The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 249–253, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-249-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 249–253, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-249-2020

  06 Nov 2020

06 Nov 2020

PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE

J. A. Sartori1, J. B. Sbruzzi2, and E. L. Fonseca1 J. A. Sartori et al.
  • 1Laboratório de Geotecnologias Aplicadas, Dept. of Geography, Geosciences Institute, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
  • 2Remote Sensing Graduate Program, Universidade Federal do Rio Grande do Sul, Brazil

Keywords: Lagoon, Shoreline change, Morphodynamics, Lagoa do Peixe

Abstract. This work aims to define the basic parameters for the automatic mapping of the channel between the Lagoa do Peixe and the Atlantic Ocean, which is located in the municipalities of Tavares and Mostardas, Rio Grande do Sul state, Brazil. The automatic mapping is based on an unsupervised classification of Landsat 8 satellite images at the Google Earth Engine cloud computing platform. The images used were selected to present both channel situations (opened and closed). Three images were selected with acquisition dates that presented the open channel and three that presented the closed channel. Each image was classified using the K-means clustering method, using separately band 6, band 7 (both located at shortwave infrared - SWIR) and the Normalized Difference Water Index (NDWI). Once the number of clusters must be defined a priori by the analyst, as well as the training sample area, these parameters were tested over the dataset and clustering results were compared. All of the generated clusters maps were analyzed over 10 random points, identifying the clustering hits and errors. Due to the absence of reference maps, all the final clustering maps for each date were compared with the composite true color image from the same acquisition date. The NDWI cluster maps showed the best results in separating water and non-water pixels.