CA-Markov Model Research Articles

Geo-spatial analyses of meandering rivers, assessing past and future impacts on bank landforms and LULC changes

ABSTRACT Morphological investigation of rivers is difficult but is one of the vital topics for the management of highly meandering river systems. The study employs GIS to analyse 32 years of Barak River data, addressing challenges in quantifying the morphology of meandering rivers. Earth observatory data from 1990 to 2022 track channel bank shifts and Land Use/Land Cover (LULC) alterations. Although the digital shoreline analysis system (DSAS) model identified long-term bank line migration analysis of shorelines, it's use is very new for rivers, especially in terms of the meandering of rivers. Conversely, research on long-term meandering river morphology in the Barak River channel has not been conducted despite evident land-use changes. Using DSAS for bank positions and CA–Markov models for LULC , future projections until 2043 reveal ongoing Barak River bank shifts and significant LULC pattern alterations. The findings of the research were validated through RMSE, Student's t-test, chi-square, and the kappa coefficient. Forecasts indicate that rapid urbanization impacts river morphology. The methodology is crucial for managing highly meandering rivers, particularly in contexts involving vulnerable river systems.

Impacts of Land Use and Land Cover Change on Non-Point Source Pollution in the Nyabarongo River Catchment, Rwanda

The Nyabarongo river catchment in Rwanda has experienced significant changes in its land use and land cover (LULC) in recent decades, with profound implications for non-point source pollution. However, there are limited studies on non-point pollution caused by nutrient loss associated with land use and land cover changes in the catchment. This study investigates the spatiotemporal impacts of these changes on water quality considering nitrogen and phosphorus within the catchment from 2000 to 2020 and 2030 as a projection. The SWAT model was used in analysis of hydrological simulations, while the CA–Markov model was used for the future projection of LULC in 2030. The results revealed (1) the important changes in LULC in the study area, where a decrease in forestland was observed with a considerable increase in built-up land, grassland, and cropland; (2) that the R2 and NSE of the TN and TP in the runoff simulation in the catchment were all above 0.70, showing good applicability during calibration and validation periods; (3) that from 2000 to 2020 and looking to the projection in 2030, the simulated monthly average TN and TP levels have progressively increased from 15.36 to 145.71 kg/ha, 2.46 to 15.47 kg/ha, 67.2 to 158.8 kg/ha, and 9.3 to 17.43 kg/ha, respectively; and (4) that the most polluted land use types are agriculture and urban areas, due to increases in human activities as a consequence of population growth in the catchment. Understanding the patterns and drivers of these changes is critical for developing effective policies and practices for sustainable land management and protection of water resources.

Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios

Abstract Mountainous landslides are expected to worsen due to environmental changes, yet few studies have quantified their future risks. To address this gap, we conducted a comprehensive analysis of the eastern Hindukush region of Pakistan. A geospatial database was developed, and logistic regression was employed to evaluate baseline landslide susceptibility for 2020. Using the latest CMIP6 models under three Shared Socioeconomic Pathways (SSPs) and the CA-Markov model, we projected future rainfall and land use/land cover patterns for 2040, 2070, and 2100, respectively. Our results reveal significant changes in future rainfall and land use patterns, particularly in the long-term future (2070 and 2100). Future landslide susceptibility was then predicted based on these projections. By 2100, high-risk landslide areas are expected to increase substantially under all SSP scenarios, with the largest increases observed under SSP5-8.5 (56.52%), SSP2-4.5 (53.55%), and SSP1-2.6 (22.45%). By 2070, high-risk areas will rise by 43.08% (SSP1-2.6), 40.88% (SSP2-4.5), and 12.60% (SSP5-8.5). However, by 2040, the changes in high-risk areas are minimal compared to the baseline, with increases of 9.45% (SSP1-2.6), 1.69% (SSP2-4.5), and 7.63% (SSP5-8.5). These findings provide crucial insights into the relationship between environmental changes and landslide risks and support the development of climate risk mitigation, land use planning, and disaster management strategies for mountainous regions.

