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Markov Chains Based Land Cover Estimation Model Development: The Case of Ankara Province (Markov Zincirleri Temelli Arazi Örtüsü Tahmin Modeli Geliştirilmesi: Ankara İli Örneği)
Abstract and Figures
Land cover changes due to many factors ranging initially from the numerical size and density of the population to socio-economic characteristics and technology, from land policies to natural factors. Most of the land cover change is human-induced and involves many economic and ecological problems. Using the land by protecting it is of great importance to solve these problems before they worsen and to take precautions. Temporal monitoring of land cover changes and improving Geographical Information Systems and Remote Sensing technologies provide decision makers with many opportunities for the future of this change. The speed, direction and type of land cover change are identified with the developed forecast models, thus forming a basis for planning for sustainable land use. Although many probability methods are used to make land cover estimations, one common practice is Markov chain model. In Markov chains, a special class of random processes, two or more results that emerge based on repeated observations can be determined by means of probability laws. The main objective of this study is to estimate the 2018 land cover of Ankara province with Markov chains technique. In this context, CORINE 1990, 2000, 2006 and 2012 data sets were evaluated according to first level land cover classes. In the model for 2012 land cover estimation, 92% accuracy was achieved based on the first three data sets. With the addition of 2012 CORINE data to the model, evaluation for 2018 was made and the accuracy of the model was found to be 90% for 2018 based on the added data set. According to these latest estimation data, the change of land cover in the province will continue to develop in favor of artificial areas and against agricultural areas.
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Land use and land cover (LULC) change corresponds to the greatest transformations that occur on the earth’s surface under physical, human and socio-economic geographical conditions. Increasing demand for residential and agricultural lands has been transforming all land classes and this should be investigated in the long term. In this study, we aim to determine LULC change and land use simulation in Diyarbakır with Geographical Information System (GIS) and Remote Sensing (RS) techniques. For this purpose, satellite images from 1984, 2002, and 2020 were classified at different levels by an object-based classification method. Accuracy assessments of the classified images were made and change detection analyses were performed using TerrSet software. The LULC changes were also estimated in different scenarios using a future land use simulation model (FLUS). The results show that natural and semi-natural areas are rapidly disappearing due to urban growth between 1984 and 2020. The results of the land use simulation show that by 2038, while the agricultural, pasture and water bodies will decrease, the built-up areas will increase. It is estimated that the city, which has developed in a west-northwest direction, will expand in the future and grow between Elazığ and Şanlıurfa Boulevard.
Kentsel büyüme, sahip olduğu farklı dinamiklere bağlı olarak zaman içerisinde mekânsal kullanım değişikliklerine yol açmaktadır. Bu değişiklerin yol açabileceği sorunlara karşı tedbirli olmak ve planlama yapabilmek için gelecekteki kent alanın yayılımını tahmin etmek oldukça önemlidir. Bu amaçla Eskişehir kentsel alanı 1984-2020 tarihleri baz alınarak incelenmiş, uygulanan CA-Markov analizi ile 2056 yılındaki kentsel büyüme alanı ön görülmüştür. Çalışmanın sonuçlarına göre hızlı kentleşme süreci yaşayan Eskişehir'de kentsel büyümenin gelecekte de devam edeceği tespit edilmiştir. Ayrıca büyümenin yönünün kuzeybatı-güneydoğu aksında devam etmesi beklenmektedir. Çalışmanın bulgularının Eskişehir için yapılan bir kentsel büyüme senaryosunda, başta kent plancıları olmak üzere, tüm karar alıcılara fayda sağlaması beklenmektedir. Urban growth is based on many different dynamics and leads to spatial use changes over time. Understanding the urban growth process is important for predicting and planning the future development of cities. For this purpose, Eskişehir urban area was examined based on the dates 1984-2020, and the urban growth area in 2056 was predicted with the CA-Markov analysis applied. According to the results of the study, it has been determined that urban growth will continue in the future in Eskişehir, which is experiencing a rapid urbanization process. In addition, the direction of growth is expected to continue in the northwest-southeast axis. The findings of the study are expected to benefit all decision makers, especially urban planners, in an urban growth scenario for Eskişehir.
Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.
ÖzBu çalışmada, doğal alanlar için risk oluşturan kentleşmenin ve doğal
alanlardaki değişimin zamansal gelişiminin izlenmesi, ileriye yönelik olarak
bir gelişim modeli oluşturulması amaçlanmıştır. Çalışma bölgesi olarak seçilen
İğneada Koruma Alanı ekolojik açıdan hassas, çok sayıda flora ve fauna için
habitat alanı olma özelliği taşıyan, sadece Türkiye için değil, dünya ölçeğinde
önemli bir ekosistemdir. Kaynakların verimliliklerinin ve koruma-kullanma
dengesinin sağlanması, ekosistemin geleceği açısından büyük önem taşımaktadır.
Bu amaçla, alanın karakteristiğinin belirlenmesi, zaman içerisindeki değişimi
ve değişimin ne yönde olduğunun belirlenmesi gerekmektedir.
Çalışmada 1984, 1990, 2000 ve 2010 tarihli
Landsat 5 TM uydu görüntüleri kullanılarak arazi örtüsü ve
kullanımları hazırlanmıştır. Bu arazi
örtüsü/kullanım verileri çalışmanın modelleme kısmında referans veri
olarak kullanılmıştır. Bölgenin
gelecekte nasıl şekilleneceğinin tahmini için; Markov zincirleri yöntemine
dayalı Stokastik Markov Modeli (ST_Markov), Hücresel Özişleme Tabanlı Markov
Modeli (CA_Markov) ve Çok Katmanlı Algılayıcılı Yapay Sinir Ağı ile Çalışan
Markov Modeli (MLP_Markov) modelleri kullanılmış ve test alanı 2010 yılı için
modellenmiştir. Mevcut 2010 yılı arazi kullanımı ile 2010 yılı için elde edilen
model sonuçları karşılaştırılmış, en yüksek doğruluğun Hücresel Özişleme
Tabanlı Markov Modeli (CA_Markov) ve Çok Katmanlı Algılayıcılı Yapay Sinir Ağı
ile Çalışan Markov Modeli (MLP_Markov) modelleri ile elde edildiği tespit
edilmiştir. MLP_Markov modeli’nin alansal değerlendirme bakımından daha yüksek
doğruluk vermesi, çok değişkenli olması ve değişimin dinamik olduğu alanlarda
kullanıldığında daha verimli sonuçlar vermesi nedeniyle 2030 yılı modellemesi
için tercih edilmiş ve sonuçlar
sunulmuştur.
Mobile phone usage is daily increasing. Tendency of the user preferences in long term is considerably important for the companies which produce products for the mobile phone market. In this study was taken by 503 people who named the mobile phone brand they are currently using and the brand they used before. To calculate the future market shares of the companies, Markov chain analysis which takes the current and the previous, not any other which was used before the previous one, mobile phone brand choices of the participants into consideration is used. Steady-state vector which is to be found at the end of the Markov chains analysis corresponds to the future market shares of the companies. The study also evaluated showing that gender differences in brand preference.
In this study, the change of land cover in Ankara province was investigated within the scope of CORINE data in two periods, 2000-2006 and 2006-2012, over 12 years. Based on the year 2000, it was determined that the land cover of Ankara was distributed horizontally and vertically, and the grade of land covered by this land cover in terms of land use ability was determined. By following periodical changes, it was determined that there is a negative linear relationship between the urban development of Ankara and the agricultural areas in the land cover changes in Ankara. During the 12-year period, settlement areas in both periods became the largest growing land cover class, with the capital Ankara having an important place in this class. Indeed, the only effective factor in the negative change of agriculture and pasture lands is settlement areas. The continuing growth of the city of Ankara over land cultivated suitable for agriculture is an indication that the unintended use of agricultural land will increase even further. In this process, a negative change was observed in agriculture and pasture lands, while a positive change was observed in other land cover types at very small rates. According to the Puyravaud formula, the annual rate of change of each type of land cover was calculated at the last stage of the study, and it was foreseen about the land cover of Ankara Province in 2030, considering that the conditions did not change
ZET Arazi kullanım/örtüsü değişikliği, farklı ekonomik aktiviteler ve aktörlerin geniş bir varyasyon içeren düzlemdeki farklı ölçeklerdeki etkileşim süreçlerinin sonucudur. Bu süreç, günümüzde kentsel alanların çeperinde yoğunlaşmakta ve bu alanlardaki arazi kullanımını/örtüsünü hızla değiştirmektedir. Bu hızlı değişimin belirlenmesi ve değişimin modellenmesi, bu alanlardaki doğal ve kültürel kaynakların sınırlı olduğu göz önüne alındığında, kaynakların akılcı kullanılması açısından elzemdir. Bu önemden hareketle, çalışmada, Malatya kent merkezine içine alan 24 x 22,5 km'lik bir çerçevenin içerisinde 590 km 2 ' lik alanda Arazi Değişim Modeli (Land Change Model-LCM) kullanılarak arazi kullanımı/örtüsü değişikliği modellenmiştir. Model için 1985, 2000, 2015 yıllarına ait LANDSAT uydu görüntülerinin obje tabanlı sınıflandırılması yapılarak her bir yıla ait 12 arazi örtüsü/kullanımı sınıfı belirlenmiş ve 1985-2000 ve 2000-2015 dönemleri için ayrı ayrı arazi örtüsü/kullanımı sınıflarının değişimleri hesaplanmış ve değişimi üç zamanda modelleyen/simüle eden modeller oluşturulmuştur. 1. Model, 1985 ve 2000 yıllarına ait arazi örtüsü/kullanımı değişimi temel alınarak oluşturulmuş ve 2015 yılına ait arazi değişimi modellenmiştir. Elde edilen tahmin model, 2015 yılına ait gerçek zamanlı arazi örtüsü/ kullanım durumu ile karşılaştırılmış ve % 72 örtüşme gözlemlenmiştir. 