Journal of Natural Hazards and Environment
Research Article
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Temporal Variability of the Batumi Delta Wetlands

Year 2023, Volume 9, Issue 1, 101 - 111, 27.01.2023

Bülent TURGUT

https://doi.org/10.21324/dacd.1172810

Abstract

Wetlands are invaluable ecosystems as they provide important services, but these areas are rapidly degrading due to anthropogenic pressures and natural changes/transformations. It is thought that this negative change will increase in the upcoming period due to the climate change and the rising demand for soil and water. Determining the changes in wetlands is very important in terms of management planning of these areas. NDWI (Normalized Difference Water Index) is a widely used method for determining the hydrological characteristics of wetlands. This study was carried out to determine the temporal variability of wetlands in the Batumi Delta and the relationship of this change with basic climate parameters such as average temperature and total precipitation. For this purpose, the average temperature and total precipitation amount of the months of April, May, June, July, August, and September in the period between 2016-2021 of the study area and the NDWI values of these time periods were determined. Green and NIR bands of Sentinel2 satellite images were used to determine NDWI values. QGIS software was used to process satellite images, and ArcGIS software was used to determine and map NDWI values. As a result of the study, it was seen that NDWI values changed by months and years, and the temperature was more effective than precipitation on this change.


Keywords

Geographic Information Systems, Ecology, Regression, NDWI

References

  • Achmad A., Zainuddin Muftiadi, M., (2019), The relationship between land surface temperature and water index in the urban area of a tropical city, IOP Conference Series Earth and Environmental Science 365(1), 012013. doi: 10.1088/1755-1315/365/1/012013.
  • Açıksarı, E., Akçay, Ö., Avşar, E.Ö., (2018), SENTINEL-1, PolSAR ve SENTINEL-2 Optik Uydu Görüntülerinin Füzyon ile Sınıflandırılması, VII. Uzaktan Algılama-CBS Sempozyumu (UZAL-CBS 2018), 18-21 Eylül, Eskişehir.
  • Bala R., Prasad R., Yadav V.P., Sharma J., (2018), A Comparative Study of Land Surface Temperature with Different Indices on Heterogeneous Land Cover Using Landsat 8 Data, ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 389-394.
  • Bhangale U., More S., Shaikh T., Patil S., More N., (2020), Analysis of Surface Water Resources Using Sentinel-2 Imagery, Procedia, 171, 2645-2654.
  • Dereli, M.A., (2019), Sentinel-2A Uydu Görüntüleri ile Giresun İl Merkezi için Kısa Dönem Arazi Örtüsü Değişiminin Belirlenmesi, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19, 361-368.
  • Du Y., Zhang Y., Ling F., Wang Q., Li W., Li X., (2016), Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band, Remote Sensing, 8, 354. doi: 10.3390/rs8040354.
  • Erdoğan, M.A., Sönmez, F., Beberoğlu, S., (2014), Baraj göllerinde su seviyelerinin uzaktan algılama ve CBS ile tahmini: Adana Seyhan Baraj Gölü Örneği. 5. Uzaktan Algılama-CBS Sempozyumu (UZAL-CBS 2014), 14-17 Ekim, İstanbul.
  • Fennessy S., Jacobs A., Kentula M.E., (2007), An evaluation of rapid methods for assessing the ecological condition of wetlands, Wetlands, 27(3), 543–560.
  • Gao B., (1996), NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58(3), 257-266.
  • Han, Q., Niu, Z., (2020). Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set. Remote Sensing, 12(17), 2675. doi: 10.3390/rs12172675.
  • Huang C., Chen Y., Wu J.P., (2014), Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery, International Journal of Applied Earth Observation and Geoinformation, 2014 (26), 350–362.
  • Kool J., Lhermitte S., Hrachowitz M., Bregoli F., McClain M.E., (2022), Seasonal inundation dynamics and water balance of the Mara Wetland, Tanzania based on multi-temporal Sentinel-2 image classification, International Journal of Applied Earth Observation and Geoinformation, 109,102766. doi: 10.1016/j.jag.2022.102766.
  • Li W.B., Du Z.Q., Ling F., Zhou D.B., Wang H.L., Gui Y.M., Sun B.Y., Zhang X.M., (2013), A comparison of land surface water mapping using the normalized difference water index from TM, ETM plus and ALI, Remote Sensing, 5, 5530–5549.
  • Li W., Qin Y., Sun Y., Huang H., Ling F., Tian L., Ding Y., (2016), Estimating the relationship between dam water level and surface water area for the Danjiangkou Reservoir using Landsat remote sensing images, Remote Sensing Letter, 2016 (7), 121–130.
