Uydu görüntüleri kullanılarak kıyı şeridi değişimi analizi ve gelecekteki konumunun belirlenmesi için etkili bir yaklaşım: Burdur Gölü örneği

Yıl 2024, , 61 - 72, 15.03.2024

Nuray Baş

https://doi.org/10.17714/gumusfenbil.1259676

Cited By: 1

Öz

Göl kıyı şeridi değişikliklerinin sulak alanın biyolojik çeşitliliği ve ekosistemleri üzerinde önemli etkileri bulunmaktadır. Bu çalışma, Türkiye'deki Burdur Gölü'nün 2013-2023 yılları arasındaki kıyı değişiminin belirlenmesi amaçlamıştır. Bu çerçevede Landsat-7 (TM) ve Landsat 8(OLI) görüntüleri ile hem uzaktan algılama tabanlı yaklaşım hem de Sayısal Kıyı Şeridi Analiz Sistemi (DSAS) yöntemi kullanılmıştır. Kıyı bölgesi boyunca kıyı şeridi değişim oranlarını tahmin etmek için Son Nokta Oranı (EPR), Doğrusal Regresyon Hızı (LRR) ve Net Kıyı Şeridi Mesafesi (NSM) gibi istatistiksel parametreler hesaplanılmıştır. Göl ile kıyı bölgesi arasındaki ayrımı vurgulamak için ise, Normalleştirilmiş Fark Bitki Örtüsü İndeksi (NDVI) ve Tasseled Cap Analizi adlı hibrit bir algoritma kullanılmıştır. En fazla kıyı şeridi değişikliği gölün kuzeydoğu kesiminde gözlenmiş olup, 2013-2023 dönemi için EPR değeri 543,12 m/yıl, LRR değeri ise 610,07 m/yıl olarak sonuçlanmıştır. Göl-kara yönünde meydana gelen değişim EPR -4,91 m/yıl ve LRR -3,17 m/yıl olarak yalnızca küçük bir miktarda gözlemlenmiştir. Göl alanı ise, 2013-2023 yılları arasında 139 km2'den 118 km2'ye kadar düşmüştür. Elde edilen sonuçlar, eğer otoritelerce önlem alınmazsa göl alanının 2033 yılına kadar %14, 2043 yılına kadar ise %27 oranında kayıp yaşayacağını göstermektedir.

Anahtar Kelimeler

Burdur Gölü, Nsm-Epr-Lrr istatistikleri, Kıyı değişimi, Tasseled cap

An effective approach for analysis of shoreline change and determination of its future location using satellite imagery: A case study of the Lake Burdur, Turkey

Öz

Lake shoreline changes can have a significant impact on the biodiversity and ecosystems of wetland. This study was aimed to calculate the coastal change of Lake Burdur in Turkey during the elapsed period from 2013 to 2023. Within this framework both remote sensing based aproach and Digital Shoreline Analysis System (DSAS) was performed using Landsat-7 (TM) and Landsat-8 (OLI) images. To estimate shoreline change rates along the coastal zone, statistical parameters such as End Point Rate (EPR), Linear Regression Rate (LRR), and Net Shoreline Movement (NSM) were calculated. A hybrid algorithm, Normalized Difference Vegetation Index (NDVI) and Tasseled Cap Analysis, is utilized to emphasize the distinction between the lake bodies and coastal zone. The maximum shoreline change in the northeast part of the lake was observed, and it resulted in a change of 543.12 m/yr for EPR and 610.07 m/yr for LRR statistics in the 2013-2023 time period. The lake to land position has only been observed in a small amount which are resulted in for EPR -4.91 m/yr. and -3.17 m/yr for LRR statistics. The lake area decreased from 139 km2 to 118 km2 between 2013 and 2023. The results indicate that if the decision-maker does not measure, the area of the lake will be lost by 14% until 2033 and 27% until 2043.