Impacts of Large Hydropower Projects on the Ecological Environment of Watersheds: A Case Study of Ertan Reservoir Area

Ertan Hydroelectric Power Station was the first large-scale water conservancy facility in western China, and with its completion, the ecological pattern of the reservoir area has changed dramatically; however, the changes in habitat quality before and after the completion of the reservoir have not yet been systematically monitored and evaluated. In this study, we analyzed the spatial and temporal characteristics of the remote sensing ecological index (RSEI), used CA-Markov model and GeoDetector, evaluated the ecological environment of the Ertan Reservoir area in each five-year period from 1995 to 2020, simulated the ecological quality of the Ertan Reservoir area in 2025, and revealed the nine drivers and their interactions affecting ecological quality of the watershed from a geologic point of view, and finally put forward reasonable planning and targeted protection suggestions. Finally, rational planning and targeted protection recommendations were proposed. The main results were as follows: (1) After the completion of the Ertan Hydropower Station, the spatial distribution of the RSEI in the reservoir area varied significantly, with a trend of “rising-declining-rising” in time, and the area share of “good” area increased the most, by 19.83%. (2) The degree of grading of each driving factor had different degrees of influence on the size of the RSEI value, and its interaction enhanced the spatial differentiation of the RSEI. (3) The average value of the RSEI is 0.66 in 2025, and the ecological environment quality will show a steady improvement in five years. The results of the study can provide a reference for constructing RSEI indicators for large hydropower facilities.

Comparison of various models for multi-scenario simulation of land use/land cover to predict ecosystem service value: A case study of Harbin-Changchun Urban Agglomeration, China

Understanding the intricate changes in land use and land cover (LULC) transformations, as well as accurately quantifying the ecosystem services value (ESV), holds paramount importance in achieving sustainable development goals. However, previous studies have notably neglected conducting empirical evaluations across various LULC prediction models under identical conditions within the same geographical region. Additionally, the majority of relevant studies primarily concentrate on local scales. In this study, we used CA-Markov, Future Land Use Simulation (FLUS) and Patch-generating Land Use Simulation (PLUS) models to simulate the dynamics of LULC, respectively. Subsequently, we successfully projected the future LULC patterns and corresponding ESV within the Harbin-Changchun Urban Agglomeration (HCUA), China. During the period spanning from 2000 to 2020, a persistent reduction of 2067 km2 in farmland was witnessed within the HCUA, while an accompanying expansion of 4081 km2 in built-up land occurred concurrently. The ESV in HCUA experienced a fluctuating trend. There was an initial decline of 7.443 × 109 yuan during the first decade, which was subsequently followed by an increase of 4.615 × 109 yuan during the latter decade. PLUS, FLUS, and CA-Markov models can all simulate the LULC of HCUA, but in decreasing order of accuracy and ease of use, with OA values of 0.8987, 0.8944 and 0.8651, and Kappa values of 0.8217, 0.8150 and 0.7860, respectively. The PLUS model was chosen to predict the future LULC. Then four development scenarios were set. Our optimization results indicate that it is advisable to restrict the area of built-up land within 17000 km2 in 2030. Moreover, under the economic and ecological balance (EEB) scenario, both ESV and economic benefits are projected to increase compared to the business as usual (BAU) scenario. This study offers valuable insights and serves as a significant reference for future research endeavors focused on optimizing land use.

The Effect of Climate Change and Future Land Use Using the CA-Markov Model on the Streamflow of the Talar River in Mazandaran Province

The Effect of Climate Change and Future Land Use Using the CA-Markov Model on the Streamflow of the Talar River in Mazandaran Province

Government Policy Influence on Land Use and Land Cover Changes: A 30-Year Analysis

This study investigated land use and land cover (LULC) patterns and changes in the Bandon Bay area of Thailand from 1991 to 2021 using satellite imagery, the first comprehensive effort to assess historical LULC trends over the past 30 years and forecast future LULC scenarios using the CA-Markov model for 2031, 2041, and 2051. Results showed the predominant LULC during 1991-2001 was the abandoned paddy fields, and during 2006-2021 was the oil palm plantations. During 1991-2001, the abandoned paddy fields changed significantly, with a net gain of 59.28 km2. From 2001-2011 and 2011-2021, the oil palm plantations experienced the most crucial change, with a net gain of 292.94 km2 and 70.06 km2. In 2031, 2041, and 2051, the LULC was predicted to be oil palms, shrimp farms, mangroves, and urban and built-up lands. The LULC changes were consistent with the government policies implemented and indicated government policy as a driving force in LULC dynamics on Bandon Bay area forestry, aquaculture, and agriculture, particularly on oil palm cultivation. Government management and regulation on land use is crucial for reducing the expansion of agricultural areas, especially oil palm plantations and aquaculture areas, to mitigate negative impacts on the Bandon Bay ecosystem. Doi: 10.28991/ESJ-2024-08-05-06 Full Text: PDF

