the city. The prediction of LULC changes and LST was done for year 2030 using Cellular Automata Markov Chain Model. The accuracy of classified and prediction maps was checked using Kappa Index. The LULC analysis revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020. Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and − 1.1% (vegetation). Furthermore, a declining trend in the barren land and water bodies was also observed over time. These LULC changes were found affecting the LST of study area. The transformation of vegetation cover into built-up area resulted in an increase in LST over time. A notable rise of 4.5 °C (summer) and 5.7 °C (winter) in the mean LST of Sialkot was observed during 1989 to 2020 and further increases are anticipated in year 2030. This study calls for attention of the policy makers to reduce human impact on the local climate of the city. The study will also help city developers in analyzing the urban population growth trend, finding suitable location to built new infrastructure by governmental authorities and how the rising temperature can affect energy demand and agriculture production of the city in future.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model.

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

Assessment of future prediction of urban growth and climate change in district Multan, Pakistan using CA-Markov method

This research utilized remote sensing (RS) technology to analyze and monitor land use land cover (LULC) and climate change. This work mainly involves three components: (i) investigating the historical trends of LULC and land surface temperature (LST) using Landsat data for 1990, 2005, and 2020. (ii)Evaluate the impact of climate change on urban temperature for increased heat stress in the Multan region and (iii) Future predictions of LULC and LST for 2035 and 2050 by the CA-Markov Chain model. Supervised classification techniques were employed to classify the images into various LULC classes. Historical trends of LULC revealed that over the study period, the built-up area in Multan has increased by almost 30,435 Ha (8.34%), which would continue to reach40003 ha (10.96%) from 1990 to 2020 at the cost of vegetation loss, which is countlessly decreasing and will get from 283,145 Ha (−77.57%) in 2035 to264443 Ha (−71.4%) till 2050. Further, the findings about LST revealed that over the region, the average LST values have increased from 1.2 °C over the last two decades and will additionally include 1.8 °C until 2050.This study underscores the importance of RS technology in understanding past LULC changes and predicting future land transformations and temperature variations. The results contribute valuable insights for policymakers, land managers, and urban planners to make informed decisions for sustainable LULC and climate adaptation strategies in the face of growing urbanization and climate change challenges.

An Assessment of Landscape and Land Use/Cover Change and Its Implications for Sustainable Landscape Management in the Chittagong Hill Tracts, Bangladesh

Human-caused environmental change has profoundly impacted resource management and land use patterns in Bangladesh’s Chittagong Hill Tracts. This study used multi-temporal Landsat images from 1998, 2008, and 2018 to analyze land use and land cover changes, particularly those associated with forest cover changes, in Bangladesh’s Chittagong Hill Tracts. Using object-based image classification, Landsat images from 1998, 2008, and 2018 were separated into four categories based on their dominant land use and land cover features: forest, grassland, water bodies, and bare land. Post-classification comparison was used to assess the degree and frequency of change, and this method was further developed to evaluate the balance, fluctuation, and adaptation of forests. In addition, the spatial structure of land cover and temporal trajectories related to changes in forest cover were studied. The CA–Markov chain model was also used to anticipate the 2048 LULC map. The image classification of the years 1998, 2008, and 2018 showed that the overall accuracy was 89.65%, 84.44%, and 86.26%; producer accuracy was 90.00%, 68.75%, and 72.22%; and the Kappa coefficient was 85.68, 82.84, and 76.36, respectively. The results showed that between 1998 and 2018, forest cover increased by 58.03%, transforming grassland to forest; grassland increased by 29.50%, converting bare land to grassland; and forest conversion to grassland was 13.34%. In addition, the result of the landscape metric revealed that during the whole study period, class level indicated a fragmentation of forest, bare land, grassland, and water in the CHT, and landscape level indicated by Shannon’s Diversity Index and Shannon’s Evenness Index showed a slight decrease in the land. Based on the CA–Markov model, forest area is predicted to expand to 9129 Km2 in 2048; however, other land uses (bare land and grassland) continue to decrease. This substantial increase in forest cover results from effective forest management based on community forestry practices and the successful execution of Bangladesh’s national forest strategy. However, as Bangladesh’s population rises, so does the country’s need for lumber/timber. Bangladesh’s government should revise its forest policy to meet the local community’s needs without endangering the forest, and policymakers must take climate change seriously. Our strategy for evaluating the critical indicators of changes in forest cover and pathways of change will aid in connecting these patterns to the dynamics of change, such as deforestation and reforestation. It would therefore serve as a framework for developing effective conservation and management plans for the Chittagong Hill regions in Bangladesh.

