Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model

Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules, as well as to predict future urban growth. For accuracy assessment, the CA-Markov model was validated using a kappa coefficient. An 83% overall accuracy was observed for the kappa index statistics, which indicates the excellent performance of the proposed model. Finally, based on the CA transition rules and the transition area matrix produced from the Markov chain model-based calibration process, the future urban growth in Seremban for 2020 and 2030 was simulated

Urban Growth Prediction of Special Economic Development Zone in Mae Sot District, Thailand

Since the ASEAN Economic Community (AEC) was activated in 2015, eight subdistricts of Mae Sot district in Tak province, Thailand have been regarded as special economic development zones (SEZ) due to their situation on the border of Thailand near the pathway of the East-West Economic Corridor project (EWEC). Thus, the Thai Government is developing many infrastructure projects there, and the urban areas are likely to expand, as the population is increasing dramatically. The study of land use could aid in more efficient decision-making in urban planning, and could mitigate the effects of uncontrolled urban development. Based on this background, land use change monitoring was performed based on Remote Sensing and Geographic Information System (GIS) using high-resolution satellite images. The images were captured by QuickBird in 2006, and by Thaichote in 2011 and 2016. The object-based classification considers not only the reflectance of the pixels but also the size, shape, color, smoothness, and compactness of the objects. This technique will bring higher accuracy to land use classification. The eCognition Developer was employed in this study for object-based classification. The mean and standard deviation of the original band was used for principle component analysis (PCA), and the normalized difference vegetation index (NDVI) was also applied to land use classification. The types of land use were divided into five categories that followed the definitions given by the Land Development Department of Thailand (LDD): agricultural area, forest, urban area, water body, and miscellaneous land. The results of land use classification showed that urban areas increased drastically year by year. The GIS dataset for land use compiled by the LDD was employed to evaluate the accuracy of our results. The overall accuracies based on the images captured in 2006 and 2011 were 86.00% and 79.88%, respectively. To evaluate urban growth in 2015, the states of land use in 2006 and 2011 were applied to a Markov Chain and Cellular Automata model (CA-Markov), which is a model for the prediction of land use change from one period to another. The Markov model evaluates the transition probability matrix to project future change, while CA-Markov performs the spatial variations in cell time transition and neighborhood based on its element cell space, cell states, time steps, transition rules, and neighbors. The accuracy of the land use prediction obtained from CA-Markov in 2016 was evaluated by comparing it with land use classification from the object-based classification of the image captured by Thaichote in 2016. The overall accuracy was 68.45%. The pattern of land use change detected from both the projection map and the classification map showed that the urban area would spread following the development of transportation infrastructure, and would encroach on the agricultural areas, while forest areas would become agricultural areas

