PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE
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...
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2020-11-01
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doaj-8ee9d73eec3044bc8e17ebb4583f10602020-11-25T04:07:13ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-11-01XLII-3-W12-202024925310.5194/isprs-archives-XLII-3-W12-2020-249-2020PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINEJ. A. Sartori0J. B. Sbruzzi1E. L. Fonseca2Laboratório de Geotecnologias Aplicadas, Dept. of Geography, Geosciences Institute, Universidade Federal do Rio Grande do Sul, Porto Alegre, BrazilRemote Sensing Graduate Program, Universidade Federal do Rio Grande do Sul, BrazilLaboratório de Geotecnologias Aplicadas, Dept. of Geography, Geosciences Institute, Universidade Federal do Rio Grande do Sul, Porto Alegre, BrazilThis 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 <i>a priori</i> 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.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/249/2020/isprs-archives-XLII-3-W12-2020-249-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. A. Sartori J. B. Sbruzzi E. L. Fonseca |
spellingShingle |
J. A. Sartori J. B. Sbruzzi E. L. Fonseca PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
J. A. Sartori J. B. Sbruzzi E. L. Fonseca |
author_sort |
J. A. Sartori |
title |
PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE |
title_short |
PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE |
title_full |
PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE |
title_fullStr |
PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE |
title_full_unstemmed |
PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE |
title_sort |
preliminary analysis for automatic tidal inlets mapping using google earth engine |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2020-11-01 |
description |
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 <i>a priori</i> 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. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/249/2020/isprs-archives-XLII-3-W12-2020-249-2020.pdf |
work_keys_str_mv |
AT jasartori preliminaryanalysisforautomatictidalinletsmappingusinggoogleearthengine AT jbsbruzzi preliminaryanalysisforautomatictidalinletsmappingusinggoogleearthengine AT elfonseca preliminaryanalysisforautomatictidalinletsmappingusinggoogleearthengine |
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1724429543007059968 |