Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China
Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X21000212 |
| _version_ | 1818461294293942272 |
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| author | Jiang Qun'ou Xu Lidan Sun Siyang Wang Meilin Xiao Huijie |
| author_facet | Jiang Qun'ou Xu Lidan Sun Siyang Wang Meilin Xiao Huijie |
| author_sort | Jiang Qun'ou |
| collection | DOAJ |
| description | Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir. |
| first_indexed | 2024-12-14T23:43:51Z |
| format | Article |
| id | doaj.art-a366d2c3f29b467da2d24764f6afe33f |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-12-14T23:43:51Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-a366d2c3f29b467da2d24764f6afe33f2022-12-21T22:43:26ZengElsevierEcological Indicators1470-160X2021-05-01124107356Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, ChinaJiang Qun'ou0Xu Lidan1Sun Siyang2Wang Meilin3Xiao Huijie4School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China; Key Laboratory of Soil and Water Conservation and Desertification Prevention, Beijing Forestry University, Beijing 100083, China; Jinyun Forest Ecosystem Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China; Corresponding author.School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaMonitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir.http://www.sciencedirect.com/science/article/pii/S1470160X21000212UAV hyper spectral remote sensing dataMachine learning algorithmRetrieval modelWater quality parameterTotal nitrogen concentration |
| spellingShingle | Jiang Qun'ou Xu Lidan Sun Siyang Wang Meilin Xiao Huijie Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China Ecological Indicators UAV hyper spectral remote sensing data Machine learning algorithm Retrieval model Water quality parameter Total nitrogen concentration |
| title | Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China |
| title_full | Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China |
| title_fullStr | Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China |
| title_full_unstemmed | Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China |
| title_short | Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China |
| title_sort | retrieval model for total nitrogen concentration based on uav hyper spectral remote sensing data and machine learning algorithms a case study in the miyun reservoir china |
| topic | UAV hyper spectral remote sensing data Machine learning algorithm Retrieval model Water quality parameter Total nitrogen concentration |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X21000212 |
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