Cellular Automata Markov Research Articles

Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

Estimating changes in carbon stocks of forest vegetation in Hunan Province using the cellular automata-Markov model 259 269

Estimating changes in carbon stocks of forest vegetation in Hunan Province using the cellular automata-Markov model 259 269

Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran

An urban heat island is one of the most vital environmental risks in urban areas. The aim of this study was to assess the spatial-temporal patterns of land use changes and heat island intensity for the city of Babol, Iran, between 1985 and 2015 and to predict likely future heat island intensity variations. Multi-temporal Landsat images were acquired and analysed in this study. First, single channel algorithms were used to calculate the land surface temperature (LST), and a Maximum Likelihood Algorithm was utilized to classify images. Second, land use changes (LUCs) and LST were examined, and the relationship between the fractional vegetation cover (FVC) and land-use changes was analysed using the normalized land surface temperature. By using the mean and the standard deviation of the normalized thermal images, the area was divided into five thermal categories: very low, low, medium, high and very high. Then, by applying the heat island intensity index, the heat island changes in the studied period were investigated. Possible future land use changes were investigated using a cellular automata-Markov model, and the heat island intensity changes were anticipated. The results indicate that the area of built-up land increased by 92% between 1985 and 2015, and that the area of agricultural land noticeably decreased. The built-up land changes trend has an inverse relationship with the trend of FVC changes and follows the same trend as the normalized surface temperature changes. Most changes in the surface temperature of the area are located within 0–800 m of a built-up area. The main reason for these changes could be the conversion of agricultural and green space land areas into built-up land. The largest area of the temperature categories in all years is the medium temperature category, which covers the suburban land areas. The low- and very low-temperature categories, which follow a decreasing trend, are related to land areas far from the city. In addition, the high- and very high-temperature categories, whose areas increased annually, are adjacent to the city core and the exits from the city. The average surface temperature in all land uses increased during the studied period. Nevertheless, the rate of temperature rise is higher in the built-up uses. The index ratio of the heat island during this period shows an increasing trend, and its value changed from 0/5 in 1985 to 0/67 in 2015. An increase in the heat island intensity has a direct relationship with the area population growth and, thus, the increase in the built-up land area. Anticipation of land use changes and the process of heat island intensity variations for the studied area show alarming results that call for decision-makers to address this important challenge.

Scenario-based analysis of land-use competition in central Iran: Finding the trade-off between urban growth patterns and agricultural productivity

This study attempts to evaluate different land-use scenarios in a highly growing region and also to find the trade-off between urbanization and agricultural productivity in the Isfahan City, central Iran. In doing so, several modeling tools including satellite image processing, Multi Criteria Evaluation (MCE), Cellular Automata-Markov Chain (CA-MC) model and scenario analysis are employed to delineate highly competitive zones and also to detect sustainable trajectories of future urban growth in the region. In this regard, suitability surfaces depicting the potential of the area for urbanization and agricultural activities are created using the MCE method and these layers were then used to produce continuous and categorized layers of land-use competition in the research area. These layers are also implemented to develop multiple growth scenarios through the CA-MC model and possible land-use competition zones are identified under historical and trend-breaking options. Finally, by integrating the total set of urban growth layers derived from predictive scenarios in this study, an innovative categorization scheme is developed to study the effect of each scenario on the intensity of land-use competition and agricultural productivity loss in the area. The results of this study provide a holistic insight regarding current conditions of the Isfahan City and also a simulation environment for planners to evaluate the possible outcomes of their decisions on fertile soils and future urban growth patterns.

Quantifying the Spatiotemporal Pattern of Urban Expansion and Hazard and Risk Area Identification in the Kaski District of Nepal

