Research Article
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Year 2023, , 70 - 81, 15.06.2023
https://doi.org/10.30897/ijegeo.1240074

Abstract

References

  • Acharya, T. D., Subedi, A., Lee, D. H. (2018). Evaluation of water indices for surface water extraction in a landsat 8 scene of Nepal. Sensors (Switzerland), 18(8), 1–15. https://doi.org/ 10.3390/s18082580
  • Aher, S., Kantamaneni, K., Deshmukh, P. (2017). Detection and Delineation of Water Bodies in Hilly Region using CartoDEM SRTM and ASTER GDEM Data. Remote Sensing of Land, 1(1), 41–52. https://doi.org/10.21523/gcj1.17010103
  • Al-Rahlawee, A. T. H., Rahebi, J. (2021). Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm. Multimedia Tools and Applications, 80(18), 28217–28243.
  • Bangira, T. (2019). Mapping surface water in complex and heterogeneous environments using remote sensing. Stellenbosch: Stellenbosch University.
  • Bangira, T., Alfieri, S. M., Menenti, M., Van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351.
  • Basar, S., Ali, M., Ochoa-Ruiz, G., Zareei, M., Waheed, A., Adnan, A. (2020). Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. Plos One, 15(10), e0240015.
  • Bhuju, U. R., Khadka, M., Neupane, P. K., Adhikari, R. (2010). A Map Based Inventory of Lakes in Nepal. Nepal Journal of Science and Technology, 11, 173–180. https://doi.org/10.3126/njst.v11i0.4141
  • Biggs, J., Von Fumetti, S., Kelly-Quinn, M. (2017). The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers. Hydrobiologia, 793(1), 3–39.
  • Brakenridge, R., Anderson, E. (2006). MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. In Transboundary floods: reducing risks through flood management (pp. 1–12). Springer.
  • Budha, P. B., Bhardwaj, A. (2019). Landslide Extraction from Sentinel-2 Image in Siwalik of Surkhet District, Nepal. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(5/W2), 9–15. https://doi.org/10.5194/isprs-annals-IV-5-W2-9-2019
  • Cai, L., Shi, W., Miao, Z., Hao, M. (2018). Accuracy assessment measures for object extraction from remote sensing images. Remote Sensing, 10(2). https://doi.org/10.3390/rs10020303
  • Colditz, R. R., Souza, C. T., Vazquez, B., Wickel, A. J., Ressl, R. (2018). Analysis of optimal thresholds for identification of open water using MODIS-derived spectral indices for two coastal wetland systems in Mexico. International Journal of Applied Earth Observation and Geoinformation, 70, 13–24.
  • 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(4). https://doi.org/10.3390/rs8040354
  • Gazioğlu, C. (2018). Biodiversity, Coastal Protection, Promotion and Applicability Investigation of the Ocean Health Index for Turkish Seas. International Journal of Environment and Geoinformatics, 5(3), 353-367, doi. 10.30897/ijegeo.484067
  • González-González, A., Clerici, N., Quesada, B. (2022). A 30 m-resolution land use-land cover product for the Colombian Andes and Amazon using cloud-computing. International Journal of Applied Earth Observation and Geoinformation, 107, 102688.
  • Habitat, U. N. (2013). State of the world’s cities 2012/2013: Prosperity of cities. Routledge.
  • Habitat, U. N. (2015). Habitat III issue paper 22—informal settlements. New York: UN Habitat.
  • Huang, C., Chen, Y., Zhang, S., Wu, J. (2018). Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics, 56(2), 333–360.
  • Jawak, S. D., Luis, A. J. (2015). A Rapid Extraction of Water Body Features from Antarctic Coastal Oasis Using Very High-Resolution Satellite Remote Sensing Data. Aquatic Procedia, 4(Icwrcoe), 125–132. https://doi.org/10.1016/j.aqpro.2015.02.018
  • Kaplan, G., Avdan, U. (2017). Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4W4), 271–277. https://doi.org/10.5194/isprs-annals-IV-4-W4-271-2017
  • Kavzoğlu, T., Yılmaz, E, Ö., Çölkesen, İ., Sefercik, U. G., Gazioğlu, C. (2023). Detection and Monitoring of Mucilage Formations Using Pixel Based Convolutional Neural Networks: The Case Study of Izmit Gulf, Turkey, Mucilage Problem in the Sea of Marmara, 233-251.
  • Kavzoğlu, T., Tonbul, H., Çölkesen, İ., Sefercik, U. G. (2021). The Use of Object-Based Image Analysis for Monitoring 2021 Marine Mucilage Bloom in the Sea of Marmara. International Journal of Environment and Geoinformatics, 8(4), 529-536, doi.10. 30897/ijegeo.990875
