Erçek Gölü (Van) Kapalı Havzası Arazi Kullanım/Arazi Örtüsü Değişiklerinin Uzaktan Algılama Yöntemi Kullanılarak Belirlenmesi

Yıl 2024, Cilt: 29 Sayı: 2, 514 - 529, 31.08.2024

Erkan Dişli , Zehra Şapcı Ayaş

https://doi.org/10.53433/yyufbed.1440273

Öz

Bu çalışmada Erçek Gölü Kapalı Alt Havzası (EGKH) genelinde meydana gelen mekânsal-zamansal değişim süreçlerini belirlemek amacı ile 2006, 2012, 2016, 2018, 2020 ve 2022 yıllarına ait arazi kullanım/arazi örtüsü (AKAÖ) verileri ve 2016, 2018, 2020, 2022 yıllarına ait normalleştirilmiş bitki örtüsü indeksi (NBÖİ) ve normalleştirilmiş fark su indeksi (NFSİ) kullanılmıştır. AKAÖ, NBÖİ ve NFSİ haritalarını oluşturmak için ArcGIS 10.8 programında Copernicus-Land Monitoring Service ve ESRI- Sentinel-2 Land Cover Explorer uydu görüntüleri kullanılmış ve AKAÖ haritasından elde edilen görüntüler kendi içerisinde yedi alt sınıfa (yerleşim yeri, orman, su kütlesi, sulak alan, tarım alanı, çıplak arazi ve kar/buz örtüsü) ayrılmıştır. AKAÖ görüntülerine göre 2006-2020 yılları arasında yerleşim yeri ve çıplak arazi alanlarında artış tarım alanlarında ise azalmanın meydana geldiği gözlemlenmiştir. Havza genelinde 2016-2022 yılları arasında pozitif (en yüksek) NBÖİ değerlerinin 0.822 ile 0.865 arasında değiştiğini ve 0.6-1 değer aralığı bazı yıllar arasında dönüşümlü artış gösterse de sağlıklı bitki örtüsü veya geniş ormanlık alanların yayılım çok sınırlı kaldığını göstermektedir.
2016-2022 yıları arasında pozitif (en yüksek) ve negatif (en düşük) NFSİ değerleri arasında dönüşümlü bir artış ve 2022 yılında ise azalış eğilimi gözlenmiştir. EGKH’da en önemli su kütlesi olan Erçek Gölü’nün en geniş alana 110.9 km2 ile 2020 yılında ve en düşük alana ise 2022 yılında 107.24 km2 sahip olduğu görülmüştür. İlgili yıllar arasında göl alanında yaklaşık %2.23 oranında meydana gelen azalmanın arazi kullanım/arazi örtüsündeki yerleşim alanları ve tarımsal faaliyetleri içerebilecek insan faaliyetlerinin bir sonucu olarak azalış gösterdiği sonucuna varılmıştır.

Anahtar Kelimeler

Arazi kullanım/arazi örtüsü Normalleştirilmiş bitki örtüsü indeksi, Erçek Gölü, Normalleştirilmiş bitki örtüsü indeksi, Normalleştirilmiş fark su indeksi, Sulak alan, Uzaktan algılama, Van

Determination of Land Use/Land Cover Changes in Erçek Lake (Van) Closed Basin Using Remote Sensing Method

