1 January 2011 Spectral mapping of the Paraíba do Sul River plume (Brazil) using multitemporal Landsat images
Natalia de Moraes Rudorff, Milton Kampel, Carlos Eduardo de Rezende
Author Affiliations +
Abstract
Coastal zones are influenced by oceanic, atmospheric, and continental forces, which make them highly vulnerable to climate and anthropogenic changes. The Paraíba do Sul River (PSR) Estuary (Brazil), is especially affected by intensive industrial, urban, and rural activities, along its catchment area. Few works, though, have been done concerning the impacts of these changes. Remote sensing is, thus, an important and unique tool to assess the past scenes for a temporal analysis. The present work aimed to analyze spatial-temporal trends of the PSR plume from 1985 to 2009, using Landsat 5 TM images. Two spectral classification methods were used to map the river plume: maximum likelihood and spectral linear mixture analysis (SLMA). The images corresponded to the months of greatest river discharge, totalizing 11 cloud-free images. Geographical, radiometric, and atmospheric corrections were applied to the five spectral bands used for the classification. Both methods showed good results, however the SLMA provided more information of the water constituent's distribution. The sediment river plume and inner shelf phytoplankton dominated waters showed a negative trend associated with a diminishing of the river discharge. Further works concern in situ validation of the classifications, bio-optical modeling, and more investigations of climate and anthropogenic changes on the PSR.
©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Natalia de Moraes Rudorff, Milton Kampel, and Carlos Eduardo de Rezende "Spectral mapping of the Paraíba do Sul River plume (Brazil) using multitemporal Landsat images," Journal of Applied Remote Sensing 5(1), 053550 (1 January 2011). https://doi.org/10.1117/1.3630220
Published: 1 January 2011
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CITATIONS
Cited by 11 scholarly publications.
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KEYWORDS
Reflectivity

Earth observing sensors

Atmospheric corrections

Landsat

Water

Image classification

Error analysis

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