The present study utilized time-series Landsat images to explore the spatiotemporal dynamics of urbanization and land use/land-cover (LULC) change in the Kaski District of Nepal from 1988 to 2016. For the specific overtime analysis of change, the LULC transition was clustered into six time periods: 1988–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016. The classification was carried out using a support vector machine (SVM) algorithm and 11 LULC categories were identified. The classified images were further used to predict LULC change scenarios for 2025 and 2035 using the hybrid cellular automata Markov chain (CA-Markov) model. Major hazard risk areas were identified using available databases, satellite images, literature surveys, and field observations. Extensive field visits were carried out for ground truth data acquisition to verify the LULC maps and identify multihazard risk areas. The overall classification accuracy of the LULC map for each year was observed to be from 85% to 93%. We explored the remarkable increase in urban/built-up areas from 24.06 km2 in 1988 to 60.74 km2 by 2016. A majority of urban/built-up areas were sourced from cultivated land. For the six time periods, totals of 91.04%, 78.68%, 75.90%, 90.44%, 92.35%, and 99.46% of the newly expanded urban land were sourced from cultivated land. Various settlements within and away from the city of Pokhara and cultivated land at the river banks were found at risk. A fragile geological setting, unstable slopes, high precipitation, dense settlement, rampant urbanization, and discrete LULC change are primarily accountable for the increased susceptibility to hazards. The predicted results showed that the urban area is likely to continue to grow by 2025 and 2035. Despite the significant transformation of LULC and the prevalence of multiple hazards, no previous studies have undertaken a long-term time-series and simulation of the LULC scenario. Updated district-level databases of urbanization and hazards related to the Kaski District were lacking. Hence, the research results will assist future researchers and planners in developing sustainable expansion policies that may ensure disaster-resilient sustainable urban development of the study area.

Prediction of land-use change and its driving forces in an ecological restoration watershed of the Loess hilly region

Land-use change is one of the important topics of regional ecological restoration research, but it exhibits high interdependencies in social-ecological systems which make it difficult to predict spatiotemporal variability and identify the main drivers. We employ long-term dynamic predictions utilizing an integrated cellular automata–Markov (CA–Markov) model and geographic information system technology, and reveal trends of land-use change and the driving forces in an ecological restoration watershed of the Loess hilly region. Here, we show that dry land showed a rising transfer trend to shrub land, sparse forestland, middle-coverage grassland, low-coverage grassland, and rural settlements from 1995 to 2010. Dry land, forestland, sparse forestland, middle-coverage grassland, and rural settlements will maintain a slight decreasing trend from 2010 to 2020, but other forestland, low-coverage grassland, and reservoir or pond may have a gradual increasing trend. Farmland and forestland in 2050 decrease by 8.17% and increase by 46.3%, respectively, compared with that in 1980. Grassland shows an overall downward trend. Water area increases first and then decreases. Rural settlements increase rapidly with a growth rate of 88.8% from 1980 to 2050. The returning farmland policy and the decline of agricultural population may be the main driving forces and positively affect the transfer of dry land to forestland. Our results may provide underlying insights needed to guide the planning and management of regional land resources.

SPATIAL PROJECTION OF LAND USE AND ITS CONNECTION WITH URBAN ECOLOGY SPATIAL PLANNING IN THE COASTAL CITY, CASE STUDY IN MAKASSAR CITY, INDONESIA

The arrangement of coastal ecological space in the coastal city area aims to ensure the sustainability of the system, the availability of local natural resources, environmental health and the presence of the coastal ecosystems. The lack of discipline in the supervision and implementation of spatial regulations resulted in inconsistencies between urban spatial planning and land use facts. This study aims to see the inconsistency between spatial planning of the city with the real conditions in the field so it can be used as an evaluation material to optimize the planning of the urban space in the future. This study used satellite image interpretation, spatial analysis, and projection analysis using markov cellular automata, as well as consistency evaluation for spatial planning policy. The results show that there has been a significant increase of open spaces during 2001-2015 and physical development was relatively spreading irregularly and indicated the urban sprawl phenomenon. There has been an open area deficits for the green open space in 2015-2031, such as integrated maritime, ports, and warehousing zones. Several islands in Makassar City are predicted to have their built-up areas decreased, especially in Lanjukang Island, Langkai Island, Kodingareng Lompo Island, Bone Tambung Island, Kodingareng Keke Island and Samalona Island. Meanwhile, the increase of the built up area is predicted to occur in Lumu Island, Barrang Caddi Island, Barrang Lompo Island, Lae-lae Island, and Kayangan Island. The land cover is caused by the human activities. Many land conversions do not comply with the provision of percentage of green open space allocation in the integrated strategic areas, established in the spatial plan. Thus, have the potential of conflict in the spatial plan of marine and small islands in Makassar City.

