CA-Markov Model Research Articles

Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.

Analysis of land use and land-cover pattern to monitor dynamics of Ngorongoro world heritage site (Tanzania) using hybrid cellular automata-Markov model

Assessment of land-use and land-cover (LULC) change of any region is one of the prominent features used in environmental resource management and its overall sustainable development. This study analyzed the LULC changes of Ngorongoro Conservation Area (NCA) and its surroundings using Remote Sensing and Geographical Information System integrated with Cellular Automata-Markov model. The LULC maps for the years 1995, 2005, and 2016 were classified using unsupervised and supervised classification procedure, and projected for 2025 and 2035 under business-as-usual scenario using the CA–Markov model. The results indicated maximum gains and losses in cultivated land and woodland in the study duration, respectively. The projected LULC for the period 2025 to 2035 showed a reduction in bushland, forest, water, and woodland, but an intensification in cultivated land, grassland, bare land, and the built-up area. The natural forests with high environmental values were found to be continuously declining under the current land management trend, causing the loss in the NCA's ecological values. For sustainable management, the authorities must reach conciliation between the existing LULC patterns change and ecosystem services monitoring. A rational land use plan must be made to control the increase of cultivated land and built-up area counting a rational land use plan and ecosystem services protection guidelines. Decision makers should involve stakeholder to support improved land use management practices for balanced and sustainable ecosystem services strategies.

An integrated approach for simulation and prediction of land use and land cover changes and urban growth (Case study: Sanandaj city in Iran)

One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logistic regressioncellular automata (LR-CA), and the weight of evidence-cellular automata (WOE-CA) for modeling LULC changes was evaluated, and the results of these models were compared with the CA-Markov model. According to the results of the study, ANN-CA, LR-CA, and WOE-CA models, with an accuracy of more than 80%, are efficient and effective for modeling LULC changes and growth of urban areas.

Determination and Prediction of Flood Risk Based on Multi-Criteria Decision Analysis (Mcda) Combining with Ca-Markov Model

Determination and Prediction of Flood Risk Based on Multi-Criteria Decision Analysis (Mcda) Combining with Ca-Markov Model

基于生态系统服务功能的新疆绿洲生态安全格局识别及优化策略——以拜城县为例

识别生态保护关键区域，优化新疆县域生态空间结构，对促进绿洲区域经济与生态环境和谐发展，提升人类福祉具有重要意义。以拜城县为研究区，通过生态系统服务价值综合评定结果确定生态保护的核心"源"区，利用最小阻力模型，结合GIS空间分析技术及CA-Markov模型，识别拜城县生态安全格局，分析其发展变化趋势，探究生态空间布局调整策略，完善县域生态空间网络体系，结果表明：（1）研究期内，拜城县生态系统服务价值总量逐年上升，其中水源涵养、废物处理价值量增长最高，不同服务类型增长率差异较小。（2）分析2009、2016、2019拜城县生态安全格局特征发现，县域生态安全布局整体存在"源"地分布不均、廊道结构破碎且网络化程度低、生态脆弱区面积较大的现状特征。（3）基于2009、2016、2019年生态保护格局，同时依据生态"源"地及生态保护重要性用地的变化趋势，模拟2029年生态"源"地及其重要性等级区域分布，结合县域自然地理特征及总体发展规划，提出构筑"两带、三功能、四区、多中心、十廊道"的拜城县生态空间布局体系，为绿洲地区生态安全及区域生态文明建设提供参考。;It is important to identify the key areas of ecological protection and optimize the ecological spatial structure in Xinjiang, it means a lot to promote the harmonious development of oasis economy and ecological environment, and to enhance human welfare. Taking Baicheng County as the study area, on the basis of fully considering the level of ecosystem service function, the core ecological source areas of environmental protection were determined through the comprehensive evaluation of ecosystem service value. We also used the least resistance model, combined with the GIS spatial analysis technology and CA-Markov model, to identify the ecological security pattern, analyze its trend of development and improve the ecologically spatial network system of Baicheng County. The results showed as follows:(1) During the study period, the total value of ecosystem services in Baicheng County has increased gradually, among which the value of water conservation and waste treatment increased the most, while the growth rate of each ecosystem service type had a little difference. (2) According to the ecological security pattern of Baicheng County in 2009, 2016 and 2019, the overall environmental security layout was characterized by the uneven distribution of source areas, broken corridor structure, low network degree, and large area of ecologically fragile regions. (3) According to the ecological protection pattern in 2009, 2016 and 2019, based on the changing trend of ecological source sites and ecological protection importance sites, we simulate the distribution of ecological source sites and their importance levels in 2029. Based on the county's natural and geographical characteristics as well as the overall development plan, we also put forward the ecologically spatial layout system of Baicheng County. That is two belts, three functions, four districts, multiple centers and ten corridors, which provides a reference for the ecological security and regional ecological civilization construction in the oasis area.