Öz

In this study, to assess the spatio-temporal change processes occurring in the Erçek Lake Closed Sub-Basin (ELCSB), land use/land cover (LULC) data for the years 2006, 2012, 2016, 2018, 2020, and 2022 were analyzed, as well as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data for the years 2016, 2018, 2020, and 2022.Copernicus-Land Monitoring Service and ESRI-Sentinel-2 Land Cover Explorer satellite images were used in the ArcGIS 10.8 program to create the LULC, NDVI, and NDWI maps, and the images data obtained from the LULC map were divided into seven subclasses (built-up area, forest, water bodies, wetlands, agriculture area, bare ground and snow/ice cover). LULC images indicate that between 2006 and 2020, there was an increase in built-up areas and bare ground, accompanied by a decrease in agricultural areas. The positive (highest) (NDVI) values in the basin ranged between 0.822 and 0.865 in 2016-2022. Despite fluctuations between some years within the 0.6-1 value range, there was limited expansion of healthy vegetation or large forest areas in the basin. An alternating increase was observed between positive (highest) and negative (lowest) NFSI values between 2016 and 2022, and a decreasing trend was observed in 2022. It was observed that Lake Erçek, the most significant water body in the ELCSB, had the largest area of 110.9 km2 in 2020 and the smallest area of 107.24 km2 in 2022. It was concluded that the approximately 2.23% decrease in the lake area between the relevant years was attributed to human activities, which may include built-up areas and agricultural activities in LULC patterns.

Anahtar Kelimeler

Land use/land cover, Remote sensing, The Erçek Lake, The normalised difference vegetation index, The normalized difference water index, Wetlands, Van