  • Maltby E., Acreman M.C., (2011), Ecosystem services of wetlands: pathfinder for a new paradigm, Hydrological Science Journal, 56, 1341-1359.
  • 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, 1425–1432.
  • Mitsch W.J., Gossilink J.G., (2000), The value of wetland: importance of scale and landscape setting, Ecological Economics, 35, 25-33.
  • Palacios-Orueta A., Khanna S., Litago J., Whiting M.L., Ustin S.L. (2005), Assessment of NDVI and NDWI spectral indices using MODIS time series analysis and development of a new spectral index based on MODIS shortwave infrared bands, 1st International Conference of Remote Sensing and Geoinformation Processing, September 7-9, Trier, Germany. doi: 10.13140/2.1.1305.4400.
  • Papa F., Prigent C., Rossow W.B., (2008), Monitoring flood and discharge variations in the large siberian rivers from a multi-satellite technique, Surveys in Geophysics, 29, 297–317.
  • Pickens, A.H., Hansen, M.C., Hancher, M., Stehman, S.V., Tyukavina, A., Potapov, P., Marroquin, B., Sherani, Z., (2020). Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sensing of Environment, 243, 111792. doi: 10.1016/j.rse.2020.111792.
  • Rebelo A.J., Scheunders P., Esler K.J., Meire P., (2017), Detecting mapping and classifying wetland fragments at a landscape scale, Remote Sensing Applications: Society and Environment, 8, 212-223.
  • Ryu J.H., Won J.S., Min K.D., (2002), Waterline extraction from Landsat TM data in a tidal flat—A case study in Gomso Bay, Korea. Remote Sensing and Environment, 2002(83), 442–456.
  • Safa M., Zarkesh M.K., Ejlali F., Farsad F., (2021), The spatial autocorrelation between precipitation and vegetation Indices in the Bandar Abbas Basin, International Journal of Scientific Research and Management, 09(12): 199-214.
  • Seid M.H., (2018), Urban Landscape Dynamics and the Implication on Surface Urban Heat Island: The Case of Hawassa Town and Surrounding Area, Ethiopia, Master Thesis, Addis Ababa University School of Graduate Studies, Department of Civil and Environmental Engineering, Addis Ababa, Ethiopia.
  • Sivanpillai R., Miller S.N., (2010), Improvements in mapping water bodies using ASTER data, Ecological Information, 5, 73–78.
  • Su Y., Bale, R.C., Ma Q., Nydick K., Ray R.L., Li W., Guo Q., (2017), Emerging stress and relative resiliency of Giant sequoia groves experiencing multiyear dry periods in a warming climate, Journal of Geophysical Research: Biogeosciences, 122(11), 3063-3075.
  • Teng J., Xia S., Yu X., Duan H., Xiao H., Zhao C., (2021), Assessing habitat suitability for wintering geese by using Normalized Difference Water Index (NDWI) in a large floodplain wetland, China, Ecological Indicators, 122, 107260. doi: 10.1016/j.ecolind.2020.107260.
  • Tong B., Guo J., Xu H., Wang Y., Li H., Bian L., Zhang J., Zhou S., (2022), Effects of soil moisture, net radiation, and atmospheric vapor pressure deficit on surface evaporation fraction at a semi-arid grass site, Science of The Total Environment, 849, 157890. doi: 10.1016/j.scitotenv.2022.157890.
  • Villmow J.R., (1962), Regional pattern of climates in Europe according to the Thornthwaite classification. The Ohio Journal of Science, 62(1), 39-53.
  • Weise K., Höfe R., Franke J., Guelmami A., Simonson W., Muro J.,O’Connor B., Strauch A., Flink F., Eberle J, Mino E., Thulin S., Philipson P., Valkengoed V., Truckenbrodt J., Zander F.,Sánchez K., Schröder C., Thonfeld F., Fitoka E, Scott E., Ling M., Schwarz M., Kunz I., Thürmer G., Plasmeijer A., Hilarides L., (2020), Wetland extent tools for SDG 6.6.1 reporting from the Satellite-based Wetland Observation Service (SWOS), Remote Sensing of Environment, 247, 111892. doi: 10.1016/j.rse.2020.111892.
  • Wu J.G., (2013), Landscape sustainability science: ecosystem services and human wellbeing in changing landscapes, Landscape Ecology, 28, 999–1023.
  • Wu J.S., Feng Z., Gao Y., Peng J., (2013), Hotspot and relationship identification in multiple landscape services: a case study on an area with intensive human activities, Ecological Indicators, 29, 529–537.
  • XLSTST, (2022), XLSTAT: Statistical Software for Excel, https://www.xlstat.com/en/, [Erişim 07 Eylül 2022].
  • Yang X., Zhao S., Qin X., Zhao N., Liang L., (2017), Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening, Remote Sensing, 9(6), 596. doi: 10.3390/rs9060596.
  • Yılmaz, M., (2018), Tarımsal Yaz Ürünlerin Sentinel-2 Uydu Görüntülerinden Rastgele Orman Algoritması ile Nesne-Tabanlı Sınıflandırılması, Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.