  • Liu, C., Shi, J., Liu, X., Shi, Z., Zhu, J. (2020). Subpixel mapping of surface water in the Tibetan Plateau with MODIS data. Remote Sensing, 12(7), 1154.
  • Lizarazo, I. (2014). Accuracy assessment of object-based image classification: another STEP. International Journal of Remote Sensing, 35(16), 6135–6156. https://doi.org/10.1080/01431161.2014.943328
  • Lu, D., Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10. 1080/01431160600746456
  • Masocha, M., Dube, T., Makore, M., Shekede, M. D., Funani, J. (2018). Surface water bodies mapping in Zimbabwe using landsat 8 OLI multispectral imagery: A comparison of multiple water indices. Physics and Chemistry of the Earth, 106(May), 63–67. https://doi.org/10.1016/j.pce.2018.05.005
  • 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.
  • Merry, C. J. (2001). Basic electromagnetic radiation. Manual of Geospatial Science and Technology, 62. Nazari-Sharabian, M., Ahmad, S., Moses, K. (2018). Climate change and groundwater : a short review Climate change and groundwater : a short review. Engineering, Technology and Applied Science Research, 8(6), 3668–3672. 2
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
  • Otukei, J. R., Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27--S31.
  • Pandey, B. N., Rana, A., others. (2018). A literature survey of optimization techniques for satellite image segmentation. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), 1–5.
  • Pare, S., Kumar, A., Singh, G. K., Bajaj, V. (2020). Image segmentation using multilevel thresholding: a research review. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 1–29.
  • Rabinskiy, L. N., Tushavina, O. V, Starovoitov, E. I. (2020). Study of thermal effects of electromagnetic radiation on the environment from space rocket activity. INCAS Bulletin, 12, 141–148.
  • Sezgin, M., Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–165.
  • Suwarsono, S., Yulianto, F., Fitriana, H. L., Nugroho, U. C., Sukowati, K. A. D., Khomarudin, M. R. (2021). Detecting the surface water area in Cirata dam upstream Citarum using a water index from Sentinel-2. International Journal of Remote Sensing and Earth Sciences (IJReSES), 17(1), 1–8.
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135.
  • Verpoorter, C., Kutser, T., Seekell, D. A., Tranvik, L. J. (2014). A global inventory of lakes based on high-resolution satellite imagery. Geophysical Research Letters, 41(18), 6396–6402. https://doi.org/ 10.1002/2014GL060641
  • Verpoorter, C., Kutser, T., Tranvik, L. (2012). Automated mapping of water bodies using Landsat multispectral data. Limnology and Oceanography: Methods, 10(12), 1037–1050.
  • Villamagna, A. M., Murphy, B. R. (2010). Ecological and socio-economic impacts of invasive water hyacinth (Eichhornia crassipes): a review. Freshwater Biology, 55(2), 282–298.
  • Wang, Y., Zang, S., Tian, Y. (2020). Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series. Chaos, Solitons \Fractals, 140, 110116.
  • Weise, K., Höfer, R., Franke, J., Guelmami, A., Simonson, W., Muro, J., O’Connor, B., Strauch, A., Flink, S., Eberle, J., others. (2020). Wetland extent tools for SDG 6.6. 1 reporting from the Satellite-based Wetland Observation Service (SWOS). Remote Sensing of Environment, 247, 111892.
  • Xing, L., Niu, Z. (2019). Mapping and analyzing China’s wetlands using MODIS time series data. Wetlands Ecology and Management, 27(5), 693–710.
  • Yan, D., Huang, C., Ma, N., Zhang, Y. (2020). Improved landsat-based water and snow indices for extracting lake and snow cover/glacier in the Tibetan plateau. Water (Switzerland), 12(5). https://doi.org/10. 3390/W12051339
  • Yang, F., Guo, J., Tan, H., Wang, J. (2017). Automated extraction of urban water bodies from ZY-3 multi-spectral imagery. Water (Switzerland), 9(2). https://doi.org/10.3390/w9020144
  • Yang, P., Song, W., Zhao, X., Zheng, R., Qingge, L. (2020). An improved Otsu threshold segmentation algorithm. International Journal of Computational Science and Engineering, 22(1), 146–153.
  • Zhai, K., Wu, X., Qin, Y., Du, P. (2015). Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo-Spatial Information Science, 18(1), 32–42. https://doi.org/10.1080/10095020. 2015.1017911
  • Zhan, X., Sohlberg, R. A., Townshend, J. R. G., DiMiceli, C., Carroll, M. L., Eastman, J. C., Hansen, M. C., DeFries, R. S. (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83(1–2), 336–350.

