Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample

Abstract

Currently, it is very important to identify and use the most appropriate methods in the management of limited resources and to reach a conclusion in a short time period by using the technology in an effective manner to fastly obtain information in high quality. Remote sensing (RS) techniques are used as a very effective tool for this purpose. Obtaining information about various parameters without direct contact with the objects provides advantages in terms of both time and cost. RS technologies are used in various different disciplines. One of the most important application areas where these technologies are used is to monitor urban development by the help of the satellite images. Determination of urban land use in detail is important for decision-makers, planners, practitioners and researchers to conduct effective planning activities. In this study the change in land cover and land use between the years of 1999 and 2016 in the central district of Kastamonu was investigated; land use and exchange groups were formed. First, satellite images of the study area were classified by controlled classification method and their accuracy was calculated. The classified satellite images are used to model the probable land area, its usage and changes in 2033 by using Artificial Neural Networks (ANN) approach. According to this, changes in the field between the years of 1999 and 2016 are given as follows; 7.8% decrease for forest areas, 10.8% increase for water areas, 13.9% decrease for agricultural areas and 10.9% increase for construction areas. Based on the results, it was thought that it is a feasible and practical tool to determine the change of land cover and land use to predict the course of the future. The ANN approach used in this study is predicted to become an important decision support system for planners and decision makers.

Keywords

• Geographic Information Systems,Remote Sensing,Land Cover/Land Use,Artificial Neural Networks

Arazi Örtüsü ve Kullanımının Zamansal ve Mekânsal Değişiminin Yapay Sinir Ağları ile Modellenmesi: Kastamonu Örneği

Öz

Sınırlı olan doğal kaynakların yönetiminde en uygun yöntemleri tespit etmek ve kullanmak, teknolojinin etkin kullanılmasıyla kaliteli bilgiyle kısa zamanda sonuca ulaşmak günümüzde son derece önemlidir. Uzaktan algılama (UA) teknikleri bu bakımdan çok etkili bir araç olarak kullanılmaktadır. Objelerle doğrudan temas olmaksızın çeşitli parametreler hakkında bilgiler edinmek hem zaman hem de maliyet açısından avantajlar sağlamaktadır. UA teknolojileri birbirinden farklı birçok disiplinde kullanılmaktadır. Bu teknolojilerin kullanıldığı en önemli uygulama alanlarından biri de uydu görüntüleri yardımıyla kentsel gelişimin izlenmesidir. Kentsel arazi kullanımının detaylı olarak belirlenmesi karar vericiler, planlayıcılar, uygulayıcılar ve araştırmacılar için etkili planlama faaliyetleri yürütebilmeleri açısından önemlidir. Bu çalışmada Kastamonu ili merkez ilçesine ait 1999 - 2016 yılları arasındaki arazi örtüsü ve arazi kullanımının değişimi incelenmiş; arazi kullanımı ve değişimi grupları oluşturulmuştur. Öncelikle çalışma alanına ait uydu görüntüleri kontrolsüz sınıflandırma metoduyla sınıflandırılmış ve doğruluk dereceleri hesaplanmıştır. Sınıflandırılan uydu görüntüleri Yapay Sinir Ağları (YSA) yaklaşımı ile çalışma alanının 2033 yılındaki muhtemel arazi örüsü, kullanımı ve değişimi modellenmiştir. Buna göre çalışma alanında 1999 yılı ile 2016 yılı arasında meydana gelen değişim; ormanlık alanlar için %7.8 azalma, su alanları için %10.8 artma, tarım alanları için %13.9 azalma ve yapılaşma alanları için %10.9 artma şeklinde gerçekleştiği tespit edilmiştir. Elde edilen sonuçlar ile arazi örtüsü ve arazi kullanımı değişiminin tespit edilmesi ve gelecekte nasıl bir seyir izleyeceğinin tahmin edilebilmesi için uygulanabilir pratik bir araç olduğu düşüncesine varılmıştır. Bu çalışmada kullanılan YSA yaklaşımının planlayıcı ve karar vericiler için önemli bir karar destek sistemi aracı olacağı öngörülmektedir.

