Remote sensing of surface water quality in relation to catchment condition in Zimbabwe

2017 ◽  
Vol 100 ◽  
pp. 13-18 ◽  
Author(s):  
Mhosisi Masocha ◽  
Amon Murwira ◽  
Christopher H.D. Magadza ◽  
Rafik Hirji ◽  
Timothy Dube
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality

scholarly journals Remote sensing and gis applications in determining shoreline and surface water quality changes in Bayelsa state, Nigeria

1970 ◽  
Vol 10 (5) ◽  
pp. 572-587
Author(s):  
A.O. Adebola ◽  
T.H.T Ogunribido ◽  
S.A. Adegboyega ◽  
M.O. Ibitoye ◽  
A.A Adeseko
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Landsat Imagery ◽  
Surface Water Quality ◽  
Remote Sensing And Gis ◽  
Shoreline Changes ◽  
Gis Applications ◽  
Gis Environment ◽  
Coastal Landform

The study of shoreline changes is essential for updating the changes in shoreline maps and management of natural resources as the shoreline is one of the most important features on the earth’s surface. Shorelines are the key element in coastal GIS that provide information on coastal landform dynamics. The purpose of this paper is to investigate shoreline changes in the study area and how it affects surface water quality using Landsat imagery from 1987 to 2016. The image processing techniques adopted involves supervised classification, object-based image analysis, shoreline extraction and image enhancement. The data obtained was analyzed and maps were generated and then integrated in a GIS environment. The results indicate that LULC changes in wetland areas increases rapidly during the years (1987-2016) from 34.83 to 38.96%, vegetation cover reduces drastically through the year which range from 30% to 20%. Polluted surface water was observed to have decreased from 30% to 20% during 1984-2010 and reduced by about 3% in 2016. In addition, the result revealed the highest level of erosion from 1987 to 2016 which is -49.60% against the highest level of accretion of 13.39% EPR and NSM -1400 erosion against 350 accretions. It was also observed that variations in shoreline changes affect the quality of surface water possibly due to shoreline movement hinterland. This study has demonstrated that through satellite remote sensing and GIS techniques, the Nigerian coastline can adequately be monitored for various changes that have taken place over the years.Key Words: Shoreline, Remote Sensing, Erosion, Accretion, GIS 

scholarly journals Development of Remote Sensing Based Models for Surface Water Quality

2013 ◽  
Vol 42 (8) ◽  
pp. 1044-1051 ◽  
Author(s):  
Tahir Ali Akbar ◽  
Quazi K. Hassan ◽  
Gopal Achari
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality

scholarly journals Surface Water Quality Assessment Using Remote Sensing, Gis and Artificial Intelligence

2019 ◽  
Vol 1 (1) ◽  
pp. 113-122
Author(s):  
A. KC ◽  
A. Chalise ◽  
D. Parajuli ◽  
N. Dhital ◽  
S. Shrestha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Satellite Imagery ◽  
Surface Water Quality ◽  
Water Quality Assessment ◽  
The Relationship ◽  
The City

The deterioration of surface water quality occurs due to the presence of various types of pollutants from human activities such as agriculture, industry, construction, deforestation, etc. 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 labour 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, which are difficult for decision-makers to understand without providing the overall patterns of surface water quality. To such problem, Remote Sensing has been a blessing because of its low cost, spatial continuity and temporal consistency. The relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. This study aims to develop techniques for estimating the concentration of both optical and non-optical SWQPs from Satellite Imagery (Landsat8) which supports coastal studies and mapping the complex relationship between satellite multi-spectral signature and concentration of SWQPs. It will also focus on classifying the most significant SWQPs that contribute to both spatial and temporal surface water quality. In contrast to traditionally performed surface water quality assessment methods, this research project will be focused on identifying such parameters incorporating the new and evolving machine intelligence that is Artificial Intelligence (AI). Significant number of samples have to be collected along with the GPS data which is used to model the relationship. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) will be developed to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. The study area chosen for this research is Bijayapur River of distance approximately 10 km flowing above, through and down the Pokhara city. The sole purpose of this research is to examine the water quality before it flows through the city and analysing after it passes through the city.

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