Cellular Automata Markov Chain Research Articles

Predictive modeling of desertification in Jibia Local Government Area of Katsina State, Nigeria

This study examines desertification in Jibia Local Government Area of Katsina State, Nigeria. Landsat TM 1990, Landsat ETM plus 2000 and Landsat 8 of 2015 with spatial resolutions of 15 and 30 m were used to compute quantitative area coverage via a supervised classification scheme in ERDAS IMAGINE 9.2. An accuracy assessment was undertaken to ascertain the accuracy level of the classification in which none of the images had an accuracy level of below 90% and a kappa coefficient of below 0.87. Cellular automata markov chain analysis model within IDRISI Kilimanjaro software was used to predict changes in desertification process up to year 2030. Results on classified satellite imageries showed a considerable decrease in vegetation and an increase in desertification. Between the years 1990 and 2000 (10 years) about 17.8% of the total study area was affected by desertification, that means approximately 1.8% of the area was affected yearly. However, between 2000 and 2015 the rate increased where more than 100% of the total land area was affected at the rate of 8.0% annually. Between 1990 and 2015, total land area affected by desertification amounted to 277.4sqkm. Results on prediction indicated desertification increase from 450.84sqkm in 2015 to 485.25sqkm of the total land area to be engulfed by desertification by the year 2030.

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

The fast-paced urbanization of recent decades entails that many regions are facing seemingly uncontrolled land-use changes (LUCs) that go hand in hand with a range of environmental and socio-economic challenges. In this paper, we use an integrated cellular automata–Markov chain (CA–MC) model to analyze and predict the urban expansion of and its impact on LUC in the city of Bahir Dar, Ethiopia. To this end, the research marshals high-resolution Landsat images of 1991, 2002, 2011, and 2018. An analytical hierarchy process (AHP) method is then used to identify the biophysical and socioeconomic factors underlying the expansion in the research area. It is shown that, during the period of study, built-up areas are rapidly expanding in the face of an overall decline of the farmland and vegetation cover. Drawing on a model calibration for 2018, the research predicts the possible geographies of LUC in the Bahir Dar area for 2025, 2034, and 2045. It is predicted that the conversions of other land-use types into built-up areas will persist in the southern, southwestern, and northeastern areas of the sprawling city, which can mainly be traced back to the uneven geographies of road accessibility, proximity to the city center, and slope variables. We reflect on how our findings can be used to facilitate sustainable urban development and land-use policies in the Bahir Dar area.

Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia

Permanent conversion of woodland to large-scale commercial agriculture, pastures or urban areas and temporary or partial removal of indigenous trees for shifting cultivation and selective logging remained major environmental challenges in the tropical region. Cognizant of the environmental changes prevailing in the pastoral and agro-pastoral areas of Southern Ethiopia, we have examined the past conversion of woodland to other land uses through the analysis of Landsat Multi-spectral scanner (MSS) 1973, Thematic Mapper(TM) 1986, Enhanced Thematic Mapper (ETM+) 2003, Operational Land Imagery (OLI) 2017 and then projected the future change in land use/cover (LUC) as well. We have employed Cellular Automata Markov chain model to simulate and predict LUC changes between 2017 and 2060. Four spatial driver variables such as distance to road and settlement, slope and elevation were used to run the simulation. Prior to the prediction, we have simulated the LUC of 2017 using transition potential maps of 2003 and transition matrix between 1973 and 2003. The predictive power of the model was then examined by comparing the reference and simulated LUC maps of 2017and also using the kappa index. A good correlation was obtained between the reference and simulated LUC maps of 2017. In addition, the computed kappa index was above 0.9, which implies that the model is effective in predicting change in LUC. The analysis result revealed that in the entire monitoring period (1973–2017) the area lost 89,875 ha of woodland. The loss is expected to continue during the period 2017–2060, with an estimated loss of 32,423 ha of woodland, if a proper measure is not taken against the continuous loss of woodland. Thus, relentless efforts are needed to rehabilitate the already degraded land and also minimize the potential loss of woodland in the future through the implementation of conservation – livelihood approach, REDD + project, and sustainable land use management strategies.

