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A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data
Pekel, F; Vancutsem, C; Bastin, L; Clerici, M; Vanbogaert, E.; Bartholome, E; Defourny, P. (2014). A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data. Remote Sens. Environ. 140: 704-716.
In: Remote Sensing of Environment. Elsevier: New York,. ISSN 0034-4257, more
Peer reviewed article  

Available in Authors 
    VLIZ: Open Repository 274954 [ OMA ]

Author keywords
    MODIS; Water surface detection; Time series analysis; Color spacetransformation; HSV; Spatial and temporal dynamics; Multi-temporal;Multi-spectral; Pixel-based image analysis

Authors  Top 
  • Pekel, F
  • Vancutsem, C
  • Bastin, L
  • Clerici, M
  • Vanbogaert, E., more
  • Bartholome, E
  • Defourny, P., more

    In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objectives of this study are to develop, demonstrate and validate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near real-time at the African continental scale as a first step towards global scale coverage. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. Two different validations were done at the continental scale over Africa: i) The algorithm validation checked the ability of the proposed algorithm to perform as effectively as human interpretation of the image: it showed an accuracy of 96.6% and no commission errors. ii) The product validation was carried out by using an independent dataset derived from high resolution imagery: the continental permanent water surface product showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at the African continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties.

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