Batum Deltası Sulak Alanlarının Zamansal Değişimi

Year 2023, Volume 9, Issue 1, 101 - 111, 27.01.2023

Bülent TURGUT

https://doi.org/10.21324/dacd.1172810

Abstract

Sulak alanlar sağladıkları hizmetler nedeniyle çok değerli ekosistemlerdir, ancak bu alanlar antropojenik baskılar ve doğal değişimler/dönüşümler nedeniyle hızlı bir şekilde bozulmaktadırlar. Bu olumsuz değişimin önümüzdeki süreçte iklim değişikliği ve artan toprak ve su talebi nedeniyle daha da artacağı düşünülmektedir. Sulak alanlarda görülen değişimlerin belirlenmesi bu alanların yönetim planlamaları açısından oldukça önemlidir. NDWI (Normalized Difference Water Index), sulak alanların hidrolojik özelliklerinin belirlenmesinde yaygın olarak kullanılan bir yöntemdir. Bu çalışma, Batum Deltasında bulunan sulak alanların zamansal değişimini ve bu değişimin ortalama sıcaklık ve toplam yağış gibi temel iklim parametreleri ile olan ilişkilerini belirlemek amacıyla yürütülmüştür. Bu amaçla çalışma alanına ait 2016-2021 yılları arasındaki periyotta Nisan, Mayıs, Haziran, Temmuz, Ağustos ve Eylül aylarına ait ortalama sıcaklık ve toplam yağış miktarları ile yine bu zaman dilimlerine ait NDWI değerleri belirlenmiştir. NDWI değerlerinin belirlenmesinde Sentinel2 uydu görüntülerine ait yeşil ve NIR bantları kullanılmıştır. Uydu görüntülerinin işlenmesinde QGIS ve NDWI değerlerinin belirlenmesi ve haritalanmasında ise ArcGIS yazılımı kullanılmıştır. Çalışma sonucunda, NDWI değerlerinin aylara ve yıllara göre değişim gösterdiği ve bu değişim üzerinde sıcaklığın yağıştan daha etkili olduğu görülmüştür.

