Cellular Automata Markov Chain Research Articles

Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City, Indonesia

The phenomenon of urban growth has become an important issue that affects the land use system and land cover in a region for several reasons, such as population growth and the economy. This phenomenon has also become one of the main environmental issues lately because it has devastated urban ecosystems. Ternate Tengah District has the highest population growth rate in Ternate City and has experienced extensive urban development due to several reasons, such as the pace of urbanization, economic growth, and population. Urbanization accelerates the demand to land for living. As a result, there will be gaps or disparities between land needs and available land, a decline in environmental carrying capacity, and potential environmental harm in the future. Spatial modeling of future land covers is needed to provide data on policy-making. GIS and remote sensing methods have been widely introduced, but the most effective one is CA-Markov. This model has been used in various areas worldwide, but its application to predicting land use change in the populous city of a small island under threat of volcanic hazards like Ternate is limited. This study aims to evaluate and forecast the land-use changes brought on by urbanization in Ternate City's Central Ternate District. We used a cellular automata-Markov chain to examine and forecast land cover changes in 2002, 2012, 2022, and 2032. The findings indicate that residential area development will increase along with population expansion and land demand. The results of this study can support the policy-making related to the future arrangement and utilization of space in The Central Ternate District.

Unplanned urban built-up growth creates problem in human adaptability: Evidence from a growing up city in eastern Himalayan foothills

The unprecedented and unplanned urban growth in Indian cities is a great threat to the sustainable urban development and urban resilience. Therefore, the present study emphasized in identifying rapid urban growth, future resilience, and human adaptability in Siliguri Municipal Corporation (SMC) area. For this purpose, the land use land cover (LULC) map has been prepared for 2001, 2011 and 2021 and then cellular automata-Markov chain (CA-MC) with four suitable fuzzy factors have been incorporated to simulate the prediction of built-up areas in 2031. Finally, future human adaptation susceptibility zone map has been prepared using the prediction-adaptation-resilience (PAR) approach. The built-up area has been increased by 10.25 km2 in last 20 years while other natural landscape has been reduced by the encroachment of urban pavement surface towards peripheral regions. The ward wise human adaptability zone indicates that total 14 municipal wards will be in most vulnerable situation within 2031. Consequently, water logging, sewage issues, congestions, environmental issues, solid waste issues will be the most challenging issue. So, the outcome of this study may be useful for the policy makers to take proper planning for built-up area development and give attention to increase the green space in the down town region.

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data.

Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on land surface temperature (LST) of Lahore as well as land use and land cover (LULC) with a cellular automata Markov chain (CA-Markov chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long-term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) to 720.31 (38.71%) km2. Meanwhile, barren land, water, and vegetation area was decreased from 728.63 (39.16%) to 544.83 (29.28%) km2, from 64.85 (3.49%) to 34.78 (1.87%) km2, and from 724.53 (38.94%) to 560.63 (30.13%) km2, respectively. In addition, urban index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R2 = 0.87 with respect to retrieved LST. According to CA-Markov chain analysis, we can predict the growth of built-up area from 830.22 to 955.53 km2 between 2032 and 2046, based on the development from 1990 to 2018. As urban index as the predictor anticipated that the LST 20-23°C and 24-27°C, regions would all decline in coverage from 5.30 to 4.79% and 15.79 to 13.77% in 2032 and 2046, while the temperature 36-39°C regions would all grow in coverage from 15.60 to 17.21% of the city. Our results indicate severe conditions, and the authorities should consider some strategies to mitigate this problem. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.

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.

Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change in Finima Nature Park Bonny Island, Nigeria

Land Use Land Cover (LULC) is naturally dynamic, thus change is inevitable. However, its changes have greatly increased to a frightening proportion as a result of high rate of anthropogenic and natural processes. Consequently, land use and land cover classes of Finima Nature Park were classified and changes were observed within the last 33 years (1987 - 2021) and predicted for the next 33 years (2021 – 2054).
 Remote sensing and Geographic Information System (GIS) were used to achieve the goal. Coordinate points were collected from the various land use and land cover classes as a reference data for accuracy assessment of the classification. Landsat imageries of 1987, 1999, 2010 and 2021 were acquired from The United States Geological Survey (USGS). The imageries were pre-processed, processed and classified into various LULC classes using Maximum Likelihood Classification in Idrisi and ArcGis10.5. Confusion matrix and Cellular Automata (CA) Markov Chain algorithm were used for accuracy assessment and prediction of LULC respectively.
 Results showed that dense vegetation, sparse vegetation, bare land, and water body were the main LULC class in 1987 and 1999, while dense vegetation, sparse vegetation, bare land, water body and built up were observed in the years 2010, 2021 and 2054. The Kappa Coefficient values were 93%, 81%, 83% and 90% for 1987, 1999, 2010 and 2021 respectively; an indication of strong accuracy of the classification. Generally, there were changes in land use and land cover within the study periods. However, changes were mostly observed in the areas of water body and bare lands closer to the sea coast. Hence, the sea was implicated as the major driver of land use and land cover change in the park. The slight decrease in dense vegetation and sparse vegetation from 1999 to 2021, and 2021 to 2054 underscores the importance and benefit of conservation.