Application of geospatial technology for the land use/land cover change assessment and future change predictions using CA Markov chain model

Application of geospatial technology for the land use/land cover change assessment and future change predictions using CA Markov chain model

Projection of urban land surface temperature: An inter- and intra-annual modeling approach

An easily applied and efficiently exploited approach was proposed for the prediction of inter- and intra-annual land surface temperature (LST) in a city's central zone. Chengdu is chosen as the study area to demonstrate the applicability of the novel approach. Built-up areas were extracted using Landsat images in 2008, 2013, and 2019 and projected for 2030 using the cellular automata-Markov chain model, indicating that built-up areas grew nearly 109% during 2008–2019 in Chengdu and will continue to grow up to 2030. Employing the multiple linear regression, the LST of built-up areas in 2019 was predicted using the normalized difference built-up index (NDBI) and the mean LST of its surrounding pixels (LST-R3) in 2013 (R2 = 0.981). The model's intra-annual application for 2019 showed a mean LST deviation of 0.93 °C. According to the model's inter-annual application, the projected LST of the built-up areas in 2030 will increase to 36.5 ± 2.0 °C. The results can contribute to achieving a thermally comfortable urban environment by providing insights into the impact of urban expansion on the urban heat island (UHI) phenomenon under the limited availability of continuous LST data.

Model Dinamika Spasial Perubahan Tutupan Lahan dan Daya Dukung Lahan Permukiman Kota Ambon Tahun 2031

The rate of population growth that continues to increase in Ambon city with the availability of suitable land is relatively constant, resulting in inconsistencies between land requirements and available land. This study aims to analyze land cover changes in Ambon City in 2013, 2018, 2023 and predict land cover in 2031 using the Cellular Automata Markov Chain (CAMC) and the carrying capacity index of residential areas. The driving factors used in this study were elevation, slope, distance from the coastline, distance from the main road, distance from the river, distance from the center of economic activity and distance from protected areas. Based on the predictions of the Cellular Automata Markov Chain model in 2031, the built-up land cover has increased in area to 4,958.33 ha or 49,583,268 m2, with a predicted population of Ambon City in 2031 of 2,445,961 people. The calculation of the carrying capacity of residential land in 2031 in Ambon City results in the carrying capacity index of residential land of 1.27 m²/capita, meaning that if DDPm > 1 indicates the carrying capacity of residential land is high and still able to accommodate residents to live and build houses in the area. In other words, Ambon City can still accommodate an increase in settlements of 1.27 m²/capita.

Impact of urban and peri-urban growth on arable land (1976–2029) in a medium sized city of Shire Indaselassie, North Western Tigray, Ethiopia