Keywords

Coğrafi Bilgi Sistemleri, Ekoloji, Regresyon, NDWI

References

  • Achmad A., Zainuddin Muftiadi, M., (2019), The relationship between land surface temperature and water index in the urban area of a tropical city, IOP Conference Series Earth and Environmental Science 365(1), 012013. doi: 10.1088/1755-1315/365/1/012013.
  • Açıksarı, E., Akçay, Ö., Avşar, E.Ö., (2018), SENTINEL-1, PolSAR ve SENTINEL-2 Optik Uydu Görüntülerinin Füzyon ile Sınıflandırılması, VII. Uzaktan Algılama-CBS Sempozyumu (UZAL-CBS 2018), 18-21 Eylül, Eskişehir.
  • Bala R., Prasad R., Yadav V.P., Sharma J., (2018), A Comparative Study of Land Surface Temperature with Different Indices on Heterogeneous Land Cover Using Landsat 8 Data, ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 389-394.
  • Bhangale U., More S., Shaikh T., Patil S., More N., (2020), Analysis of Surface Water Resources Using Sentinel-2 Imagery, Procedia, 171, 2645-2654.
  • Dereli, M.A., (2019), Sentinel-2A Uydu Görüntüleri ile Giresun İl Merkezi için Kısa Dönem Arazi Örtüsü Değişiminin Belirlenmesi, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19, 361-368.
  • Du Y., Zhang Y., Ling F., Wang Q., Li W., Li X., (2016), Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band, Remote Sensing, 8, 354. doi: 10.3390/rs8040354.
  • Erdoğan, M.A., Sönmez, F., Beberoğlu, S., (2014), Baraj göllerinde su seviyelerinin uzaktan algılama ve CBS ile tahmini: Adana Seyhan Baraj Gölü Örneği. 5. Uzaktan Algılama-CBS Sempozyumu (UZAL-CBS 2014), 14-17 Ekim, İstanbul.
  • Fennessy S., Jacobs A., Kentula M.E., (2007), An evaluation of rapid methods for assessing the ecological condition of wetlands, Wetlands, 27(3), 543–560.
  • Gao B., (1996), NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58(3), 257-266.
  • Han, Q., Niu, Z., (2020). Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set. Remote Sensing, 12(17), 2675. doi: 10.3390/rs12172675.
  • Huang C., Chen Y., Wu J.P., (2014), Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery, International Journal of Applied Earth Observation and Geoinformation, 2014 (26), 350–362.
  • Kool J., Lhermitte S., Hrachowitz M., Bregoli F., McClain M.E., (2022), Seasonal inundation dynamics and water balance of the Mara Wetland, Tanzania based on multi-temporal Sentinel-2 image classification, International Journal of Applied Earth Observation and Geoinformation, 109,102766. doi: 10.1016/j.jag.2022.102766.
  • Li W.B., Du Z.Q., Ling F., Zhou D.B., Wang H.L., Gui Y.M., Sun B.Y., Zhang X.M., (2013), A comparison of land surface water mapping using the normalized difference water index from TM, ETM plus and ALI, Remote Sensing, 5, 5530–5549.
  • Li W., Qin Y., Sun Y., Huang H., Ling F., Tian L., Ding Y., (2016), Estimating the relationship between dam water level and surface water area for the Danjiangkou Reservoir using Landsat remote sensing images, Remote Sensing Letter, 2016 (7), 121–130.
  • Maltby E., Acreman M.C., (2011), Ecosystem services of wetlands: pathfinder for a new paradigm, Hydrological Science Journal, 56, 1341-1359.
  • 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, 1425–1432.
  • Mitsch W.J., Gossilink J.G., (2000), The value of wetland: importance of scale and landscape setting, Ecological Economics, 35, 25-33.
  • Palacios-Orueta A., Khanna S., Litago J., Whiting M.L., Ustin S.L. (2005), Assessment of NDVI and NDWI spectral indices using MODIS time series analysis and development of a new spectral index based on MODIS shortwave infrared bands, 1st International Conference of Remote Sensing and Geoinformation Processing, September 7-9, Trier, Germany. doi: 10.13140/2.1.1305.4400.
