Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal)
The Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/7/1090 |
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| author | Osvaldo J. R. Pereira Eder R. Merino Célia R. Montes Laurent Barbiero Ary T. Rezende-Filho Yves Lucas Adolpho J. Melfi |
| author_facet | Osvaldo J. R. Pereira Eder R. Merino Célia R. Montes Laurent Barbiero Ary T. Rezende-Filho Yves Lucas Adolpho J. Melfi |
| author_sort | Osvaldo J. R. Pereira |
| collection | DOAJ |
| description | The Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere, the water pH being one of the key factors in controlling the biogeochemical functioning and, consequently, production and emission of GHGs in these lakes. Here, we present a new field-validated classification of the Nhecolândia lakes using water pH values estimated based on a cloud-based Landsat (5 TM, 7 ETM+, and 8 OLI) 2002–2017 time-series in the Google Earth Engine platform. Calibrated top-of-atmosphere (TOA) reflectance collections with the Fmask method were used to ensure the usage of only cloud-free pixels, resulting in a dataset of 2081 scenes. The pH values were predicted by applying linear multiple regression and symbolic regression based on genetic programming (GP). The regression model presented an R<sup>2</sup> value of 0.81 and pH values ranging from 4.69 to 11.64. A lake mask was used to extract the predicted pH band that was then classified into three lake classes according to their pH values: Freshwater (pH < 8), oligosaline (pH 8–8.9), and saline (≥9). Nearly 12,150 lakes were mapped with those with saline waters accounting for 7.25%. Finally, a trend surface map was created using the ALOS PRISM Digital Surface Model (DSM) to analyze the correlation between landscape features (topography, connection with the regional drainage system, size, and shape of lakes) and types of lakes. The analysis was in consonance with previous studies that pointed out that saline lakes tend to occur in lower positions compared to freshwater lakes. The results open a relevant perspective for the transfer of locally acquired experimental data to the regional balances of the Nhecolândia lakes. |
| first_indexed | 2024-03-11T10:10:54Z |
| format | Article |
| id | doaj.art-71717c041a9e4a9493f089cba366fd99 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T10:10:54Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-71717c041a9e4a9493f089cba366fd992023-11-16T14:33:23ZengMDPI AGRemote Sensing2072-42922020-03-01127109010.3390/rs12071090Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal)Osvaldo J. R. Pereira0Eder R. Merino1Célia R. Montes2Laurent Barbiero3Ary T. Rezende-Filho4Yves Lucas5Adolpho J. Melfi6IEE, NUPEGEL, Universidade de São Paulo, São Paulo 05508-010, BrazilIEE, NUPEGEL, Universidade de São Paulo, São Paulo 05508-010, BrazilCENA, NUPEGEL, Universidade de São Paulo, Piracicaba 13400-970, BrazilGET, IRD, CNRS, UPS, OMP Toulouse 31400, FranceFAENG, Universidade Federal do Mato Grosso do Sul, Campo Grande 79079-900, BrazilUniversité de Toulon, Aix Marseille Université, CNRS, IM2NP, 83041 Toulon CEDEX 9, FranceIEE, NUPEGEL, Universidade de São Paulo, São Paulo 05508-010, BrazilThe Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere, the water pH being one of the key factors in controlling the biogeochemical functioning and, consequently, production and emission of GHGs in these lakes. Here, we present a new field-validated classification of the Nhecolândia lakes using water pH values estimated based on a cloud-based Landsat (5 TM, 7 ETM+, and 8 OLI) 2002–2017 time-series in the Google Earth Engine platform. Calibrated top-of-atmosphere (TOA) reflectance collections with the Fmask method were used to ensure the usage of only cloud-free pixels, resulting in a dataset of 2081 scenes. The pH values were predicted by applying linear multiple regression and symbolic regression based on genetic programming (GP). The regression model presented an R<sup>2</sup> value of 0.81 and pH values ranging from 4.69 to 11.64. A lake mask was used to extract the predicted pH band that was then classified into three lake classes according to their pH values: Freshwater (pH < 8), oligosaline (pH 8–8.9), and saline (≥9). Nearly 12,150 lakes were mapped with those with saline waters accounting for 7.25%. Finally, a trend surface map was created using the ALOS PRISM Digital Surface Model (DSM) to analyze the correlation between landscape features (topography, connection with the regional drainage system, size, and shape of lakes) and types of lakes. The analysis was in consonance with previous studies that pointed out that saline lakes tend to occur in lower positions compared to freshwater lakes. The results open a relevant perspective for the transfer of locally acquired experimental data to the regional balances of the Nhecolândia lakes.https://www.mdpi.com/2072-4292/12/7/1090pHGoogle Earth Enginetime-seriesgenetic programmingLandsatlakes |
| spellingShingle | Osvaldo J. R. Pereira Eder R. Merino Célia R. Montes Laurent Barbiero Ary T. Rezende-Filho Yves Lucas Adolpho J. Melfi Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal) Remote Sensing pH Google Earth Engine time-series genetic programming Landsat lakes |
| title | Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal) |
| title_full | Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal) |
| title_fullStr | Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal) |
| title_full_unstemmed | Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal) |
| title_short | Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolândia Lakes (Brazilian Pantanal) |
| title_sort | estimating water ph using cloud based landsat images for a new classification of the nhecolandia lakes brazilian pantanal |
| topic | pH Google Earth Engine time-series genetic programming Landsat lakes |
| url | https://www.mdpi.com/2072-4292/12/7/1090 |
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