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A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS

Yıl 2025, Cilt: 14 Sayı: 1, 1 - 13, 29.01.2025
https://doi.org/10.18036/estubtdc.1509648

Öz

Observation, monitoring, and characterization of land changes in natural ecosystems that are under the influence of many natural or anthropogenic environmental factors are very important in terms of taking effective and sustainable management decisions and protecting them. Today, remote sensing methods facilitate continuous and controlled spatial change monitoring studies, especially in large areas, with the many different methods and techniques they provide, and thus offer cost and time effective solutions. In this study, it was aimed to determine the changes in land and water potential of the Porsuk dam lake and its near surroundings, located between Eskişehir and Kütahya provinces, using remote sensing methods over a 10-year period. In this context, Landsat satellite data for the years 2014 and 2024 and the days with the least cloudiness were obtained, and normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) calculations were made on these data using the ArcGis/ArcMap program. Later, the obtained results were compared and changes in land and water potential were determined. According to the results of NDVI analysis, it was determined that the presence of forests (4.78%) and areas with herbaceous vegetation (5.56%) increased in the 10-year period, whereas soil (-2.70%), tree/shrub (-1.26%) areas and the water body decreased (-5.87%). According to the results of NDWI analysis, it was determined that dry (2.02%) and moderately dry (10.81%) areas increased, while water body (-8.87%) and humid areas (-11.71%) decreased. The results were also supported by surface temperature analysis. Since the results obtained from the study include data on temporal and spatial changes, it is thought that they will contribute to future planning, management and decision-making processes and studies to be carried out in this field.