  • Papa F., Prigent C., Rossow W.B., (2008), Monitoring flood and discharge variations in the large siberian rivers from a multi-satellite technique, Surveys in Geophysics, 29, 297–317.
  • Pickens, A.H., Hansen, M.C., Hancher, M., Stehman, S.V., Tyukavina, A., Potapov, P., Marroquin, B., Sherani, Z., (2020). Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sensing of Environment, 243, 111792. doi: 10.1016/j.rse.2020.111792.
  • Rebelo A.J., Scheunders P., Esler K.J., Meire P., (2017), Detecting mapping and classifying wetland fragments at a landscape scale, Remote Sensing Applications: Society and Environment, 8, 212-223.
  • Ryu J.H., Won J.S., Min K.D., (2002), Waterline extraction from Landsat TM data in a tidal flat—A case study in Gomso Bay, Korea. Remote Sensing and Environment, 2002(83), 442–456.
  • Safa M., Zarkesh M.K., Ejlali F., Farsad F., (2021), The spatial autocorrelation between precipitation and vegetation Indices in the Bandar Abbas Basin, International Journal of Scientific Research and Management, 09(12): 199-214.
  • Seid M.H., (2018), Urban Landscape Dynamics and the Implication on Surface Urban Heat Island: The Case of Hawassa Town and Surrounding Area, Ethiopia, Master Thesis, Addis Ababa University School of Graduate Studies, Department of Civil and Environmental Engineering, Addis Ababa, Ethiopia.
  • Sivanpillai R., Miller S.N., (2010), Improvements in mapping water bodies using ASTER data, Ecological Information, 5, 73–78.
  • Su Y., Bale, R.C., Ma Q., Nydick K., Ray R.L., Li W., Guo Q., (2017), Emerging stress and relative resiliency of Giant sequoia groves experiencing multiyear dry periods in a warming climate, Journal of Geophysical Research: Biogeosciences, 122(11), 3063-3075.
  • Teng J., Xia S., Yu X., Duan H., Xiao H., Zhao C., (2021), Assessing habitat suitability for wintering geese by using Normalized Difference Water Index (NDWI) in a large floodplain wetland, China, Ecological Indicators, 122, 107260. doi: 10.1016/j.ecolind.2020.107260.
  • Tong B., Guo J., Xu H., Wang Y., Li H., Bian L., Zhang J., Zhou S., (2022), Effects of soil moisture, net radiation, and atmospheric vapor pressure deficit on surface evaporation fraction at a semi-arid grass site, Science of The Total Environment, 849, 157890. doi: 10.1016/j.scitotenv.2022.157890.
  • Villmow J.R., (1962), Regional pattern of climates in Europe according to the Thornthwaite classification. The Ohio Journal of Science, 62(1), 39-53.
  • Weise K., Höfe R., Franke J., Guelmami A., Simonson W., Muro J.,O’Connor B., Strauch A., Flink F., Eberle J, Mino E., Thulin S., Philipson P., Valkengoed V., Truckenbrodt J., Zander F.,Sánchez K., Schröder C., Thonfeld F., Fitoka E, Scott E., Ling M., Schwarz M., Kunz I., Thürmer G., Plasmeijer A., Hilarides L., (2020), Wetland extent tools for SDG 6.6.1 reporting from the Satellite-based Wetland Observation Service (SWOS), Remote Sensing of Environment, 247, 111892. doi: 10.1016/j.rse.2020.111892.
  • Wu J.G., (2013), Landscape sustainability science: ecosystem services and human wellbeing in changing landscapes, Landscape Ecology, 28, 999–1023.
  • Wu J.S., Feng Z., Gao Y., Peng J., (2013), Hotspot and relationship identification in multiple landscape services: a case study on an area with intensive human activities, Ecological Indicators, 29, 529–537.
  • XLSTST, (2022), XLSTAT: Statistical Software for Excel, https://www.xlstat.com/en/, [Erişim 07 Eylül 2022].
  • Yang X., Zhao S., Qin X., Zhao N., Liang L., (2017), Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening, Remote Sensing, 9(6), 596. doi: 10.3390/rs9060596.
  • Yılmaz, M., (2018), Tarımsal Yaz Ürünlerin Sentinel-2 Uydu Görüntülerinden Rastgele Orman Algoritması ile Nesne-Tabanlı Sınıflandırılması, Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.

