Urmiye Gölü Örneğinde Arazi/Arazi Değişimi Tespit Prosedüründe Hücresel Otomata Markov Yöntemi İle Nesne Tabanlı Sınıflandırma Yaklaşımının Uygulanması

Abstract

Mevcut araştırmanın temel amacı, nesne tabanlı bir görüntü analizi (OBIA) işlemi uygulayarak Urmiye Gölü'nün kuzey kıyılarındaki Arazi Örtüsü / Arazi Kullanım Değişiklikleri (LC / LUC) modellerinde değişiklikleri ortaya koymaktı. Buna bağlı olarak, görüntü işleme prosedürleri aşamasında, Urmiye Gölü yüzeylerindeki uzamsal değişiklikler, 1987'den 2016'ya kadar Landsat görüntülerinden dikkatlice alınmıştır. Ardından, ikinci aşamada, Misho Dağı'nın güney yamaçları için LC / LU değişim modelleri kesin olarak tanımlandı. Elde edilen modeller, görüntü sınıflandırma prosedürlerinde yaklaşık% 92,54 genel bir hassasiyet ve% 91'lik bir Kappa katsayısı gösterdi. Son aşamada, Hücresel Otomata-Markov (CA-Markov) yönteminin tanıtılması ve bir geçiş matrisinin ayarlanmasıyla, LC / LU modellerinde uzamsal değişiklikler, çalışma alanı içinde 2020 yılına kadar yaklaşan yıllar boyunca aşamalı olarak simüle edilmiştir.

Nihai modeller, Urmia Gölü yüzeyinde, belirli su hacimlerinin eşlik ettiği ve azalan eğilimde anlamlı bir düşüş olduğunu göstermektedir, bu da tuzlu toprakların miktarının anlamlı şekilde arttığını vurgulamaktadır. Bu zararlı eğim, esas olarak Urmiye Gölü'nün kıyı bölgelerinde, kritik bir hiper-salin durumunun eşlik ettiği LC / LU tiplerinde en son değişikliklerin ortaya çıkmasıyla bitki örtüsünün tiplerinde kritik bir azalmaya neden olur. Mevcut önemli değişikliklerin uygulanması, yerel biyotik ve abiyotik bileşenlerin çoğunun ciddi çevresel olumsuz gözlemlerle taklit tehlikeler içinde olduğuna işaret etmektedir. LC / LU’ta bu tür hızlı bir şekilde meydana gelen devrim niteliğindeki değişiklikler, acil durumdaki tehlike ekosistemlerinde ve hassas iklimsel alt sistemlerde var olan çeşitli kritik etkiler yaratacaktır.

Keywords

• Arazi Kullanım Değişiklikleri,Nesneye Dayalı Yaklaşım ve CA-Markov Yöntemi,Urmiye Gölü

APPLYING AN OBJECT-BASED CLASSIFICATION APPROACH THROUGH A CELLULAR AUTOMATA-MARKOV METHOD IN LANDCOVER/LANDUSE CHANGE DETECTION PROCEDURE "CASE OF THE URMIA LAKE"

Abstract

The main aim of the present research was to reveal changes on Land-Cover/Land-Use Changes (LC/LUC) patterns in the in the northern coast of the Urmia Lake by applying an object-based image analysis (OBIA) process. Accordingly, in the image process procedures stage, spatial changes on the Urmia Lake surfaces were carefully acquired from the Landsat imageries, since 1987 to 2016. Then, in the second stage, LC/LU change patterns have been precisely delineated, for the southern hillsides of the Misho Mountain. The resulting models showed an overall accuracy of nearly about 92.54% and a Kappa coefficient of 91% in the image classification procedures. In the final stage, by introducing a Cellular Automata-Markov (CA-Markov) method and setting a transition matrix, the spatial changes on the LC/LU patterns have been progressively simulated for the approaching years till year 2020 inside the study area.

The final models illustrate a meaningful significant decrease in the Urmia Lake surface, accompanying by certain water volumes diminishing tendency, highlighting the fact that the amount of salty lands are meaningfully increasing. This harmful inclination has successively causes a critical diminishing on the vegetation’s types by emerging the most recent changes on LC/LU  types  accompanying  by  a  critical  hyper-saline  condition  mainly around  the  coastal  parts  of the  Urmia  Lake.

Implementations of the current significant changes strongly pointing up that the majority of local biotic and abiotic components are in imitate dangers with serious environmental negative observations. Such rapidly occurring revolutionized changes on LC/LU will impose various critical effects on the existing in danger ecosystems and vulnerable climatic sub-systems in immediate prospect.

Keywords

• Landuse Changes,Object-Based Approach and CA-Markov Method,Urmia Lake

References

1. Adhikari, S., Southworth, J. Simulating forest cover changes of Bannerghatta National Park based on a CA-Markov model: a remote sensing approach. Remote Sensing, 2012 4(10), 3215-3243.
2. Ahmed, B., Ahmed, R. Modeling urban land cover growth dynamics using multi‑temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 2012. 1 (1), 3-31.
3. Balzter, H., Markov chain models for vegetation dynamics. Ecological Modelling, 2000.126 (2-3), 139-154.
4. Behera, D. M., Borate, S. N., Panda, S. N., Behera, P. R., Roy, P. S. Modelling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model–A geo-information based approach. Journal of earth system science, 2012. 121 (4), 1011-1024.
5. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 2004,58 (3-4), 239-258.
6. Blaschke, T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 2010. 65 (1), 2-16.
7. Campbell, J. B., Wynne, R. H. Introduction to remote sensing (Vol. 5): Guilford Press: New York, NY, USA. 2011.
8. Chander, G., Markham, B. L., Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 2009. 113 (5), 893-903.

9. Eastman, J. R. IDRISI Taiga guide to GIS and image processing. Clark Labs Clark University, Worcester, MA. 2009.
10. Hadi, S. J., Shafri, H. Z., Mahir, M. D. Modelling LULC for the period 2010-2030 using GIS and Remote sensing: a case study of Tikrit, Iraq. Paper presented at the IOP conference series: earth and environmental science. 2014.
11. Justice, C., Townshend, J., Vermote, E., Masuoka, E., Wolfe, R., Saleous, N., Morisette, J. An overview of MODIS Land data processing and product status. Remote sensing of environment, 2002. 83 (1-2), 3-15.
12. Kamusoko, C., Aniya, M., Adi, B., Manjoro, M. Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 2009,29 (3), 435-447.
13. Keshtkar, H., Voigt, W. A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2016. 2 (1), 10.
14. Li, S., Jin, B., Wei, X., Jiang, Y., Wang, J. Using CA-Markov model to model the spatiotemporal change of land use/cover in Fuxian Lake for decision support. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015. 2 (4), 163.
15. Lillesand, T., Kiefer, R. W., Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons.
16. Liu, Y., Guo, Q., Kelly, M. A framework of region-based spatial relations for non- overlapping features and its application in object based image analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 2008. 63 (4), 461-475.
17. Meinel, G., Neubert, M. A comparison of segmentation programs for high resolution remote sensing data. International Archives of Photogrammetry and Remote Sensing, 2004. 35 (Part B), 1097-1105.
18. Neubert, M., Herold, H., Meinel, G. Evaluation of remote sensing image segmentation quality–further results and concepts. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2006.36 (4/C42).
19. Pontius, G. R., Malanson, J. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 2005. 19 (2), 243-265.
20. Pontius Jr, R. G., Peethambaram, S., Castella, J.-C. Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers, 2011. 101 (1), 45-62.
21. Rahman, A., Kumar, S., Fazal, S., Siddiqui, M. A. Assessment of land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 2012. 40 (4), 689-697.
22. Rasuly, A. A., Mahdian, M. Moharrami. M. and Derrafshi, A. Signifying of the Urmia Lake Landuse Changes By Object-Oriented Image Processing Techniques. 2016.
23. Rasuly, A. A. Principle of applied remote sensing: image processing: Press Office: University of Tabriz, Tabriz, Iran. 2009.
24. Roy, D. P., Wulder, M., Loveland, T. R.,Woodcock, C., Allen, R., Anderson, M., Kennedy, R. Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of environment, 2014. 145, 154-172.
25. Sánchez-Reyes, U. J., Niño-Maldonado, S., Barrientos-Lozano, L., Treviño-Carreón, J. Assessment of land use-cover changes and successional stages of vegetation in the natural protected area Altas Cumbres, Northeastern Mexico, using Landsat satellite imagery. Remote Sensing, 2017. 9 (7), 712.
26. Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., Macomber, S. A. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote sensing of environment, 2001. 75 (2), 230-244.