MODEL PREDIKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN PENDEKATAN SISTEM INFORMASI GEOGRAFIS DAN CELLULAR AUTOMATA MARKOV CHAIN: STUDI KASUS KABUPATEN ROKAN HULU, PROVINSI RIAU

Rokan Hulu Regency, Riau Province, is one of the largest oil palm plantation centers in Indonesia. The large growth of palm oil plantations in recent years has threatened the existence of protected areas and increased deforestation rates. This study aims to (1) find out the extent of land use change in Rokan Hulu Regency in 2000-2012, (2) predict land use change in Rokan Hulu regency by 2030. This research uses Geographic Information System approach with prediction model of Cellular Automata (CA) Markov Chain. The driving factors of land use change as input of CA Markov Chain model consist of distance to road, distance to river, distance to disturbance area, distance to Plantation Forest, distance to Forest Clearance Permits and slope data. The results showed that the area of deforestation reached 60.38% from 2000 to 2012, mostly in the northeastern region of Rokan Hulu Regency. The prediction model of land use change has an accuracy of 68.15% based on the kappa index agreement (KIA). By 2030, the area of forest land in Rokan Hulu is predicted to be 3.13%

Modelling urban sprawl using fuzzy cellular automata model for the city of Jaipur

The unorganised, unplanned, uncontrolled, and unauthorised development has been termed as urban sprawl and often referring to as a complex pattern of transportation, social, economic, and land use development. The rapid increase in population and economic development are the primary influences of urban sprawl. Urban sprawl is the major problem of metropolitan areas all over the world especially developing countries. The impact of urban sprawl can be noted as pollution, traffic, loss of prime agricultural land, deforestation, congestion of places, and water pollution. In this paper, we have proposed a fuzzy-CA-Markov model for modelling urban sprawl. The factors which influence the urban sprawl is considered as fuzzy parameter like accessibility from local road, accessibility from main road, accessibility from major road, slop, altitude, and density. The future prediction of urban sprawl was measured through Markov's cellular automata model.

A cost-effective and efficient framework to determine water quality monitoring network locations

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata–Markov chain model (CA–Markov) was applied to simulate future change in non-point sources during the period 2026–2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.

Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

Understanding the hydrological response of a watershed to land use/land cover (LULC) changes is imperative for water resources management planning. The objective of this study was to analyze the hydrological impacts of LULC changes in the Andassa watershed for a period of 1985–2015 and to predict the LULC change impact on the hydrological status in year 2045. The hybrid land use classification technique for classifying Landsat images (1985, 2000 and 2015); Cellular-Automata Markov (CA-Markov) for prediction of the 2030 and 2045 LULC states; the Soil and Water Assessment Tool (SWAT) for hydrological modeling were employed in the analyses. In order to isolate the impacts of LULC changes, the LULC maps were used independently while keeping the other SWAT inputs constant. The contribution of each of the LULC classes was examined with the Partial Least Squares Regression (PLSR) model. The results showed that there was a continuous expansion of cultivated land and built-up area, and withdrawing of forest, shrubland and grassland during the 1985–2015 periods, which are expected to continue in the 2030 and 2045 periods. The LULC changes, which had occurred during the period of 1985 to 2015, had increased the annual flow (2.2%), wet seasonal flow (4.6%), surface runoff (9.3%) and water yield (2.4%). Conversely, the observed changes had reduced dry season flow (2.8%), lateral flow (5.7%), groundwater flow (7.8%) and ET (0.3%). The 2030 and 2045 LULC states are expected to further increase the annual and wet season flow, surface runoff and water yield, and reduce dry season flow, groundwater flow, lateral flow and ET. The change in hydrological components is a direct result of the significant transition from the vegetation to non-vegetation cover in the watershed. This suggests an urgent need to regulate the LULC in order to maintain the hydrological balance.

Monitoring and Modeling of Spatiotemporal Urban Expansion and Land-Use/Land-Cover Change Using Integrated Markov Chain Cellular Automata Model

Spatial–temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial–temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA–Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA–Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations.

Modelling urban dynamics in rapidly urbanising Indian cities

Metropolitan cities in India are emerging as major economic hubs with an unprecedented land use changes and decline of environmental resources. Globalisation and consequent relaxations of Indian markets to global players has given impetus to rapid urbanisation process. Urbanisation being irreversible and rapid coupled with fast growth of population during the last century, contributed to serious ecological and environmental consequences. This necessitates monitoring and advance visualisation of spatial patterns of landscape dynamics for evolving appropriate management strategies towards sustainable development approaches. This study visualises the growth of Indian mega cities Delhi, Mumbai, Pune, Chennai and Coimbatore, through Cellular Automata Markov model considering the influence of agent(s) of urban growth through soft computing techniques. CA Markov model is considered to be one of most effective algorithm to visualise the growth of urban spatial structures. Prediction of growth using agent based modelling considering the spatial patterns of urbanisation during the past four decades has provided insights to the urban dynamics. The industrial, infrastructural, socio-economic factors significantly influence the urban growth compared to the biophysical factors. Visualisation of urban growth suggest agents driven growth in the cities and its surroundings with large land use transformations in urban corridors and upcoming Industrial and ear marked developmental zones. Integrating local agents of urban growth help in identifying specific regions of intense growth, likely challenges and provide opportunities for evolving appropriate management strategies towards sustainable cities during the 21st century.

