Türkiye'nin Burdur ilinin kırsal bir yerleşim merkezinde mermer ocaklarının genişlemesi ve arazi örtüsü değişikliklerinin uydu görüntüleri ve CBS kullanılarak izlenmesi

Abstract

Bu çalışma, 1995-2020 yılları arasında mermer ocaklarının yüzey alanlarındaki artışa dikkat çekmek ve arazi örtüsü tiplerindeki değişiklikleri izlemek amacıyla Burdur ili Karamanlı ilçesinde gerçekleştirilmiştir. Coğrafi bilgi sistemi araçları ve maksimum olabilirlik kontrollü sınıflandırma yaklaşımı bu çalışma için metodolojik bir temel sağlamıştır. Bu çalışmada kullanılan Landsat uydu görüntüleri 1995, 2000, 2005, 2010, 2015 ve 2020 için elde edilmiştir. Beş arazi örtüsü sınıfı belirlenmiştir ve bu sınıflar arasında yapay yüzeyler, tarımsal alanlar, orman ve yarı doğal alanlar, su yüzeyleri ve mermer ocakları bulunmaktadır. İlgili görüntüler sınıflandırıldıktan sonra değişiklik tespiti yapılmıştır. 25 yıllık dönemde yapay yüzeylerde % 56,86 artış, tarımsal alanlarda % 0,09 azalma, orman ve yarı doğal alanlarda % 13,42 azalma ve su yüzeylerinde % 13,76 azalma belirlenmiştir. Çalışma alanında en dikkat çekici değişiklik mermer ocaklarında meydana gelmiştir. Mermer ocakları % 891,44 oranında çok ciddi bir artış göstermiştir (148,41 hektardan 1577,52 hektara). Mermer ocaklarının daha çok orman ve yarı doğal alanlara geçiş yaptığı görülmüştür. Bu çalışmanın bulguları, çevre koruma stratejilerinin geliştirilmesi ve mekânsal planlama çalışmaları için önemli bir arka plan sağlayacaktır.

Keywords

• Mermer ocakları
• arazi örtüsü değişimleri
• çevresel koruma
• arazi kullanım planlaması
• CBS
• uzaktan algılama

Monitoring of marble quarries expansion and land cover changes using satellite images and GIS on a rural settlement of Burdur province, Turkey

Abstract

This study was actualized in Karamanlı district of Burdur province in order to draw attention to the increase in the surface areas of marble quarries and to monitor the changes of land cover types during the period from 1995 to 2020. Geographic information system tools and supervised maximum likelihood classification approach provided a methodological basis for this study. Landsat satellite images used in this study were obtained for 1995, 2000, 2005, 2010, 2015 and 2020. Five land cover classes have been identified, and these classes include artificial surfaces, agricultural areas, forest and semi-natural areas, water surfaces, and marble quarries. Change detection was performed after classifying the relevant images. During 25-year period, an increase of 56.86% in artificial surfaces, a decrease of 0.09% in agricultural areas, a decrease of 13.42% in forest and semi-natural areas and a decrease of 13.76% in water surfaces were designated. The most striking change in the study area occurred in marble quarries. Marble quarries showed a very serious increase of 891.44% (from 148.41 hectares to 1577.52 hectares). It has been observed that the marble quarries are mostly transitioned to forest and semi-natural areas. The findings of this study will provide an important background for the development of environmental protection strategies, and spatial planning studies.

Keywords

• Marble quarries
• land cover changes
• environmental conservation
• land use planning
• GIS
• remote sensing

References

1. Farahani, H., & Bayazidi, S., Modeling the assessment of socio-economical and environmental impacts of sand mining on local communities: A case study of Villages Tatao River Bank in North-western part of Iran, Resources Policy, 2018, 55, 87-95.
2. Amirshenava, S., & Osanloo, M., A hybrid semi-quantitative approach for impact assessment of mining activities on sustainable development indexes, Journal of Cleaner Production, 2019, 218, 823-834.
3. Mouflis, G. D., Gitas, I. Z., Iliadou, S., & Mitri, G. H., Assessment of the visual impact of marble quarry expansion (1984–2000) on the landscape of Thasos island, NE Greece, Landscape and Urban Planning, 2008, 86(1), 92-102.
4. Ozcelik, M., Environmental pollution and its effect on water sources from marble quarries in western Turkey, Environmental Earth Sciences, 2016, 75(9), 796.
5. Atibu, E. K., Lacroix, P., Sivalingam, P., Ray, N., Giuliani, G., Mulaji, C. K., ... & Poté, J., High contamination in the areas surrounding abandoned mines and mining activities: An impact assessment of the Dilala, Luilu and Mpingiri Rivers, Democratic Republic of the Congo, Chemosphere, 2018, 191, 1008-1020.
6. Mandal, I., & Pal, S., COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas, Science of the Total Environment, 2020, 732, 139281.
7. Afonso, T. F., Demarco, C. F., Pieniz, S., Quadro, M. S., Camargo, F. A., & Andreazza, R., Bioprospection of indigenous flora grown in copper mining tailing area for phytoremediation of metals, Journal of Environmental Management, 2020, 256, 109953.
8. Gül, M., Zorlu, K., & Gül, M., Assessment of mining impacts on environment in MuğlaAydın (SW Turkey) using Landsat and Google Earth imagery, Environmental Monitoring and Assessment, 2019, 191(11), 655.

9. Moeletsi, R., & Tesfamichael, S., Quantifying land cover changes caused by granite quarries from 1973-2015 using Landsat data. In GISTAM (pp. 196-204), (2018).
10. Chowdhury, M., Hasan, M. E., & Abdullah-Al-Mamun, M. M., Land use/land cover change assessment of Halda watershed using remote sensing and GIS, The Egyptian Journal of Remote Sensing and Space Science, 2020, 23(1), 63-75.
11. Uysal, M., Turgut, B., Polat, N., Dereli, M. A., & Yalçın, M., Determination of boron minerals in open pit mines with remote sensing techniques, Afyon Kocatepe University Journal of Science and Engineering, 2017, 17(4), 270-276 (In Turkish).
12. Gürsoy, M., & Büyüksağiş, İS., Analyze of a sample mining field by remote sensing method, El-Cezeri Journal of Science and Engineering, 2017, 4(3), 518-540 (In Turkish).
13. Kabisch, N., Selsam, P., Kirsten, T., Lausch, A., & Bumberger, J., A multi-sensor and multitemporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes, Ecological Indicators, 2019, 99, 273-282.
14. Colkesen, I., & Kavzoglu, T., Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using sentinel-2 and Landsat OLI imagery, Remote Sensing Letters, 2017, 8(11), 1082-1091.
15. Temiz, F., Bozdag, A., & Durduran, S. S., Analysis of Land-Use Change in Denizli City Center through Geographical Information Systems, Fresenius Environmental Bulletin, 2018, 27(9), 6129-6136.
16. Dereli, M. A., Determination of short-term land cover change by Sentinel-2A satellite imagery for Giresun city center, Afyon Kocatepe University Journal of Science and Engineering, 2019, 19(2), 361-368 (In Turkish).
17. Dogru, A. O., Goksel, C., David, R. M., Tolunay, D., Sözen, S., & Orhon, D., Detrimental environmental impact of large scale land use through deforestation and deterioration of carbon balance in Istanbul Northern Forest Area, Environmental Earth Sciences, 2020, 79(270), 270.
18. Rudke, A. P., de Souza, V. A. S., dos Santos, A. M., Xavier, A. C. F., Rotunno Filho, O. C., & Martins, J. A., Impact of mining activities on areas of environmental protection in the southwest of the Amazon: A GIS-and remote sensing-based assessment, Journal of Environmental Management, 2020, 263, 110392.
19. Elagouz, M. H., Abou-Shleel, S. M., Belal, A. A., & El-Mohandes, M. A. O., Detection of land use/cover change in Egyptian Nile Delta using remote sensing, The Egyptian Journal of Remote Sensing and Space Science, 2020, 23(1), 57-62.
20. Dereli, M. A., & Tercan, E., Assessment of shoreline changes using historical satellite images and geospatial analysis along the Lake Salda in Turkey, Earth Science Informatics, 2020, 13, 709-718.
21. Ustaoglu, E., & Aydınoglu, A. C., Suitability evaluation of urban construction land in Pendik district of Istanbul, Turkey, Land Use Policy, 2020, 99, 104783.
22. Yildirim, V., & Bediroglu, S., A geographic information system‐based model for economical and eco‐friendly high‐speed railway route determination using analytic hierarchy process and least‐cost‐path analysis, Expert Systems, 2019, 36(3), e12376.
23. Orhan, O., Yakar, M., & Ekercin, S., An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems, Arabian Journal of Geosciences, 2020, 13(17), 1-17.
24. Nyimbili, P. H., & Erden, T., GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul, Turkey, Socio-Economic Planning Sciences, 2020, 71, 100860.
25. Tercan, E., & Dereli, M. A., Development of a land suitability model for citrus cultivation using GIS and multi-criteria assessment techniques in Antalya province of Turkey, Ecological Indicators, 2020, 117, 106549.
26. Tercan, E., Saracoglu, B. O., Bilgilioğlu, S. S., Eymen, A., & Tapkın, S., Geographic information system-based investment system for photovoltaic power plants location analysis in Turkey, Environmental Monitoring and Assessment, 2020, 192, 297.
27. Tercan, E., Tapkın, S., Latinopoulos, D., Dereli, M. A., Tsiropoulos, A., & Ak, M. F., A GISbased multi-criteria model for offshore wind energy power plants site selection in both sides of the Aegean Sea, Environmental Monitoring and Assessment, 2020, 192, 652.
28. Bonifazi, G., Cutaia, L., Massacci, P., & Roselli, I., Monitoring of abandoned quarries by remote sensing and in situ surveying, Ecological Modelling, 2003, 170(2-3), 213-218.
29. Malaviya, S., Munsi, M., Oinam, G., & Joshi, P. K., Landscape approach for quantifying land use land cover change (1972–2006) and habitat diversity in a mining area in Central India (Bokaro, Jharkhand), Environmental Monitoring and Assessment, 2010, 170(1-4), 215-229.
30. Darwish, T., Khater, C., Jomaa, I., Stehouwer, R., Shaban, A., & Hamzé, M., Environmental impact of quarries on natural resources in Lebanon, Land Degradation & Development, 2011, 22(3), 345-358.
31. Ozcan, O., Musaoglu, N., & Seker, D. Z., Environmental impact analysis of quarrying activities established on and near a river bed by using remotely sensed data, Fresenius Environmental Bulletin, 2012, 21(11), 3147-3153.
32. Kekovalı, K., & Kalafat, D., Detecting of mining-quarrying activities in Turkey using satellite imagery and its correlation with daytime to nighttime ratio analysis, Journal of the Indian Society of Remote Sensing, 2014, 42(1), 227-232.
33. LaJeunesse Connette, K. J., Connette, G., Bernd, A., Phyo, P., Aung, K. H., Tun, Y. L., ... & Songer, M., Assessment of mining extent and expansion in Myanmar based on freelyavailable satellite imagery, Remote Sensing, 2016, 8(11), 912.
34. Akanwa, A. O., Okeke, F. I., Nnodu, V. C., & Iortyom, E. T., Quarrying and its effect on vegetation cover for a sustainable development using high-resolution satellite image and GIS, Environmental Earth Sciences, 2017, 76(14), 505.
35. Yadav, S. K., & Borana, S. L., Monitoring and temporal study of mining area of Jodhpur City using remote sensing and GIS, Int. Research Journal of Eng and Technology, 2017, 4(10), 2.
36. Pericak, A. A., Thomas, C. J., Kroodsma, D. A., Wasson, M. F., Ross, M. R., Clinton, N. E., ... & Amos, J. F., Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine, PloS one, 2018, 13(7), e0197758.
37. Bernardo, N., Watanabe, F., Rodrigues, T., & Alcântara, E., Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image, Advances in Space Research, 2017, 59(9), 2335-2348.
38. Lhissou, R., El Harti, A., Maimouni, S., & Adiri, Z., Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions, Remote Sensing Applications: Society and Environment, 2020, 20, 100420.
39. Kavzoglu, T., & Colkesen, I., A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 2009, 11(5), 352-359.
40. Ali, M. Z., Qazi, W., & Aslam, N., A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier, The Egyptian Journal of Remote Sensing and Space Science, 2018, 21, S29-S35.
41. Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K., Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh, The Egyptian Journal of Remote Sensing and Space Science, 2018, 21(1), 37-47.