Spatiotemporal land cover dynamics and drivers for Dhidhessa River Basin (DRB), Ethiopia

Land cover change drives scale-dependent impacts on the environment. Effective environmental resources management, therefore, requires an understanding of the spatiotemporal land cover dynamics and their impacts at watershed scale. However, an integrated study that includes spatiotemporal land cover dynamics and the relative importance of drivers of the changes is missing for many basins in Ethiopia including the Dhidhessa River Basin (DRB). The objective of this study is to quantify historic and future land cover dynamics and to identify the relative importance of the drivers of the changes for the DRB. The post-classification comparison method and the cellular automata–Markov chain models were used for the analysis. The major land cover change driving factors were analyzed using the partial least square regression technique. The result revealed increasing waterbody, settlement, grassland, and agricultural lands at the expense of forest, and bush and shrubland in the past and future 30 years. Population density, slope, elevation, temperature, and rainfall were the drivers of land cover changes where population pressure is the primary driver. The observed land cover changes in the DRB could result in environmental degradation. Quantifying these impacts is, therefore, crucial to plan and implement informed land resources management strategies.

Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios

The climate and Land Use/Land Cover (LULC) changes evince the considerable impact on water balance components by altering the hydrological processes. So, the present work focuses on the evaluation of the combined impact of both the climate and LULC changes along with and without water storage structures on water balance components of the Krishna river basin, India under present and future scenarios with the help of Soil Water and Assessment Tool (SWAT). Sequential Uncertainty Fitting algorithm (SUFI-2) was used for the model calibration and validation, which were carried out at the Vijayawada gauge station. The coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) values obtained during the calibration period were 0.63 and 0.61, respectively, whereas, in validation, these values were found to be 0.61 and 0.56, indicates satisfactory results. The results showed that the model simulations and performance were significantly influenced by the presence of water storage structures, whereas the LULC changes were effective at the sub-watershed level. Future LULC maps of 2025, 2055, and 2085 were simulated from the Cellular Automata (CA) Markov Chain model, and they were used along with future climate projections to investigate its impact on water balance components. The climate model projects an increase of water balance components specifically, surface runoff, streamflow, and water yield, except for evapotranspiration in the future. Whereas, the future LULC changes may influence in offsetting the streamflow 20 to 30% reference to the observed flow. Thus, LULC changes were significantly influenced the model simulations; therefore, it is essential to consider the LULC changes along with climate scenarios in climate change studies. Overall, the surface runoff, water yield, and streamflow may increase by 50% under Representative Concentration Pathway (RCP) 4.5, and they may double under the RCP 8.5 scenario by the end of the century.

Modeling flood plain wetland transformation in consequences of flow alteration in Punarbhaba river in India and Bangladesh

Ecologically precious and economically remunerative wetland resources in the riparian flood plain of Punarbhaba river basin are under massive transformation in post hydrological alteration phase triggered mainly by damming. It affects the people who are directly and indirectly depends on these resources. The present work intends to measure the degree and direction of wetland transformation in reference to the river flow change using multi-date satellite images. For addressing this issue, modeling of river flow change, wetland area, wetland depth, wetland water appearance consistency within wetland and landscape transformations have been done in pre and post dam periods (dam over Punarbhaba river is installed on 1992) and future state is also predicted. Modeling wetland transformation using advance geo-spatial and machine learning techniques like combined cellular automata-markov chain, water presence frequency approach, spatial nonlinear regression (adaptive spline) is quite new in such data sparse condition. NDWI intensity transformation modeling is a novel approach and it is successfully applied in this work. Few major findings of this works are (1) river flow is declined in post dam phase and further predicted to be declined in coming days (2) reduction of peak flow has reduced wetland area by 34.52% in post dam period and predicted to be decreased by 22.66% by 2030 (3) NDWI intensity of wetland is also declined by 37.87% and it is caused for drying out 40.26 km2 shallower wetland. Simulated trend shows further decline of wetland depth in predicted model. (4)10.56% highly consistent wetland is transformed into inconsistent wetland in terms of water presence (5) encroachment of agriculture land and infrastructural development have enhanced the degree of wetland fragmentation.

Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner.

