5 August 2013 Use of satellite derived vegetation indices for the detection of water pipeline leakages in semiarid areas
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Proceedings Volume 8795, First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013); 879507 (2013) https://doi.org/10.1117/12.2028241
Event: First International Conference on Remote Sensing and Geoinformation of Environment, 2013, Paphos, Cyprus
Abstract
Remote sensing may be used for quick and cost effective detection and monitoring of water leakages, since traditional field survey methods for detection of water pipeline leakages are costly and time consuming. Vegetation indices are widely used by researchers for many applications. Among them, NDVI, RVI and SAVI are indices that can be used for pipeline leakage detection. In this study, the above vegetation indices were evaluated based on Landsat ETM+ multispectral images in a multi-temporal mode. The evaluation was performed in the semiarid environment in Cyprus, in order to detect the position of points/areas where water leakage occurs and to examine the accuracy of the vegetation indices in detecting such events. In addition, a low altitude system was used to record spectral differences before and after a leakage event. The results showed that there are leakage points that could be detected using satellite images due to the increasing and decreasing of the surrounding vegetation affected by the water leaked of the pipeline. Other characteristics such as the soil type and precipitation were also examined. Finally, the low altitude system highlighted the advantages of using such non contact techniques for monitoring water leakages.
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Athos Agapiou, Leonidas Toulios, Kyriacos Themistocleous, Skevi Perdikou, Dimitrios D. Alexakis, Apostolos Sarris, Giorgos Toulios, Chris R. I. Clayton, Helena Phinikaridou, Andreas Manoli, Diofantos G. Hadjimitsis, "Use of satellite derived vegetation indices for the detection of water pipeline leakages in semiarid areas", Proc. SPIE 8795, First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013), 879507 (5 August 2013); doi: 10.1117/12.2028241; https://doi.org/10.1117/12.2028241
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