Anahtar Kelimeler

Lake Burdur, Nsm-Epr-Lrr statistics, Shoreline change, Tasseled cap

Kaynakça

• Adebisi, N., Balogun, A.L., Mahdianpari, M., & Min, T.H. (2021). Assessing the impacts of rising sea level on coastal morpho-dynamics with automated high-frequency shoreline mapping using multi-sensor optical satellites. Remote Sensing, 13, 3587. https://doi.org/10.3390/rs13183587
• Aishi, A.F., & Hasan, K. (2022). Time-series analysis of landcover dynamics and their relation with coastline migration along Kuakata coast, Bangladesh using remote sensing techniques. Geology, Ecology and Landscapes. https://doi.org/10.1080/24749508.2022.2097374
• Alevkayalı, Ç., Atayeter, Y., Yayla, O., Bilgin, T., & Akpınar, H. (2023). Burdur Gölü’nde uzun dönemli kıyı çizgisi değişimleri ve iklim ilişkisi: Zamansal-mekânsal eğilimler ve tahminler. Türk Coğrafya Dergisi, 82, 37-50. https://doi.org/10.17211/tcd.1287976
• Appeaning Addo, K. (2013). Shoreline morphological changes and the human factor. Case study of Accra Ghana. Journal of Coastal Conservation, 17(1), 85–91. https://doi.org/10.1007/s11852-012-0220-5
• Amrouni, O., Hzami, A., & Heggy, E. (2019). Photogrammetric assessment of shoreline retreat in North Africa: anthropogenic and natural drivers. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 73–92. https://doi.org/10.1016/j.isprsjprs.2019.09.001
• Ayalke, Z. G., Şişman, A., & Akpinar, K. (2023). Shoreline extraction and analyzing the effect of coastal structures on shoreline changing with remote sensing and geographic information system: Case of Samsun, Turkey. Regional Studies in Marine Science, 61, 2352-4855. https://doi.org/10.1016/j.rsma.2023.102883
• Ataol, M. (2010). Burdur Gölü’nde seviye değişimleri. Coğrafi Bilimler Dergisi, 8(1), 77-92. https://doi.org/10.1501/Cogbil_0000000105
• Ball, G.H., & Hall, D.J. (1965). Isodata, a novel method of data analysis and pattern classification. Standford Research Institute. https://apps.dtic.mil/sti/pdfs/AD0699616.pdf
• Bahadır, M., & Özdemir, M.A. (2011). Acıgöl havzasının sayısal topoğrafik analiz yöntemleri ile morfometrik jeomorfolojisi. The Journal of International Social Research, 4(18), 323–344. https://hdl.handle.net/11630/8163
• Barik, K.K., Annaduari, R., Mohanty, P. C., Mahendra, R.S., Tripathy, J. K., & Mitra, D. (2019). Statistical assessment of long-term shoreline changes along the Odisha Coast. Indian Journal of Geo Marine Sciences, 48(12), 1990-1998. http://nopr.niscpr.res.in/handle/123456789/52790
• Gülle, İ., Turna, I., Güçlü, S.S., Küçük, F., & Gülle, P. (2008). The vertical profile of water temperature, dissolved oxygen, pH and conductivity in Lake Burdur, Turkey. Ege University Journal of Fisheries & Aquatic Sciences, 25(4), 283–287. https://doi.org. 10.12714/egejfas.2008.25.4.5000156609
• Canbaz, O., Gürsoy, Ö., & Gökce, A. (2018). Detecting clay minerals in hydrothermal alteration areas with integration of aster image and spectral data in Kösedağ-Zara (Sivas), Turkey. Journal of the Geoological Society of India, 91, 483–488. https://doi.org/10.1007/s12594-018-0882-1
• Canbaz, O., Gürsoy, Ö., & Gökçe, A. (2017). Determination of hydrothermal alteration areas by aster satellite images: Ağmaşat Plato-Zara (Sivas) / Turkey sample. Cumhuriyet Science Journal, 38(3), 419-426. https://doi.org/10.17776/csj.340473
• Carvalho, R.C., Kennedy, D.M., Niyazi, Y., Cleach, C., Konlechner, T.M., & Ierodiaconou, D. (2020). Structure‐from‐motion photogrammetry analysis of historical aerial photography: determining beach volumetric change over decadal scales. Earth Surface Processes Landforms, 45, 2540–2555. https://doi.org/ 10.1002/esp.4911
• Gözükara, G., Altunbaş, S., & Sarı, M. (2020). Zamansal ve mekansal değişimlerin eski göl tabanlarındaki toprak oluşumu, gelişimi ve morfolojisi üzerine etkisi. Harran Tarım ve Gıda Bilimleri Dergisi, 24(1): 96-110. https://doi.org/ 10.29050/harranziraat.581874
• Davraz, A., Şener, E., & Şener, Ş. (2019). Evaluation of climate and human effects on the hydrology and water quality of Burdur Lake, Turkey. Journal of African Earth Science, 158, 103569. https://doi.org/10.1016/j.jafrearsci.2019.103569
• Dey, M., Sakthivel, P.S., & Jena, B.K. (2021). A shoreline change detection (2012-2021) and forecasting using digital shoreline analysis system (DSAS) Tool: a case study of Dahej Coast, Gulf of Khambhat, Gujarat, India. The Indonesian Journal of Geography, 53(2). https://doi.org/10.22146/ijg.56297
• Dervisoğlu, A., Yağmur, N., Firatli, E., Musaoğlu, N., & Tanik, A. (2022). Spatio-temporal assessment of the shrinking Lake Burdur, Turkey. International Journal of Environment and Geoinformatics (IJEGEO), 9(2), 169-176. https://doi.org/10.30897/ijegeo. 1078781