3. Model, 1985 ve 2015 yıllarına ait arazi örtüsü/kullanımı değişimini temel alan ve 2045 yılına ait arazi değişimi modellenmiştir. Bu çalışmada, uzaktan algılanmış veriler, modeller için her döneme ait yol katmanı ve Sayısal Yükseklik Modeli, sosyo ekonomik ve demografik veriler kullanılarak, Malatya kenti ve çevresindeki gelecekteki arazi kullanım değişiminin modellenmesi hedeflenmiştir. ABSTRACT MODELLING OF LAND USE/COVER IN MALATYA, THE SIMULATION OF 2025-2045 LAND USE/COVER The change of land use/cover is a result of the interaction processes on different scales in different platforms including various economic activities and actors. This process is getting intensive in the vicinity of urban areas today and changing the land use/cover in these areas in a fast way. It is significant to determine this change and model the change in terms of using the sources wisely considering the limited natural and cultural sources in these areas. Based on this significance, in this study, the land use/cover change was modeled by using Land Change Model – LCM in an area of 590 km 2 within the borders of a framework of 24 x 22,5 km including the city centre of Malatya. For the model, 12 land cover/use classes for each year were determined by making object-based classifications of the satellite images of LANDSAT belonging to 1985, 2000 and 2015 and the respective changes of land use/cover classes were estimated for the periods of 1985-2000 and 2000-2015. It was created the models simulating the change in 3 different times. Model 1 was created based on the land cover/use change for 1985 and 2000 and the land change for 2015 was modeled. The attained estimation model was compared with the real-time land cover/use for 2015 and an overlapping of 72% was observed. In model 3, the land change for 2045 was modeled based on the land cover/use change for the years of 1985-2015. In this study, it was aimed to model the land use change in future in Malatya and its surrounding by using remote sensing data, road layers of each period for the models, Digital Elevation Model, socioeconomic and demographic data .
Land use and land cover (LULC) models are essential for analyzing LULC change and predicting land use requirements and are valuable for guiding reasonable land use planning and management. However, each LULC model has its own advantages and constraints. In this paper, we explore the characteristics of LULC change and simulate future land use demand by combining a CLUE-S model with a Markov model to deal with some shortcomings of existing LULC models. Using Beijing as a case study, we describe the related driving factors from land-adaptive variables, regional spatial variables and socio-economic variables and then simulate future land use scenarios from 2010 to 2020, which include a development scenario (natural development and rapid development) and protection scenarios (ecological and cultivated land protection). The results indicate good consistency between predicted results and actual land use situations according to a Kappa statistic. The conversion of cultivated land to urban built-up land will form the primary features of LULC change in the future. The prediction for land use demand shows the differences under different scenarios. At higher elevations, the geographical environment limits the expansion of urban built-up land, but the conversion of cultivated land to built-up land in mountainous areas will be more prevalent by 2020; Beijing, however, still faces the most pressure in terms of ecological and cultivated land protection.
An attempt has been made to explore and evaluate the Cellular Automata (CA) Markov modelling to monitor and predict the future land use and land cover (LULC) scenario in a part of Brahmaputra River basin using LULC maps derived from multi-temporal satellite images. CA Markov is a combined cellular automata/Markov chain/multi-criteria/multi-objective land allocation (MOLA) LULC prediction procedure that adds an element of spatial contiguity as well as knowledge base of the likely spatial distribution of transitions to Markov chain analysis. Evidence likelihood map was used for as knowledge base of the likely spatial procedure in CA Markov model. The predicting quantity and predicting location change have been analysed and statistically evaluated. The validation statistics indicated how well the comparison map agreed and disagreed with the reference map. Predicted results accuracy is slightly higher when compare to others studies of LULC change using CA Markov approaches.
Abstract
Detecting land-use change has become of concern to environmentalists, conservationists and land use planners due to its impact on natural ecosystems. We studied land use/land cover (LULC) changes in part of the northwestern desert of Egypt and used the Markov-CA integrated approach to predict future changes. We mapped the LULC distribution of the desert landscape for 1988, 1999, and 2011. Landsat Thematic Mapper 5 data and ancillary data were classified using the random forests approach. The technique produced LULC maps with an overall accuracy of more than 90%. Analysis of LULC classes from the three dates revealed that the study area was subjected to three different stages of modification, each dominated by different land uses. The use of a spatially explicit land use change modeling approach, such as Markov-CA approach, provides ways for projecting different future scenarios. Markov-CA was used to predict land use change in 2011 and project changes in 2023 by extrapolating current trends. The technique was successful in predicting LULC distribution in 2011 and the results were comparable to the actual LULC for 2011. The projected LULC for 2023 revealed more urbanization of the landscape with potential expansion in the croplands westward and northward, an increase in quarries, and growth in residential centers. The outcomes can help management activities directed toward protection of wildlife in the area. The study can also be used as a guide to other studies aiming at projecting changes in arid areas experiencing similar land use changes.