Kaynakça

• Abebe, G., Getachew, D., & Ewunetu, A. (2022). Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences, 4, 30. https://doi.org/10.1007/s42452-021-04915-8
• Adab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65, 1723-1743. https://doi.org/10.1007/s11069-012-0450-8
• Allam, M., Bakr, N., & Elbably, W. (2019). Multi-temporal assessment of land use/land cover change in arid region based on landsat satellite imagery: Case study in Fayoum Region, Egypt. Remote Sensing Applications:Society and Environment, 14, 8-19. https://doi.org/10.1016/j.rsase.2019.02.002
• Ambastha, K., Hussain, S. A., & Badola, R. (2007). Resource dependence and attitudes of local people toward conservation of Kabartal wetland: A case study from the Indo-Gangetic plains. Wetlands Ecology and Management, 15, 287-302. https://doi.org/10.1007/s11273-006-9029-z
• Anand, V., & Oinam, B. (2020). Future land use land cover prediction with special emphasis on urbanization and wetlands. Remote Sensing Letters, 11(3), 225-234. https://doi.org/10.1080/2150704X.2019.1704304
• Arveti, N., Etikala, B., & Dash, P. (2016). Land use/land cover analysis based on various comprehensive geospatial data sets: a case study from Tirupati area, south India. Advance in Remote Sensing, 5(2), 73-82. http://dx.doi.org/10.4236/ars.2016.52006
• Ballut-Dajud, G.A. Sandoval Herazo, L.C., Fernández-Lambert, G., Marín-Muñiz, J.L., López Méndez, M.C., & Betanzo-Torres, E.A. (2022). Factors affecting wetland loss: A review. Land, 11(3), 434. https://doi.org/10.3390/land11030434
• Belal, A. A., & Moghanm, F. S. (2011). Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Sciences, 142, 73-79. https://doi.org/10.1016/j.ejrs.2011.09.001
• Bhatta, B. (2011). Remote sensing and GIS. Oxford University Press: New Delhi, India.
• Bijeesh, T.V., & Narasimhamurthy, K. N. (2019, March). A comparative study of spectral indices for surface water delineation using Landsat 8 images (pp.1-5). In 2019 IEEE International Conference on Data Science and Communication (IconDSC), Bangalore, India. https://doi.org/10.1109/IconDSC.2019.8816929
• Chen, S., Chen, B., & Fath, B. D. (2013). Ecological risk assessment on the system scale: A review of state-of-the-art models and future perspectives. Ecological Modelling, 250, 25-33. https://doi.org/10.1016/j.ecolmodel.2012.10.015
• Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482. https://doi.org/10.1016/j.rsase.2021.100482
• Di Gregorio, A., & Jansen, L.J.M. (2000). Land cover classification system (LCCS): Classification concepts and user manual. Food and Agriculture Organization of the United Nations: Rome, Italy.
• Dişli, E. (2015). Hydrology and water chemistry of Lake Burdur, South-West Anatolia, Turkey. International Journal of Ecosystems and Ecology Science (IJEES), 4, 525-536.
• Dişli, E. (2017). Hydrochemical characteristics of surface and groundwater and suitability for drinking and agricultural use in the Upper Tigris River Basin, Diyarbakır-Batman, Turkey. Environmental Earth Sciences, 76, 500. https://doi.org/10.1007/s12665-017-6820-5
• Dişli, E. (2018). Murgul Bakır Madeni-Damar Atık Barajı (Artvin) alanındaki yeraltı ve yüzey suyu kaynaklarının hidrojeolojik özellikleri ve boya deneyi. Çukurova University Journal of the Faculty of Engineering and Architecture, 33(1), 163-178.
• Elmahdy, S. I., Ali, T. A., Mohamed, M. M., Howari, F. M., Abouleish, M., & Simonet, D. (2020). Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the Northern Emirates, UAE using random forest, Kernel logistic regression and Naive Bayes Tree models. Frontiers Environmental Science, 8(102), 1-23. https://doi.org/10.3389/fenvs.2020.00102
• Essa, W., Verbeiren, B., van der Kwast, J., Van de Voorde, T., & Batelaan, O. (2012). Evaluation of the DisTrad thermal sharpening methodology for urban areas. International Journal of Applied Earth Observation and Geoinformation, 19, 163-172. https://doi.org/10.1016/j.jag.2012.05.010
• Faruque, Md. J., Vekerdy, Z., Hasan, Md. Y., Islam, K. Z., Young, B., Ahmed, M. T., … & Kundu, P. (2022). Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human induced mangrove forests areas in Bangladesh. Remote Sensing Applications: Society and Environment, 25, 100699. https://doi.org/10.1016/j.rsase.2022.100699
• Ganie, M. A., & Nusrath, A. (2016). Determining the vegetation indices (NDVI) from Landsat 8 satellite data. International Journal of Advanced Research, 4(8), 1459-1463. http://dx.doi.org/10.21474/IJAR01/1348
