SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China
Recently, the frequency of cyanobacterial blooms in lakes across Northeast China has noticeably increased, because of the combined effect of global climate change and human activities. Phycocyanin (PC), a distinctive pigment found in cyanobacteria, plays a pivotal role in detecting and predicting ha...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124005077 |
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| author | Changchun Song Yipei Xu Chong Fang Chi Zhang Zhuohang Xin Zhihong Liu |
| author_facet | Changchun Song Yipei Xu Chong Fang Chi Zhang Zhuohang Xin Zhihong Liu |
| author_sort | Changchun Song |
| collection | DOAJ |
| description | Recently, the frequency of cyanobacterial blooms in lakes across Northeast China has noticeably increased, because of the combined effect of global climate change and human activities. Phycocyanin (PC), a distinctive pigment found in cyanobacteria, plays a pivotal role in detecting and predicting harmful cyanobacterial blooms. A novel segmented PC concentration remote sensing inversion model was developed using the support vector regression (SVR) algorithm and Sentinel-3 OLCI data to examine the spatiotemporal variations in PC concentrations across 11 representative lakes in Northeast China. The model showed high accuracy (PC ≤ 45 μg/L: R2 = 0.882, MAPE = 34.53 %, MAE = 3.96 μg/L; PC > 45 μg/L: R2 = 0.814, MAPE = 31.80 %, MAE = 70.58 μg/L). Significant variations in PC concentrations were observed across the watersheds in Northeast China. Over seven years, the PC concentrations declined in half of the lakes. Although no significant changes in the annual average PC concentrations were noticed, intra-annual fluctuations were observed. Seasonal patterns in PC concentrations were also observed. The contribution rates of natural and anthropogenic factors were determined using spatiotemporal data on PC concentrations from 2016 to 2022 and redundancy analysis. The primary drivers of PC concentrations varied across different spatial and temporal scales. These findings highlight the monitoring of PC concentrations in the lakes of Northeast China using Sentinel-3 OLCI images. This study can provide valuable insights for water quality management authorities, enabling them to understand the changing characteristics and trends of PC concentrations and to issue early warnings of cyanobacterial blooms. |
| format | Article |
| id | doaj-art-52200d0c90284017af9dfd74084d2f33 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Ecological Informatics |
| spelling | doaj-art-52200d0c90284017af9dfd74084d2f332025-01-19T06:24:40ZengElsevierEcological Informatics1574-95412025-03-0185102965SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast ChinaChangchun Song0Yipei Xu1Chong Fang2Chi Zhang3Zhuohang Xin4Zhihong Liu5Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaFaculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Resources and Environmental Information System, Beijing 100101, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China; Corresponding author at: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaFaculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaFaculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaRecently, the frequency of cyanobacterial blooms in lakes across Northeast China has noticeably increased, because of the combined effect of global climate change and human activities. Phycocyanin (PC), a distinctive pigment found in cyanobacteria, plays a pivotal role in detecting and predicting harmful cyanobacterial blooms. A novel segmented PC concentration remote sensing inversion model was developed using the support vector regression (SVR) algorithm and Sentinel-3 OLCI data to examine the spatiotemporal variations in PC concentrations across 11 representative lakes in Northeast China. The model showed high accuracy (PC ≤ 45 μg/L: R2 = 0.882, MAPE = 34.53 %, MAE = 3.96 μg/L; PC > 45 μg/L: R2 = 0.814, MAPE = 31.80 %, MAE = 70.58 μg/L). Significant variations in PC concentrations were observed across the watersheds in Northeast China. Over seven years, the PC concentrations declined in half of the lakes. Although no significant changes in the annual average PC concentrations were noticed, intra-annual fluctuations were observed. Seasonal patterns in PC concentrations were also observed. The contribution rates of natural and anthropogenic factors were determined using spatiotemporal data on PC concentrations from 2016 to 2022 and redundancy analysis. The primary drivers of PC concentrations varied across different spatial and temporal scales. These findings highlight the monitoring of PC concentrations in the lakes of Northeast China using Sentinel-3 OLCI images. This study can provide valuable insights for water quality management authorities, enabling them to understand the changing characteristics and trends of PC concentrations and to issue early warnings of cyanobacterial blooms.http://www.sciencedirect.com/science/article/pii/S1574954124005077PhycocyaninOLCIRemote sensingSupport vector regressionDriving factors |
| spellingShingle | Changchun Song Yipei Xu Chong Fang Chi Zhang Zhuohang Xin Zhihong Liu SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China Ecological Informatics Phycocyanin OLCI Remote sensing Support vector regression Driving factors |
| title | SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China |
| title_full | SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China |
| title_fullStr | SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China |
| title_full_unstemmed | SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China |
| title_short | SVR model and OLCI images reveal a declining trend in phycocyanin levels in typical lakes across Northeast China |
| title_sort | svr model and olci images reveal a declining trend in phycocyanin levels in typical lakes across northeast china |
| topic | Phycocyanin OLCI Remote sensing Support vector regression Driving factors |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124005077 |
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