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Modelling Dunes from Lençóis Maranhenses National Park (Brazil): Largest dune field in South America.

André Luís Silva Dos SantosHélder Pereira BorgesCelso Henrique Leite Silva JuniorRaimundo Nonato Piedade JuniorDenilson da Silva Bezerra
Published in: Scientific reports (2019)
This paper presents a digital elevation model (DEM) of the dunes found in the Lençóis Maranhenses National Park, an environmental protection area located in the Maranhão state (Brazil). The DEM supports the modeling studies of sand-dune evolution using multi-temporal satellite images and ground truth data, obtained through the post-processed kinematic Global Navigation Satellite System (GNSS) positioning. The study area is located at the border of three major neotropical ecosystems: the Amazonia, Caatinga, and Brazilian savanna. It is located in the northeastern state of Maranhão and encompasses the largest dune fields in the country. Wide shrubby areas (restingas, in Portuguese), lakes, mangroves, and a multitude off reshwater lagoons compose the park's natural environments. The objective of the present study is to create an DEM that can evidence the complex dynamics of dune formation in the study area with use of GNSS. Geodetic techniques and precision mapping were employed to monitor the short-term coastal dynamics. The use of GNSS receivers is justified by the difficulty of mapping the dune's features using conventional methods such as theodolite, level, and total station systems, due to their high cost, time restriction sand low data precision. Surface surveys were carried out annually between December 2015 and January 2017 to create a DEM. The study results reveal that the area has a negative volumetric balance of erosion and a preferential direction of sediment transport by wind, which may justify the pattern of advancement and retraction observed in the dunes of the studied area.
Keyphrases
  • high resolution
  • gene expression
  • drinking water
  • heavy metals
  • mass spectrometry
  • climate change
  • machine learning
  • optical coherence tomography
  • human health
  • organic matter