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Comparison of Remote Sensing Classification Techniques for Water Body Detection: A Case Study in Atikhisar Dam Lake (Çanakkale)

Year 2019, Volume: 40 Issue: 3, 650 - 661, 30.09.2019
https://doi.org/10.17776/csj.556440

Abstract

Water resources management is
one of the most important issues of today. Satellite remote sensing have been
successfully used to detect the presence of water bodies. In this study, four
remote sensing methods: (1) normalized difference water index (NDWI), (2)
support vector machine (SVM), (3) geographic object-based image analysis
(GEOBIA) and (4) NDWI supported GEOBIA (GEOBIA_NDWI) were examined for water
body area detection. For this purpose, Atikhisar Dam Lake, the only water
source of Çanakkale central district of Turkey was selected as study area. As
remote sensing data nine multitemporal Landsat-8 Operational Land Imager (OLI)
multispectral satellite images between 2013 and 2017 were used. For the
accuracy assessment, area values extracted from the used methods were tested
with in-situ measurement lake area values. The main issues discussed in this
study can be specified as follows: (i) Is pixel-based classification SVM or
object-based image classification GEOBIA more successful in the water body
detection?, (ii) Are the image classification methods (SVM and GEOBIA) or the
water index (NDWI) more successful in the water body detection? and (iii) What
is the contribution of NDWI to GEOBIA_NDWI (GEOBIA_NDWI) classification in the
water body detection? The results show that meteorological factors and irrigation
were influential in lake area variations. NDWI was found to be superior to
other methods in determining water body and allowed for better detection of the
lake boundary. Additionally, NDWI made a better separation of the land cover
classes adjacent to water at the border. The object based GEOBIA was better
than the pixel based SVM for distinguishing water and other land cover classes
adjacent to border. GEOBIA_NDWI lake area results were more accurate than the
standard object-based classification. Mixed pixels out of the lake area was
determined less in the NDWI and GEOBIA_NDWI results.