Landscape pattern prediction method based on ANN-CA-Markov coupling model

The simulation and prediction of landscape patterns can provide a reference for the formulation of ecological space security policies and management and control measures. Therefore, it is necessary to use objective and accurate scientific methods to make accurate simulation predictions. In this study, the quantitative prediction of the Markov model is combined with the advantages of the space-time simulation of the CA model, and the artificial neural network (ANN) module is used for calculation and coupling correction. The Kappa coefficient and the FoM (figure of merit) index are used to test the simulation accuracy. Based on this process, the changes of the landscape pattern in Mudanjiang City at three time points in 2000, 2010 and 2020 were analyzed, and the simulation prediction was made based on this. The experimental results show that the CA-Markov model using the ANN module for coupling correction can be tested for accuracy in the study area, and its Kappa coefficient is 0.834 and the FoM index is 0.001. The model meets the requirements of the landscape pattern for the accuracy of simulation and prediction, and the prediction and analysis of the study area is carried out, and the accurate prediction results are obtained.The results of the study were: Looking at the change in landscape pattern over the 20 years from 2000 to 2020, significant changes occurred in the areas of arable land and artificial surface. The total area of arable land decreased by nearly 130 km2, while the area of artificial surface (i.e., built-up land) increased by about 167 km2. The forest area initially decreased and then increased, with an overall increase of approximately 56 km2, while the water and grassland areas first increased and then decreased. In terms of spatial distribution, the changes in agricultural and forest land are mainly concentrated in the central area where urban development is intensive and in the northern forest area. The results of this study are quite important for policy formulation, according to which it was found that human activities, especially urbanization, have led to a decrease in the area of forested land, and therefore the implementation of the natural forest protection policy can lead to a sustained increase in the area of forested land.

Impact of urban spatial dynamics and blue-green infrastructure on urban heat islands: A case study of Guangzhou using Local Climate Zones and predictive modeling

Amidst mounting concerns regarding health risks associated with excessive carbon emissions, attention has turned to the heat mitigation ability of blue-green infrastructure (BGI). BGI offers heat mitigation through transpiration cooling, effectively countering the urban heat island (UHI) effect while facilitating energy conservation and carbon reduction. This study adopted the InVEST model to assess the spatial pattern of UHI and heat mitigation service based on the Local Climate Zone (LCZ) classification, along with quantifying the energy savings in Guangzhou for 2019. The results revealed that the UHI effect exhibited greater intensity in the southwest built-up area. Compact Lowrise (LCZ-3) and Heavy Industry (LCZ-10) areas demonstrated higher UHI intensity, while Water (LCZ-G) and Dense Trees (LCZ-A) areas showcased the highest heat mitigation index (HMI) and optimal cooling capacities. Three urban development scenarios were devised to simulate the UHI distribution, heat mitigation capacity, and energy savings by 2025, using the CA-Markov model. In comparison to the Baseline scenario, the Environmental Protection scenario showed a modest 3.67 % decrease in the built-up area, but a noteworthy increase in HMI and energy saving, by 21.2 % and 6.6 % respectively. This scenario can serve as a reference pathway for urban energy conservation and sustainable development.

Land/use land /cover dynamics and future scenario of Mayurakshi river basin by random forest and CA–Markov model

Land/use land /cover dynamics and future scenario of Mayurakshi river basin by random forest and CA–Markov model

Forecasting Wetland Transformation to Dust Source by Employing CA-Markov Model and Remote Sensing: A Case Study of Shadgan International Wetland

Forecasting Wetland Transformation to Dust Source by Employing CA-Markov Model and Remote Sensing: A Case Study of Shadgan International Wetland

Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.

