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2016 | T. 55 | 59--72
Tytuł artykułu

Wykrywanie wody na zdjęciach optycznych Sentinel-2 na podstawie wskaźników wodnych

Treść / Zawartość
Warianty tytułu
EN
The detection of water on Sentinel-2 imagery based on water indices
Języki publikacji
PL
Abstrakty
EN
Copernicus Programme managed by the European Commission and implemented in partnership with i.a. the European Space Agency (ESA) provides free access to satellite data from Sentinel mission including Sentinel-2 high resolution optical satellite data. The aim of the research was to recognize opportunities of water detection on Sentinel-2 imagery. Satellite data was analyzed before and after atmospheric correction. A number of tests were carried out using indices selected from the literature. Based on the gained experience, a new index for water detection has been proposed, Sentinel Water Mask (SWM), specially adapted for Sentinel-2 images. Its construction is based on the highest difference between spectral values of water surface and other land cover forms. SWM provides quick and effective detection of water which is especially important in flood assessment for crisis management. Research was performed on unprocessed images of Sentinel-2 Level-1C and images after atmospheric correction (Level-2A). Water was detected with the use of threshold values determined by the visual interpretation method. The accuracy of the obtained water masks was assessed on the basis of validation points. The performed analysis allowed to indicate indices, which enable estimation of areas covered by water on Sentinel-2 images with high classification accuracy, this is: AWEInsh (Automated Water Extraction Index), MNDWI (Modified Normalized Difference Water Index), NDWIMcFeeters (Normalized Difference Water Index). Their application allowed for achievement of overall accuracy of water detection oscillating around 95% and high Kappa coefficient. The usage of the proposed SWM index leads to slightly better results (more than 96%). The sensitivity to the selection of threshold values of analyzed indices was assessed and then the optimal threshold ranges were determined. The optimal threshold value for NDWIMcFeeters should be included in the value range (0.1, 0.2), for MNDWI (0.2, 0.3) and for SWM (1.4, 1.6). The unambiguous threshold range for AWEInsh index was impossible to indicate due to the large range of values.
Wydawca

Rocznik
Tom
Strony
59--72
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Centrum Badań Kosmicznych PAN, Zespół Obserwacji Ziemi, ul. Bartycka 18A, 00-716 Warszawa, arobak@cbk.waw.pl
autor
  • Centrum Badań Kosmicznych PAN, Zespół Obserwacji Ziemi, ul. Bartycka 18A, 00-716 Warszawa, agadawska@cbk.waw.pl
  • Centrum Badań Kosmicznych PAN, Zespół Obserwacji Ziemi, ul. Bartycka 18A, 00-716 Warszawa, mmilczarek@cbk.waw.pl
  • Centrum Badań Kosmicznych PAN, Zespół Obserwacji Ziemi, ul. Bartycka 18A, 00-716 Warszawa, stlewiński@cbk.waw.pl
Bibliografia
  • Chandrasekar K., Roy P.S., 2010, Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product, International Journal of Remote Sensing, 31(15), 3987-4005.
  • Du Y., Zhang Y., Ling F., Wang Q., Li W., Li X., 2015, 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.
  • Feyisa G.L., Meilby H., Fensholt R., Proud S.R, 2014, Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery, Remote Sensing of Environment, 140, 23-35.
  • Fisher A., Danaher T., 2013, A Water Index for SPOT5 HRG Satelite Imagery, New South Wales, Australia, Determined by Linear Discrimining Analysis, Remote Sensing, 5, 5907-5925.
  • Fisher A., Flood N., Danaher T., 2016, Comparing Landsat water index methods for automated water classification in eastern Australia, Remote Sensing of Environment, 175, 167-182.
  • Hardisky, M., Klemas V., Smart R., 1983, The Influences of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Reflectance of Spartina Alterniflora Canopies, Photogrammetric Engineering and Remote Sensing, 48(1), 77-84.
  • Hunt E.R., Rock B.N., 1989, Detection of changes in leaf water content using Near- and Middle-Infrared reflectances, Remote Sensing of Environment, 30(1), 43-54.
  • Jackson T.J., Chen D., Cosh M., Li F., Anderson M., Walthall C., Doriaswamy P., Hunt E.R., 2004, Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans, Remote Sensing of Environment, 92, 475-482.
  • Jiang H., Feng M., Zhu Y., Lu N., Huang J., Xiao T., 2014, An Automated Method for Extracting Rivers and Lakes from Landsat Imaginery, Remote Sensing, 6, 5067-5089.
  • Kai C., Nan J., 2006, The Study of Automatically Extracting Water Information in City Zone Based On SPOT5 Image, IEEE International Geoscience & Remote Sensing Symposium, 1481-1484.
  • Kwak Y., Iwami Y, 2014, Nationwide flood inundation mapping in Bangladesh by using modified land surface water index, ASPRS 2014 Conference, 23-28 march 2014, Louisville, Kentucky.
  • Lacaux J.P., Tourre Y.M., Vignolles C., Ndione J.A., Lafaye M., 2007, Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal, Remote Sensing of Environment, 106, 66-74.
  • Li W., Du Z., Ling F., Zhou D., Wang H., Gui Y., Sun B., Zhang X., 2013, A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI, Remote Sensing, 5, 5530-5549.
  • 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.
  • Memon A.A., Muhammad S., Rahman S., 2015, Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012, The Egyptian Journal of Remote Sensing and Space Sciences 18, 99-106.
  • Rogers A.S., Kearney M.S., 2004, Reducing signature variability in unmixing coastal marsh thematic mapper scenes using spectral indices, International Journal of Remote Sensing 25(12), 2317-2335.
  • Singh K., Ghosh M., Sharma S.R., 2016, WSB-DA: Water Surface Boundary Detection Algorithm Using Landsat 8 OLI Data, IEEE Journal of Selected Topics in Applied EO and RS 9, no. 1, 363-368.
  • Tetteh G.O., Schönert M., 2015, Automatic Generation of Water Mask from RapidEye Images, Journal of Geoscience and Environment Protection, 3, 17-23.
  • Verpoorter C., 2012, Kutser T., Tranvik L., 2012, Automated mapping of water bodies using Landsat multispectral data, Limnology and Oceanography-Methods 10, 1037-1050.
  • Wilson E.H., Sader S.A., 2002, Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sensing of Environment, 80(3), 385-396.
  • Xu H., 2006 Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery, International Journal of Remote Sensing, 27(14), 3025-3033.
  • 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, 32-42.
  • Źródła internetowe:
  • https://sentinel.esa.int/web/sentinel/missions/sentinel-2
  • http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Overview4
  • https://scihub.copernicus.eu/
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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