• Hepdeniz, K. (2020). Determination of Burdur Lake's areal change in upcoming years using geographic information systems and the artificial neural network method. Arabian Journal of Geoscience, 13, 1143. https://doi.org/10.1007/s12517-020-06137-5
• Kalman, R. (1960). A new approach to linear filtering and prediction problems, Journal of Basic Engineering, 82(1), 35–45. https://doi.org/10.1115/1.3662552
• Kale, M. M., Ataol, M., & Tekkanat, İ. S. (2019). Assessment of shoreline alterations using a digital shoreline analysis system: a case study of changes in the Yeşilırmak Delta in northern Turkey from 1953 to 2017. Environment Monioring and Assessment, 191, 398. https://doi.org/10.1007/s10661-019-7535-8
• Kuleli, T., & Bayazıt, S. (2022). Development of a method to measure the sustainability of coastal uses. Environmental Development and Sustainability, 25(6), 5141-5161. https://doi.org/10.1007/s10668-022-02259-w
• Kuleli, T. (2010). Quantitative analysis of shoreline changes at the Mediterranean Coast in Turkey. Environmental Monitoring and Assessment, 167(1-4), 387–397. https://doi.org/10.1007/s10661-009-1057-8
• Kuleli T. (2011). Automatic detection of shoreline change on coastal ramsar wetlands of Turkey. Ocean Engineering 38(10), 1141–1149. https://doi:10.1016/j.oceaneng.2011.05.00
• Lowe, M. K., Adnan, F.A.F., Hamylton, M., Carvalho, R.C., & Woodroffe, C.D. (2019). Assessing reef-island shoreline change using uav-derived orthomosaics and digital surface models. Drones, 3(2), 44. https://doi.org/10.3390/drones3020044
• Mishra, M., Acharyya, T., Chand, P., Guimarães Santos, C. A., Kar, D., Das, P.P., Pattnaik, N., Silva, R.M., & Medeiros do Nascimento, T.V. (2021). Analyzing shoreline dynamicity and the associated socioecological risk along the Southern Odisha Coast of India using remote sensing-based and statistical approaches. Geocarto International, 37(14), 3991–4027. https://doi.org/10.11080/10106049.2021.188200
• Mitri, G., Nader, M., Dagher, M.A., & Gebrael, K. (2020). Investigating the performance of sentinel-2A and landsat 8 imagery in mapping shoreline changes. Journal of Coastal Conservation, 24(40). https://doi.org/10.1007/s11852-020-00758-4
• Nassar, K., Mahmod, E.W., Fath, H., Masria, A., Nadaoka, K., & Negm, A. (2018). Shoreline change detection using DSAS technique: Case of north Sinai coast, Egypt. Marine Georesources Geotechnology, 37(1), 81-95. https://doi.org/10.1080/1064119X.2018.1448912
• Pradhan, B., Rizeei, H.M., & Abdulle, A. (2018). Quantitative assessment for detection and monitoring of coastline dynamics with temporal Radarsat images. Remote Sensing, 10(11), 1705. https://doi.org/10.3390/rs10111705 Qiao, G., Mi, H., Wang, W., Tong, X., Li, Z., Li, T., &Hong, Y. (2018). 55-year (1960–2015) spatiotemporal shoreline change analysis using historical disp and landsat time series data in Shanghai. International Journal of Applied Earth Observation and Geoinformation, 68, 238–251. https://doi.org/10.1016/j.jag.2018.02.009
• Sabuncu, A. (2020). Burdur Gölü kıyı şeridindeki değişiminin uzaktan algılama ile haritalanması, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 20(4), (623-633). https://doi.org/10.35414/akufemubid.711653
• Samarawickrama, U., Piyaratne, D., & Ranagalage, M. (2017). Relationship between NDVI with Tasseled cap indices: a remote sensing based analysis. International Journal of Innovative Research Technology, 3(12).
• Santra, M., Dwivedi, C.S., & Pandey, A.C. (2023). Quantifying shoreline dynamics in the Indian Sundarban delta with Google Earth Engine (GEE)-based automatic extraction approach. Tropical Ecology, https://doi.org/10.1007/s42965-023-00321-w
• Sarp, G., & Özçelik, M. (2017). Water body extraction and change detection using time series: a case study of Lake Burdur, Turkey. Journal of Taibah University for Science, 11(3), 381-391. https://doi.org/ 10.1016/j.jtusci.2016.04.005
• Survey, U.-U.S.G, Earth Explorer. (2022, 10 January). https://earthexplorer.usgs.gov/
• Şener, E., Davraz, A., & Ismailov, T. (2005). The monitoring Burdur Lake water level changes with multi-time monitoring satellite images, (in Turkish). Türkiye Kuvaterner Sempozyumu (TURQUA-V) (pp. 148-15). Istanbul.
• Tamer, Y., Berberoğlu, E., & Gülle, İ. (Ed.). (2020). Burdur’un doğası. Doğa Koruma ve Milli Parklar 6. Bölge Müdürlüğü.
• Thieler, E., Himmelstoss, E., Zichichi, J., & Ergül, A. (2009). The Digital Shoreline Analysis System (DSAS) version 4.0. an ArcGIS extension for calculating shoreline change. US Geological Survey. https://doi.org/10.3133/ofr20081278
• TÜİK- Veri Portalı. (2022, 11 Eylül). https://data.tuik.gov.tr/Kategori/GetKategori?p=Nufus-ve-Demografi-109
• Wang, H., Xu, D., Zhang, D., Pu, Y., & Luan, Z. (2022). Shoreline dynamics of Chongming Island and driving factor analysis based on landsat images. Remote Sensing, 14, 3305. https://doi.org/10.3390/rs14143305