Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data
Nutrient pollution, also known as eutrophication, is a severe environmental problem that leads to harmful algal blooms in water bodies and affects water supplies for human use. This study aims to determine the trophic state of the reservoir based on the trophic state index (TSI) calculated from gro...
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Environmental Research Institute, Chulalongkorn University
2025-01-01
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| Series: | Applied Environmental Research |
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| Online Access: | https://ph01.tci-thaijo.org/index.php/aer/article/view/258309 |
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| author | Thong Nguyen Hoang Van Tran Thi |
| author_facet | Thong Nguyen Hoang Van Tran Thi |
| author_sort | Thong Nguyen Hoang |
| collection | DOAJ |
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Nutrient pollution, also known as eutrophication, is a severe environmental problem that leads to harmful algal blooms in water bodies and affects water supplies for human use. This study aims to determine the trophic state of the reservoir based on the trophic state index (TSI) calculated from ground monitoring data of Secchi depth (SD), total phosphorus (TP), and chlorophyll-a (Chl-a) obtained from 35 points in 2 survey periods during the late rainy season to early dry season 2012–2013 and May 2023. In addition, we combined remote sensing data to spatially estimate the eutrophication situation across the reservoir through correlation analysis and determine the best regression models. The results of correlation analysis between ground monitoring values and spectral values from remote sensing algorithms showed that the two parameters, SD and TP, correlated best with the NIR/BLUE algorithm. In contrast, the Chl-a parameter correlated best with the NIR/RED algorithm. From there, we mapped the spatial distribution of parameters and trophic state according to TSI in the entire Dau Tieng Reservoir based on the spectral values from remote sensing algorithms and regression models. Analysis results showed that, as of May 2023, the Dau Tieng Reservoir showed signs of eutrophication in most areas; some areas also showed signs of hyper-eutrophication, causing the risk of harmful algal blooms. The results achieved in this study will be a valuable source of consultation, supporting environmental management to minimize nutrient pollution in the Dau Tieng Reservoir water source.
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| format | Article |
| id | doaj-art-a7840f7530854523beed323f639682f9 |
| institution | Kabale University |
| issn | 2287-075X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Environmental Research Institute, Chulalongkorn University |
| record_format | Article |
| series | Applied Environmental Research |
| spelling | doaj-art-a7840f7530854523beed323f639682f92025-02-11T11:08:20ZengEnvironmental Research Institute, Chulalongkorn UniversityApplied Environmental Research2287-075X2025-01-01471Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data Thong Nguyen Hoang0Van Tran Thi1Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology, Ho Chi Minh City, VietnamFaculty of Environment and Natural Resources, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam Nutrient pollution, also known as eutrophication, is a severe environmental problem that leads to harmful algal blooms in water bodies and affects water supplies for human use. This study aims to determine the trophic state of the reservoir based on the trophic state index (TSI) calculated from ground monitoring data of Secchi depth (SD), total phosphorus (TP), and chlorophyll-a (Chl-a) obtained from 35 points in 2 survey periods during the late rainy season to early dry season 2012–2013 and May 2023. In addition, we combined remote sensing data to spatially estimate the eutrophication situation across the reservoir through correlation analysis and determine the best regression models. The results of correlation analysis between ground monitoring values and spectral values from remote sensing algorithms showed that the two parameters, SD and TP, correlated best with the NIR/BLUE algorithm. In contrast, the Chl-a parameter correlated best with the NIR/RED algorithm. From there, we mapped the spatial distribution of parameters and trophic state according to TSI in the entire Dau Tieng Reservoir based on the spectral values from remote sensing algorithms and regression models. Analysis results showed that, as of May 2023, the Dau Tieng Reservoir showed signs of eutrophication in most areas; some areas also showed signs of hyper-eutrophication, causing the risk of harmful algal blooms. The results achieved in this study will be a valuable source of consultation, supporting environmental management to minimize nutrient pollution in the Dau Tieng Reservoir water source. https://ph01.tci-thaijo.org/index.php/aer/article/view/258309EutrophicationNutrient pollutionTSIRemote sensingReservoir |
| spellingShingle | Thong Nguyen Hoang Van Tran Thi Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data Applied Environmental Research Eutrophication Nutrient pollution TSI Remote sensing Reservoir |
| title | Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data |
| title_full | Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data |
| title_fullStr | Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data |
| title_full_unstemmed | Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data |
| title_short | Spatial Estimation of Trophic State for Reservoir Using Ground Monitoring and Remote Sensing Data |
| title_sort | spatial estimation of trophic state for reservoir using ground monitoring and remote sensing data |
| topic | Eutrophication Nutrient pollution TSI Remote sensing Reservoir |
| url | https://ph01.tci-thaijo.org/index.php/aer/article/view/258309 |
| work_keys_str_mv | AT thongnguyenhoang spatialestimationoftrophicstateforreservoirusinggroundmonitoringandremotesensingdata AT vantranthi spatialestimationoftrophicstateforreservoirusinggroundmonitoringandremotesensingdata |