Cellular Automata Markov Chain Research Articles

Application of modified cellular automata Markov chain model: forecasting land use pattern in Lebanon

This article modifies the use of the Cellular Automata Markov Chain Model to predict future land use pattern in Lebanon, and compares it to the current developed model. LandSat images of years 2000, 2009 and 2018 are used to generate land use maps within the geographic information system. Current developed model was generated by integrating Population density data with land use classification maps to decompose the built-up development to three sub-classes: High, Medium and Low-density built-up land uses. Simulations of future land use pattern over the year 2018 based on these two prediction models reveal that the Modified Cellular Automata Markov Chain Modelling technique is more accurate than the Extended Markov Chain model. Spatial effects of built-up densities are validated in this study. Consequently, the extension of the Cellular Automata Markov Chain Model represents an innovative tool for regional and urban planning to forecast potential locative distribution of old and new urban agglomeration. The sequential shift of the urban areas among different density classes in addition to the interactions of urban agglomerations should be employed as a guiding tool for decision-makers and planners during the phase of developing new population and economic strategies, new urban Masterplan and during the process of enacting/developing new land-use policies. In the final part of the study, a simulation of land use pattern for the year 2036 is generated using TerrSet v.18 software and an analysis of the outcome for the forecasted map is discussed.

Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub-catchment, Zimbabwe

The purpose of this study was to use current and historic land use/land cover (LULC) changes to predict future LULC using coupled Cellular Automata–Markov chain model. Landsat time series data for the month of June were used to determine LULC changes for the years 1984, 1995, 2005 and 2015 using random forest classification system in R. We then used Markov Chain algorithm with multiple perceptron in land change modeler to simulate future LULC dynamics for the years 2025, 2035 and 2045. Contingent valuation method was employed to estimate ecological value of wetlands. Results for the LULC analysis for the period between 1984 and 2015 show a decline in woodland and wetland area owing to natural and human factors. We noted a reduced net change for grassland, woodland and mountain vegetation covers losing by − 4%, − 5.2%, − 2% and − 4% change, respectively. While, riverine vegetation, crop field and rock outcrop gained by 0.5%, 4% and 10.2%, respectively. A notable dramatic decline in wetland area was recorded between 1995 and 2005 losing about 125.53 ha, which could be attributed to recurrent droughts of 1995, 1998, 2002 and 2005. Major conversions were from wetland cover to crop fields suggesting agricultural encroachment onto the wetland area. Wetland area, thus, significantly decreased by 6% (p < 0.05) amounting to 236.52 ha in the last 30 years. Coupled CA–Markov chain model results for the years 2025, 2035 and 2045 predicted a further increase in cropfield at the expense of woodland and wetland area. In particular, wetland area is expected to further shrink by 46% (72.67) by the year 2045. Contingent valuation results show that every respondent concurs that wetlands have deteriorated to a very bad state in comparison to their state in the twentieth century. The cited reduced wetland health, biodiversity of both flora and fauna, water quality and quantity and general wetland area. The present state is estimated to be far less than 10% of the total value of wetlands 20 years ago. Quantifying such future wetland area changes is important not only for pure-scientific purposes but also for appropriately developing locally relevant and sustainable management strategies for wetland conservation.

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. 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. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

Abstract Understanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

Perubahan penggunaan/ tutupan lahan dan prediksi perubahan penggunaan/ tutupan lahan di Kabupaten Lampung Selatan

Land cover/land use is one of factors which is important in land management. The changes of land cover can affect on the polcy in an area. Land cover/land use of South Lampung Regency is potentially changes. Economy, social, and population are factors that affect the change in land cover. The aim of this research were to analyze spatial change of land cover/land use in South Lampung Regency period 2007-2019 and predicting future land cover/landuse of 2031. Analysis was carried out applying Cellular automata markov chain of land cover spatial model. The result showed that Kappa for image classification was 0.86. Dominant land cover type in South Lampung Regency was plantation field of 35.47 – 36.18%. Model validation of 2031 predicted land cover was 0.946, while The types of land cover that increase in 2031 were built up area and paddy field of 96.8% and 86.5%, respectively, while paddy fields, forest, moor, body of water, plantation, shurb were decreasing approximately 16.2, 14.9, 13, 7.7, 4.1 and 1.0%. Paddy field that had the most area of decline as prediction results of 16.2%.

An Integrative Modelling Approach to Analyse Landscape Dynamics Through Intensity Analysis and Cellular Automata-Markov Chain Model

The goal of this study is offer a deep understanding of the landscape dynamics in the Gorgan Township, the Golestan Province, Iran. Landsat satellite imagery of two different time thresholds, i.e. the years 1992 and 2011, was acquired from the US Geological Survey database and the changes were quantified for the Gorgan area covering a 19-year time span. Furthermore, an integrated Cellular Automata-Markov Chain (CA-MC) model was applied to predict future changes up to the year 2030. We used the intensity analysis method to compare the historical dynamics of different land categories at multiple levels. The results indicated that during the 19 years, the built-up and forest areas increased by 2.33% and 0.27%, respectively, while agriculture and remnant vegetation decreased by 2.43% and 0.24%, respectively. The CA-MC model illustrated that in the following 19 years, the built-up areas could increase by 2.45%. An intensity analysis revealed that forest gains and losses were dormant while remnant vegetation gains and losses were active. The built-up area’s gains and water bodies’ losses were active and stationary during both time intervals. The transitions from water bodies and remnant vegetation to agriculture were regularly targeting and stationary, while the transition from forest to agriculture was regularly avoiding and stationary. Our findings also indicated a heavy systematic transition from agriculture to built-up areas. Regarding the increasing population growth and urbanisation in the region, the outcomes of this study can help make informed decisions for the management and protection of natural resources in the study area.

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.