References

  • [1] Demirel K. and Kavdır Y., Effect of Soil Water Retention Barriers on Turfgrass Growth and Soil Water Content, Irrigation Science, 31-4 (2013) 689-700.
  • [2] Genç L., Demirel K., Çamoglu G., Asık S. and Smith S. Determination of plant water stress using spectral reflectance measurements in watermelon (citrullus vulgaris), American-Eurasian Journal of Agricultural & Environmental Sciences, 11-2 (2011) 296-304.
  • [3] Çamoğlu G., Demirel K., Genc L. Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper, Quantitative InfraRed Thermography Journal, 15-1 (2018) 81-94.
  • [4] Özelkan E. and Karaman M., The Analysis of the Effect of Meteorological and Hydrological Drought on Dam Lake via Multitemporal Satellite Images: A Case Study in Atikhisar Dam Lake (Çanakkale), Omer Halisdemir University Journal of Engineering Sciences, 7-2 (2018) 1023-1037.
  • [5] Karaman M., Budakoglu M., Uca Avci Z.D., Özelkan E., Bülbül A., Civas M. and Tasdelen S., Determination of Seasonal Changes in Wetlands Using CHRIS/Proba Hyperspectral Satellite Images: A Case Study from Acigöl (Denizli), Turkey, Journal of Environmental Biology, 36 (2015) 73-83.
  • [6] Liu Z., Yao Z. and Wang R., Assessing Methods of Identifying Open Water Bodies Using Landsat 8 OLI Imagery, Environmental Earth Sciences, 75-10 (2016) 1-13.
  • [7] Karaman M., Özelkan E. and Tasdelen S., Influence of Basin Hydrogeology in the Detectability of Narrow Rivers by Sentinel2-A Satellite Images: A Case Study in Karamenderes (Çanakkale), Journal of Natural Hazards and Environment, 4 (2018) 140-155.
  • [8] Ji L., Zhang L. and Wylie B., Analysis of Dynamic Thresholds for the Normalized Difference Water Index, Photogrammetric Engineering & Remote Sensing, 75-11 (2009) 1307–1317.
  • [9] Du Z., Li W., Zhou D., Tian L., Ling F., Wang H., Gui Y. and Sun B., Analysis of Landsat-8 OLI Imagery for Land Surface Water Mapping, Remote Sensing Letters, 5-7 (2014) 672-681.
  • [10] Gürsoy Ö., Atun R. Investigating surface water pollution by integrated remotely sensed and field spectral measurement data: A case study, Polish Journal of Environmental Studies, 28-4 (2019) 2139-2144.
  • [11] Gürsoy Ö., Birdal A., Özyonar F., Kasaka E. Determining and monitoring the water quality of Kizilirmak River of Turkey: First results, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3 (2015) 1469-1474.
  • [12] Kavzoğlu T. and Çölkesen İ., Destek Vektör Makineleri ile Uydu Görüntülerinin Sınıflandırılmasında Kernel Fonksiyonlarının Etkilerinin İncelenmesi, Harita Dergisi, 144 (2010) 73-82.
  • [13] Gürsoy Ö and Altun R., Comparison of Spectral Classification Methods in Water Quality, Cumhuriyet Science Journal, 39-2 (2018) 543-549.
  • [14] Kalkan K. and Maktav D., Nesne Tabanlı ve Piksel Tabanlı Sınıflandırma Yöntemlerinin Karşılaştırılması (IKONOS Örneği). In: III. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 12-15 October, Gebze – Kocaeli, Türkiye, 2010.
  • [15] Belgiu M. and Drăguţ L., Comparing Supervised and Unsupervised Multiresolution Segmentation Approaches for Extracting Buildings from Very High Resolution Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 96 (2014) 67-75.
  • [16] Çölkesen İ., Yomralıoğlu T. and Kavzoğlu T., Obje Tabanlı Sınıflandırmada Bölgeleme Esasına Dayalı Ölçek Parametresi Tespiti: WorldView-2 Uydu Görüntüsü Örneği, Harita Dergisi., 154, (2015) 9-18.
  • [17] Blaschke T., Object Based Image Analysis for Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 65 (2010) 2–16.
  • [18] McFeeters S.K., The Use of Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features, International Journal of Remote Sensing, 17 (1996) 1425–1432.
  • [19] Xu H., Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery, International Journal of Remote Sensing, 27-14 (2006) 3025-3033.
  • [20] Pôssa E.M. and Maillard P., Precise Delineation of Small Water Bodies from Sentinel-1 Data using Support Vector Machine Classification, Canadian Journal of Remote Sensing, 44-3 (2018) 179-190.
  • [21] Uca Avci Z.D., Karaman M., Ozelkan E., Kumral M., Budakoglu M., OBIA Based Hierarchical Image Classification for Industrial Lake Water, Science of the Total Environment, 487 (2014) 565-573.
  • [22] Karaman M., Budakoglu M., Uca Avci D.U., Ozelkan E., Bulbul A., Civas M., Tasdelen S., Determination of Seasonal Changes in Wetlands Using CHRIS/Proba Hyperspectral Satellite Images: A Case Study from Acigöl (Denizli), Turkey, Journal of Environmental Biology, 36-1 (2015) 73.
  • [23] Korzeniowska K. and Korup O., Object-Based Detection of Lakes Prone to Seasonal Ice Cover on the Tibetan Plateau, Remote Sensing, 9-4 (2017) 339.
  • [24] Olmanson L.G. and Bauer M.E., Land cover Classification of the Lake of the Woods/Rainy River Basin by Object-Based Image Analysis of Landsat and Lidar Data, Lake and Reservoir Management, 33-4 (2017) 335-346.
  • [25] Kaplan G. and Avdan U., Object-based Water Body Extraction Model Using Sentinel-2 Satellite Imagery, European Journal of Remote Sensing, 50-1 (2017) 137-143.
  • [26] Şensoy S., Demircan M., Ulupınar Y. and Balta Z., Türkiye İklimi, Turkish State Meteorological Service Report. URL: https://www.mgm.gov.tr/FILES/genel/makale/13_turkiye_iklimi.pdf Retrieved February 10, 2019.
  • [27] Chen G., Özelkan E., Singh K.K., Zhou J., Brown M.R. and Meentemeyer R.K., Uncertainties in Mapping Forest Carbon in Urban Ecosystems, Journal of Environmental Management, 187 (2017) 229-238.
  • [28] Özelkan E., Sağlık A., Sümer S.K., Bedir M. and Kelkit A., Kentleşmenin Tarım Alanları Üzerine Etkisinin Uzaktan Algılama ile İncelenmesi–Çanakkale Örneği, Çanakkale Onsekiz Mart Üniversitesi Ziraat Fakültesi Dergisi, 6 (2018) 123-134.

Su Kütlesi Belirlemede Farklı Sınıflandırma Yöntemlerinin Karşılaştırılması: Atikhisar Barajı (Çanakkale) Örneği

Year 2019, Volume: 40 Issue: 3, 650 - 661, 30.09.2019
https://doi.org/10.17776/csj.556440

