Catchment characterization to support water monitoring and management decisions using remote sensing
Abstract This study implements remote sensing (RS) and geographic information system techniques in deriving physical and spectral characteristics of a catchment to aid in water quality monitoring. This approach is conducted by utilizing RS datasets like digital elevation model (DEM), satellite image...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-04-01
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| Series: | Sustainable Environment Research |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s42834-019-0008-5 |
| _version_ | 1828474722632859648 |
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| author | Michelle V. Japitana Alexander T. Demetillo Marlowe Edgar C. Burce Evelyn B. Taboada |
| author_facet | Michelle V. Japitana Alexander T. Demetillo Marlowe Edgar C. Burce Evelyn B. Taboada |
| author_sort | Michelle V. Japitana |
| collection | DOAJ |
| description | Abstract This study implements remote sensing (RS) and geographic information system techniques in deriving physical and spectral characteristics of a catchment to aid in water quality monitoring. This approach is conducted by utilizing RS datasets like digital elevation model (DEM), satellite images, and on-site spectral measurements. A Shuttle Radar Topography Mission DEM was used for extracting physical profiles while Landsat Operational Land Imager was utilized to extract land cover information. This method was tested in a 22,000-ha catchment with dominant agricultural lands where large-scale mining companies are also operating actively. The land cover classification has an overall accuracy of 97.66%. Forest (50%) and cropland (32%) are the most dominant land cover within the catchment. The spectral signature of waters at designated sampling points was measured to evaluate its correlation to water quality data like pH and dissolved oxygen (DO). The correlation between the level of pH and reflectance implies a positive relationship (R2 of 0.548) while that of DO and reflectance gives a negative correlation (R2 of 0.634). Results of this study demonstrate the practical advantage of exploiting remotely-sensed data in profiling and characterizing a catchment as it provides valuable information in understanding and mitigating contamination in an area. Through these RS-derived catchment profiles, insights on the contaminant’s concentration and possible sources can be identified. The graphical and statistical analysis of the spectral data prove its potential in developing water quality models and maps. |
| first_indexed | 2024-12-11T06:03:25Z |
| format | Article |
| id | doaj.art-8b3b1c3ce8cb4c33ab14149e7fff8ddb |
| institution | Directory Open Access Journal |
| issn | 2468-2039 |
| language | English |
| last_indexed | 2024-12-11T06:03:25Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Sustainable Environment Research |
| spelling | doaj.art-8b3b1c3ce8cb4c33ab14149e7fff8ddb2022-12-22T01:18:22ZengBMCSustainable Environment Research2468-20392019-04-0129111010.1186/s42834-019-0008-5Catchment characterization to support water monitoring and management decisions using remote sensingMichelle V. Japitana0Alexander T. Demetillo1Marlowe Edgar C. Burce2Evelyn B. Taboada3School of Engineering, University of San CarlosSchool of Engineering, University of San CarlosSchool of Engineering, University of San CarlosSchool of Engineering, University of San CarlosAbstract This study implements remote sensing (RS) and geographic information system techniques in deriving physical and spectral characteristics of a catchment to aid in water quality monitoring. This approach is conducted by utilizing RS datasets like digital elevation model (DEM), satellite images, and on-site spectral measurements. A Shuttle Radar Topography Mission DEM was used for extracting physical profiles while Landsat Operational Land Imager was utilized to extract land cover information. This method was tested in a 22,000-ha catchment with dominant agricultural lands where large-scale mining companies are also operating actively. The land cover classification has an overall accuracy of 97.66%. Forest (50%) and cropland (32%) are the most dominant land cover within the catchment. The spectral signature of waters at designated sampling points was measured to evaluate its correlation to water quality data like pH and dissolved oxygen (DO). The correlation between the level of pH and reflectance implies a positive relationship (R2 of 0.548) while that of DO and reflectance gives a negative correlation (R2 of 0.634). Results of this study demonstrate the practical advantage of exploiting remotely-sensed data in profiling and characterizing a catchment as it provides valuable information in understanding and mitigating contamination in an area. Through these RS-derived catchment profiles, insights on the contaminant’s concentration and possible sources can be identified. The graphical and statistical analysis of the spectral data prove its potential in developing water quality models and maps.http://link.springer.com/article/10.1186/s42834-019-0008-5Water quality monitoringReflectanceGISSite characterization |
| spellingShingle | Michelle V. Japitana Alexander T. Demetillo Marlowe Edgar C. Burce Evelyn B. Taboada Catchment characterization to support water monitoring and management decisions using remote sensing Sustainable Environment Research Water quality monitoring Reflectance GIS Site characterization |
| title | Catchment characterization to support water monitoring and management decisions using remote sensing |
| title_full | Catchment characterization to support water monitoring and management decisions using remote sensing |
| title_fullStr | Catchment characterization to support water monitoring and management decisions using remote sensing |
| title_full_unstemmed | Catchment characterization to support water monitoring and management decisions using remote sensing |
| title_short | Catchment characterization to support water monitoring and management decisions using remote sensing |
| title_sort | catchment characterization to support water monitoring and management decisions using remote sensing |
| topic | Water quality monitoring Reflectance GIS Site characterization |
| url | http://link.springer.com/article/10.1186/s42834-019-0008-5 |
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