Analysis of landscape and local vegetation changes through the use of semi-automatic image classifiers: application in cases of environmental disasters

The fire at the Ultracargo Petrochemical Terminal in the Port of Santos (SP) in 2015 caused severe environmental impacts, including atmospheric, water, and soil contamination. The analysis of land cover, particularly local vegetation affected by disasters, is important for identifying, quantifying, and monitoring changes in the landscape, as well as for impact management. In this context, the use of semi-automatic classifiers, which combine automated satellite image analysis with human intervention, has proven effective in tracking these changes. In this study, satellite images from the MapBiomas platform from 1985, 2014, 2015, and 2022 were used to analyze and compare changes in the landscape and local vegetation, through semi-automatic classification, in the area of the Port of Santos where the industrial accident occurred Keywords:Environmental Accidents; Remote Sensing; Mangrove.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-07
DOI
https://doi.org/10.5281/zenodo.14052811
Primary Topic
Oil Spill Detection and Mitigation
Type
article
Field-Weighted Citation Impact
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article

Analysis of landscape and local vegetation changes through the use of semi-automatic image classifiers: application in cases of environmental disasters

Zenodo (CERN European Organization for Nuclear Research)
Oil Spill Detection and Mitigation
article

Analysis of landscape and local vegetation changes through the use of semi-automatic image classifiers: application in cases of environmental disasters

article en

Abstract

The fire at the Ultracargo Petrochemical Terminal in the Port of Santos (SP) in 2015 caused severe environmental impacts, including atmospheric, water, and soil contamination. The analysis of land cover, particularly local vegetation affected by disasters, is important for identifying, quantifying, and monitoring changes in the landscape, as well as for impact management. In this context, the use of semi-automatic classifiers, which combine automated satellite image analysis with human intervention, has proven effective in tracking these changes. In this study, satellite images from the MapBiomas platform from 1985, 2014, 2015, and 2022 were used to analyze and compare changes in the landscape and local vegetation, through semi-automatic classification, in the area of the Port of Santos where the industrial accident occurred Keywords:Environmental Accidents; Remote Sensing; Mangrove.

Zenodo (CERN European Organization for Nuclear Research)
Universidade Santa Cecília (BR)
Openalex Percentile: Top 79%
Oil Spill Detection and Mitigation
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Analysis of landscape and local vegetation changes through the use of semi-automatic image classifiers: application in cases of environmental disasters · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS