№5|2020

PIPELINE SYSTEMS

DOI 10.35776/MNP.2020.05.08
UDC 628.144:628.179.34:528.88

Guseinli El’mir Imran ogly, Alieva A. D.

A new method for remote detection of water leaks in pipelines

Summary

The possibility of developing a new method for remote detection of water leaks in pipelines is considered. To develop the recommendations on the optimal implementation of altitude (aircraft or satellite) sounding in order to detect water leaks in pipelines, the main physical processes that affect the results of detecting water leaks by remote sensing are analyzed. The need for taking into account the following physical effects is shown: the dependence of the reflective spectrum of the soil on its moisture content; the dependence of the optical density of atmospheric aerosol (AOD) on the relative humidity (RH); inverse relationship between the air temperature and humidity; temperature dependence of the reflected signal of the soil due to soil drying. A new method for detecting leaks in pipelines is proposed that involves re-driving the water pipeline route and comparing the obtained spectrometric results taking into account the influence of the air temperature on the soil moisture and the degree of the atmospheric aerosol humidity.

Key words

, , , , ,

The further text is accessible on a paid subscription.
For authorisation enter the login/password.
Or subscribe

Список цитируемой литературы

  1. Diofantos G. Hadjimitsis, Athos Agapiou, Kyriacos Themistocleus, Dimitrios D. Alexakis, Giorgos Toulis, Skevi Perdikou, Apostolos Sarris, Leonidas Toulis, Chris Clayton. Detection of water pipes and leakages in rural water supply networks using remote sensing techniques. https://cdn.intechopen.com/pdfs/45188/InTech-Detection_of_water_pipes_and_leakages_in_rural_water_supply_networks_using_remote_sensing_techniques.pdf (date of the application 24.09.2019).
  2. Huang Y., Fipps G., Maas J. S., Fletcher S. R. Airborne remote sensing for detection of irrigation canal leakage. Irrigation and Drainage, 2010, v. 59, pp. 524–553.
  3. Trishchenko P. A., Cihlar J., Zhanqing L. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment, 2002, v. 81, pp. 1–18.
  4. Dong ZiPeng, Yu Xing, Li XingMin, Dai Jin. Analysis of variation trends and causes of aerosol optical depth in Shaanxi province using MODIS data. Chinese Science Bulletin. December 2013, v. 58, pp. 4486–4496. DOI: 10.1007/s11434=013-5991-z.
  5. Athos Agapiou, Dimitrios D. Alexakis, Kyriacos Themistocleus, Diofantos G. Hadjimitsis. Water leakage detection using remote sensing, field spectroscopy and GIS in semiarid areas of Cyprus. Urban Water Journal, 2014. DOI: 10.1080/1573062X.2014.975726.
  6. Xiao Z., Jiang H., Zhou G., Chen J., Zhang R. Characteristic of aerosol optical thickness as well the relationship with NDVI in the Yangtze River Delta. China. Terrestrial, Atmospheric and Oceanic Sciences, 2013, v. 24, pp. 863–876. DOI: 10.3319/TAO.2013.05.02.01(A).
  7. Chen T., de Jeu R. A. M., Liu Y. Y., van der Werf G. R., Dolman A. J. Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia. Remote Sensing of Environment, 2014, v. 140, pp. 330–338.
  8. Robert L. Andrew, Huade Guan, OkkeBatelaan. Large – scale vegetation response to terrestrial moisture storage changes. Hydrology and Earth System Sciences, 2017, v. 21, pp. 4469–4478. https://doi.org/10.5194/hess-21-4469-2017 (date of the application 24.09.2019).
  9. Shwan Seeyan, Broder Merkel, Rudy Abo. Investigation of the relationship between groundwater level fluctuation and vegetation cover by using NDVI for Shaqlawa Basin, Kurdistan Region – Iraq. Journal of Geography and Geology, 2014, v. 6, no. 3. ISSN 1919-9779. E-ISSN 1916-9787. Published by Canadian Center of Science and Education.
  10. Wendy H. Wood, Shawn j. Marshall, Shannon E. Fargey. Daily measurements of near – sufacehumidty from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010. Earth System Science Data, 2019, v. 11, pp. 23–34.

vstmag engfree 200x100 2

mvkniipr ru