Anahtar Kelimeler

• Coğrafi Bilgi Sistemleri,Uzaktan Algılama,Arazi Örtüsü/Arazi Kullanımı,Yapay Sinir Ağları

Kaynakça

1. Blackwell W J, Chen F W (2009). Neural Networks in Atmospheric Remote Sensing. [Boston]: Artech House, Inc.
2. Blumenthal R L (2013). Remote Sensing. Salem Press Encyclopedia Of Science
3. Zhang Y (2006) Land Surface Temperature Retrieval from CBERS-02 IRMSS Thermal İnfrared Data and its Applications in Quantitative Analysis of Urban Heat Island Effect. J. Remote Sens., 10: 789-797.
4. Veldkamp A, Verburg P H (2004). Modelling Land Use Change And Environmental Impact. Journal of Environmental Management, Volume 72, Issues 1–2, Pages 1-3, https://doi.org/ 10.1016/j.jenvman.2004.04.004.
5. Watson R T, Noble I R, Bolin B, Ravindranath N H, Verardo D J, Dokken D J (2000). Land use, land-use change and forestry. A Special Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge: Cambridge University.
6. Pocewicz A, Nielsen-Pincus M, Goldberg C S, Johnson M H, Morgan P, Force J E, ... & Vierling L (2008). Predicting Land Use Change: Comparison of Models Based on Landowner Surveys and Historical Land Cover Trends. Landscape Ecology, 23(2), 195-210.
7. Almeida C M, Gleriani J M, Castejon E F, Soares‐Filho B S (2008). Using Neural Networks and Cellular Automata for Modelling Intra‐Urban Land‐Use Dynamics, International Journal of Geographical Information Science, 22:9, 943-963, DOI: 10.1080/13658810701731168.
8. Brown D G, Pijanowski B C, Duh J D (2000). Modeling the Relationships Between Land Use and Land Cover on Private Lands in the Upper Midwest, USA. Journal of Environmental Management, 59(4), 247-263.