Abstract

Su kaynakları yönetimi günümüzün en önemli
konularının başında gelmektedir. Su kütlelerinin varlığının tespitinde uydudan
uzaktan algılama başarı ile kullanılmaktadır. Bu çalışmada, (1) normalize
edilmiş fark su indisi (NDWI), (2) destek vektör makinaları (DVM), (3) coğrafi
nesne-tabanlı görüntü analizi (GEOBIA) ve NDWI destekli GEOBIA (GEOBIA_NDWI)
uzaktan algılama yöntemleri su kütlesini belirleyebilmek için incelenmiştir. Bu
amaçla, Türkiye’nin Çanakkale İl’inin Merkez İlçe’sinin tek su kaynağı olan
Atikhisar Baraj Gölü çalışma alanı olarak tercih edilmiştir. Uzaktan algılama
verisi olarak, 2013 ve 2017 yılları arasında temin edilmiş, dokuz adet çok
zamanlı Landsat-8 Operational Land Imager (OLI) multispektral uydu görüntüsü
kullanılmıştır. Kullanılan yöntemlerden elde edilen sonuçların doğruluk analizi
için, yerinde ölçülen göl alanı değerleri kullanılmıştır. Bu çalışmada ele
alınan ana konular şu şekilde sıralanabilir: (i) Piksel tabanlı sınıflandırma
DVM mi yoksa nesne tabanlı sınıflandırma GEOBIA mı su kütlesi belirlemede daha
başarılıdır?, (ii) Görüntü sınıflandırma yöntemleri mi (DVM ve GEOBIA) yoksa su
indisi mi (NDWI) su kütlesi belirlemede daha başarılıdır? ve (iii) NDWI’ın
GEOBIA_NDWI sınıflandırmasına su kütlesi belirlemede katkısı nedir? Sonuçlar
meteorolojik etkenlerin ve sulamanın göldeki değişimlerde etkili olduğunu
göstermektedir. NDWI göl alanı belirlemede daha başarılı bulunmuştur ve göl
sınırı belirlemede daha iyi sonuç vermektedir. Ek olarak, NDWI göl kenarında su
ile temas eden sınıfları daha iyi ayırabilmektedir. Nesne tabanlı GEOBIA suyla
temas eden arazi örtüsü sınıflarını piksel tabanlı DVM’den daha iyi
ayırabilmektedir. GEOBIA_NDWI sonuçları standart nesne tabanlı sınıflandırmadan
daha doğrudur. NDWI ve GEOBIA_NDWI sonuçlarında göl alanı dışında su olarak
atanmış piksel sayısı daha azdır. 