Assessing the sustainability of small hydropower sites in the Citarum Watershed, Indonesia employing CA-Markov and SWAT models

ABSTRACT This study investigates the sustainability of potential small hydropower sites within the Citarum Watershed in West Java, Indonesia. The Citarum River, with a catchment area of 6,090 km2, plays a crucial role in regional water supply and hydroelectric power generation. However, environmental challenges such as deforestation, land use changes, and sedimentation pose significant risks. We employed the CA-Markov model integrated with IDRISI TerrSet software and the Soil and Water Assessment Tool to predict future land use changes and assess water supply, soil erosion, and sedimentation impacts for 2030 and 2040. The analysis revealed diverse trends in water yield across different catchment areas, with some regions showing increased water availability and others facing declines. High erosion and sedimentation rates were identified as critical issues affecting hydropower efficiency. The study highlights the need for comprehensive watershed management strategies, including reforestation, sustainable land management practices, and sediment management, to enhance the sustainability of small hydropower projects. Our findings underscore the importance of integrating environmental considerations into hydropower development to ensure the ecological integrity of the Citarum Watershed.

Evaluation of Urban Resilience and Its Influencing Factors: A Case Study of the Yichang–Jingzhou–Jingmen–Enshi Urban Agglomeration in China

With the increasing frequency of various uncertainties and disturbances faced by urban systems, urban resilience is one of the vital components of the sustainability of modern cities. An indicator system is constructed to measure the resilience levels of the Yichang–Jingzhou–Jingmen–Enshi (YJJE) urban agglomeration during 2010–2023 based on four domains—economy, ecology, society, and infrastructure. This paper analyzes the spatiotemporal differentiation of resilience in YJJE in conjunction with the entropy weight method, Getis–Ord Gi* model, and robustness testing. Then, the factor contribution model is used to discern key driving elements of urban resilience. Finally, the CA-Markov model is implemented to predict urban resilience in 2030. The results reveal that the values of resilience in YJJE increase at a rate of 3.25%/a and continue to rise, with the differences among cities narrowing over the examined period. Furthermore, the urban resilience exhibits a significant spatially heterogeneity distribution, with Xiling, Wujiagang, Xiaoting, Yidu, Zhijiang, Dianjun, Dangyang, Yuan’an, Yiling, and Duodao being the high-value agglomerations of urban resilience, and Hefeng, Jianli, Shishou, and Wufeng being the low-value agglomerations of urban resilience. The marked heterogeneity of resilience in the YJJE urban agglomeration reflects the disparity in economic progress across the study area. The total amount of urban social retail, financial expenditure per capita, GDP per capita, park green space area, urban disposable income per capita, and number of buses per 10,000 people surface as the key influencing factors in relation to urban resilience. Finally, the levels of resilience among cities within YJJE will reach the medium level or higher than medium level in 2030. Xiling, Wujiagang, Xiaoting, Zhijiang, Dianjun, Dangyang, and Yuan’an will remain significant hot spots of urban resilience, while Jianli will remain a significant cold spot. In a nutshell, this paper can provide scientific references and policy recommendations for policymakers, urban planners, and researchers on the aspects of urban resilience and sustainable city.

Driving forces and prediction of urban open spaces morphology: The case of Shanghai, China using geodetector and CA-Markov model

Urban open spaces offer both environmental and social benefits. However, comprehensive studies that integrate both quantitative and qualitative evaluations of the factors driving change in these spaces and their long-term predictions are lacking. Most existing studies concentrate on land-use development rather than conducting empirical research specific to urban open spaces in Shanghai. This study addresses this gap by employing a geographic detector (geodetector) to analyze the influence of various driving factors on open-space changes. These factors were then used as weight values in a multicriteria CA-Markov model to simulate and predict change in Shanghai's urban open spaces by 2050. The advantage of analyzing driving forces lies in their ability to capture the multifactor synergy influencing change in urban open spaces, aligning with the aim of this study to quantitatively evaluate the interaction between natural, climatic, and socioeconomic factors. Additionally, semi-structured interviews were conducted with 10 policymakers and planners to assess the reliability of the quantitative predictions. The results indicate that socioeconomic factors are the primary drivers of change in urban open spaces. Specifically, the interaction between the normalized difference vegetation index (NDVI) and population density (PD) emerged as the most influential variables. For prediction outcomes, the unconstrained scenario predicts a decrease in open-space area from 5610.94 km2 in 2020 to 5124.36 km2 in 2050. The planning intervention scenario anticipates minimal changes in Shanghai's total urban open-space area with almost no floating changes. However, the economic development scenario predicts a rapid decline in open spaces. Experts and planners evaluated these three scenarios and confirmed the reliability and accuracy of the prediction models. The methods and findings of this study can support zoning planning for urban open-space systems in other cities and regions.