Urban and peri-urban areas have been growing rapidly globally due to population increase and other factors. However, poor urban land use planning caused remarkable impacts on the loss of potentially arable land. This study aimed to analyze the spatio-temporal patterns of urban and peri-urban expansion and its impact on arable land in the Shire Indaselassie city, North Western Tigray. Multi-temporal and spectral Landsat images were used as an input and processed using TerrSet, ERDAS imagine 2015, and ArcGIS 10.8.1 softwares. The Cellular Automata-Markov Chain model was used to predict the future urban and peri-urban areas. Pearson correlation coefficient was applied to examine the association among the population, urban, peri-urban growth, and its impact on arable land in Stata/SE v14. The findings revealed that urban and peri-urban lands increased by 7.5 km2 (22.1%) and 1.0 km2 (2.9%), while arable land decreased by − 0.1 km2 (− 0.2%) from 1976 to 2019. It is also predicted to increase by 1.0 km2 (2.80%) and 0.9 km2 (2.70%) until 2029, while arable land is anticipated to shrink by − 1.0 km2 (− 3.0%). The relationship among population growth, urban, and peri-urban land shows positive (r = 0.942, p = 0.058; r = 0.985, p = 0.016). However, arable land has been negatively correlated with population growth, urban, and peri-urban lands (r = − 0.610, p = 0.392). The result of this research is indispensable for urban planners and decision makers to optimize urban and peri-urban expansion related information without jeopardizing the size of arable lands.

Multi-scenario simulation of land use and land cover based on shared socioeconomic pathways: The case of coastal special economic zones in China

Urban land-use change simulations without considering the sustainable planning policies, especially in special economic park highly concerned by planners, might lack the reliability and availability. Thus, this study proposes a novel planning support systems integrating the Cellular Automata Markov chain model and Shared Socioeconomic Pathways (CA-Markov-SSPs) for predicting the changing of land use and land cover (LULC) at the local and system level by using a novel machine learning-driven, multi-source spatial data modelling framework. Using multi-source satellite data of coastal special economic zones from 2000 to 2020 as a sample, calibration validation based on the kappa indicates a highest average reliability above 0.96 from 2015 to 2020, and the cultivated land and built-up land classes of LULC is the most significant changes in 2030 by using the transition matrix of probabilities, the other classes except water bodies continue to increase. And the non-sustainable development scenario can be prevented by the multiple level collaboration of socio-economic factors. This research intended to help decision makers to confine irrational urban expansion and achieve the sustainable development.

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Estimation of Water Balance for Anticipated Land Use in the Potohar Plateau of the Indus Basin Using SWAT

Land Use/Land Cover (LU/LC) change is among the dominant driving factors that directly influence water balance by transforming hydrological responses. Consequently, a thorough comprehension of its impacts is imperative for sustainable water resource planning and development, notably in developing worlds such as Pakistan, where agriculture is a major livelihood. This research intends to assess the continuing changes in LU/LC and evaluate their probable repercussions on the hydrological regime of the Potohar Plateau. The maximum likelihood classification (MLC) algorithm for land use classification of the high-resolution satellite imageries, the Cellular-Automata Markov Chain Model (CA-MCM) for the projection of LU/LC maps, and the Soil and Water Assessment Tool (SWAT) in tandem with SWAT-CUP for hydrological modeling were employed in this research. The high-resolution climatic dataset (10 × 10 km) was used in SWAT. The LU/LC analysis revealed a continual propagation of agricultural and built-up lands at the detriment of forest and barren land during the last three decades, which is anticipated to continue in the future, too. Hydrological model calibrations and validations were performed on multi-basins, and the performance was evaluated using different statistical coefficients, e.g., the coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS). The results yielded that the model performed very well and demonstrated the model’s robustness in reproducing the flow regime. The water balance study revealed that the anticipated LU/LC changes are projected to decrease the mean annual surface runoff, water yield, and streamflow due to an increase in percolation, lateral flow, sub-surface flow, and evapotranspiration. More significant variations of the water balance components were observed at the sub-basin level, owing to the heterogeneous spatial distribution of LU/LC, than at the basin level. The outcome of this study will provide pragmatic details to legislative bodies to develop land and water management ameliorative strategies to harness hydrological changes sustainably.