References

  • [1] Demirel K. and Kavdır Y., Effect of Soil Water Retention Barriers on Turfgrass Growth and Soil Water Content, Irrigation Science, 31-4 (2013) 689-700.
  • [2] Genç L., Demirel K., Çamoglu G., Asık S. and Smith S. Determination of plant water stress using spectral reflectance measurements in watermelon (citrullus vulgaris), American-Eurasian Journal of Agricultural & Environmental Sciences, 11-2 (2011) 296-304.
  • [3] Çamoğlu G., Demirel K., Genc L. Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper, Quantitative InfraRed Thermography Journal, 15-1 (2018) 81-94.
  • [4] Özelkan E. and Karaman M., The Analysis of the Effect of Meteorological and Hydrological Drought on Dam Lake via Multitemporal Satellite Images: A Case Study in Atikhisar Dam Lake (Çanakkale), Omer Halisdemir University Journal of Engineering Sciences, 7-2 (2018) 1023-1037.
  • [5] Karaman M., Budakoglu M., Uca Avci Z.D., Özelkan E., Bülbül A., Civas M. and Tasdelen S., Determination of Seasonal Changes in Wetlands Using CHRIS/Proba Hyperspectral Satellite Images: A Case Study from Acigöl (Denizli), Turkey, Journal of Environmental Biology, 36 (2015) 73-83.
  • [6] Liu Z., Yao Z. and Wang R., Assessing Methods of Identifying Open Water Bodies Using Landsat 8 OLI Imagery, Environmental Earth Sciences, 75-10 (2016) 1-13.
  • [7] Karaman M., Özelkan E. and Tasdelen S., Influence of Basin Hydrogeology in the Detectability of Narrow Rivers by Sentinel2-A Satellite Images: A Case Study in Karamenderes (Çanakkale), Journal of Natural Hazards and Environment, 4 (2018) 140-155.
  • [8] Ji L., Zhang L. and Wylie B., Analysis of Dynamic Thresholds for the Normalized Difference Water Index, Photogrammetric Engineering & Remote Sensing, 75-11 (2009) 1307–1317.
  • [9] Du Z., Li W., Zhou D., Tian L., Ling F., Wang H., Gui Y. and Sun B., Analysis of Landsat-8 OLI Imagery for Land Surface Water Mapping, Remote Sensing Letters, 5-7 (2014) 672-681.
  • [10] Gürsoy Ö., Atun R. Investigating surface water pollution by integrated remotely sensed and field spectral measurement data: A case study, Polish Journal of Environmental Studies, 28-4 (2019) 2139-2144.
  • [11] Gürsoy Ö., Birdal A., Özyonar F., Kasaka E. Determining and monitoring the water quality of Kizilirmak River of Turkey: First results, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3 (2015) 1469-1474.
  • [12] Kavzoğlu T. and Çölkesen İ., Destek Vektör Makineleri ile Uydu Görüntülerinin Sınıflandırılmasında Kernel Fonksiyonlarının Etkilerinin İncelenmesi, Harita Dergisi, 144 (2010) 73-82.
  • [13] Gürsoy Ö and Altun R., Comparison of Spectral Classification Methods in Water Quality, Cumhuriyet Science Journal, 39-2 (2018) 543-549.
  • [14] Kalkan K. and Maktav D., Nesne Tabanlı ve Piksel Tabanlı Sınıflandırma Yöntemlerinin Karşılaştırılması (IKONOS Örneği). In: III. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 12-15 October, Gebze – Kocaeli, Türkiye, 2010.
  • [15] Belgiu M. and Drăguţ L., Comparing Supervised and Unsupervised Multiresolution Segmentation Approaches for Extracting Buildings from Very High Resolution Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 96 (2014) 67-75.
  • [16] Çölkesen İ., Yomralıoğlu T. and Kavzoğlu T., Obje Tabanlı Sınıflandırmada Bölgeleme Esasına Dayalı Ölçek Parametresi Tespiti: WorldView-2 Uydu Görüntüsü Örneği, Harita Dergisi., 154, (2015) 9-18.
  • [17] Blaschke T., Object Based Image Analysis for Remote Sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 65 (2010) 2–16.
  • [18] McFeeters S.K., The Use of Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features, International Journal of Remote Sensing, 17 (1996) 1425–1432.
  • [19] Xu H., Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery, International Journal of Remote Sensing, 27-14 (2006) 3025-3033.
  • [20] Pôssa E.M. and Maillard P., Precise Delineation of Small Water Bodies from Sentinel-1 Data using Support Vector Machine Classification, Canadian Journal of Remote Sensing, 44-3 (2018) 179-190.
  • [21] Uca Avci Z.D., Karaman M., Ozelkan E., Kumral M., Budakoglu M., OBIA Based Hierarchical Image Classification for Industrial Lake Water, Science of the Total Environment, 487 (2014) 565-573.
  • [22] Karaman M., Budakoglu M., Uca Avci D.U., Ozelkan E., Bulbul A., Civas M., Tasdelen S., Determination of Seasonal Changes in Wetlands Using CHRIS/Proba Hyperspectral Satellite Images: A Case Study from Acigöl (Denizli), Turkey, Journal of Environmental Biology, 36-1 (2015) 73.
  • [23] Korzeniowska K. and Korup O., Object-Based Detection of Lakes Prone to Seasonal Ice Cover on the Tibetan Plateau, Remote Sensing, 9-4 (2017) 339.
  • [24] Olmanson L.G. and Bauer M.E., Land cover Classification of the Lake of the Woods/Rainy River Basin by Object-Based Image Analysis of Landsat and Lidar Data, Lake and Reservoir Management, 33-4 (2017) 335-346.
  • [25] Kaplan G. and Avdan U., Object-based Water Body Extraction Model Using Sentinel-2 Satellite Imagery, European Journal of Remote Sensing, 50-1 (2017) 137-143.
  • [26] Şensoy S., Demircan M., Ulupınar Y. and Balta Z., Türkiye İklimi, Turkish State Meteorological Service Report. URL: https://www.mgm.gov.tr/FILES/genel/makale/13_turkiye_iklimi.pdf Retrieved February 10, 2019.
  • [27] Chen G., Özelkan E., Singh K.K., Zhou J., Brown M.R. and Meentemeyer R.K., Uncertainties in Mapping Forest Carbon in Urban Ecosystems, Journal of Environmental Management, 187 (2017) 229-238.
  • [28] Özelkan E., Sağlık A., Sümer S.K., Bedir M. and Kelkit A., Kentleşmenin Tarım Alanları Üzerine Etkisinin Uzaktan Algılama ile İncelenmesi–Çanakkale Örneği, Çanakkale Onsekiz Mart Üniversitesi Ziraat Fakültesi Dergisi, 6 (2018) 123-134.
There are 28 citations in total.

Details

Primary Language English
Journal Section Natural Sciences
Authors

Emre Özelkan 0000-0002-2031-1610

Publication Date September 30, 2019
Submission Date April 20, 2019
Acceptance Date September 13, 2019
Published in Issue Year 2019Volume: 40 Issue: 3

Cite

APA Özelkan, E. (2019). Comparison of Remote Sensing Classification Techniques for Water Body Detection: A Case Study in Atikhisar Dam Lake (Çanakkale). Cumhuriyet Science Journal, 40(3), 650-661. https://doi.org/10.17776/csj.556440