Kaynakça

  • [1] Ellis EC, Klein Goldewijk K, Siebert S, Lightman D, Ramankutty N. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecol Biogeogr 2010; 19: 589–606.
  • [2] Fischer G, Sun L. Model based analysis of future land-use development in China, Agric Ecosyst Environ 2001; 85 (1-3): 163-176.
  • [3] Zhang J, Zhang Y. Remote sensing research issues of the National Land Use Change Program of China. ISPRS J Photogramm 2007; 62: 461–472.
  • [4] Zhu Z, Qiu S, Ye S. Remote sensing of land change: A multifaceted perspective. Remote Sens Environ 2022; 282: 113266.
  • [5] Krsnik G, Reynolds KM, Murphy P, Paplanus S, Garcia-Gonzalo J, Olabarria JRG. Forest use suitability: Towards decision-making-oriented sustainable management of forest ecosystem services. Geogr Sustain 2023; 4: 414–427.
  • [6] Beck PS, Atzberger C, Høgda KA, Johansen B, Skidmore AK. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens Environ 2006; 100 (3): 321-334.
  • [7] Ahmadi H, Nusrath A. Vegetation change Detection of Neka river in Iran by using remote sensing and GIS. Journal of Geography and Geology 2012; 2 (1): 58-67.
  • [8] Meera Gandhi G, Parthiban S, Thummalu N, Christy A. Ndvi: Vegetation change detection using remote sensing and gis – A case study of Vellore District. Procedia Comput Sci 2015; 57: 1199-1210.
  • [9] Da Ponte E, Roch M, Leinenkugel P, Dech S, Kuenzer C. Emanuel Da vd, (2017), Paraguay's Atlantic forest cover loss e satellite-based change detection and fragmentation analysis between 2003 and 2013. Appl Geogr 2017; 79: 37-49.
  • [10] Roy DP, Wulder MA, Loveland TR, Ce W, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R. et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens Environ 2014; 145: 154–172.
  • [11] Belward AS, Skøien JO. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. . ISPRS J Photogramm 2015; 103: 115–128.
  • [12] Ustin SL, Middleton EM. Current and near-term advances in Earth observation for ecological applications. Ecol Process 2021; 10: 1–57.
  • [13] Lu D, Mausel P, Brondizio E, Moran E. 2004. Change detection techniques. Int J Remote Sens 2004; 25: 2365–2401.
  • [14] Rogan J, Chen D. Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog Plann 2004; 61: 301–325.
  • [15] Bellón B, Bégué A, Seen DL, de Almeida CA, Simões M. A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. Remote Sens 2017; 9: 600.
  • [16] Huang S, Tang L, Hupy JP, Wang Y, Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J For Res 2021; 32(1):1–6.
  • [17] Lemenkova P, Debeir O. R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC. Appl Sci 2022; 12 (24): 12554.
  • [18] Ichii K, Kawabata A, Yamaguchi Y. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. Int J Remote Sens 2002; 23 (18): 3873–3878.
  • [19] Kasimati A, Psiroukis V, Darra N, Kalogrias A, Kalivas D, Taylor JA, Fountas S. Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability. Precis Agric 2023; 24:1220–1240.
  • [20] Saylan İH, Çömert R. Investigation of the success of Sentinel-2A products in mapping of burned forest areas. TUZAL 2019; 1(1), 8-15.
  • [21] Doğan HM, Kılıç OM, Yılmaz DS. Researching plant density classes of Tokat province by Landsat-7 ETM+ satellite images and geographical information systems. Journal of Agricultural Faculty of Gaziosmanpaşa University 2014; 31(1): 47-53.
  • [22] Özyavuz M, Bilgili BC, Salıcı A. Determination of vegetation changes with NDVI method. J Environ Prot Ecol 2015; 16(1): 264-273.
  • [23] Hartoyo APP, Sunkar A, Ramadani R, Faluthi S, Hidayati S. Normalized Difference Vegetation Index (NDVI) analysis for vegetation cover in Leuser Ecosystem area, Sumatra, Indonesia. Biodiversitas 2021; 22(3): 1160-1171.
  • [24] Yang S, Zhao Y, Yang D, Lan A. Analysis of vegetation NDVI changes and driving factors in the Karst concentration distribution area of Asia. Forests 2024; 15: 398.
  • [25] Hunault-Fontbonne J, Eyvindson K. (2023), Bridging the gap between forest planning and ecology in biodiversity forecasts: A review. Ecol Ind 2023; 154: 110620.
  • [26] Özvan H, Arık B, Yeler O, Şatır O, Bostan P. Determining land change using remote sensing and geographical information systems techniques: the case of lake Karataş and its surroundings, Peyzaj 2023; 5(1): 30-39.
  • [27] Szabó S, Gácsi Z, Balázs B. Spesific features of NDVI, NDWI as reflected in land cover categories. Landscape & Environment 2016; 10(3-4): 194+202.
  • [28] Özelkan E. Water Body Detection Analysis Using NDWI Indices Derived from Landsat-8 OLI. Pol J Environ Stud 2020; 29 (2): 1759-1769.
  • [29] Lamani S, Harijan C. Remote sensing and gis applications in ndwi analysis for monitoring water resources in Gadag Taluk. Int J Progressive Res Eng 2023; 3(8): 31-35.
  • [30] Bakış R, Altan M, Gümüşlüoğlu E, Tucan A, Ayday C, Önsoy H, Olgun K. Investigation of the water potential of Porsuk basin with respect to hydroelectric energy production. Eskişehir Osmangazi Üniversitesi Müh. Mim. Fak. Dergisi 2008; 11 (2): 125-162.
  • [31] Yıldız H, Mermer A, Ünal E, Akbaş F. Spatial and Temporal Analysis of Turkey Vegetation with NDVI Images. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi 2012; 21 (2): 50-56.
  • [32] Akman Y, Ketenoğlu O, Güney K, Kurt L, Tuğ GM. Bitki Ekolojisi, Palme Yayıncılık, 2004.
  • [33] Yang Z, Liu Q. Response of streamflow to climate changes in the Yellow River Basin, China. J Hydrometeorol 2011; 12(5): 1113–1126.
  • [34] Yetik AK, Arslan B, Şen B. Trends and variability in precipitation across Turkey: a multimethod statistical analysis. Theor Appl Climatol 2024; 155: 473–488.
  • [35] Lenoir J, Svenning JC. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 2014; 37: 001–014.
  • [36] Baines PG, Folland CK. Evidence for a Rapid Global Climate Shift across the Late 1960s. J Clim. DOI: 10.1175/JCLI4177.1.

A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS

Yıl 2025, Cilt: 14 Sayı: 1, 1 - 13, 29.01.2025
https://doi.org/10.18036/estubtdc.1509648

Öz

Observation, monitoring, and characterization of land changes in natural ecosystems that are under the influence of many natural or anthropogenic environmental factors are very important in terms of taking effective and sustainable management decisions and protecting them. Today, remote sensing methods facilitate continuous and controlled spatial change monitoring studies, especially in large areas, with the many different methods and techniques they provide, and thus offer cost and time effective solutions. In this study, it was aimed to determine the changes in land and water potential of the Porsuk dam lake and its near surroundings, located between Eskişehir and Kütahya provinces, using remote sensing methods over a 10-year period. In this context, Landsat satellite data for the years 2014 and 2024 and the days with the least cloudiness were obtained, and normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) calculations were made on these data using the ArcGis/ArcMap program. Later, the obtained results were compared and changes in land and water potential were determined. According to the results of NDVI analysis, it was determined that the presence of forests (4.78%) and areas with herbaceous vegetation (5.56%) increased in the 10-year period, whereas soil (-2.70%), tree/shrub (-1.26%) areas and the water body decreased (-5.87%). According to the results of NDWI analysis, it was determined that dry (2.02%) and moderately dry (10.81%) areas increased, while water body (-8.87%) and humid areas (-11.71%) decreased. The results were also supported by surface temperature analysis. Since the results obtained from the study include data on temporal and spatial changes, it is thought that they will contribute to future planning, management and decision-making processes and studies to be carried out in this field.