Optimal rural land use allocation in central China: Linking the effect of spatiotemporal patterns and policy interventions

Rural land use development is experiencing a transition stage of socioeconomic and land use development in China. Historic land use transition process and policy interventions have key influence on the applicability of land use allocation solutions in future land use management. Strategic land use allocation is therefore required to possess a good adjustment capability to the transition process. Although heuristic optimization methods have been promising to solve land use allocation problems, most of them ignored the spatially explicit effect of historic land use transition and policies. To help resolve this issue, this study aims to optimize future land use pattern in the context of rural land use development. We took Yunmeng County, one of the typical major grain producing and rapidly urbanizing areas in central China, as a case study and solved the sustainable land use allocation problem by using an improved heuristic optimization model. The model was constructed based on the integration of a spatial discrete particle swarm optimization and cellular automata-Markov simulation approach. The spatiotemporal land use patterns and policy interventions were represented by the CA-Markov as in spatially explicit transition rules, and then incorporated into the discrete PSO for optimal land use solutions. We examined the influence of the joint effect of spatiotemporal land use patterns and policy interventions on the land use allocation outcome. Our results demonstrate the robustness and potential of the proposed model, and, more importantly, indicate the significance of incorporating the spatiotemporal land use patterns and policy interventions into rural land use allocation.

Simulating the impacts of future land use change on soil erosion in the Kasilian watershed, Iran

Predicting soil erosion potential is important in watershed management. A rapidly growing Iranian population and climate change are expected to influence land use and soil sustainability. In recent years, northern Iran has experienced significant land use changes due to internal migration along the Caspian coast and conversion of forests and rangelands. Considering the effect of these changes in the future, the purpose of this study is to forecast land use patterns and investigate soil erosion scenarios using the Revised Universal Loss Equation and Markov Cellular Automata. Data from 1981 to 2011 were used as a baseline to estimate changes that might occur in 2030. The results reveal that the mean erosion potential will increase 45% from the estimated 104.52 t ha−1 year−1 in the baseline period. Moreover, the results indicate that land use change from forest area to settlements will be the most significant factor in erosion induced by land use change, showing the highest correlation among erosional factors. Projecting land use change and its effect on soil erosion indicate that conversion may be unsustainable if change occurs on land that is not suited to the use. The method predicts soil erosion under different scenarios and provides policymakers a basis for altering programs related to land use optimization and urban growth. Those results indicated the necessity of appropriate policies and regulations particularly for limiting land use changes and urban sprawl in areas of unfavorable soil erosion risk factors.

Cellular Automata Modelling in Predicting the Development of Settlement Areas, A Case Study in The Eastern District of Pontianak Waterfront City

Pontianak waterfront city as water-based urban has the potential of water resources, socio-economic, cultural, tourism and riverine settlements. Settlements areas in the eastern district of Pontianak waterfront city is located in the triangle of Kapuas river and Landak river. This study uses quantitative-GIS methods that integrates binary logistic regression and Cellular Automata-Markov models. The data used in this study such as satellite imagery Quickbird 2003, Ikonos 2008 and elevation contour interval 1 meter. This study aims to discover the settlement land use changes in 2003-2014 and to predict the settlements areas in 2020. This study results the accuracy in predicting of changes in settlements areas shows overall accuracy (79.74%) and the highest kappa index (0.55). The prediction results show that settlement areas (481.98 Ha) in 2020 and the increasingly of settlement areas (6.80 Ha/year) in 2014-2020. The development of settlement areas in 2020 shows the highest land expansion in Parit Mayor Village. The results of regression coefficient value (0) of flooding variable, so flooding did not influence to the development of settlement areas in the eastern district of Pontianak because the building’s adaptation of rumah panggung’s settlements was very good which have adjusted to the height of tidal flood.