Details

Primary Language Turkish
Subjects Environmental Sciences
Published Date Ocak 2023
Journal Section Research Articles
Authors

Bülent TURGUT> (Primary Author)
KARADENİZ TEKNİK ÜNİVERSİTESİ, ORMAN FAKÜLTESİ, ORMAN MÜHENDİSLİĞİ BÖLÜMÜ
0000-0001-7443-1100
Türkiye

Publication Date January 27, 2023
Published in Issue Year 2023, Volume 9Issue 1

Cite

Bibtex @research article { dacd1172810, journal = {Doğal Afetler ve Çevre Dergisi}, eissn = {2528-9640}, address = {}, publisher = {Artvin Çoruh University}, year = {2023}, volume = {9}, number = {1}, pages = {101 - 111}, doi = {10.21324/dacd.1172810}, title = {Batum Deltası Sulak Alanlarının Zamansal Değişimi}, key = {cite}, author = {Turgut, Bülent} }
APA Turgut, B. (2023). Batum Deltası Sulak Alanlarının Zamansal Değişimi . Doğal Afetler ve Çevre Dergisi , 9 (1) , 101-111 . DOI: 10.21324/dacd.1172810
MLA Turgut, B. "Batum Deltası Sulak Alanlarının Zamansal Değişimi" . Doğal Afetler ve Çevre Dergisi 9 (2023 ): 101-111 <http://dacd.artvin.edu.tr/en/pub/issue/75518/1172810>
Chicago Turgut, B. "Batum Deltası Sulak Alanlarının Zamansal Değişimi". Doğal Afetler ve Çevre Dergisi 9 (2023 ): 101-111
RIS TY - JOUR T1 - Temporal Variability of the Batumi Delta Wetlands AU - BülentTurgut Y1 - 2023 PY - 2023 N1 - doi: 10.21324/dacd.1172810 DO - 10.21324/dacd.1172810 T2 - Doğal Afetler ve Çevre Dergisi JF - Journal JO - JOR SP - 101 EP - 111 VL - 9 IS - 1 SN - -2528-9640 M3 - doi: 10.21324/dacd.1172810 UR - https://doi.org/10.21324/dacd.1172810 Y2 - 2022 ER -
EndNote %0 Journal of Natural Hazards and Environment Batum Deltası Sulak Alanlarının Zamansal Değişimi %A Bülent Turgut %T Batum Deltası Sulak Alanlarının Zamansal Değişimi %D 2023 %J Doğal Afetler ve Çevre Dergisi %P -2528-9640 %V 9 %N 1 %R doi: 10.21324/dacd.1172810 %U 10.21324/dacd.1172810
ISNAD Turgut, Bülent . "Batum Deltası Sulak Alanlarının Zamansal Değişimi". Doğal Afetler ve Çevre Dergisi 9 / 1 (January 2023): 101-111 . https://doi.org/10.21324/dacd.1172810
AMA Turgut B. Batum Deltası Sulak Alanlarının Zamansal Değişimi. J Nat Haz Environ. 2023; 9(1): 101-111.
Vancouver Turgut B. Batum Deltası Sulak Alanlarının Zamansal Değişimi. Doğal Afetler ve Çevre Dergisi. 2023; 9(1): 101-111.
IEEE B. Turgut , "Batum Deltası Sulak Alanlarının Zamansal Değişimi", Doğal Afetler ve Çevre Dergisi, vol. 9, no. 1, pp. 101-111, Jan. 2023, doi:10.21324/dacd.1172810
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