Exploring the predictive ability of the CA–Markov model for urban functional area in Nanjing old city

With advancements in sustainable urban development, research on urban functional areas has garnered significant attention. In recent years, Point-of-Interest, with their large volume of information and ease of acquisition, have been widely applied in research on urban functional domains. However, scholars currently focus on the identification of urban functional areas, usually relying on data from a single period, whereas research on the prediction of functional areas has not yet been well validated. Therefore, in this study, we propose a new method based on several years of POI data to predict urban functional areas. Taking Nanjing City, Jiangsu Province, as an example, we first identified the functional area distribution of the old city of Nanjing over several years using POI data and then designed multiple sets of experiments to explore the CA–Markov model’s ability to predict functional areas from various aspects, including model overall accuracy, robustness, and comparison analysis between predictions and actual situations. The results show that (1) for mixed or single functional areas, the model’s predictions over several years tend to be stable, and the accuracy of the predictions over many years indicates the robustness of the model in predicting urban functional areas. (2) For mixed functional areas in cities, model predictions largely rely on the distribution of the base years used for prediction, leading to inaccurate results; thus, it is still not applicable for simulating and predicting mixed functional areas. (3) For single functional areas in cities or primary functions within an area, the model’s predicted degree of change was close to the actual degree of change, making the results referable.

The impact of land use change on carbon storage and multi-scenario prediction in Hainan Island using InVEST and CA-Markov models

The impact of land use change on carbon storage and multi-scenario prediction in Hainan Island using InVEST and CA-Markov models

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

Wetland Distribution Prediction Based on CA–Markov Model under Current Land Use and Protection Policy in Sanjiang Plain

The conflict between grain production and wetland resource protection in plain wetland is prominent. Understanding the future impacts of current land use policies on wetlands is the key to rationally evaluating and adjusting these policies. Therefore, the objective of the research was to predict the potential distribution of Sanjiang plain wetland under the current land use and protection policy using remote sensing images and CA Markov models. Methodologically, Landsat TM remote sensing images of the Sanjiang Plain (2010–2020) were used to extract wetland distribution based on object-oriented methods, and the characteristics and patterns of wetland change caused by the land use and protection policies during this period were analyzed. A CA–Markov model was used to predict the potential distribution of Sanjiang Plain wetland in 2030, 2040, 2050, and 2060. Then, we summarized the advantages and disadvantages of current land use policies and put forward adjustment measures. The results indicate that during 2010 and 2020, the wetland area of Sanjiang Plain decreased by 22.34%. The conversion ratio of wetland to non-wetland type (mainly farmland) in the first half and the second half of the decade was 46.41% and 15.31%, respectively, and the decrease in wetland showed an obvious slowing trend. The spatial distribution prediction in future showed that the wetland area will continue to decline in 2030, and the decline will basically stop in 2040. Finally, the proportion of wetland area will remain at 8.68% of the total area of Sanjiang Plain, with that of some counties and cities less than 5%. It is concluded that, although the current land use policies in Sanjiang Plain can effectively slow down the wetland area shrinking and stabilize the spatial pattern, a very low proportion of wetland area in some areas will make it difficult for the wetland ecosystem to exert ecological functions and ensure regional ecological security. The wetland conservation managers should adjust the current land use policies according to relevant requirements of farmland protection policies and restore the areal proportion and spatial pattern of wetland in order to help with regional sustainable development.

Prediction of land use around urban metro stations using the CA-Markov model

ABSTRACT Analyzing how rail transit stations affect the land use changes around them in space and time is important for forecasting the future interaction between new stations and land use and for adjusting and optimizing planning schemes. Based on the design concept of station-city integration, this paper uses Zhongshanba Station of Guangzhou Metro as a case study. It examines the land use evolution around the station and predicts the future land use changes around the double-track transfer station of Line 5 and Line 11, using the POI kernel density and the passenger flow heat map of the existing station in the old city. The results show that the POI and passenger flow heat map are innovative driving variables that can increase the simulation accuracy of the CA-Markov model to 91.86%. The prediction results also show that public management-service land and residential land will increase while industrial land will decrease. Although garden land will expand, the surrounding ecological area is still inadequate. Finally, this paper provides some strategies for optimizing subway stations and planning urban land use.