Examining effects of climate change and land use dynamic on biophysical and economic values of ecosystem services of a natural reserve region

Ecosystem Service Valuation (ESV) is a process of evaluating and quantifying the monetary values of ESs and their functions. Using both biophysical and spatially explicit integrated models, biophysical and monetary values of key Ecosystem Services (ESs) were estimated in the Sundarbans Biosphere Region (SBR), India. Quantification was made both in time series (1982–2017) and individual years (1973, 1988, 2003, 2013, 2018, 2025, 2035, 2045) to understand the impact of climate change and land-use dynamics on the long-term ecological status of the region. The monetary and biophysical values of the ESs were then obtained from Net Primary Productivity (NPP) models, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Cellular Automata Markov Chain Model (CA-Markov). NPP increased significantly during the first half period (1982–1999), but significantly declined during the second period (2000–2017). The highest estimated ESVs (US$ ha−1) was found for habitat service (30780), nutrient cycling (12626), and gas regulation (7224.81), whereas, lower ESVs were approximated for water regulation (347.81), raw material production (777.82) and waste treatment (13.57) services. Among the nine ESs evaluated, climate regulation, gas regulation, and disturbance regulation were the most important regulating services of the SBR. The combined effects of climate change and land-use dynamics on ESs are much stringent in a vulnerable region like the SBR. Most of the regulating services were closely associated with the fluctuation of land use land cover input. Thus, land management policies and land reform strategies that will encourage the conversion of productive land, especially the highly productive mangrove forest, for the development or any other financial benefits, would disturb the ideal human-nature balance of this ecosystem. The outcomes of this study also provide an important reference to the land administrators, researchers, and decision-makers to comprehend the expected social-ecological juxtaposition in a protected natural reserve region like the Sundarbans.

Prediction Model of Land Cover Changes using the Cellular Automata – Markov Chain Affected by the BOCIMI Toll Road in Sukabumi Regency

The development of a city is a manifestation of regional development. The impact of the development of a city is the occurrence of land cover changes and increase of the settlement areas. In 2002-2018 the vegetation land cover experienced a drastic decline and experienced land conversion into the settlement areas so that the area of settlement land cover increased. This study aims to analyze the spatial patterns of settlement land cover changes that are affected by the existence of BOCIMI Toll road in the Sukabumi Regency, West Java using the Cellular Automata - Markov Chain (CA-MC) method, and modeling for 2032 based on the driving factor (driving factor) applied to the model. CAMC is a simple model of a spatially distributed process in a Geographic Information System (GIS). Five variables used as driving factors, elevation, slope, distance from the river, distance from the road, distance from the toll gates and distance from the toll road. The results of the model show that there are some changes in land cover and an increase of the settlement area that is affected by the physical and infrastructure factors in Sukabumi Regency, it can be seen that the kappa value is 0.8352 or 83.52%. Further, it is necessary to analyze the Sukabumi Regency model and the Regional Spatial Plan (RTRW) to see the growth and direction of the settlement area in Sukabumi Regency.

Erosion rate prediction model ssing SWAT and CA-Markov methods (case study: Upper Ci Catih Catchment Area)

Landuse has a vital role that affects the movement of water on hidrological cycle. Around Upper Ci Catih Catchment Area in the past 10 years, many of various industries (ranging from smal-scale to large-scale industries) have increased. Increasing built-up area, especially settlements and industries can increase runoff that have an impact on land degradation such as erosion. This study focuses on the hydrological modelling made by SWAT (Soil Water Assesment Tools) and landuse change prediction using CA-Markov (Cellular Automata-Markov Chain) to find the effects of landuse changes on the erosion rate changes. Erosion rate prediction in 2032 based on landuse changes prediction in that year. This study shows that variations of Hydrologic Response Unit (HRU) conditions affects the erosion rate in every subwatershed. The result of accuracy and calibration test satisfied, which NS and R2 mark for SWAT validation is 0,56 and 0,72, and kappa coefficient value for CA-Markov model validation by 89%. The changes of landuse especially forest and irrigated rice fields is predicted in 2032 will decrease in every Subbasin. It will affect the increase of average erosion rate in every Subbasin by 15% of the erosion rate in 2017 on Upper Ci Catih Catchment Area.

CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)

Abstract. An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. Sensitivity analysis has been carried out to identify the land use land cover parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928.

