Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China
Accurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water...
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6911 |
| _version_ | 1797545832859303936 |
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| author | Jing Zhao Fujie Zhang Shuisen Chen Chongyang Wang Jinyue Chen Hui Zhou Yong Xue |
| author_facet | Jing Zhao Fujie Zhang Shuisen Chen Chongyang Wang Jinyue Chen Hui Zhou Yong Xue |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | Accurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality evaluation method based on the Markov model and remote sensing retrieval is proposed to realize the innovation of large-scale spatial monitoring across administrative boundaries. Additionally, to explore the spatiotemporal characteristics and driving factors of TSS, a new three-band remote sensing model of TSS was built by regression analysis for the inland reservoir using the synchronous field spectral data, water quality samples and remote sensing data in the trans-provincial Hedi Reservoir in the Guangdong and Guangxi Provinces of South China. The results show that: (1) The three-band model based on the OLI sensor explained about 82% of the TSS concentration variation (<inline-formula><math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.81</mn><mo>,</mo><mo> </mo><mi>N</mi><mo>=</mo><mn>34</mn><mo>,</mo><mo> </mo><mo> </mo><mi>p</mi><mo> </mo><mi>value</mi><mo><</mo><mn>0.01</mn></mrow></semantics></math></inline-formula>) with an acceptable validation accuracy (<inline-formula><math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi><mo>=</mo><mn>6.24</mn><mo> </mo><mi>mg</mi><mo>/</mo><mi mathvariant="normal">L</mi><mo>,</mo><mi>M</mi><mi>R</mi><mi>E</mi><mo>=</mo><mn>18.02</mn><mo>%</mo><mo>,</mo><mo> </mo><mi>N</mi><mo>=</mo><mn>15</mn></mrow></semantics></math></inline-formula>), which is basically the first model of its kind available in South China. (2) The TSS concentration has spatial distribution characteristics of high upstream and low downstream, where the average TSS at 31.54 mg/L in the upstream are 2.5 times those of the downstream (12.55 mg/L). (3) Different seasons and rainfall are important factors affecting the TSS in the upstream cross-border area, the TSS in the dry season are higher with average TSS of 33.66 mg/L and TSS are negatively correlated with rainfall from upstream mankind activity. Generally, TSS are higher in rainy seasons than those in dry seasons. However, the result shows that TSS are negatively correlated with rainfall, which means human activities have higher impacts on water quality than climate change. (4) The Markov dynamic evaluation results show that the water quality improvement in the upstream Shijiao Town is the most obvious, especially in 2018, the improvement in the water quality level crossed three levels and the TSS were the lowest. This study provided a technical method for remote sensing dynamic monitoring of water quality in a large reservoir, which is of great significance for remediation of the water environment and the effective evaluation of the river and lake chief system in China. |
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| spelling | doaj.art-a5528fee9d164fe5a68a438a1bc4972e2023-11-20T23:22:04ZengMDPI AGSensors1424-82202020-12-012023691110.3390/s20236911Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South ChinaJing Zhao0Fujie Zhang1Shuisen Chen2Chongyang Wang3Jinyue Chen4Hui Zhou5Yong Xue6Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaFaculty of Agriculture and Food, Kunming University of Science and Technology, Kunming 650500, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Open Laboratory of Geospatial Information Technology and Application, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaCollege of Engineering and Technology, University of Derby, Derby DE22 1GB, UKAccurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality evaluation method based on the Markov model and remote sensing retrieval is proposed to realize the innovation of large-scale spatial monitoring across administrative boundaries. Additionally, to explore the spatiotemporal characteristics and driving factors of TSS, a new three-band remote sensing model of TSS was built by regression analysis for the inland reservoir using the synchronous field spectral data, water quality samples and remote sensing data in the trans-provincial Hedi Reservoir in the Guangdong and Guangxi Provinces of South China. The results show that: (1) The three-band model based on the OLI sensor explained about 82% of the TSS concentration variation (<inline-formula><math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.81</mn><mo>,</mo><mo> </mo><mi>N</mi><mo>=</mo><mn>34</mn><mo>,</mo><mo> </mo><mo> </mo><mi>p</mi><mo> </mo><mi>value</mi><mo><</mo><mn>0.01</mn></mrow></semantics></math></inline-formula>) with an acceptable validation accuracy (<inline-formula><math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi><mo>=</mo><mn>6.24</mn><mo> </mo><mi>mg</mi><mo>/</mo><mi mathvariant="normal">L</mi><mo>,</mo><mi>M</mi><mi>R</mi><mi>E</mi><mo>=</mo><mn>18.02</mn><mo>%</mo><mo>,</mo><mo> </mo><mi>N</mi><mo>=</mo><mn>15</mn></mrow></semantics></math></inline-formula>), which is basically the first model of its kind available in South China. (2) The TSS concentration has spatial distribution characteristics of high upstream and low downstream, where the average TSS at 31.54 mg/L in the upstream are 2.5 times those of the downstream (12.55 mg/L). (3) Different seasons and rainfall are important factors affecting the TSS in the upstream cross-border area, the TSS in the dry season are higher with average TSS of 33.66 mg/L and TSS are negatively correlated with rainfall from upstream mankind activity. Generally, TSS are higher in rainy seasons than those in dry seasons. However, the result shows that TSS are negatively correlated with rainfall, which means human activities have higher impacts on water quality than climate change. (4) The Markov dynamic evaluation results show that the water quality improvement in the upstream Shijiao Town is the most obvious, especially in 2018, the improvement in the water quality level crossed three levels and the TSS were the lowest. This study provided a technical method for remote sensing dynamic monitoring of water quality in a large reservoir, which is of great significance for remediation of the water environment and the effective evaluation of the river and lake chief system in China.https://www.mdpi.com/1424-8220/20/23/6911total suspended solidsprogress degreeMarkov modelremote sensingriver chief system |
| spellingShingle | Jing Zhao Fujie Zhang Shuisen Chen Chongyang Wang Jinyue Chen Hui Zhou Yong Xue Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China Sensors total suspended solids progress degree Markov model remote sensing river chief system |
| title | Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China |
| title_full | Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China |
| title_fullStr | Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China |
| title_full_unstemmed | Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China |
| title_short | Remote Sensing Evaluation of Total Suspended Solids Dynamic with Markov Model: A Case Study of Inland Reservoir across Administrative Boundary in South China |
| title_sort | remote sensing evaluation of total suspended solids dynamic with markov model a case study of inland reservoir across administrative boundary in south china |
| topic | total suspended solids progress degree Markov model remote sensing river chief system |
| url | https://www.mdpi.com/1424-8220/20/23/6911 |
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