Linnade laienemine Eestis: seire, analüüs ja modelleerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneLinnade laienemine, mida iseloomustab vähese tihedusega, ruumiliselt ebaühtlane ja hajutatud areng linna piiridest välja. Kuna linnade laienemine muudab põllumajandus- ja metsamaid ning väikesed muutused linnapiirkondades võivad pikaajaliselt mõjutada elurikkust ja maastikku, on hädavajalik seirata linnade ruumilist laienemist ning modelleerida tulevikku, saamaks ülevaadet suundumustest ja tagajärgedest pikemas perspektiivis. Eestis võeti pärast taasiseseisvumist 1991. aastal vastu maareformi seadus ning algas “maa” üleandmine riigilt eraomandisse. Sellest ajast peale on Eestis toimunud elamupiirkondade detsentraliseerimine, mis on mõjutanud Tallinna ümbruse põllumajandus- ja tööstuspiirkondade muutumist, inimeste elustiili muutusi ning jõukate inimeste elama asumist ühepereelamutesse Tallinna, Tartu ja Pärnu lähiümbruse. Selle aja jooksul on Eesti rahvaarv vähenenud 15,31%. Käesoleva doktoritöö eesmärgiks on "jälgida, analüüsida ja modelleerida Eesti linnade laienemist viimase 30 aasta jooksul ning modelleerida selle tulevikku", kasutades paljusid modelleerimismeetodeid, sealhulgas logistilist regressiooni, mitmekihilisi pertseptronnärvivõrke, rakkautomaate, Markovi ahelate analüüsi, mitme kriteeriumi. hindamist ja analüütilise hierarhia protsesse. Töö põhineb neljal originaalartiklil, milles uuriti linnade laienemist Eestis. Tegu on esimese põhjaliku uuringuga Eesti linnade laienemise modelleerimisel, kasutades erinevaid kaugseireandmeid, mõjutegureid, parameetreid ning modelleerimismeetodeid. Kokkuvõtteks võib öelda, et uusehitiste hajumismustrid laienevad jätkuvalt suuremate linnade ja olemasolevate elamupiirkondade läheduses ning põhimaanteede ümber.Urban expansion is characterized by the low–density, spatially discontinued, and scattered development of urban-related constructions beyond the city boundaries. Since urban expansion changes the agricultural and forest lands, and slight changes in urban areas can affect biodiversity and landscape on a regional scale in the long-term, spatiotemporal monitoring of urban expansion and modeling of the future are essential to provide insights into the long-term trends and consequences. In Estonia, after the regaining independence in 1991, the Land Reform Act was passed, and the transfer of “land” from the state to private ownership began. Since then, Estonia has experienced the decentralization of residential areas affecting the transformation of agricultural and industrial regions around Tallinn, changes in people's lifestyles, and the settling of wealthy people in single-family houses in the suburbs of Tallinn, Tartu, and Pärnu. During this period, Estonia's population has declined dramatically by 15.31%. Therefore, this dissertation aims to "monitor, analyze and model Estonian urban expansion over the last 30 years and simulate its future" using many modeling approaches including logistic regression, multi-layer perceptron neural networks, cellular automata, Markov chain Analysis, multi-criteria evaluation, and analytic hierarchy process. The thesis comprises four original research articles that studied urban expansion in Estonia. So far, this is the first comprehensive study of modeling Estonian urban expansion utilizing various sets of remotely sensed data, driving forces and predictors, and modeling approaches. The scattering patterns of new constructions are expected to continue as the infilling form, proximate to main cities and existing residential areas and taking advantage of main roads in future.https://www.ester.ee/record=b550782

Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Urbanization pattern of Greater Asmara Area for the last two decades (1989 to 2009) and a prediction for the coming ten years was studied. Satellite images and geospatial tools were employed to quantify and analyze the spatiotemporal urban land use changes during the study periods. The principal objective of this thesis was to utilize satellite data, with the application of geospatial and modeling tools for studying urban land use change. In order to achieve this, satellite data for three study periods (1989, 2000 and 2009) have been obtained from USGS. Object-Based Image Analysis (OBIA); and image classification with Nearest Neighbor algorithm in eCognition Developer 8 have been accomplished. In order to assess the validation of the classified LULC maps, Kappa measure of agreement has been used; results were above minimum and acceptable level. ArcGIS and IDRISI Andes have been employed for LUCC quantification; spatiotemporal analysis of the urban land use classes;to examine the land use transitions of the land classes and identify the gains and losses in relation to built up area; and to characterize impacts of the changes. Since, the major concern of the study was urban expansion, the LULC classes were reclassified in to built up and non-built up for further analysis. Urban sprawl has been measured using Shannon Entropy approach; results indicated the urban area has undergone a considerable sprawl. Finally, LCM has been used to develop a model, asses the prediction capacity of the developed model and predict future urban land use change of the GAA. Multi-layer perceptron Neural Network has been used to model the transition potential maps, results were successful to make ‘actual’ prediction with Markov Chain Analyst.Despite the GAA is center of development and its regional economic and social importance, its trend of growth remains the major factor for diminishing productive land and other valuable natural resources. The findings of the study indicated that, in the last twenty years the built up area has tripled in size and impacted the surrounding natural environment. Thus, the findings of this study might support decision making for sustainable urban development of GAA