Extraction of Water Bodies from Sentinel-2 Images in the Foothills of Nepal Himalaya

Year 2023, , 70 - 81, 15.06.2023
https://doi.org/10.30897/ijegeo.1240074

Abstract

This paper evaluates an integrated water body mapping method in sub Himalayan region of Nepal with optical images of Sentinel – 2 satellites of European Space Agency. The objectives of this study is to evaluating the integrated method of water body mapping with Sentinel – 2 data and to find the optimal mapping method in Sub Himalaya region. This method extracts the information on water bodies by combining image indices and near infrared band and used slope image to remove false results.. The study results indicate that difference of indices is more accurate to map the water bodies than single index method as it enhance the contrast between water bodies and other environmental features. On the basis of the accurately mapped water bodies of the study area, this research conclude that the multi spectral images from the Sentinel images can be ideal data source for water bodies monitoring with fine spatial and temporal resolution. Although smaller water bodies with high vegetation cover cannot be detected by this method, the integrated water body mapping method is suitable for the applications multi-spectral images in this field.

References

  • Acharya, T. D., Subedi, A., Lee, D. H. (2018). Evaluation of water indices for surface water extraction in a landsat 8 scene of Nepal. Sensors (Switzerland), 18(8), 1–15. https://doi.org/ 10.3390/s18082580
  • Aher, S., Kantamaneni, K., Deshmukh, P. (2017). Detection and Delineation of Water Bodies in Hilly Region using CartoDEM SRTM and ASTER GDEM Data. Remote Sensing of Land, 1(1), 41–52. https://doi.org/10.21523/gcj1.17010103
  • Al-Rahlawee, A. T. H., Rahebi, J. (2021). Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm. Multimedia Tools and Applications, 80(18), 28217–28243.
  • Bangira, T. (2019). Mapping surface water in complex and heterogeneous environments using remote sensing. Stellenbosch: Stellenbosch University.
  • Bangira, T., Alfieri, S. M., Menenti, M., Van Niekerk, A. (2019). Comparing thresholding with machine learning classifiers for mapping complex water. Remote Sensing, 11(11), 1351.
  • Basar, S., Ali, M., Ochoa-Ruiz, G., Zareei, M., Waheed, A., Adnan, A. (2020). Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization. Plos One, 15(10), e0240015.
  • Bhuju, U. R., Khadka, M., Neupane, P. K., Adhikari, R. (2010). A Map Based Inventory of Lakes in Nepal. Nepal Journal of Science and Technology, 11, 173–180. https://doi.org/10.3126/njst.v11i0.4141
  • Biggs, J., Von Fumetti, S., Kelly-Quinn, M. (2017). The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers. Hydrobiologia, 793(1), 3–39.
  • Brakenridge, R., Anderson, E. (2006). MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. In Transboundary floods: reducing risks through flood management (pp. 1–12). Springer.
  • Budha, P. B., Bhardwaj, A. (2019). Landslide Extraction from Sentinel-2 Image in Siwalik of Surkhet District, Nepal. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(5/W2), 9–15. https://doi.org/10.5194/isprs-annals-IV-5-W2-9-2019
  • Cai, L., Shi, W., Miao, Z., Hao, M. (2018). Accuracy assessment measures for object extraction from remote sensing images. Remote Sensing, 10(2). https://doi.org/10.3390/rs10020303