Future runoff assessment under climate change and land-cover alteration scenarios: a case study of the Zayandeh-Roud dam upstream watershed

Abstract In this study, hydrological responses to climate change and land-cover alteration on future runoff in the Zayandeh-Roud dam upstream watershed were assessed. In this regard, land-use maps in 1996, 2008, 2018, and 2033 were generated using Landsat time-series (TM and OLI), Support-Vector Machine (SVM), and the CA-Markov chain model, for analysing the effects of land-cover alteration on future runoff. Second, the Global Circulation Model (GCM) scenario time-series under RCP 2.6 and RCP 8.5 scenarios were downscaled to evaluate the impacts of climate change on future streamflow. Eventually, the HEC-HMS model was calibrated (1996–2018) for evaluating the impacts of climate and land-use map changes. Results showed that the percentage of the urban area and farmland in 2033 compared to 2018 were expected to grow by 0.1 and 2.39% upstream of the Eskandari station and 0.05 and 0.71% upstream of the Ghale-Shahrokh station, respectively, although the percentage of the barren area was expected to remain almost unchanged in both regions. The future stream flow of Eskandari and Ghale-Shahrokh stations in 2033 was expected to decrease by 57–63 MCM (for RCP 2.6 and RCP 8.5) and 295–403 MCM, respectively, where 68–72% and 79–86% were expected to decrease under climate change scenarios and remains are due to land-cover alteration.

Forecasted water demand using Extended Cellular Automata Markov Chain Model: case of Saida and Jezzine regions in Lebanon

Forecasted water demand using Extended Cellular Automata Markov Chain Model: case of Saida and Jezzine regions in Lebanon

Land use/land cover dynamics study and prediction in jaipur city using CA markov model integrated with road network

Land Use Land Cover (LULC) dynamics analysis is critical and should be done regularly. It draws attention to LULC developments that can be addressed before they become unmanageable disasters or circumstances. For the years 2000, 2010, and 2020, LULC change analysis was carried out in Jaipur City, Rajasthan, India. The LULC maps were created using Landsat data through a visual interpretation technique at a scale of 1:50,000. These maps were classified into vegetation, agriculture, built-up areas, barren land, and water bodies. LULC was predicted by extrapolating the current LULC change pattern. Using a Cellular Automata-Markov Chain Model (CA Markov) integrated with road network, the current LULC change trend was extrapolated and utilized to estimate the LULC map for the years 2020, 2030, 2040, and 2050. The strategy was validated by estimating LULC change for 2020 and comparing it to the actual LULC map for that year. The urban area contributed to 4. 75% in 2000 of the total area in Jaipur city. The percentage of area under urban class has increased to 9.68% in 2010 and 12.96% in 2020. The prediction based on 2000–2010 and 2010–2020 has shown an unprecedented decadal growth in the built-up area till 2050. The prediction based on the 2000–2010 period has shown a rise of 92.04% during 2020–2030, 77.13% during 2030–2040 and, 64.34% during 2040–2050. The prediction based on the 2010–2020 period has shown a rise of 102.42% during 2020–2030, 73.56% during 2030–2040 and, 54.47% during 2040–2050. This study is, therefore, calls for policy interventions to manage population and urban growth.

Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift

Modeling land use land cover (LULC) change is crucial to understand its spatiotemporal trends to protect the land resources sustainably. The appraisal of this study was to model LULC change from 1985 to 2050 owing to the business-as-usual scenario (BAU) in Gidabo River Basin (GRB) located in the Main Ethiopian Rift Valley. Different dependent and independent spatial datasets were used viz, 1985, 2003 and 2021 Landsat imagery; topography features, proximity variables, population density and evidence likelihood. Since the future projection requires the historical land use as a baseline, historical land use trends were detected using hybrid image classification procedure in ERDAS Imagine and nine major land cover classes were identified. Multi-Layer Perceptron Neural Network and Cellular Automata-Markov Chain model built-in TerrSet software were implemented to project the 2035 and 2050 LULC. The study depicts, GRB experienced significant LULC dynamics and will also be extended for the coming several years. Agriculture land, settlement and water body showed significant gains at the expense of forest, shrub and grasslands loss. Land use changes beyond land's capability played a significant role in triggering land degradation. To minimize these adverse consequences of land use change, environmentally-friendly management measures must be implemented. The outcome of this study will be helpful in providing the opportunity to develop adequate land and water resource conservation strategy plan for the future.