Study of urbanization trend prediction in Nanchang based on CA-Markov model

In order to promote the healthy development of Nanchang city in the future and maintain the virtuous cycle of urban ecosystem, this paper uses CA-Markov model to simulate and forecast the land use of Nanchang city in 2030 and explore the urbanization process of Nanchang city. Firstly, the land use changes in Nanchang from 2000 to 2020 are described, followed by land use simulations for Nanchang in 2020 as a verification of accuracy, and land use projections for Nanchang in 2030 based on accuracy requirements. The results show that: (1) the rate of urban expansion will continue to be maintained or even gradually increase from 2020 to 2030 under the expansion model based on the period 2000-2020. (2) The main factor in the change of land use types in the built-up area of Nanchang over the past two decades is socio-economic factors, with natural factors taking second place. (3) The area of land development intensity in Nanchang will reach its peak in 2030, and the expansion of the municipal districts will gradually slowdown in favour of Nanchang and Xinjian counties, which have a more extensive land area and are close to the central urban area.

Monitoring and Simulation of Land-Use Changes in the Bosomtwe District of Ghana Based on CA-Markov Model

Monitoring and Simulation of Land-Use Changes in the Bosomtwe District of Ghana Based on CA-Markov Model

Spatial planning policy evaluation to achieve food security and sustainability in Pandeglang Regency

Pandeglang Regency is designated as the agricultural center of Banten Province. Dedicating a specific area to a protected agricultural area often faces problems of not considering the population growth and food needs. Therefore, this research aims to evaluate the spatial pattern and allocation of the agricultural area based on the comparison between spatial planning policy and land-use model on the current trend in 2031 using CA-Markov model. This research uses remotely sensed data from SPOT satellites in 2006, 2012, and 2018 as the base for the 2031 model. The result shows that the number of paddy fields in Pandeglang fluctuated from 2006 until 2031. In 2031, the paddy field is predicted to increase by around 5%. This increase hasn’t been able to keep up with the population growth, as the increase of settlement area exceeds the paddy field at 27%, so that the ratio is around 1:5. The protected agricultural area policy hasn’t been implemented thoroughly, leaving most of it at the risk of conversion. Recommendation towards this challenge and the planning policy is headed towards preserving environment and agricultural area by properly allocating and considering the pattern and rate of urban growth to achieve better food security and sustainability.

Simulating Urban Expansion Based on Ecological Security Pattern-A Case Study of Hangzhou, China.