Runoff Responses to Climate and Land Use/Cover Changes under Future Scenarios

Climate and land use/cover (LUC) are the two most significant factors that directly affect the runoff process. However, most research on runoff response has focused mainly on projected climate variation, while future LUC variability has been neglected. Therefore, the objective of this study is to examine the impacts of projected climate and LUC changes on runoff. Future climate scenarios are projected using the Quantile Mapping (QM) method, and future LUC scenarios are predicted with the Cellular Automaton-Markov (CA-Markov) model. Three different scenarios are simulated and compared to evaluate their impacts: Scenario 1 (LUC of 2010 and climate during the 2011–2050 period, abbreviated S1), Scenario 2 (LUC of 2010, 2020, 2030, 2040 and 2050 and climate of the historical wet year, normal year and dry year, abbreviated S2) and Scenario 3 (LUC of 2010, 2020, 2030, 2040 and 2050 and corresponding climate projections of 2011–2020, 2021–2030, 2031–2040 and 2041–2050 period, abbreviated S3). These three scenarios are then input into the Soil and Water Assessment Tool (SWAT) model to assess runoff responses. Beijiang River Basin, located in southern China, is used in this case study. The results obtained from S1, S2 and S3 show that runoff change in this basin is mainly caused by climate change; warmer temperatures and greater precipitation increase runoff. LUC change has little influence on runoff at the whole-basin scale, but changes in runoff components are more notable in the urban area than in the natural region at the sub-watershed level. The impact of LUC change in urbanized region on runoff components differ obviously among the wet, normal and dry years, and surface runoff and groundwater are found to be more sensitive to urbanization. Runoff depth is predicted to increase in this basin under the impacts of both climate and LUC changes in the future. Climate change brings greater increase in water yield and surface runoff, whereas LUC change leads to changes in allocation between surface runoff and groundwater in the urban region.

Prediction of future urban surface temperatures using medium resolution satellite data in Harare metropolitan city, Zimbabwe

The objective of the study was to determine the impact of urban growth on future micro-climate of Harare by predicting future distribution of land use and land cover, as well as land surface temperature using Cellular Automata Markov Chain analysis. Landsat series data was used to map Land Use and Land Cover (LULC) and land surface temperature distribution during the month of October for the year 1984, 1993, 2001 and 2015. The Cellular Automata Markov Chain analysis was used to determine long term landscape transformation at 10-year time steps from 2015 to 2045. We further tested the potential of a variety of vegetation and non-vegetation indices to predict land surface temperature. Results show that the Urban Index (UI), non-vegetation index was the best predictor of surface temperature, since it had the highest correlation with retrieved surface temperature (r = 0.9831). When tested against temperature derived from thermal band in October 2015, the mean absolute percentage error of the UI derived temperature was 5.27%. Based on changes which occurred between 1984 and 2015, the Cellular Automata Markov Chain analysis predicted that high density built-up areas will increase monotonically from 470.02 in 2015 to 490.36 km2 in 2045. Green spaces were predicted as decreasing from 57.42 to 27.85 km2, while croplands also decrease from 30.27 to 16.93 km2 between 2015 and 2040. Using UI as predictor of land surface temperature, we predicted that the 18–28 °C class will decrease in coverage between 2015 and 2040, while the 36–45 °C category will increase in proportion covered from 42.5 to 58% of city. We concluded that continued urban growth will increase warming and result in high future temperatures unless mitigation efforts are strengthened. The findings of this study are important in informing future development of cities to consider growth implications on future temperatures and thermal comfort of urban residents.

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.

Modeling Future Land Cover Changes and Their Effects on the Land Surface Temperatures in the Saudi Arabian Eastern Coastal City of Dammam

Over the past several decades, Saudi cities have experienced rapid urban developments and land use and land cover (LULC) changes. These developments will have numerous short- and long-term consequences including increasing the land surface temperature (LST) of these cities. This study investigated the effects of LULC changes on the LST for the eastern coastal city of Dammam. Using Landsat imagery, the study first detected the LULC using the maximum likelihood classification method and derived the LSTs for the years 1990, 2002, and 2014. Using the classified results, it then modeled the future LULC for 2026 using the Cellular Automata Markov (CAM) model. Finally, using three thematic indices and linear regression analysis, it then modeled the LST for 2026 as well. The built-up area in Dammam increased by 28.9% between 1990 and 2014. During this period, the average LSTs for the LULC classes increased as well, with bare soil and built-up area having the highest LST. By 2026, the urban area is expected to encompass 55% of the city and 98% of the land cover is envisioned to have average LSTs over 41 °C. Such high temperatures will make it difficult for the residents to live in the area.