  • Colditz, R. R., Souza, C. T., Vazquez, B., Wickel, A. J., Ressl, R. (2018). Analysis of optimal thresholds for identification of open water using MODIS-derived spectral indices for two coastal wetland systems in Mexico. International Journal of Applied Earth Observation and Geoinformation, 70, 13–24.
  • 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(4). https://doi.org/10.3390/rs8040354
  • Gazioğlu, C. (2018). Biodiversity, Coastal Protection, Promotion and Applicability Investigation of the Ocean Health Index for Turkish Seas. International Journal of Environment and Geoinformatics, 5(3), 353-367, doi. 10.30897/ijegeo.484067
  • González-González, A., Clerici, N., Quesada, B. (2022). A 30 m-resolution land use-land cover product for the Colombian Andes and Amazon using cloud-computing. International Journal of Applied Earth Observation and Geoinformation, 107, 102688.
  • Habitat, U. N. (2013). State of the world’s cities 2012/2013: Prosperity of cities. Routledge.
  • Habitat, U. N. (2015). Habitat III issue paper 22—informal settlements. New York: UN Habitat.
  • Huang, C., Chen, Y., Zhang, S., Wu, J. (2018). Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics, 56(2), 333–360.
  • Jawak, S. D., Luis, A. J. (2015). A Rapid Extraction of Water Body Features from Antarctic Coastal Oasis Using Very High-Resolution Satellite Remote Sensing Data. Aquatic Procedia, 4(Icwrcoe), 125–132. https://doi.org/10.1016/j.aqpro.2015.02.018
  • Kaplan, G., Avdan, U. (2017). Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4W4), 271–277. https://doi.org/10.5194/isprs-annals-IV-4-W4-271-2017
  • Kavzoğlu, T., Yılmaz, E, Ö., Çölkesen, İ., Sefercik, U. G., Gazioğlu, C. (2023). Detection and Monitoring of Mucilage Formations Using Pixel Based Convolutional Neural Networks: The Case Study of Izmit Gulf, Turkey, Mucilage Problem in the Sea of Marmara, 233-251.
  • Kavzoğlu, T., Tonbul, H., Çölkesen, İ., Sefercik, U. G. (2021). The Use of Object-Based Image Analysis for Monitoring 2021 Marine Mucilage Bloom in the Sea of Marmara. International Journal of Environment and Geoinformatics, 8(4), 529-536, doi.10. 30897/ijegeo.990875
  • Liu, C., Shi, J., Liu, X., Shi, Z., Zhu, J. (2020). Subpixel mapping of surface water in the Tibetan Plateau with MODIS data. Remote Sensing, 12(7), 1154.
  • Lizarazo, I. (2014). Accuracy assessment of object-based image classification: another STEP. International Journal of Remote Sensing, 35(16), 6135–6156. https://doi.org/10.1080/01431161.2014.943328
  • Lu, D., Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10. 1080/01431160600746456
  • Masocha, M., Dube, T., Makore, M., Shekede, M. D., Funani, J. (2018). Surface water bodies mapping in Zimbabwe using landsat 8 OLI multispectral imagery: A comparison of multiple water indices. Physics and Chemistry of the Earth, 106(May), 63–67. https://doi.org/10.1016/j.pce.2018.05.005
  • 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.
  • Merry, C. J. (2001). Basic electromagnetic radiation. Manual of Geospatial Science and Technology, 62. Nazari-Sharabian, M., Ahmad, S., Moses, K. (2018). Climate change and groundwater : a short review Climate change and groundwater : a short review. Engineering, Technology and Applied Science Research, 8(6), 3668–3672. 2
  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
  • Otukei, J. R., Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27--S31.
  • Pandey, B. N., Rana, A., others. (2018). A literature survey of optimization techniques for satellite image segmentation. 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), 1–5.
