[show abstract][hide abstract] ABSTRACT: Remote sensing may be used for quick and cost effective detection and monitoring water leakages, since traditional field survey methods for detection of water pipeline leakages are costly and time consuming. The main form of satellite spectral data used for several applications is vegetation indices. Vegetation indices are mathematical equations of the main spectral data in the red and very near infrared part of electromagnetic portions which are sensitive in the vegetation behaviour. Among them, NDVI, RVI and SAVI are indices that may be used for pipeline leakages 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 addition, a low altitude system was used to record spectral differences before and after a leakage event. Based on the results found, it seems 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.
First International Conference on Remote Sensing and Geoinformation of Environment; 04/2013
[show abstract][hide abstract] ABSTRACT: Aerosol optical thickness is considered to be the most important unknown parameter of every atmospheric correction approach
for removing atmospheric effects from satellite remotely sensed images. This study presents a description of the basics of
the proposed atmospheric correction procedure, which combines the darkest object subtraction principle and the radiative transfer
equations. The method considers the true reflectance values of the selected dark targets acquired in situ and the atmospheric
parameters such as the aerosol single scattering phase function, single scattering albedo and water vapour absorption, which
are also found from ground measurements. The proposed procedure is applicable to short wavelengths such as Landsat TM band
1, 2 and ASTER band 1 in which water vapour absorption is negligible. The proposed image processing method has been tested
successfully to determine the aerosol optical thickness on Landsat-5/TM images of the Lower Thames Valley area located to
West London (UK) in the vicinity of Heathrow Airport and to Landsat TM/ETM+ and ASTER images of an area located in the vicinity
of Paphos International Airport (Cyprus). The determined aerosol optical thicknesses for the Heathrow Airport area were 0.60,
013 and 0.75 for the Landsat TM images (0.45–0.52µm) acquired on 17th of May 1985, 2nd of June 1985 and 4th of July 1985.
The determined aerosol optical thicknesses for the ASTER (0.52–0.60µm) images acquired on the 4th of February 2008, 26th
of February 2008, 17th of December and 24th of December 2007 were 0.18, 0.39, 0.49 and 0.90, respectively. The accuracy assessment
applied using the in situ spectroradiometric and sun-photometer data during the satellite overpass acquired on July–August
2008 for the Paphos area in Cyprus shows satisfactory results both for removing the atmospheric effects and for determining
the aerosol optical thickness. Indeed, the high correlation between the determined aerosol optical thickness and those extracted
from the visibility values increases the potential of the proposed method.
[show abstract][hide abstract] ABSTRACT: The recent development of satellite meteorology has allowed us to estimate spatially and frequently number of basic meteorological parameters. This paper presents the proposed methodology for retrieving visibility values based on the application of the darkest pixel atmospheric correction algorithm on satellite image data. The method is based on the use of the radiative transfer calculations followed by some key assumptions. Landsat-5 TM band 1 images (0.45–0.52μm) have been used to determine the visibility value for each image date. A direct comparison between the measured visibility data from the airport meteorological stations with the determined visibility data was performed showing high correlation values. Indeed, by relating the determined visibility data with those measured on the Heathrow Airport station in the West London (UK), a correlation coefficient of r2=0.97 has been found with the observed significance for the regression model to be less than 0.05, for four multi-temporal images acquired on 1985 and 1986. The algorithm has been tested also to Landsat TM images of the Paphos Airport area in Cyprus with satisfactory agreement between the visibilities measured at the meteorological station and those found from the images. The algorithm presented may be useful for assessing the atmospheric conditions of satellite images and also can assist the improvement and effectiveness of the available atmospheric correction algorithms.
Physics and Chemistry of The Earth - PHYS CHEM EARTH. 01/2010; 35(1):121-124.
[show abstract][hide abstract] ABSTRACT: Satellite remote sensing has been a valuable tool in providing a complete and synoptic geographical coverage of water quality in fresh water systems. The principal benefit of satellite remote sensing for inland water quality monitoring is the production of synoptic views without the need of costly in situ sampling. In addition spatial and temporal variations of water quality and trophic state in fresh water bodies such as dams and reservoirs can be mapped and assessed using satellite remotely sensed imagery. Satellite remote sensing techniques may also be used to design or improve in situ sampling monitoring programmes by locating appropriate sampling points based on the qualitative results obtained directly from the satellite images. A further benefit is the capability of establishing spectral statistical relationships of satellite data with water quality parameters. Cyprus is made attractive by the frequency of high cloud-free imagery availability and moreover due to the fact that a single Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper (ETM+) image of Cyprus covers almost the entire island. This paper examines the potential of using satellite remote sensing for the qualitative assessment of water quality in inland water bodies such as dams in Cyprus; including evaluation on spatial, temporal water quality variations and finally an assessment on trophic state.