Kaynakça

  • [1] Ellis EC, Klein Goldewijk K, Siebert S, Lightman D, Ramankutty N. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecol Biogeogr 2010; 19: 589–606.
  • [2] Fischer G, Sun L. Model based analysis of future land-use development in China, Agric Ecosyst Environ 2001; 85 (1-3): 163-176.
  • [3] Zhang J, Zhang Y. Remote sensing research issues of the National Land Use Change Program of China. ISPRS J Photogramm 2007; 62: 461–472.
  • [4] Zhu Z, Qiu S, Ye S. Remote sensing of land change: A multifaceted perspective. Remote Sens Environ 2022; 282: 113266.
  • [5] Krsnik G, Reynolds KM, Murphy P, Paplanus S, Garcia-Gonzalo J, Olabarria JRG. Forest use suitability: Towards decision-making-oriented sustainable management of forest ecosystem services. Geogr Sustain 2023; 4: 414–427.
  • [6] Beck PS, Atzberger C, Høgda KA, Johansen B, Skidmore AK. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens Environ 2006; 100 (3): 321-334.
  • [7] Ahmadi H, Nusrath A. Vegetation change Detection of Neka river in Iran by using remote sensing and GIS. Journal of Geography and Geology 2012; 2 (1): 58-67.
  • [8] Meera Gandhi G, Parthiban S, Thummalu N, Christy A. Ndvi: Vegetation change detection using remote sensing and gis – A case study of Vellore District. Procedia Comput Sci 2015; 57: 1199-1210.
  • [9] Da Ponte E, Roch M, Leinenkugel P, Dech S, Kuenzer C. Emanuel Da vd, (2017), Paraguay's Atlantic forest cover loss e satellite-based change detection and fragmentation analysis between 2003 and 2013. Appl Geogr 2017; 79: 37-49.
  • [10] Roy DP, Wulder MA, Loveland TR, Ce W, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R. et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens Environ 2014; 145: 154–172.
  • [11] Belward AS, Skøien JO. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. . ISPRS J Photogramm 2015; 103: 115–128.
  • [12] Ustin SL, Middleton EM. Current and near-term advances in Earth observation for ecological applications. Ecol Process 2021; 10: 1–57.
  • [13] Lu D, Mausel P, Brondizio E, Moran E. 2004. Change detection techniques. Int J Remote Sens 2004; 25: 2365–2401.
  • [14] Rogan J, Chen D. Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog Plann 2004; 61: 301–325.
  • [15] Bellón B, Bégué A, Seen DL, de Almeida CA, Simões M. A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. Remote Sens 2017; 9: 600.
  • [16] Huang S, Tang L, Hupy JP, Wang Y, Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J For Res 2021; 32(1):1–6.
  • [17] Lemenkova P, Debeir O. R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC. Appl Sci 2022; 12 (24): 12554.
  • [18] Ichii K, Kawabata A, Yamaguchi Y. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. Int J Remote Sens 2002; 23 (18): 3873–3878.
  • [19] Kasimati A, Psiroukis V, Darra N, Kalogrias A, Kalivas D, Taylor JA, Fountas S. Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability. Precis Agric 2023; 24:1220–1240.
  • [20] Saylan İH, Çömert R. Investigation of the success of Sentinel-2A products in mapping of burned forest areas. TUZAL 2019; 1(1), 8-15.
  • [21] Doğan HM, Kılıç OM, Yılmaz DS. Researching plant density classes of Tokat province by Landsat-7 ETM+ satellite images and geographical information systems. Journal of Agricultural Faculty of Gaziosmanpaşa University 2014; 31(1): 47-53.
  • [22] Özyavuz M, Bilgili BC, Salıcı A. Determination of vegetation changes with NDVI method. J Environ Prot Ecol 2015; 16(1): 264-273.
  • [23] Hartoyo APP, Sunkar A, Ramadani R, Faluthi S, Hidayati S. Normalized Difference Vegetation Index (NDVI) analysis for vegetation cover in Leuser Ecosystem area, Sumatra, Indonesia. Biodiversitas 2021; 22(3): 1160-1171.
  • [24] Yang S, Zhao Y, Yang D, Lan A. Analysis of vegetation NDVI changes and driving factors in the Karst concentration distribution area of Asia. Forests 2024; 15: 398.
  • [25] Hunault-Fontbonne J, Eyvindson K. (2023), Bridging the gap between forest planning and ecology in biodiversity forecasts: A review. Ecol Ind 2023; 154: 110620.
  • [26] Özvan H, Arık B, Yeler O, Şatır O, Bostan P. Determining land change using remote sensing and geographical information systems techniques: the case of lake Karataş and its surroundings, Peyzaj 2023; 5(1): 30-39.
  • [27] Szabó S, Gácsi Z, Balázs B. Spesific features of NDVI, NDWI as reflected in land cover categories. Landscape & Environment 2016; 10(3-4): 194+202.
  • [28] Özelkan E. Water Body Detection Analysis Using NDWI Indices Derived from Landsat-8 OLI. Pol J Environ Stud 2020; 29 (2): 1759-1769.
  • [29] Lamani S, Harijan C. Remote sensing and gis applications in ndwi analysis for monitoring water resources in Gadag Taluk. Int J Progressive Res Eng 2023; 3(8): 31-35.
  • [30] Bakış R, Altan M, Gümüşlüoğlu E, Tucan A, Ayday C, Önsoy H, Olgun K. Investigation of the water potential of Porsuk basin with respect to hydroelectric energy production. Eskişehir Osmangazi Üniversitesi Müh. Mim. Fak. Dergisi 2008; 11 (2): 125-162.
  • [31] Yıldız H, Mermer A, Ünal E, Akbaş F. Spatial and Temporal Analysis of Turkey Vegetation with NDVI Images. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi 2012; 21 (2): 50-56.
  • [32] Akman Y, Ketenoğlu O, Güney K, Kurt L, Tuğ GM. Bitki Ekolojisi, Palme Yayıncılık, 2004.
  • [33] Yang Z, Liu Q. Response of streamflow to climate changes in the Yellow River Basin, China. J Hydrometeorol 2011; 12(5): 1113–1126.
  • [34] Yetik AK, Arslan B, Şen B. Trends and variability in precipitation across Turkey: a multimethod statistical analysis. Theor Appl Climatol 2024; 155: 473–488.
  • [35] Lenoir J, Svenning JC. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 2014; 37: 001–014.
  • [36] Baines PG, Folland CK. Evidence for a Rapid Global Climate Shift across the Late 1960s. J Clim. DOI: 10.1175/JCLI4177.1.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Karasal Ekoloji, Ekoloji (Diğer)
Bölüm Makaleler
Yazarlar

