Urban land use and land cover change analysis and modeling a case study area Malatya, Turkey

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.This research was conducted to analyze the land use and land cover changes and to model the changes for the case study area Malatya, Turkey. The first step of the study was acquisition of multi temporal data in order to detect the changes over the time. For this purpose satellite images (Landsat 1990-2000-2010) have been used. In order to acquire data from satellite images object oriented image classification method have been used. To observe the success of the classification accuracy assessment has been done by comparing the control points with the classification results and measured with kappa. According to results of accuracy assessment the overall kappa value found around 75%. The second step was to perform the suitability analysis for the urban category to use in modeling process and it has been done using the Multi Criteria Evaluation method. The third step was to observe the changes between the defined years in the study area. In order to observe the changes land use/cover maps belongs to different years compared with cross tabulation and overlay methods, according to the results it has been observed that the main changes in the study area were the transformation of agricultural lands and orchards to urban areas. Every ten years around 1000ha area of agricultural land and orchards were transformed to urban. After detecting the changes in the study area simulation for the future has been performed. For the simulation two different methods have been used which are; the combination of Cellular Automata and Markov Chain methods and the combination of Multilayer Perceptron and Markov Chain methods with the support of the suitability analysis. In order to validate the models; both of them has been used to simulate the year 2010 land categories using the 1990 and 2000 data. Simulation results compared with the existing 2010 map for the accuracy assessment (validation). For accuracy assessment the quantity and allocation based disagreements and location and quantity based kappa agreements has been calculated. According to the results it has been observed that the combination of Multilayer Perceptron and Markov Chain methods had a higher accuracy in overall, so that this combination used for predicting the year 2020 land categories in the study area. According to the result of simulation it has been found that; the urban area would increase 1575ha in total and ~936ha of agricultural lands and orchards would be transformed to the urban area if the existing trend continued

Possibilities and limits of prospective GIS land cover modelling--a compared case study: Garrotxes (France) and Alta Alpujarra Granadina (Spain)

International audienceThis study focuses on the possibilities and the limits of a prospective GIS land cover modelling applied to two case studies (France and Spain). The methodology, based on available GIS tools, consists of using earlier land cover maps and relevant environmental factors (calibration data) to model actual, known land cover to validate the model. The model aggregates Markov chain analysis for time prediction and multi-critera evaluation, multi-objective evaluation and cellular automata to perform spatial contiguity of modelled land cover scores. The first results give an accurate, pixel by pixel prediction rate of approximately 75%. An important issue of this study consists of analysing prediction residues to improve the model

Predictive Land Use Modelling as a Tool for Regulating Urban Development in Yola, Nigeria

Analysing land use/cover (LULC) change processes and driving factors of urbanisation can help identify policy measures that can be used to efficiently regulate urban development. CA-Markov principles were adopted to predict urban sprawl to the year 2033 in Yola, Nigeria. The research incorporates socio economic factors in the driving mechanism in Cellular Automata and Markov Chain (CA-Markov) to establish rules for driving forces for urban sprawl specific to Yola Topography using experts’ opinion and residents’ perception. The results provides scientific basis and opportunity to define and apply tools and strategies for managing urban development by reconciling the imperatives of urban development and conservation of environmental resources. Keywords: LULC, CA-Markov, Modelling, Landsat Image

Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn this paper urban land use change analysis and modeling of the Concelhos of Setúbal and Sesimbra, Portugal is accomplished using multitemporal and multispectral satellite images acquired in the years 2000 and 2006 and other vector datasets. The LULC maps are first obtained using an object-oriented image classification approach with the Nearest Neighbour algorithm in Definiens. Classification is assessed using the overall accuracy and Kappa measure of agreement. These measures of accuracies are above minimum standard accepted levels. The land use dynamics, both for pattern and quantities are also studied using a post classification change detection technique together with the following selected spatial/landscape metrics: class area, number of patches, edge density, largest patch index, Euclidian mean nearest neighbor distance, area weighted mean patch fractal dimension and contagion. Urban sprawl has also been measured using Shannon Entropy approach to describe the dispersion of land development or sprawl. Results indicated that the study area has undergone a tremendous change in urban growth and pattern during the study period. A Cellular Automata Markov (CA_Markov) modeling approach has also been applied to predict urban land use change between 1990 and 2010 with two scenarios: MMU 1ha and MMU 25ha. The suitability maps (change drivers) are calibrated with the LULC maps of 1990 and 2000 using MCE and a contiguity filter. The maps of 1990 and 2000 are also used for the transition probability matrix. Then, the land use maps of 2006 are simulated to compare the result of the “prediction” with the actual land use map in that year so that further prediction can be carried out for the year 2010. This is evaluated based on the Kappa measure of agreement (Kno, Klocation and Kquanity) and produced a satisfactory level of accuracy. After calibrating the model and assessing its validity, a “real” prediction for the year 2010 is carried out. Analysis of the prediction revealed that the rate of urban growth tends to continue and would threaten large areas that are currently reserved for forest cover, farming lands and natural parks. Finally, the modeling output provides a building block for successive urban planning, for exploring how an

Soil erosion vulnerability under scenarios of climate land-use changes after the development of a large reservoir in a semi-arid area

Climate and land-use/cover changes (LUCC) influence soil erosion vulnerability in the semi-arid region of Alqueva, threatening the reservoir storage capacity and sustainability of the landscape. Considering the effect of these changes in the future, the purpose of this study was to investigate soil erosion scenarios using the Revised Universal Soil Loss Equation (RUSLE) model. A multi-agent system combining Markov cellular automata with multi-criteria evaluation was used to investigate LUCC scenarios according to delineated regional strategies. Forecasting scenarios indicated that the intensive agricultural area as well as the sparse and xerophytic vegetation and rainfall-runoff erosivity would increase, consequently causing the soil erosion to rise from 1.78 Mg ha(-1) to 3.65 Mg ha(-1) by 2100. A backcasting scenario was investigated by considering the application of soil conservation practices that would decrease the soil erosion considerably to an average of 2.27 Mg ha(-1). A decision support system can assist stakeholders in defining restrictive practices and developing conservation plans, contributing to control the reservoir's siltation

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study 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: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results 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

Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Urbanization has become a global trend under the combined influence of population growth, socioeconomic development, and globalization. Even though recent urban planning in London has been more deliberate, the relationships between climate change and urban growth in the context of economic geography are still somewhat unclear. This study relies on rainfall prediction with the aid of the Statistical DownScaling Model (SDSM), which provides the statistical foundation for future flooding potential within the urban space of London while considering major socioeconomic policies related to land use management. These SDSM findings, along with current land use policies, were included as other factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in London for 2030 and 2050. Two scenarios with the inclusion and exclusion of flood impact factor, respectively, were applied to evaluate the impact of climate change on urban growth. Findings indicated: (1) mean monthly projected precipitation derived by SDSM is expected to increase for the year 2030 in London, which will affect the flooding potential and hence the area of open space; (2) urban and open space are expected to increase > 16 and 20km 2 (in percentage of 1.51 and 1.92 compared to 2012) in 2030 and 2050, respectively, while agriculture is expected to decrease significantly due to urbanization and climate change; (3) the inclusion of potential flood impact induced from the future precipitation variability drives the development toward more open space and less urban area.The research is supported by the Global Innovation Initiative (British Council Grant No. Gll206), funded by the British Council and the Department for Business, Innovation and Skills