FIG Peer Review Journal


Surface Water Quality Assessment Using Geospatial Technologies (10247)

Deepak Parajuli, Aman K.c., Chalise Ashish, Nabin Dhital and Tejendra Kandel (Nepal)
Mr. Deepak Parajuli
Pashchimanchal Campus
Corresponding author Mr. Deepak Parajuli (email: bindaaspratiks[at], tel.: 9843225854)

[ abstract ] [ paper ] [ handouts ]

Published on the web 2020-02-28
Received 2019-10-01 / Accepted 2020-02-03
This paper is one of selection of papers published for the FIG Working Week 2020 in Amsterdam, the Netherlands and has undergone the FIG Peer Review Process.

FIG Working Week 2020
ISBN 978-87-92853-93-6 ISSN 2307-4086


The deterioration of surface water quality occurs due to the presence of various types of pollutants from human, agricultural, and industrial activities. Thus, the presence of various pollutants in water bodies can lead to deterioration of both surface water quality and aquatic life. Conventional surface water quality assessment methods are widely performed using laboratory analysis, which are labor intensive, costly, and time consuming. Moreover, these methods can only provide individual concentration of surface water quality parameters (SWQPs), measured at monitoring stations and shown in a discrete point format. Our study aims to develop techniques for estimating the concentration of both optical and non-optical SWQPs from Landsat8 which supports costal studies and mapping the complex relationship between satellite multi-spectral signature and concentration of SWQPs. In contrast to traditionally performed surface water quality assessment methods, our project is focused to identify such parameters incorporating step wise regression technique to find the relation between satellite images and SWQPs. Significant number of samples were collected along with the GPS data which were used to model the relationship. In this context, a remote-sensing framework based on regression technique a model was developed to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. Study site was chosen as Phewa lake, which is comparatively a larger water body. We collected the samples in the lake on the same day Landsat 8 Satellite passed over the city. And then measured both the optical and non-optical parameters in the laboratory. The relationship between the parameters was modelled using statistical testing and parameters. The acquired image from the satellite were processed and subjected into corrections to minimize its atmospheric and geometric errors. Individual models for each parameter was developed using correlation and regression techniques to determine the relations. The results of the relations were as expected as the resolution of the satellite images were satisfactory for the study site.
Keywords: Cartography; Positioning; Remote sensing; Risk management; Surface Water Quality Parameters (SWQPs), Landsat 8 Satellite, Satellite Image, GIS, Remote Sensing, Step Wise Regression