Responses of water balance component to land use/land cover and climate change using geospatial and hydrologic modeling in the Gidabo watershed, Ethiopia

The impact of climate change (CC) and land-use/land-cover (LULC) change on water resources poses a major threat globally. This study investigates the separate and combined responses of CC and LULC change on water balance in Gidabo watershed, Ethiopia, using Soil and Water Assessment Tool (SWAT), Cellular Automata-Markov Chain, and regional climate models. CC projection under RCP 4.5 and hydrological modeling show a significant decline in mean annual surface runoff (Q), baseflow (BF), water yield (WY), percolation (PC), and evapotranspiration (ET) during mid-century (2027–2056) as well as late-century (2061–2090) compared to the baseline period (1988–2018) data, largely due to decline in rainfall. Likewise, under RCP 8.5 scenario, the modeling results show a decrease in Q, BF, WY, PC, and ET during these periods. The LULC change (often associated with an expansion in cultivated and urban areas and a reduction in evergreen forest and grassland) alone leads to a positive synergy with an increase in mean annual Q and WY and a negative synergy with a decrease in BF, PC and ET during the period 2018 to 2075. The combined effects of climate and LULC changes have also shown a decline in Q, BF, WY, PC and ET. Overall, the climate change impacts significantly the future Q and WY. However, the combined effect of climate and LULC changes on BF, PC, and ET is more prominent than their separate impacts and it underlines the significance of this study.

Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling

Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling

Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data

Accurately simulating and predicting the urban expansion process, especially in expeditious urbanization areas, is an important aspect of managing limited land resources and adjusting flawed land use policies. This research was conducted on the basis of a high-temporal-resolution land use dataset to precisely model urban expansion in a rapidly developing zone by integrating the Artificial Neural Network (ANN), cellular automata (CA), and Markov Chain (MC). An urban suitability index (USI) map was created using ANN and fed to CA–MC to identify possible changed-to-urban cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented as simulation models for comparison. Due to its ability to capture more temporal information, LSTM outperformed MLP in modeling urban expansion dynamics over a short temporal interval. The simulated results were validated by (fuzzy) kappa simulation and the results revealed that the combination of ANN and CA–MC can precisely model the urban development locations due to its strength in revealing the nonlinear relationship between the expansion process and its drivers. The same model was applied to southern Auckland, and the compared results show that the most simulated variance is caused by the land use policies implemented by different types of governments.

Modeling of Cellular Automata Markov Chain for Predicting the Carrying Capacity of Ambon City

Ambon is a city with the highest economic and population growth in Maluku Province, which makes built-up land have high and rapid growth so that it can reduce the carrying capacity of the city's land. This can lead to an imbalance between population and available housing and may result in higher population pressure on available areas. Prediction of spatial modeling is needed as a preventive measure to prevent excessive land cover changes in the future. This study aims to analyze the carrying capacity of residential areas and spatial modeling of land cover changes in Ambon City in 2010, 2015, and 2020 using Cellular Automata Markov Chain (CAMC) and identify settlement patterns based on population density in 2031 in Ambon city. The results of the Cellular Automata Markov Chain (CAMC) analysis show that the residential area in 2031 has increased to 7,910.03 ha and the development of residential areas in 2031 in Ambon City is centered on Sirimau District.

Modeling of carbon sequestration with land use and land cover in the northeastern part of the Nile Delta, Egypt

The world is currently concerned about the phenomenon of climate change and global warming because of the natural disasters that threaten the lives of living organisms on the surface of the earth. Despite this, there is a dearth of studies dealing with reducing carbon emissions in Egypt. We needed to cover this defect in an important area of Egypt, which is the eastern delta region including Manzala Lake. Land use and land cover (LU/LC) represents a vital role in carbon regulation in coastal areas. Our study uses three Landsat images in 2001, 2013, and 2021 to estimate the amount of carbon storage and sequestration and used Land Change Modeler (LCM) with Cellular Automata Markov Chain (CA-MC) model to predict land cover changes in 2031. Results show that in the period between 2013 and 2021, about half of the floating plants have been destroyed and removed. Our results demonstrate that the increased area for agricultural lands from 2013 to 2021 (2306 Mg C/km2) leads to an increased amount of carbon stoke but the decreased area for floating plants (-4508 Mg C/km2) leads to a decrease in carbon storage. Results showed that a remarkable decrease in carbon storage in the future was attributed to anthropogenic activities in the study area and also as a result of the un exploiting of environmental resources. Finally, the total cost of carbon was 1323.4, 1437.9, 1184.8, and 1296.5 $US/ha for 2001, 2013, 2021, and 2032, respectively. Results showed that the minimum cost for 2021 that a noticeable shortage due to the removal of vegetation and an increase the area of lake water. These conversions have to some extent affected the capacity of coastal habitats to sequester and store CO2. For carbon storage, we recommend afforestation of desert areas. The current design for carbon storage and sequestration is a good guide for government and decision makers for suitable sustainable development, climatic regulation, and carbon budget.

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.

Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

Wetland change prediction of Ogun-River Basin, Nigeria: Application of cellular automata Markov and remote sensing techniques

Wetland changes are very serious ecosystem problems, which stem from unrestrained human and environmental actions. Despite significant attention in environmental management research, the means to tackle wetland changes are still gaining momentum in science and research within developing countries. With the increasing availability of remote sensing data and flexible solutions, finding the right solution to wetland changes becomes a key research and policy agenda. Herein, we assessed the wetland change prediction of Ogun River (OR) basin, Nigeria, during 1999, 2009, and 2019 by analyzing land cover change (LCC) images using cellular automata-Markov (CA-Markov) chain and remote sensing (RS) techniques. The acquired shuttle radar topographic mapper and Landsat remotely sensed data were applied to create thematic-maps of the normalized difference vegetation index (NDVI), normalized difference salinity index (NDSI), and digital elevation model (DEM) of the study area. The CA-Markov required the supervised classification of the land cover maps of 1999, 2009, and 2019. The results of the elevation, NDVI, and NDSI revealed the qualitative differences in the study area. The LCC analysis indicated that farmlands and built-up areas increased by 54.56 and 33.21 %, respectively. However, waterbodies, wetlands, and vegetation decreased by 0.42, 3.53, and 8.28 % respectively. These findings agree with the CA-Markov with an accuracy exceeding 70 %, and, thus predict the wetland changes for the year 2030. The major attributions to wetland cover variations in the study area are built-up encroachment with extensive, spontaneous, and uncontrolled agricultural activities. This study, through its findings, provides relevant guidelines and information on wetland changes required by stakeholders for environmental policy, planning, and sustainability.

Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency

Land cover changes based on cellular automata for surface temperature in Semarang Regency has increased significantly due to the continuous rise in its population. Therefore, this study aims to identify, analyze and predict multitemporal land cover changes and surface temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 and 8 based on supervised classification, while Land Surface Temperature (LST) was calculated from its thermal bands. The collected data were analyzed for accuracy through observation, while Cellular Automata - Markov Chain was used to predict the associated changes in 2028. The result showed that there are 4 land cover maps with 5-year intervals from 2003 to 2018 at an accuracy of more than 85%. Furthermore, the existing land covers were dominated by forest with decreasing trend, while the built-up area continuously increased. The existing Land surface temperature range from 20.6°C to 36.6°C, at an average of 28.2°C and a yearly increase of 0.07°C. The temperature changes are positively correlated with the occurrence of land conversion. Land cover predictions for 2028 show similar forest dominance, with a 23,4% built-up area at a surface temperature of 28.9°C.
 Keywords: Land cover change; Cellular Automata-Markov Chain; Land Surface Temperature
 Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember
 
 
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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

Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran)

Our study uses regional-scale maps to quantify carbon storage and sequestration from different land use types to evaluate the effects of future land use scenarios. We developed an integrated modeling approach to assess the spatiotemporal impacts of land use/cover change (LUCC) on the provision and value of the carbon storage and sequestration during the historical period (2000–2019) and predicted scenarios (2019–2046) in the Jiroft plain, Iran. We integrated several analytic tools for our analysis, which was comprised of Google Earth Engine (GEE), Cellular Automata Markov Chain (CA-MC) model, Intensity Analysis (IAA), and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Our results demonstrate that: (1) agriculture and urban expansion led to a considerable decrease in carbon storage, mainly due to rapid deforestation from 2000–2019; (2) if the historical trend continues under the business as usual (BAU) scenario, it will lead to considerable social costs due to the loss of stored carbon in the plain (2,624,113 Mg) with an annual average sequestration loss of −475,547 Mg; (3) the downward carbon sequestration trend could potentially be reversed by employing the environmentally sound planning (ESP) scenario that is estimated to save 3,705,491 Mg in carbon storage, with annual average sequestration gain of + 605,830 Mg. The design scenarios provide a useful guide for policymakers and local governments to help understand the potential outcomes of the various development strategies, which will ultimately lead to more effective ecosystem management.