Received 10 July 2013
Received in revised form 19 September 2013
Accepted 3 October 2013
Available online 30 October 2013
Keywords: 1. Introduction Vignolles, Ndione, & Lafaye, 2007), food security (agriculture, cattle d surface parameters y of other parameters, nd surface reflectance, ying surface is covered
Remote Sensing of Environment 140 (2014) 704–716
Contents lists available at ScienceDirect
Remote Sensing o .ethe Convention on Climate Change (UNFCCC) (GCOS, 2011). Beyond the
ECV, a timely global water surfaces monitoring capacity would provide all countries with access to critical information on floods and droughts, by water or not. Substantial errors in the underlying water mask are known to pervade into these parameters and any product derived from them (Carroll, Townshend, DiMiceli, Noojipady, & Sohlberg, 2009).changing climate impacts the hydrological cycle and interferes with freshwater resources (Ohmura & Wild, 2002), lake maps and water level are two recognized critical parameters among the Essential Climate
Variables (ECV) required to support climate change analyses called for by and water is critical for the production of lan from remote sensing data products. The accurac such as land surface temperature, active fires a can be improved by defining whether the underlIn 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 to support sustainable development policies and activities (Vörösmarty et al., 2000). As breeding, aquaculture, and gardening) (Breman, Groot, & Van Keulen, 2001; Brouwer, 2003, Haas, Bartholomé, Lambin, & Vanacker, 2011;
Hein, 2006; Thebaud & Batterbury, 2001), and biodiversity protection (UNESCO, 2009; Vörösmarty et al., 2000).
Besides all of these, an accurate time-dependent depiction of landboth of which have dramatic economic and h
Lettenmaier, 2003). Such monitoring is cruci including human and animal health (Ceccat ⁎ Corresponding author. Tel.: +39 0332 783725.
E-mail address: firstname.lastname@example.org (J.-F. Pekel). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All ri http://dx.doi.org/10.1016/j.rse.2013.10.008MODIS
Water surface detection
Time series analysis
Color space transformation
Spatial and temporal dynamics
Pixel-based image analysisIn 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 tomonitor them innear 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. © 2013 Elsevier Inc. All rights reserved.Article history:a b s t r a c ta r t i c l e i n f oA near real-time water surface detection m transformation of MODIS multi-spectral tim
J.-F. Pekel a,⁎, C. Vancutsem a, L. Bastin b, M. Clerici a, E. a Joint Research Centre, European Commission, Italy b School of Engineering and Applied Science, Aston University, United Kingdom c Earth and Life Institute, Université catholique de Louvain, Belgium j ourna l homepage: wwwuman impacts (Alsdorf & al for many applications o, 2010; Lacaux, Tourre, ghts reserved.thod based on HSV e series data nbogaert c, E. Bartholomé a, P. Defourny c f Environment l sev ie r .com/ locate / rseRemote 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 (Goetz,
Gardiner, & Viers, 2008). Large reflectance datasets at medium resolution with high revisit frequency are becoming widely available. It is therefore possible to develop, assess and implement methodologies to wetlands in Alaska derived from L-band radar imagery acquired by
JAXA's JERS-1 SAR is available (Whitcomb, Moghaddam, McDonald, 705J.-F. Pekel et al. / Remote Sensing of Environment 140 (2014) 704–716map water surfaces and monitor their evolution in near real-time at global scale, based on such data.
However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures. The spectral properties of water are determined by the electromagnetic interaction of light with the constituent components of water via absorption or scattering processes. These constituents are: phytoplankton (chlorophyll-a), suspended sediments (i.e. solid particulate matter with a diameter smaller than 2 mm resulting from erosion processes) and colored dissolved organic matter (CDOM) resulting from the degradation of biological organisms. All these constituents vary in character and amount according to the limnological/optical types, season, cyclical change of biological activity and human impact, and play an important role in determining intensity of the absorption and scattering processes (Arst, 2003). Consequently, the water-leaving radiance detected by the sensor shows great spatial and temporal variability, which makes the reliable discrimination of water particularly difficult (Gond, Bartholomé,