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

Year 2019, , 536 - 550, 01.09.2019
https://doi.org/10.36306/konjes.612489

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.

References

  • 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.
  • 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.
  • Balzter, H., Markov chain models for vegetation dynamics. Ecological Modelling, 2000.126 (2-3), 139-154.
  • 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.
  • 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.
  • Blaschke, T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 2010. 65 (1), 2-16.
  • Campbell, J. B., Wynne, R. H. Introduction to remote sensing (Vol. 5): Guilford Press: New York, NY, USA. 2011.
  • 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.
  • Eastman, J. R. IDRISI Taiga guide to GIS and image processing. Clark Labs Clark University, Worcester, MA. 2009.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Lillesand, T., Kiefer, R. W., Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • Rasuly, A. A., Mahdian, M. Moharrami. M. and Derrafshi, A. Signifying of the Urmia Lake Landuse Changes By Object-Oriented Image Processing Techniques. 2016.
  • Rasuly, A. A. Principle of applied remote sensing: image processing: Press Office: University of Tabriz, Tabriz, Iran. 2009.
  • 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.
  • 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.
  • 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.

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

Year 2019, , 536 - 550, 01.09.2019
https://doi.org/10.36306/konjes.612489

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.

References

  • 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.
  • 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.
  • Balzter, H., Markov chain models for vegetation dynamics. Ecological Modelling, 2000.126 (2-3), 139-154.
  • 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.
  • 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.
  • Blaschke, T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 2010. 65 (1), 2-16.
  • Campbell, J. B., Wynne, R. H. Introduction to remote sensing (Vol. 5): Guilford Press: New York, NY, USA. 2011.
  • 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.
  • Eastman, J. R. IDRISI Taiga guide to GIS and image processing. Clark Labs Clark University, Worcester, MA. 2009.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Lillesand, T., Kiefer, R. W., Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • Rasuly, A. A., Mahdian, M. Moharrami. M. and Derrafshi, A. Signifying of the Urmia Lake Landuse Changes By Object-Oriented Image Processing Techniques. 2016.
  • Rasuly, A. A. Principle of applied remote sensing: image processing: Press Office: University of Tabriz, Tabriz, Iran. 2009.
  • 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.
  • 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.
  • 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.
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Ramiz Mammadov This is me

Ali Akbar Rasuly This is me

Hanieh Mobasher This is me

Keyvan Mohamadzadeh This is me

Publication Date September 1, 2019
Submission Date November 8, 2018
Acceptance Date December 6, 2018
Published in Issue Year 2019

Cite

IEEE R. Mammadov, A. A. Rasuly, H. Mobasher, and K. Mohamadzadeh, “APPLYING AN OBJECT-BASED CLASSIFICATION APPROACH THROUGH A CELLULAR AUTOMATA-MARKOV METHOD IN LANDCOVER/LANDUSE CHANGE DETECTION PROCEDURE ‘CASE OF THE URMIA LAKE’”, KONJES, vol. 7, no. 3, pp. 536–550, 2019, doi: 10.36306/konjes.612489.