Remote sensing Estimating Water pH Using Cloud-Based Landsat Images for a New Classification of the Nhecolandia Lakes (Brazilian Pantanal)

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Pereira:2020:RS,

• author = "Osvaldo Pereira and Eder Merino and Celia Montes and Laurent Barbiero and Ary Rezende-Filho and Yves Lucas and Adolpho Melfi",
• title = "Remote sensing Estimating Water {pH} Using Cloud-Based {Landsat} Images for a New Classification of the {Nhecolandia} Lakes ({Brazilian Pantanal})",
• journal = "Remote Sensing",
• year = "2020",
• volume = "12",
• number = "7",
• pages = "1090",
• keywords = "genetic algorithms, genetic programming, google earth engine, acidity, pH, time-series, landsat, satellite observation, lakes, environmental sciences, environmental engineering, sciences of the universe, physics, geochemistry",
• publisher = "MDPI",
• ISSN = "2072-4292",
• URL = "https://hal.archives-ouvertes.fr/hal-02528909",
• URL = "https://hal.archives-ouvertes.fr/hal-02528909/document",
• URL = "https://hal.archives-ouvertes.fr/hal-02528909/file/55%20-%20RemoteSensing-2020.pdf",
• URL = "https://www.mdpi.com/2072-4292/12/7/1090/pdf",
• DOI = "doi:10.3390/rs12071090",
• size = "21 pages",
• abstract = "The Nhecolandia 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 Nhecol^andia 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 2 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 (at least 9). Nearly 12,150 lakes were mapped with those with saline waters accounting for 7.25percent. Finally, a trend surface map was created using the ALOS PRISM Digital Surface Model (DSM) to analyse 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 Nhecolandia lakes.",
• annote = "Universidade de Sao Paulo (USP); Laboratoire des Mecanismes et Transfert en Geologie (LMTG) ; Universite Toulouse III - Paul Sabatier (UT3) ; Universite Federale Toulouse Midi-Pyrenees-Universite Federale Toulouse Midi-Pyrenees-Observatoire Midi-Pyrenees (OMP) ; Universite Federale Toulouse Midi-Pyrenees-Centre National de la Recherche Scientifique (CNRS); Universidade Federal de Mato Grosso do Sul (UFMS); Institut des Materiaux, de Microelectronique et des Nanosciences de Provence (IM2NP) ; Aix Marseille Universite (AMU)-Universite de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Laboratoire des Mecanismes et Transfert en Geologie",
• description = "International audience",
• identifier = "hal-02528909",
• language = "en",
• oai = "oai:HAL:hal-02528909v1",
• relation = "info:eu-repo/semantics/altIdentifier/doi/10.3390/rs12071090",
• rights = "info:eu-repo/semantics/OpenAccess",

}

Genetic Programming entries for Osvaldo Pereira Eder Merino Celia Montes Laurent Barbiero Ary Rezende-Filho Yves Lucas Adolpho Melfi

Citations