9. Lambin E F, Rounsevell M D A, Geist H J (2000). Are Agricultural Land-Use Models Able to Predict Changes in Land-Use Intensity? Agriculture, Ecosystems & Environment, 82(1-3), 321-331.
10. Lakes T, Müller D, Krüger C (2009). Cropland Change in Southern Romania: a Comparison of Logistic Regressions and Artificial Neural Networks. Landscape Ecology, 24(9), 1195.
11. Gardner M W, Dorling S R (1998). Artificial Neural Networks (The Multilayer Perceptron)—A Review of Applications in The Atmospheric Sciences. Atmospheric Environment, 32(14-15), 2627-2636.
12. Kavzoglu T, Mather P M (2003). The Use of Backpropagating Artificial Neural Networks in Land Cover Classification. International Journal of Remote Sensing, 24(23), 4907-4938.
13. Dai E, Wu S, Shi W, Cheung C K, Shaker A (2005). Modeling Change-Pattern-Value Dynamics on Land Use: An Integrated GIS and Artificial Neural Networks Approach. Environmental Management, 36(4), 576-591.
14. Çiftçi B B, Kuter S, Akyürek Z, Weber G W (2017). Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 179.
15. Martínez-Vega J, Díaz A, Nava J M, Gallardo M, Echavarría P (2017). Assessing Land Use-Cover Changes and Modelling Change Scenarios in Two Mountain Spanish National Parks. Environments, 4(4), 79.
16. Babu J Suresh, Dr. T Sudha (2018). Analysis and Detection of Deforestation Using Novel Remote-Sensing Technologies with Satellite Images. 2018 IADS International Conference on Computing, Communications & Data Engineering (CCODE). Available at SSRN: https://ssrn.com/abstract=3187151 or http://dx.doi.org/10.2139/ssrn.3187151.
17. Li Xia, Anthony Gar-On Yeh (2002). Neural-Network-Based Cellular Automata for Simulating Multiple Land Use Changes Using GIS, International Journal of Geographical Information Science, 16:4, 323-343, DOI: 10.1080/13658810210137004
18. Brown D G, Walker R, Manson S, Seto K (2012). Modeling Land Use and Land Cover Change. In Land Change Science (pp. 395-409). Springer, Dordrecht.
19. Basse R M, Omrani H, Charif O, Gerber P, Bódis K (2014). Land use Changes Modelling Using Advanced Methods: Cellular Automata and Artificial Neural Networks. The Spatial and Explicit Representation of Land Cover Dynamics At The Cross-Border Region Scale. Applied Geography, 53, 160-171.
20. Kavzoglu T, Colkesen I (2009). A Kernel Functions Analysis for Support Vector Machines for Land Cover Classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
21. Were K, Bui D T, Dick Ø B, Singh B R (2015). A Comparative Assessment of Support Vector Regression, Artificial Neural Networks, And Random Forests for Predicting and Mapping Soil Organic Carbon Stocks Across An Afromontane Landscape. Ecological Indicators, 52, 394-403.
22. Jiménez A, Vilchez F, González O, Flores S (2018). Analysis of the Land Use and Cover Changes in the Metropolitan Area of Tepic-Xalisco (1973–2015) through Landsat Images. Sustainability, 10(6), 1860.
23. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting Land-Cover and Land-Use Change in the Urban Fringe: A Case in Morelia city, Mexico. Landscape and Urban Planning, 55(4), 271-285.
24. Weng Q (2002). Land Use Change Analysis in the Zhujiang Delta of China Using Satellite Remote Sensing, GIS and Stochastic Modelling. Journal of Environmental Management, 64(3), 273-284.
25. Wu Q, Li H Q, Wang R S, Paulussen J, He Y, Wang M, Wang Z (2006). Monitoring and Predicting Land Use Change in Beijing Using Remote Sensing and GIS. Landscape and Urban Planning, 78(4), 322-333.
26. Fan F, Wang Y, Wang Z (2008). Temporal and Spatial Change Detecting (1998–2003) and Predicting of Land Use and Land Cover in Core Corridor of Pearl River Delta (China) by Using TM and ETM+ Images. Environmental Monitoring and Assessment, 137(1-3), 127.
27. Pal M, Mather P M (2003). An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification. Remote Sensing of Environment, 86(4), 554-565.
28. Seto K C, Fragkias M (2005). Quantifying Spatiotemporal Patterns of Urban Land-Use Change in Four Cities of China with Time Series Landscape Metrics. Landscape Ecology, 20(7), 871-888.
29. Tayyebi A, Pijanowski B C, Linderman M, Gratton C (2014). Comparing Three Global Parametric and Local Non-Parametric Models to Simulate Land Use Change in Diverse Areas of The World. Environmental Modelling & Software, 59, 202-221.
30. Sunar Erbek F, Özkan C, Taberner M (2004). Comparison of Maximum Likelihood Classification Method with Supervised Artificial Neural Network Algorithms for Land Use Activities, International Journal of Remote Sensing, 25:9, 1733-1748, DOI: 10.1080/0143116031000150077.
31. Rogan J, Franklin J, Stow D, Miller J, Woodcock C, Roberts D (2008). Mapping land-Cover Modifications Over Large Areas: A Comparison of Machine Learning Algorithms. Remote Sensing of Environment, 112(5), 2272-2283.
32. Öztemel E (2003). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık.
33. Elmas Ç (2011). Yapay Zekâ Uygulamaları. İstanbul: Seçkin Yayıncılık.
34. Babapour R, Naghdi R, Ghajar I, Ghodsi R (2015) Modeling the Proportion of Cut Slopes Rock in Forest Roads Using Artificial Neural Network and Ordinal Linear Regression. Environmental Monitoring and Assessment. 187(7), 446.
35. Buğday E (2018). Application of Artificial Neural Network System Based on ANFIS Using GIS for Predicting Forest Road Network Suitability Mapping. Fresenius Environmental Bulletin, Volume 27 – No. 3/2018 pages 1656-1668.
36. Fatemi, M. H. (2004). Prediction of the electrophoretic mobilities of some carboxylic acids from theoretically derived descriptors. Journal of Chromatography A, 1038(1-2), 231-237.
37. An, D., Ko, H. H., Gulambar, T., Kim, J., Baek, J. G., Kim, S. S. (2009). A semiconductor yields prediction using stepwise support vector machine. In Assembly and Manufacturing, 2009. ISAM 2009. IEEE International Symposium on (pp. 130-136). IEEE. URL – 1 http://www.nik.com.tr/content_sistem.asp?id=41 (20 Nisan 2018)
38. Nicolas J M, Inglada J, Tupin F (2014). Remote Sensing Imagery. London: Wiley-ISTE. Weng Q (2010). Urban environmental studies. B. Warf (Eds.). Encyclopedia of Geography, (s. 2927-2933). Thousand Oaks, CA: Sage Reference.
39. Duran C (2007). Uzaktan Algılama Teknikleri ile Bitki Örtüsü Analizi. Doğu Akdeniz Ormancılık Araştırma Müdürlüğü DOA Dergisi (Journal of DOA). 13,45-67.
40. Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement. 20(1), 37-46.