Modelling future land use scenarios based on farmers’ intentions and a cellular automata approach

Different mechanisms drive land use and land cover changes (LUCC). This paper presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: (1) A0 – current social and economic trend; (2) A1 – intensified agricultural production; (3) A2 – reduced agricultural production; and (4) B0 - increasing demand for urban development. LUCC models are applied to a Torres Vedras (Portugal) case study. This territory is located in a peri-urban area near Lisbon dominated by forest and agricultural land, which has been suffering considerable urban pressure in the last decades. Farmers — major agents of agricultural land use change — were interviewed to obtain their LUCC intentions according to the scenarios studied. To model LUCC a Cellular automata-Markov chain approach was applied. Our results suggest that significant LUCC will occur depending on their intentions in the different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the biggest drop in non-irrigated arable land, and the highest growth in forest in the A2 scenario; and (3) the greatest urban growth was recognized in the B0 scenario. To verify if the fitting simulations performed well, techniques to measure agreement and quantity-allocation disagreements were applied.These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future.

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

ABSTRACTUnderstanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.

The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region

This paper conveys the findings of a study of the development of Semarang Metropolitan Region (SMR) over two decades. The developments seen were urbanisation and its impact on Surface Urban Heat Islands (SUHI). This research used remote sensing data satellite imagery of Landsat 5 T M and Landsat 8 OLI to see land cover change from 1998 to 2018. Furthermore, to predict the land cover change, this research used a Stochastic Cellular Automata-Markov Chain (SCA-MC) algorithm. Measurements of the LST and SUHI used Landsat products with Thermal Infra-Red Sensors (TIRS) Band 10 (2018) and TIRS Band 6 (1998). The classification of the results from 1998 to 2018 indicates a widespread change in the built-up area of about 74.62%, revealing urban expansion in the SMR. Moreover, there has been a decline in the vegetation canopy of 36.14%, resulting in an increase in average surface temperature of 2–5 °C. Areas with increased surface temperatures reached more than 30 °C, which is an increase of 245.57 km2 from 1998 to 2018. Furthermore, the SCA-MC prediction suggests the scenario to mitigate the SUHI through Spatial planning instruments. Thus, control of development is essential to the sustainability of the SMR, through land use planning scenarios.

Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India

Assessment of past and future urban growth processes helps the decision makers to evaluate and formulate the policy documents. In an attempt to make such assessments, this study compares three commonly used urban growth models: Multicriteria Cellular Automata-Markov Chain (MCCA-MC), Multi-Layer Perception Markov Chain (MLP-MC), and the Slope, Land use, Exclusion, Urban Extent, Transportation and Hillshade (SLEUTH). This study has taken into account the land use and land cover data for the years, 1977, 1992, 2000, 2008, 2016 and prepared driving variables for urban growth. The KAPPA index of agreement indicates that the MCCA-MC, MLP-MC and SLEUTH models avoid errors by 94%, 93%, and 92% respectively. Models forecast that about 156.96 km2, 157.43 km2 and 142.43 km2 built-up areas will emerge through the process of urbanization by 2031 in the city of Udaipur. However, this assessment identified that all the models are embodied with their own advantages and disadvantages while serving specific purposes. While the MCCA-MC and MLP-MC provides a good account of the urban spread, the SLEUTH identifies the new isolated growth centres more accurately.

Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico

In this study, we analyzed the land use and land cover (LULC) change in a hydrologic basin in the western coast of central Mexico and its regional variation in carbon storage. Three thematic maps (1986, 2001 and 2017) were produced by using pixel-based unsupervised classification techniques of Landsat images. LULC maps for 2017, 2033 and 2050 were modeled using a Cellular Automata Markov chain, validating their predictive power using Kappa variations. The InVEST software was used to estimate the carbon stored in four reservoirs, analyzing their variations over time (1986–2050). Accuracy assessment for the classifications revealed satisfactory results with an Overall accuracy of 83% and a Kappa coefficient of 0.76. Results show that the main landscape modifier was the exposed soils class increasing by 65% its extent in 1986, with a net increase around 87,900 ha. The evergreen forest (EF) and the tropical dry forest (TDF) classes showed a decrease along the period analyzed, displaying a net loss of 57,200 ha and 47,200 ha, respectively. Projected land cover changes followed the same trend observed, with a decreasing tendency in the EF and TDF coverages as consequence of exposed soil class increase. This change implies a reduction in the estimated total carbon stock, decreasing from 362.9 Tg C in 1986, to 336.2 Tg C in 2017. According to our model, if the trend detected in this analysis continues, it is expected a reduction to 317.9 Tg C in 2050. This approach combining Cellular Automata and Markov chain analysis with the InVEST model provides elements to estimate changes on carbon storage resulting from landscape changes. It also allows the identification of potential changes in the selected land use classes, providing with technical elements that could help to decision makers to better maintain the ecological integrity of the drainage basin.

