Edinburgh Research Explorer

Using optical and microwave, modeled and airborne data to identify water leaks from rural aqueducts

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationSPIE Proceedings
EditorsManfred Ehlers
PublisherSPIE
Pages459-468
Number of pages10
Volume4886
DOIs
Publication statusPublished - 14 Mar 2003

Abstract

The development of techniques for the detection of water leaks from underground pipelines is seen as a high profile activity by water companies and regulators. This is due to increasing water demands and problems with current leak detection methods. In this paper optical reflectance and microwave backscatter models (SAIL + PROSPECT and RT2) were used to try and identify optimal indices for detecting water leaks amongst a variety of different land cover types at different growth stages. Results suggest that red/near infrared and red/middle infrared ratios show potential for leak detection. Given the sensitivity of L-band radar to moisture, and the ability to separate contributions from canopy and ground surface, it is possible to detect saturated soils through vegetation canopies. The results of both approaches are used to infer limits of detection in terms of season and meteorological conditions for a range of land covers. Preliminary findings suggest that leaks may be optimally detected when canopy height is low, surrounding soil is dry, and the leak has been present for more than 14 days. The modelled data is compared with L - band fully polarimetric E-SAR data, and 200 channel HYMAP hyperspectral airborne data which were acquired over an 8km section of the Vrynwy aqueduct (UK), which included a high concentration of leaks. Data was acquired as part of the British National Space Centre (BNSC) and Natural Environmental Research Council (NERC), SAR and Hyperspectral Airborne Campaign (SHAC) in June 2000. The results from this work suggest that remote sensing is both an effective and feasible tool for leak identification.

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