Optical method for water pollution remote sensing

1998 ◽  
Author(s):  
Yi He ◽  
Jian Wu
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Optical Method

scholarly journals Application of Remote Sensing Techniques for Water Pollution Monitoring

1979 ◽  
Vol 2 (4) ◽  
pp. 193-200
Author(s):  
Yoshinori ISHII ◽  
Tsunemasa IMAIZUMI ◽  
Yoshinori MIYAZAKI
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Pollution Monitoring ◽  
Remote Sensing Techniques

scholarly journals An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

2019 ◽  
Vol 12 (1) ◽  
pp. 43 ◽  
Author(s):  
Maurício Araújo Dias ◽  
Erivaldo Antônio da Silva ◽  
Samara Calçado de Azevedo ◽  
Wallace Casaca ◽  
Thiago Statella ◽  
...  
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Anomaly Detection ◽  
Synthetic Data ◽  
Open Water ◽  
Surveillance Systems ◽  
Environmental Disasters ◽  
Detection Strategy ◽  
Potential Applications ◽  
Static Images

The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings.

Applications of Excimer Laser Based Remote Sensing Systems to Problems Related to Water Pollution

1994 ◽  
pp. 289-305
Author(s):  
R. Fantoni ◽  
R. Barbini ◽  
F. Colao ◽  
A. Palucci ◽  
S. Ribezzo
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Excimer Laser ◽  
Sensing Systems ◽  
Remote Sensing Systems

Remote Sensing of Atmospheric Turbulence by Means of a Fast Optical Method: A Comparison with SimultaneousIn SituMeasurements

1980 ◽  
Vol 19 (7) ◽  
pp. 834-838 ◽  
Author(s):  
M. Azouit ◽  
J. Vernin ◽  
R. Barletti ◽  
G. Ceppatelli ◽  
A. Righini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Turbulence ◽  
Optical Method

scholarly journals THE EVALUATION OF WATER POLLUTION WITH THE HELP OF REMOTE SENSING TOOLS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 403-408
Author(s):  
P. Šádek ◽  
J. Struhár
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Water Quality ◽  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Pollution Assessment ◽  
Vegetation Period ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Landsat 8

<p><strong>Abstract.</strong> With the growing population, there is a growing demand for quality drinking water. Especially in developing parts of the world, this is a serious problem. The aim of this work is to test remote sensing methods for water quality monitoring. The presented part of the project is focused on introducing the process of water pollution assessment using vegetation indices, which are derived only using RGB images. Water quality monitoring is based on satellite imagery Landsat 8 and UAV images Phantom 3. As reference data was used in-site measurements in profiles points. In-site measurements were repeated every month in the vegetation period from April to September. Based on regression analysis, the equation for the calculation of the amount of chlorophyll and the statistical evaluation of the quality of these equations is derived for each vegetation index. The best results were achieved using the ratio aquatic vegetation index (RAVI) and ExG (Excess green) indices of 97% and 96.8% respectively.</p>

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