Kübra Günbey 0000-0003-1589-9699

Harun Böcük 0000-0002-4480-5295

Yayımlanma Tarihi 29 Ocak 2025
Gönderilme Tarihi 3 Temmuz 2024
Kabul Tarihi 22 Ağustos 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 14 Sayı: 1

Kaynak Göster

APA Günbey, K., & Böcük, H. (2025). A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji, 14(1), 1-13. https://doi.org/10.18036/estubtdc.1509648
AMA Günbey K, Böcük H. A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS. Estuscience - Life. Ocak 2025;14(1):1-13. doi:10.18036/estubtdc.1509648
Chicago Günbey, Kübra, ve Harun Böcük. “A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS”. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji 14, sy. 1 (Ocak 2025): 1-13. https://doi.org/10.18036/estubtdc.1509648.
EndNote Günbey K, Böcük H (01 Ocak 2025) A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji 14 1 1–13.
IEEE K. Günbey ve H. Böcük, “A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS”, Estuscience - Life, c. 14, sy. 1, ss. 1–13, 2025, doi: 10.18036/estubtdc.1509648.
ISNAD Günbey, Kübra - Böcük, Harun. “A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS”. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji 14/1 (Ocak 2025), 1-13. https://doi.org/10.18036/estubtdc.1509648.
JAMA Günbey K, Böcük H. A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS. Estuscience - Life. 2025;14:1–13.
MLA Günbey, Kübra ve Harun Böcük. “A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS”. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji, c. 14, sy. 1, 2025, ss. 1-13, doi:10.18036/estubtdc.1509648.
Vancouver Günbey K, Böcük H. A REMOTE SENSING APPROACH OF LAND AND WATER CONTENT CHANGE BETWEEN 2014 AND 2024 TO THE PORSUK DAM AND ITS NEAR SURROUNDINGS. Estuscience - Life. 2025;14(1):1-13.