Disordered urban expansion has encroached on a large amount of ecological land, resulting in the steady degradation of urban ecology, which has an adverse effect on the sustainable development of the region. An ecological security pattern can effectively control urban expansion, and it is of great significance to balance urban development and ecological protection. In order to analyze the impact of ecological security patterns on urban expansion, Hangzhou was taken as an example, the CA-Markov model and FLUS model were used to simulate the urban expansion pattern in 2030 under the natural development scenario and the ecological security scenario. The results showed that (1) the ecological source area in the study area is 630.90 km2 and was mainly distributed in the western mountainous area. There are 14 ecological corridors, primarily composed of valleys and rivers. Ecological nodes are mainly distributed on the north and south sides of the main urban area. (2) From 2000 to 2018, the annual increase index (AI) of construction land decreased in the northeast and southeast directions but increased in the northwest and southwest directions, and in the northeast direction the value was always the highest. Except for the southwest direction, the average annual growth rate (AGR) of construction land in the other directions decreased. At a distance from the city center of 30 km, AI was relatively higher and was increasing, while AGR was declining. At a distance of 30–45 km, both AI and AGR were increasing, indicating that the focus of construction land was moving outwards. (3) From 2018 to 2030, under both natural development scenario and ecological security scenario, construction land would keep expanding, but the construction land area, proportion, AI, and AGR of the latter would both be smaller than the former, indicating that the ecological security pattern can effectively curb urban expansion. Because of a large amount area of ecological sources, the expansion of construction land in the southwest direction would be constrained, especially under the ecological security scenario. The methods and results of this study can provide theoretical and application references for urban planning and green development in metropolises.

Simulating spatiotemporal land use change in middle and high latitude regions using multiscale fusion and cellular automata: The case of Northeast China

Global warming-induced land use change in middle and high latitude regions remains an important and challenging topic for sustainable development. Although a large amount of remote sensing imageries is available for extracting land use information, there are unavoidable inconsistencies among land use products from different platforms. This study proposes a framework which integrates multiscale fusion method and cellular automata to simulate spatiotemporal land use change in middle and high latitude regions. Five types of remote sensing-based land use products were used to extract land use information of Northeast China in 2001, 2009, and 2017. The results demonstrate that the fused land use product from the integration of remote sensing-multiscale fusion and cellular automata has a higher accuracy. The simulated land use map for 2017 using CA-Markov model was validated by the actual land use classification of 2017, and the stochastic Kappa (Kno), location related Kappa (Klocation) and traditional Kappa (Kstandard) were 0.90, 0.94 and 0.86, respectively. The results of land use change from 2001 to 2017 show the biggest increase (5.9%) in farmland, mainly from grassland and forest. The predicted results indicate that this trend will persist until 2025 with a continuous augmentation of farmland in Northeast China. The areas of grassland will decrease by 4.3% from 2017 to 2025, with a major conversion to farmland and forest. The increasing farmland is a potential threat to ecology environment in the region as it will heavily sacrifice grassland and forest. These findings provide useful information on understanding spatiotemporal land use change in middle and high latitude regions and allow us maintaining food security for sustainable development.

Evaluation of the Efficiency of the CA-Markov Model in Predicting Spat-Temporal Changes of Land Use in Dust Source of South and Southeast Ahwaz

Evaluation of the Efficiency of the CA-Markov Model in Predicting Spat-Temporal Changes of Land Use in Dust Source of South and Southeast Ahwaz

The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)

The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km2 (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km2 (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km2 (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots.

Study on the Impact of Land-Use Change on Runoff Variation Trend in Luojiang River Basin, China

To reveal the influence process of land use changes on runoff variation trends, this paper takes the Luojiang River of China as the study area, and the Soil and Water Assessment Tool (SWAT) model was constructed to quantitatively analyze the impact of different land uses on runoff formation in the watershed, and used the Cellular Automata-Markov (CA-Markov) model to predict future land use scenarios and runoff change trends. The results show that: (1) the SWAT model can simulate the runoff in the Luojiang River basin; (2) the runoff in the Luojiang River basin has a decreasing trend in recent 10 years, caused by the decrease of rainfall and runoff due to changes in land use; (3) the forecast shows that the land-use changes in the basin will lead to an increase in runoff coefficient in 2025. The increase of the runoff coefficient will bring some adverse effects, and relevant measures should be taken to increase the water storage capacity of urban areas. This study can help plan future management strategies for the study area land coverage and put forward a preventive plan for the possible adverse situation of runoff variation.

Ecosystem services value assessment and forecasting using integrated machine learning algorithm and CA-Markov model: an empirical investigation of an Asian megacity

The ecosystem services in an area are quite dependent on its ambient land use and land cover (LULC) attributes. Here we assess the spatio-temporal distribution of Ecosystem Services Value (ESV) for the years 1990, 2000, 2010, 2020, based on the then existing LULC aspects of the Kolkata Urban Agglomeration in eastern India. Further, these are simulated for 2030, 2040 and 2050 to determine the future potential ESV. The respective LULC layers were extracted from Landsat images using the support vector machine method and future projections were done using Markov Chain-Cellular Automata models. Results reveal that all LULC aspects are likely to significantly decrease except built-up tracts. The available ESV shall concomitantly decline by 29.7%, especially due to wetland loss. The ESV patterns also showed strong spatial correlation/clustering, with higher ESV patches locating along rivers/wetlands. These results can better inform management of high-value EVS components for sustaining/improving the urban environmental quality.

Mitigating Drought Conditions under Climate and Land Use Changes by Applying Hedging Rules for the Multi-Reservoir System

Climate and land use changes have substantially affected hydrologic cycles and increased the risk of drought. Reservoirs are one of the important means to provide resilience against hydrologic variability and achieve sustainable water management. Therefore, adaptive reservoir operating rules are needed to mitigate their adverse effects. In this study, the Hanjiang River Basin in southeast China was selected as the study area. Future climate and land use projections were produced by the Delta method and CA-Markov model, respectively. Future climate forcings and land use patterns were then incorporated into a distributed hydrologic model to evaluate river flow regime shifts. Results revealed that climate and land use changes may lead to severe drought conditions in the future. Lower flows are shown to be more sensitive to environmental changes and a decline of monthly flows could reach up to nearly 30% in the dry season. To address the threat of increasing drought uncertainties in the water supply system, the aggregation-decomposition method incorporated with hedging rules was applied to guide the multi-reservoir operation. Parameters of optimal hedging rules were obtained by a multi-objective optimization algorithm. The performance of hedging rules was evaluated by comparison to standard operating policies and conventional operating rules with respect to reliability, resiliency, vulnerability, and sustainability indices. Results showed that the multi-reservoir system guided by hedging rules can be more adaptive to the environmental changes.

Vegetation Coverage Prediction for the Qinling Mountains Using the CA–Markov Model

The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index data (GIMMS NDVI, SPOT NDVI, and MODIS NDVI) were compared at first. SPOT, NDVI, and MODIS NDVI were used for calculating the vegetation coverage in the Qinling Mountains. Based on the CA–Markov model, the vegetation coverage grades in 2008, 2010, and 2013 were used to simulate the vegetation coverage grade in 2025. The results show that the grades of vegetation coverage of the Qinling Mountains calculated by SPOT, NDVI, and MODIS NDVI are highly similar. According to the prediction results, the grade of vegetation coverage in the Qinling Mountains has a rising trend under the guidance of the policy, particularly in urban areas. Most of the vegetation coverage transit from low vegetation coverage to middle and low vegetation coverage. The grades of the vegetation coverage, which were predicted by the CA–Markov model using SPOT, NDVI, and MODI NDVI, are consistent in spatial distribution and temporal variation.

Exploring and Predicting the Individual, Combined, and Synergistic Impact of Land-Use Change and Climate Change on Streamflow, Sediment, and Total Phosphorus Loads