• Gibbs, J. P. (2000). Wetland loss and biodiversity conservation. Conservation Biology, 14(1), 314-317.
• Gilmore, S., Saleem, A., & Dewan, A. (2015). Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data. In Research@Locate’15 (pp. 100-108). http://SunSITE. Informatik. RWTH-Aachen. DE/Publications/CEUR-WS/.
• Guo, M., Li, J., Sheng, C., Xu, J., & Wu, L. (2017). A Review of wetland remote sensing. Sensors, 17(4), 777. https://doi.org/10.3390/s17040777
• He, X., Gao, Y., Niu, J., & Zhao, Y. (2011). Landscape pattern changes under the impacts of urbanization in the Yellow River Wetland-taking Zhengzhou as an example. Procedia Environmental Sciences, 10, 2165 -2169. https://doi.org/10.1016/j.proenv.2011.09.339
• İrcan, M. R., & Duman, N. (2022). Van Gölü Havzası’ndaki maksimum ve minimum sıcaklıkların trend analizi. Türk Cografya Dergisi, 80, 39-52. https://doi.org/10.17211/tcd.1079628
• Jamal, S., & Ahmad, W.S. (2020). Assessing land use land cover dynamics of wetland ecosystems using Landsat satellite data. SN Applied Sciences, 2, 1891. https://doi.org/10.1007/s42452-020-03685-z
• Jensen, J. (2007). Remote sensing of the environment: An Earth resource perspective, 2nd ed. Pearson Prentice Hall.
• Kumar, G., & Singh, K. K. (2020). Mapping and Monitoring the Selected Wetlands of Punjab, India, Using Geospatial Techniques. Journal of the Indian Society of Remote Sensing, 48, 615-625. https://doi.org/10.1007/s12524-020-01104-9
• Liang, S. (2005). Quantitative remote sensing of land surfaces. John Wiley & Sons.
• Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2008). Remote sensing and image interpretation. 6th ed. Wiley: New York.
• Malekmohammadi, B., Uvo, C.B., Moghadam, N.T., Noori, R., & Abolfathi, S. (2023). Environmental risk assessment of wetland ecosystems using bayesian belief networks. Hydrology, 10(1), 16. https://doi.org/10.3390/hydrology10010016
• Mancino, G., Nolè, A., Ripullone, F., & Ferrara, A. (2014). Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy. iForest- Biogeosciences and Forestry, 7(2), 75-84. https://doi.org/10.3832/ifor0909-007
• Martinuzzi, S., Gould, W. A., Ramos Gonzalez, O. M., Martinez Robles, A., Calle Maldonado, P., Pérez-Buitrago, N., & Fumero Caban, J. J. (2008). Mapping tropical dry forest habitats integrating Landsat NDVI, Ikonos imagery, and topographic information in the Caribbean Island of Mona. Revista de Biología Tropical, 56(2), 625-639.
• McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714
• Molly, A. (2022). Assessment of land use change impacts on wetland hydrology and vegetation health using NDWI and NDVI respectively. A case study of Tochi wetland, Oyam district. (PhD), A research dissertation submitted to the school of Forestry, Environmental and Geographical Sciences in Partial Fulfilment for the award of the degree of bachelor of Environmental Science of Makerere University.
• Nicacias, M. (2009). Evaluating the effect of moisture stress on tomato using non-destructive remote sensing techniques. (PhD), University of Limpopo, Mankweng, South Africa.
• Nsubuga, F. W. N., Botai, J. O., Olwoch, J. M., dew Rautenvbach, C. J., Kalumba, A. M., Tsela, P., & Mearns, K. F. (2017). Detecting changes in surface water area of Lake Kyoga sub-basin using remotely sensed imagery in a changing climate. Theoretical and Applied Climatology, 127, 327-337. https://doi.org/10.1007/s00704-015-1637-1
• Onamuti, O. Y., Okogbue, E. C., & Orimoloye, I. R. (2017). Remote sensing appraisal of Lake Chad shrinkage connotes severe impacts on green economics and socio-economics of the catchment area. Royal Society Open Science, 4(11), 171120. https://doi.org/10.1098/rsos.171120
• Onyango, D. O., & Opiyo, S. B. (2022), Detection of historical landscape changes in Lake Victoria Basin, Kenya, using remote sensing multi-spectral indices. Watershed Ecology and the Environment, 4, 1-11. https://doi.org/10.1016/j.wsee.2021.12.001
• Orimoloye, I. R., Kamba, A. M., Mazinyon, S. P., & Nel, W. (2020). Geospatial analysis of wetland dynamics: Wetland depletion and biodiversity conservation of Isimangaliso Wetland, South Africa. Journal of King Saud University-Science, 32(1), 90-96. https://doi.org/10.1016/j.jksus.2018.03.004
• Ostad-Ali-Askari, K. (2022). Review of the effects of the anthropogenic on the wetland environment. Applied Water Science, 12, 260. https://doi.org/10.1007/s13201-022-01767-4
• Öztürk, M., & Dişli, E. (2022). Hydrochemical and environmental isotopes characteristic of groundwater and controlling factors for waters' chemical composition in the Iron-Copper mine area (Elazığ, SE Turkey). Environmental Chemistry, 19(6), 350-374. http://doi.org/10.1071/en22070
• Paludan, C., Alexeyev, F. E., Drews, H., Fleischer, S., Fuglsang, A., Kindt, T., … & Wolter, K. (2002). Wetland management to reduce Baltic Sea eutrophication. Water Science & Technology, 45(9), 87-94. https://doi.org/10.2166/wst.2002.0211
• Reed, B. C., Loveland, T. R., & Tieszen L. L. (1996). An approach for using AVHRR data to monitor U.S. great plains grasslands. Geocarto International, 11(3), 13-22. https://doi.org/10.1080/10106049609354544
• Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973, December). Monitoring vegetation systems in the great plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium, Greenbelt.
• Smail, R. Q. S., & Dişli, E. (2023). Assessment and validation of groundwater vulnerability to nitrate and TDS using based on a modified DRASTIC model: a case study in the Erbil Central Sub-Basin, Iraq. Environmental Monitoring and Assessments, 195, 567. https://doi.org/10.1007/s10661-023-11165-1
• Singh, S. K., Mustak, Sk., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata markov chain models using earth observation datasets and geo-information. Environmental Processes, 2, 61-78. https://doi.org/10.1007/s40710-015-0062-x
• Singh, S, Kumar, P., Parijat, R., Gonengcil, B., & Rai, A. (2023). Establishing the relationship between land use land cover, normalized difference vegetation index and land surface. Geography and Sustainability, 5(2), 265-275. https://doi.org/10.1016/j.geosus.2023.11.006
• Sreenivasulu, G., Jayaraju, N., Kishore, K., & Lakshmi Prasad, T. (2014). Landuse and landcover analysis using remote sensing and GIS: A case study in and round Rajampet, Kadapa District, Andhra Pradesh, India. Indian Journal of Scientific Research, 8, 123-129.
• Tawfeeq, J. MS., Dişli, E., & Hamed, M. H. (2024). Hydrogeochemical evolution processes, groundwater quality, and non-carcinogenic risk assessment of nitrate-enriched groundwater to human health in different seasons in the Hawler (Erbil) and Bnaslawa Urbans, Iraq. Environmental Science and Pollution Research, 31, 26182-26203. https://doi.org/10.1007/s11356-024-32715-1
• Uddin, M. J., & Mondal, C. (2020). Effect of earth covering and water body on land surface temperature (LST). Journal of Civil Engineering, Science and Technology, 11(1), 45-56. https://doi.org/10.33736/jcest.2065.2020
• Xiao, Y., Hao, Q., Zhang, Y., Zhu, Y., Yin, S., Qin, L., & Li, X. (2022). Investigating sources, driving forces and potential health risks of nitrate and fluoride in groundwater of a typical alluvial fan plain. Science of the Total Environment, 802,149909. https://doi.org/10.1016/j.scitotenv.2021.149909
• Xu, H. (2006). Modification of normalized difference water ındex (NDWI) to enhance open water features in remotely sensed ımagery. International Journal of Remote Sensing, 27(14), 3025-3033. https://doi.org/10.1080/01431160600589179
• Verma, P., Raghubanshi, A., Srivastava, P. K., & Raghubanshi, A. S. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Modeling Earth Systems and Environment, 6, 1045-1059. https://doi.org/10.1007/s40808-020-00740-x
• Zhang, L., Wang, M.-H., Hu, J., & Ho, Y.-S. (2010). A review of published wetland research, 1991-2008: Ecological engineering and ecosystem restoration. Ecological Engineering, 36(8), 973-980. https://doi.org/10.1016/j.ecoleng.2010.04.029
• Zhang, Z., Fan, Y., Jiao, Z., Wang, X., & Wu, Q. (2022). Baseline-based soil salinity index (BSSI): a new soil salinity index for monitoring soil salinization. IEEE International Geoscience and Remote Sensing Symposium. IEEE, Kuala Lumpur, Malaysia. https://doi.org/10.1109/IGARSS46834.2022.9883453
• Zhao, Y., Hu, C., Dong, X., & Li, J. (2023). NDVI characteristics and influencing factors of typical ecosystems in the Semi-Arid Region of Northern China: A case study of the Hulunbuir Grassland. Land, 12, 713. https://doi.org/10.3390/land12030713
• Zsuffa, I., Van Dam, A. A., Kaggwa, R. C., Namaalwa, S., Mahieu, M., Cools, J., & Johnston, R. (2014). Towards decision support-based integrated management planning of papyrus wetlands: A case study from Uganda. Wetlands Ecology Management, 22, 199-213. https://doi.org/10.1007/s11273-013-9329-z