ANALISIS DAN PREDIKSI PERUBAHAN TUTUPAN LAHAN DI DAS CITARUM HULU

Land cover is one of factors which is important in watershed management. The changes of land cover in a watershed can affect the hydrology abstraction. Land cover of upstream Citarum watershed is potentially changes. Economy, social, population, and natural hazard are factors that affect the change in land cover. The aim of this research were to analyze spatial change of land cover in upstream Citarum watershed period 2006-2014 and predicting future land cover of 2030 as usual and applying a policy to conserve paddy field and forest. Analysis was carried out applying Cellular automata markov chain of land cover spatial model. The result showed that Kappa and total accuracy for image classification were 0.89 and 0.92. Dominant land cover type in upstream Citarum watershed was paddy field of 25.99 – 26.06%. The average of land cover changes approximately < 1% with conversion of paddy field to built up area is the dominant change. Model validation of 2014 predicted land cover was 0.992, while Kappa for optimum iteration of 7 was 0.994. The land cover for period 2006-2030 was change approximately < 1%. The types of land cover that increase in 2030 were built up area and mixed farming of 0.21% and 0.11%, respectively, while plantation forest, moor, paddy field, and bareland were decreasing approximately 0.17, 0.23, 0.62 and 0.02%. The predicted land cover of 2030 under protection of paddy filed and forest scenario was trigger the increasing of mixed farming 0.36%, while moor and bareland were decrease of 0.22 and 0.10%.

Evaluating and modeling the spatiotemporal pattern of regional-scale salinized land expansion in highly sensitive shoreline landscape of southeastern Iran

Taking an area of about 2.3×104 km2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies.

Dynamic trade-off analysis of multiple ecosystem services under land use change scenarios: Towards putting ecosystem services into planning in Iran

This study dynamically analyzes the trade-off between three ecosystem services (ESs), including soil retention, habitat quality, and food supply in the Gorganrood watershed, northeastern Iran. In this regard, several analytic tools including the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model, Cellular Automata-Markov Chain (CA-MC) model, Intensity Analysis and Trade-Off (TO) index are employed to dynamically link the process of land use change with the temporal flow of ESs. Two scenarios of business as usual (BAU) and environmentally sound planning (ESP) are developed to produce land use layers under various planning strategies. Accordingly, the BAU scenario is more successful at providing a landscape with a higher acreage of agricultural fields and rangelands. Based on tradeoff index results, the BAU scenario establishes a landscape in which food supply is intensified by declining land capability for providing soil retention and habitat quality services. In contrast, the ESP scenario generates a landscape in which the outputs of the TO index indicate that habitat quality and soil retention benefits are improved by decreasing the potential of the area to supply food. This study emphasizes that a dynamic trade-off analysis of multiple ESs can assist planners and policy makers to make informed decisions and undertake strategic planning in Iran.

A modeling approach to path dependent and non-path dependent urban allocation in a rapidly growing region

The present paper combines the strengths of path dependent and non-path dependent modeling approaches to detect sustainable urban development profiles in a rapidly expanding region in Iran. In doing so, the Cellular Automata-Markov Chain (CA-MC) model is calibrated and validated using an integrative application of Multi Criteria Evaluation (MCE) method and Multi Layer Perceptron Neural Network (MLPNN) algorithm in addition to applying different Kappa-based metrics. The CA-MC model is then employed to project urban growth trajectories in the targeted research location. By combining the results of different urban allocation approaches, highly suitable zones for future development of the area are detected. These lands were not identifiable if each approach was implemented alone. The potential lands for future urbanization are evaluated and ranked in terms of their connectivity and compactness as well as their effect on other land-use categories such as forest and agriculture. The suggested methodology not only improves the calibration of a spatially-explicit model, but also it provides a wider range of options for policy makers. The findings of this study are useful to municipal decision makers in order to protect against further ecological consequences of ill-planned and uncontrolled urbanization.