State of the Art on Artificial Intelligence in Land Use Simulation

[Abstract] This review presents a state of the art in artificial intelligence applied to urban planning and particularly to land-use predictions. In this review, different articles after the year 2016 are analyzed mostly focusing on those that are not mentioned in earlier publications. Most of the articles analyzed used a combination of Markov chains and cellular automata to predict the growth of urban areas and metropolitan regions. We noticed that most of these simulations were applied in various areas of China. An analysis of the publication of articles in the area over time is included.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (ref. ED431G/01 and ED431D 2017/16), the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002 and UNLC13-13-3503), and the European Regional Development Funds (FEDER). CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia,” supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaria Xeral de Universidades” (grant no. ED431G 2019/01)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G 2019/0

Recent Progress in Urbanisation Dynamics Research

This book is dedicated to urbanization, which is observed every day, as well as the methods and techniques of monitoring and analyzing this phenomenon. In the 21st century, urbanization has gained momentum, and the awareness of the significance and influence of this phenomenon on our lives make us take a closer look at it not only with curiosity, but also great attention. There are numerous reasons for this, among which the economy is of special significance, but it also has many results, namely, economic, social, and environmental. First of all, it is a spatial phenomenon, as all of the aspects can be placed in space. We would therefore like to draw special attention to the results of urbanization seen on the Earth's surface and in the surrounding space. The urbanization–land relation seems obvious, but is also interesting and multi-layered. The development of science and technology provides a lot of new tools for observing urbanization, as well as the analyses and inference of the phenomenon in space. This book is devoted to in-depth analysis of past, present and future urbanization processes all over the world. We present the latest trends of research that use experience in the widely understood geography of the area. This book is focused on multidisciplinary phenomenon, i.e., urbanization, with the use of the satellite and photogrammetric observation technologies and GIS analyses

Inductive pattern-based land use/cover change models: A comparison of four software packages

International audienceLand use/cover change (LUCC), as an important factor in global change, is a topic that has recently received considerable attention in the prospective modeling domain. There are many approaches and software packages for modeling LUCC, many of them are empirical approaches based on past LUCC such as CLUES , DINAMICA EGO, CA_MARKOV and Land Change Modeler (both available in IDRISI). This study reviews the possibilities and the limits of these four modeling software packages. First, a revision of the methods and tools available for each model was performed, taking into account how the models carry out the different procedures involved in the modeling process: quantity of change estimate, change potential evaluation, spatial allocation of change, reproduction of temporal and spatial patterns, model evaluation and advanced modeling options. Additional considerations, such as flexibility and user friendliness were also taken into account. Then, the four models were applied to a virtual case study to illustrate the previous descriptions with a typical LUCC scenario that consists of four processes of change (conversion of forest to two different types of crops, crop abandonment and urban sprawl) that follow different spatial patterns and are conditioned by different drivers. The outputs were compared to assess the quantity of change estimates, the change potential and the simulated prospective maps. Finally, we discussed some basic criteria to define a " good " model

Predicting spatial and decadal of land use and land cover change using integrated Cellular Automata Markov chain model based scenarios (2019–2049) Zarriné-Rūd River Basin in Iran

Effective land use and land cover (LULC) change assessment requires tools to measure past, current, and based on them to create a future scenario. LULC changes are unavoidable in the world, particularly in developing countries. Since LULC are too dynamic and complicated without the identification of appropriate methods and approaches the future perdition will be less accurate. Therefore, the integrated Cellular Automata Markov chain (CA-Markov) model is known as a capable estimator. In this study, LULC changes in Zarriné-Rūd River Basin (ZRB) in Iran was analyzed based on different images and data extracted from satellite data in 1989 and 2019 to create the LULC scenario in 2049. The model was validated using actual and projected to 2019. The overall agreement on two extracted maps was 97.85% in 1989 and 96.55% in 2019. The more detailed analysis of validation of calibration based on the kappa showed the highest data reliability of 0.98 in 1989 and 0.95 in 2019, respectively. According to the transition matrix of probabilities, the most significant changes in the ZRB based on the past scenario (1989–2019) is in rainfed and built up land classes of LULC in 2049. Concurrently, the other classes continue to decline except irrigated agriculture and water bodies. The results obtained showed that the pasture and mountain LULC class had continued to reduce more than other classes. Furthermore, water resources and the amount of the precipitation in past and future are important to spatial and temporal expansion on LULC classes