  • Pare, S., Kumar, A., Singh, G. K., Bajaj, V. (2020). Image segmentation using multilevel thresholding: a research review. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 1–29.
  • Rabinskiy, L. N., Tushavina, O. V, Starovoitov, E. I. (2020). Study of thermal effects of electromagnetic radiation on the environment from space rocket activity. INCAS Bulletin, 12, 141–148.
  • Sezgin, M., Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146–165.
  • Suwarsono, S., Yulianto, F., Fitriana, H. L., Nugroho, U. C., Sukowati, K. A. D., Khomarudin, M. R. (2021). Detecting the surface water area in Cirata dam upstream Citarum using a water index from Sentinel-2. International Journal of Remote Sensing and Earth Sciences (IJReSES), 17(1), 1–8.
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135.
  • Verpoorter, C., Kutser, T., Seekell, D. A., Tranvik, L. J. (2014). A global inventory of lakes based on high-resolution satellite imagery. Geophysical Research Letters, 41(18), 6396–6402. https://doi.org/ 10.1002/2014GL060641
  • Verpoorter, C., Kutser, T., Tranvik, L. (2012). Automated mapping of water bodies using Landsat multispectral data. Limnology and Oceanography: Methods, 10(12), 1037–1050.
  • Villamagna, A. M., Murphy, B. R. (2010). Ecological and socio-economic impacts of invasive water hyacinth (Eichhornia crassipes): a review. Freshwater Biology, 55(2), 282–298.
  • Wang, Y., Zang, S., Tian, Y. (2020). Mapping paddy rice with the random forest algorithm using MODIS and SMAP time series. Chaos, Solitons \Fractals, 140, 110116.
  • Weise, K., Höfer, R., Franke, J., Guelmami, A., Simonson, W., Muro, J., O’Connor, B., Strauch, A., Flink, S., Eberle, J., others. (2020). Wetland extent tools for SDG 6.6. 1 reporting from the Satellite-based Wetland Observation Service (SWOS). Remote Sensing of Environment, 247, 111892.
  • Xing, L., Niu, Z. (2019). Mapping and analyzing China’s wetlands using MODIS time series data. Wetlands Ecology and Management, 27(5), 693–710.
  • Yan, D., Huang, C., Ma, N., Zhang, Y. (2020). Improved landsat-based water and snow indices for extracting lake and snow cover/glacier in the Tibetan plateau. Water (Switzerland), 12(5). https://doi.org/10. 3390/W12051339
  • Yang, F., Guo, J., Tan, H., Wang, J. (2017). Automated extraction of urban water bodies from ZY-3 multi-spectral imagery. Water (Switzerland), 9(2). https://doi.org/10.3390/w9020144
  • Yang, P., Song, W., Zhao, X., Zheng, R., Qingge, L. (2020). An improved Otsu threshold segmentation algorithm. International Journal of Computational Science and Engineering, 22(1), 146–153.
  • Zhai, K., Wu, X., Qin, Y., Du, P. (2015). Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo-Spatial Information Science, 18(1), 32–42. https://doi.org/10.1080/10095020. 2015.1017911
  • Zhan, X., Sohlberg, R. A., Townshend, J. R. G., DiMiceli, C., Carroll, M. L., Eastman, J. C., Hansen, M. C., DeFries, R. S. (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83(1–2), 336–350.
There are 47 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Kumod Lekhak 0000-0002-1040-4776

Pawan Rai 0000-0002-8423-1898

Padam Bahadur Budha 0000-0003-1275-3854

Publication Date June 15, 2023
Published in Issue Year 2023

Cite

APA Lekhak, K., Rai, P., & Budha, P. B. (2023). Extraction of Water Bodies from Sentinel-2 Images in the Foothills of Nepal Himalaya. International Journal of Environment and Geoinformatics, 10(2), 70-81. https://doi.org/10.30897/ijegeo.1240074