Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data
Inland surface water availability is a serious global sustainability challenge. Hence, there is a need to monitor surface water availability, in order to better manage it under an increasingly changing planet. So far, a comprehensive effort to understand changes in inland surface water availability and dynamics is lacking. Remote sensing instruments provide an opportunity to monitor surface water availability on a global scale, but they also introduce significant computational challenges. In this chapter, we present an unsupervised method that overcomes several challenges inherent in remote sensing data to effectively monitor changes in surface water bodies. Using an independent validation dataset, we compare the proposed method with two cluster algorithms (K-MEANS and EM) as well as an image segmentation algorithm (normal-cut). We show that our method is more efficient and reliable.
KeywordsSpatiotemporal data mining Spatiotemporal clustering Changes of water extent
- Gao H, Birkett C, Lettenmaier DP (2012) Global monitoring of large reservoir storage from satellite remote sensing. Water Resour Res 48(9)Google Scholar
- MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Berkeley, vol 1, No. 14, pp 281–297Google Scholar
- McLachlan G, Krishnan T (2007) The EM algorithm and extensions, vol 382. John Wiley & Sons, New YorkGoogle Scholar
- Pang-Ning T, Steinbach M, Kumar V et al (2006) Introduction to data mining. PearsonGoogle Scholar
- US Geological Survey and NASA. Land Processes Distributed Active Archive Center (LP DAAC). https://lpdaac.usgs.gov/