The present study predicts and assesses the individual, combined, and synergistic effect of land-use change and climate change on streamflow, sediment, and total phosphorus (TP) loads under the present and future scenarios by using the Soil and Water Assessment Tool (SWAT). To predict the impacts of climate and land-use change on streamflow, sediment, and TP loads, there are 46 scenarios composed of historical climate, baseline period climate, eight climate models of Coupled Model Intercomparison Project phase 5 (CMIP5) of two representative emission pathways (RCP4.5 and RCP8.5), after downscaled and bias-corrected, two observed land-use maps (LULC 1995, LULC 2015) and the projected two future land-use maps (LU2055 and LU 2075) with the help of CA-Markov model to be fed into SWAT. The central tendency of streamflow, sediment, and TP loads under future scenarios is represented using the annual average. The intra-/inter-annual variation of streamflow, sediment, and TP loads simulated by SWAT is also analyzed using the coefficient of variation. The results show that future land-use change has a negligible impact on annual streamflow, sediment, TP loads, and intra-annual and inter-annual variation. Climate change is likely to amplify the annual streamflow and sediment and reduce the annual TP loads, which is also expected to reduce its inter-/intra-annual variation of TP loads compared with the baseline period (2000–2019). The combined impact of land-use and climate change on streamflow, sediment, and TP loads is greater than the sum of individual impacts for climate change and land-use change, especially for TP loads. Moreover, the synergistic impact caused by the interaction of climate and land use varies with variables and is more significant for TP loads. Thus, it is necessary to consider the combined climate and land-use change scenarios in future climate change studies due to the non-negligible synergistic impact, especially for TP loads. This research rare integrates the individual/combined/synergistic impact of land-use and climate change on streamflow, sediment, and TP loads and will help to understand the interaction between climate and land-use and take effective climate change mitigation policy and land-use management policy to mitigate the non-point source pollution in the future.

Hydrologic responses to climate and land-use/land-cover changes in the Bilate catchment, Southern Ethiopia

Abstract The likely effects of climate and land-use/land-cover (LULC) changes on hydrologic processes in Bilate catchment, Ethiopia were evaluated. The study emphasizes the evaluation of individual and combined impacts on hydrologic responses of climate and LULC changes. Climatic scenarios included a downscaled regional climate model from CORDEX-Africa. The CA–Markov model was used to project LULC. The results revealed that distinct changes on hydrologic responses occurred which follow the direction of climate and LULC changes. A 30.87% decline in rainfall resulted in about 4.09, 1.43 and 3.57% decline in runoff, groundwater and water yield, respectively. A rise in mean temperature by 1.3 °C resulted in a 7 and 0.8% increase in potential and actual evapotranspiration, respectively. Runoff, groundwater and water yield are projected to decrease by 11.24, 12.54 and 11.54%; however, potential and actual evapotranspiration are projected to increase by 19 and 14.7%, respectively, under combined climate and LULC changes. The joint effects of climate and LULC changes on hydrologic responses in the forthcoming were higher than the variation trend of climate or LULC change alone. Climate change compared with LULC change has a higher impact on hydrologic responses. The results obtained provide further insight into future water balance, and assistance in water resources planning and management.

Land Use Optimization and Simulation of Low-Carbon-Oriented—A Case Study of Jinhua, China

Land-use change is an important contributor to atmospheric carbon emissions. Taking Jinhua city in eastern China as an example, this study analyzed the effects on carbon emissions by land-use changes from 2005 to 2018. Then, carbon emissions that will be produced in Jinhua in 2030 were predicted based on the land-use pattern predicted by the CA-Markov model. Finally, a low-carbon optimized land-use pattern more consistent with the law of urban development was proposed based on the prediction and planning model used in this study. The results show that (1) from 2005 to 2018, the area of land used for construction in Jinhua continued to increase, while woodland and cultivated land areas decreased. Carbon emissions from land use rose at a high rate. By 2018, carbon emissions had increased by 1.9 times compared to 2015. (2) During the 2010–2015 period, the total concentration of carbon emissions decreased due to decreases in both the rate of growth in construction land and the rate of decline in a woodland area, as well as an adjustment of the energy structure and the use of polluting fertilizer and pesticide treatments. (3) The carbon emissions produced with an optimal land-use pattern in 2030 are predicted to reduce by 19%. The acreage of woodland in Jinhua’s middle basin occupied by construction land and cultivated land is predicted to reduce. The additional construction land will be concentrated around the main axis of the Jinhua-Yiwu metropolitan area and will exhibit a characteristic ribbon-form with more distinct clusters. The optimized land-use pattern is more conducive to carbon reduction and more in line with the strategy of regional development in the study area. The results of this study can be used as technical support to optimize the land-use spatial pattern and reduce urban land’s contribution to carbon emissions.

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India.

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. It was carried out in four steps: (1) change analysis, (2) transition potential, (3) change prediction, and (4) model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding.