Evolution of river geomorphology to water quality impact using remote sensing and GIS technique

2019 ◽  
Vol 149 ◽  
pp. 258-273 ◽  
Author(s):  
Mohd Khairul Amri Kamarudin ◽  
Aliyu Muhammad Nalado ◽  
Mohd Ekhwan Toriman ◽  
Hafizan Juahir ◽  
Roslan Umar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing And Gis ◽  
Gis Technique ◽  
River Geomorphology ◽  
Water Quality Impact

Water Quality Assessment of River Ganga using Remote Sensing and GIS Technique

2015 ◽  
Vol 4 (1) ◽  
pp. 1253-1261 ◽  
Author(s):  
Anju Panwar ◽  
◽  
Sapana Bartwal ◽  
Sourabh Dangwal ◽  
Ashok Aswal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Remote Sensing And Gis ◽  
Gis Technique ◽  
River Ganga

Determining Wheat Crop Acreage Based on Remote Sensing and GIS Technique in Jabalpur, India

2021 ◽  
pp. 63-69
Author(s):  
Umakant Rawat ◽  
Ankit Yadav ◽  
P. S. Pawar ◽  
Aniket Rajput ◽  
Devendra Vasht ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Wheat Crop ◽  
Crop Acreage ◽  
Gis Technique

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 

An Investigation on Sudden Change in Water Quality of Brahmaputra River Using Remote Sensing and GIS

2020 ◽  
Vol 43 (7) ◽  
pp. 619-623
Author(s):  
Thota Sivasankar ◽  
Suranjana B. Borah ◽  
Ranjit Das ◽  
P. L. N. Raju
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Sudden Change ◽  
Remote Sensing And Gis ◽  
Brahmaputra River

scholarly journals Remote sensing and GIS application for river runoff and water quality modeling in a hilly forested watershed of Japan

2010 ◽  
Vol 13 (2) ◽  
pp. 198-216 ◽  
Author(s):  
Binaya R. Shivakoti ◽  
Shigeo Fujii ◽  
Shuhei Tanaka ◽  
Hirotaka Ihara ◽  
Masashi Moriya
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Cover ◽  
Spatial Information ◽  
Remote Sensing And Gis ◽  
Water Quality Modeling ◽  
Model Verification ◽  
Modeling Framework ◽  
Area Index ◽  
Distributed Modeling

The main objective of this study is to present a simplified distributed modeling framework based on the storage balance concept of a Tank Model and by utilizing inputs from remote sensing data and GIS analysis. The modeling process is simplified by (1) minimizing the number of parameters with unknown values and 2) retaining important characteristics (such as land cover, topography, geology) of the study area in order to account for spatial variability. Remote sensing is used as a main source of distributed data and the GIS environment is used to integrate spatial information into the model. Remote sensing is utilized mainly to derive land cover, leaf area index (Lai) and transpiration coefficient (Tc). Topographic variables such as slope, drainage direction and soil topographic index (Tindex) are derived from a digital elevation model (DEM) using GIS. The model is used to estimate evapotranspiration (Et) loss, river flow rate and selected water quality parameters (CODMn and TP). Model verification adopted a comparison of estimated results with observed data collected at different temporal scales (storm events, daily, alternate days and every 10 days). A simplified distributed modeling framework coupled with remote sensing and GIS is expected to be an alternative to complex distributed modeling processes, which required values of parameters usually unavailable at a grid scale.

scholarly journals Land Use/Land Cover Change Detection Study Using Remote Sensing and GIS Technique in Puthimari River Basin-A Transboundary Basin Between Bhutan and India

2021 ◽  
pp. 26-34
Author(s):  
Swapnali Barman ◽  
Jaivir Tyagi ◽  
Waikhom Rahul Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
River Basin ◽  
Remote Sensing And Gis ◽  
Rural Settlement ◽  
Land Use Land Cover ◽  
Dense Vegetation ◽  
Gis Technique

Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.

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