Physics and Chemistry of the Earth, Parts A/B/C. 01/2010;
[show abstract][hide abstract] ABSTRACT: Although there have been many studies conducted on the use of satellite remote sensing for water quality monitoring and assessment in inland water bodies, relatively few studies have considered the problem of atmospheric intervention of the satellite signal. The problem is especially significant when using time series multi-spectral satellite data to monitor water quality surveillance in inland waters such as reservoirs, lakes, and dams because atmospheric effects constitute the majority of the at-satellite reflectance over water. For the assessment of temporal variations of water quality, the use of multi-date satellite images is required so atmospheric corrected image data must be determined. The aim of this study is to provide a simple way of monitoring and assessing temporal variations of water quality in a set of inland water bodies using an earth observation- based approach. The proposed methodology is based on the development of an image-based algorithm which consists of a selection of sampling area on the image (outlet), application of masking and convolution image processing filter, and application of the darkest pixel atmospheric correction. The proposed method has been applied in two different geographical areas, in UK and Cyprus. Mainly, the method has been applied to a series of eight archived Landsat-5 TM images acquired from March 1985 up to November 1985 of the Lower Thames Valley area in the West London (UK) consisting of large water treatment reservoirs. Finally, the method is further tested to the Kourris Dam in Cyprus. It has been found that atmospheric correction is essential in water quality assessment studies using satellite remotely sensed imagery since it improves significantly the water reflectance enabling effective water quality assessment to be made.
Environmental Monitoring and Assessment 01/2009; 159(1-4):281-92. · 1.59 Impact Factor
[show abstract][hide abstract] ABSTRACT: 0920-4741 (Print) Determination of turbidity is a common component of water-quality assessments. In regions where there are a lot of inland waters such as dams, sampling even a small proportion of those dams for monitoring and assessing water quality is cost prohibitive. Satellite remote sensing has the potential to be a powerful tool for assessing water quality over large spatial scales. The overall objective of this study was to examine whether Landsat-5 TM (Thematic Mapper) and Landsat-7 ETM+ (Enhanced Thematic Mapper) could be used to measure turbidity across theKourris Dam, which is the biggest dam in Cyprus. This paper presents the results obtained by applying the linear regression analysis in order to examine the relationship between the turbidity measurements measured in-situ during the satellite overpass against at-satellite atmospheric corrected reflectance values. It has been found that the reflectance, after atmospheric correction, at LandsatTMBands 1 and 3 is strongly related with turbidity levels after linear regression analysis. The most significant correlation was occurred when reflectance in TM band 3 and logarithmic reflectance in TM band 3 were correlated with turbidity measurements. Indeed, the correlation coefficient (R) when atmospheric corrected reflectance (ρ) in the LandsatTMband 3 were correlated against turbidity, before atmospheric correction was R = 0.38 and after atmospheric correction was R = 1; and when atmospheric corrected logarithmic reflectance (Log ρ) in the Landsat TM band 3 were correlated against turbidity, before atmospheric correction was R = 0.46 and after atmospheric correction was R = 1.
Water Resources Management 01/2006; · 2.26 Impact Factor
[show abstract][hide abstract] ABSTRACT: The Covariance Matrix Method (CMM) uses the statistical relationship between all the selected bands of a satellite sensor simultaneously, rather than one at a time as in the regression method. It examines the set of variances and covariance between all band pairs in the image data and CMM provides an average pixel correction for a specified part of a satellite image. It is necessary to know a priori a value for the atmospheric path radiance on one spectral band. From this, CMM enables the estimation of the atmospheric path radiances in all the other bands. Dark pixels must be present in the CMM technique. Indeed, the authors suggest an improved CMM atmospheric correction algorithm. This methodology has been presented as an improved revised version of the CMM atmospheric approach. The authors provide a critical assessment of the suitability of the CMM atmospheric correction using Landsat TM image data of an area consisting low reflectance targets that have been used for several environmental monitoring applications. The proposed improved method produces retrieved surface reflectance within the range of the ground measurements.