Predicting spatial and decadal of land use and land cover change using integrated cellular automata Markov chain model based scenarios (2019–2049) Zarriné-Rūd River Basin in Iran

Effective land use and land cover (LULC) change assessment requires tools to measure past, current, and based on them to create a future scenario. LULC changes are unavoidable in the world, particularly in developing countries. Since LULC are too dynamic and complicated without the identification of appropriate methods and approaches the future perdition will be less accurate. Therefore, the integrated Cellular Automata Markov chain (CA-Markov) model is known as a capable estimator. In this study, LULC changes in Zarriné-Rūd River Basin (ZRB) in Iran was analyzed based on different images and data extracted from satellite data in 1989 and 2019 to create the LULC scenario in 2049. The model was validated using actual and projected to 2019. The overall agreement on two extracted maps was 97.85% in 1989 and 96.55% in 2019. The more detailed analysis of validation of calibration based on the kappa showed the highest data reliability of 0.98 in 1989 and 0.95 in 2019, respectively. According to the transition matrix of probabilities, the most significant changes in the ZRB based on the past scenario (1989–2019) is in rainfed and built up land classes of LULC in 2049. Concurrently, the other classes continue to decline except irrigated agriculture and water bodies. The results obtained showed that the pasture and mountain LULC class had continued to reduce more than other classes. Furthermore, water resources and the amount of the precipitation in past and future are important to spatial and temporal expansion on LULC classes.

Assessment of Uncertainties in Modelling Land Use Change with an Integrated Cellular Automata–Markov Chain Model

Assessment of Uncertainties in Modelling Land Use Change with an Integrated Cellular Automata–Markov Chain Model

Impacts of land use/land cover and climate changes on soil erosion in Muga watershed, Upper Blue Nile basin (Abay), Ethiopia

BackgroundSoil erosion is one of the major threats in the Ethiopian highlands. In this study, soil erosion in the Muga watershed of the Upper Blue Nile Basin (Abay) under historical and future climate and land use/land cover (LULC) change was assessed. Future LULC was predicted based on LULC map of 1985, 2002, and 2017. LULC maps of the historical periods were delineated from Landsat images, and future LULC was predicted using the CA–Markov chain model. Precipitation for the future period was projected from six regional circulation models. The RUSLE model was used to estimate the current and future soil erosion rate in Muga watershed.ResultsThe average annual rate of soil erosion in the study area was increased from about 15 t ha−1 year−1 in 1985 to 19 t ha−1 year−1 in 2002, and 19.7 t ha−1 year−1 in 2017. Expansion of crop cultivation and loss of vegetation caused an increase in soil erosion. Unless proper measure is taken against the LULC changes, the rate of soil loss is expected to increase and reach about 20.7 t ha−1 year−1 in 2033. In the 2050s, soil loss is projected to increase by 9.6% and 11.3% under RCP4.5 and RCP8.5, respectively, compared with the baseline period. Thus, the soil loss rate is expected to increase under both scenarios due to the higher erosive power of the future intense rainfall. When both LULC and climate changes act together, the mean annual soil loss rate shows a rise of 13.2% and 15.7% in the future under RCP4.5 and RCP8.5, respectively, which is due to synergistic effects.ConclusionsThe results of this study can be useful for formulating proper land use planning and investments to mitigate the adverse effect of LULC on soil loss. Furthermore, climate change will exacerbate the existing soil erosion problem and would need for vigorous proper conservation policies and investments to mitigate the negative impacts of climate change on soil loss.