Trabalho Apresentado em Evento

Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia

A working group of three institutions was set up to develop this study: University of Applied Sciences Eberswalde (Germany), National Institute for Space Research (INPE, Brazil) and National Institute for Amazon Research (INPA, Brazil). The main task is to apply in the Guarayos region (Bolivia), the...

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Autor principal: Ugarte, H. Ferrufino
Outros Autores: Zawila-Niedzwiecki, T., Santos, João Roberto dos, Maldonado, Francisco Dario
Grau: Trabalho Apresentado em Evento
Idioma: English
Publicado em: International Geoscience and Remote Sensing Symposium (IGARSS) 2020
Assuntos:
Detection Algorithm
Radiometric Rotation Technique
Tropical Forest
Algorithms
Data Reduction
Remote Sensing
Space Research
Radiometry
Acesso em linha: https://repositorio.inpa.gov.br/handle/1/19995
id oai:repositorio:1-19995
recordtype dspace
spelling oai:repositorio:1-19995 Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia Ugarte, H. Ferrufino Zawila-Niedzwiecki, T. Santos, João Roberto dos Maldonado, Francisco Dario Detection Algorithm Radiometric Rotation Technique Tropical Forest Algorithms Data Reduction Remote Sensing Space Research Radiometry A working group of three institutions was set up to develop this study: University of Applied Sciences Eberswalde (Germany), National Institute for Space Research (INPE, Brazil) and National Institute for Amazon Research (INPA, Brazil). The main task is to apply in the Guarayos region (Bolivia), the multi-temporal change detection algorithm "RCEN multi-spectral". The study area is located in Guarayos-Bolivia, characterized by two main high forests landscapes, "The Amazon Region" and "The Brazilian Paranaense Region"; the approach of the change detection is taken under multivariate analysis (three spectral bands), with data coming from two kinds of sensor ETM+/Landsat-7 and CCD/CBERS-2. The image detection was transformed from continuous image (floating-point) to thematic, through slicing and labeling process. Hence it is possible to discriminate five thematic classes: two related to degradation, two referring to regeneration and one of no-change. The change detection map shows: in the timeframe studied 11% of all area under study presents deforestation patterns, on the other hand the regeneration class is not significant. In conclusion the methodology has good performance and it is evolving in landscapes with high humidity complications. © 2007 IEEE. 2020-06-16T17:30:35Z 2020-06-16T17:30:35Z 2007 Trabalho Apresentado em Evento https://repositorio.inpa.gov.br/handle/1/19995 10.1109/IGARSS.2007.4424059 en Pags. 5302-5305 Restrito International Geoscience and Remote Sensing Symposium (IGARSS)
institution Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional
collection INPA-RI
language English
topic Detection Algorithm
Radiometric Rotation Technique
Tropical Forest
Algorithms
Data Reduction
Remote Sensing
Space Research
Radiometry
spellingShingle Detection Algorithm
Radiometric Rotation Technique
Tropical Forest
Algorithms
Data Reduction
Remote Sensing
Space Research
Radiometry
Ugarte, H. Ferrufino
Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia
topic_facet Detection Algorithm
Radiometric Rotation Technique
Tropical Forest
Algorithms
Data Reduction
Remote Sensing
Space Research
Radiometry
description A working group of three institutions was set up to develop this study: University of Applied Sciences Eberswalde (Germany), National Institute for Space Research (INPE, Brazil) and National Institute for Amazon Research (INPA, Brazil). The main task is to apply in the Guarayos region (Bolivia), the multi-temporal change detection algorithm "RCEN multi-spectral". The study area is located in Guarayos-Bolivia, characterized by two main high forests landscapes, "The Amazon Region" and "The Brazilian Paranaense Region"; the approach of the change detection is taken under multivariate analysis (three spectral bands), with data coming from two kinds of sensor ETM+/Landsat-7 and CCD/CBERS-2. The image detection was transformed from continuous image (floating-point) to thematic, through slicing and labeling process. Hence it is possible to discriminate five thematic classes: two related to degradation, two referring to regeneration and one of no-change. The change detection map shows: in the timeframe studied 11% of all area under study presents deforestation patterns, on the other hand the regeneration class is not significant. In conclusion the methodology has good performance and it is evolving in landscapes with high humidity complications. © 2007 IEEE.
format Trabalho Apresentado em Evento
author Ugarte, H. Ferrufino
author2 Zawila-Niedzwiecki, T.
Santos, João Roberto dos
Maldonado, Francisco Dario
author2Str Zawila-Niedzwiecki, T.
Santos, João Roberto dos
Maldonado, Francisco Dario
title Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia
title_short Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia
title_full Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia
title_fullStr Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia
title_full_unstemmed Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia
title_sort change detection in the amazon rainforest with radiometric rotation technique rcen multi-spectral case study: guarayos - bolivia
publisher International Geoscience and Remote Sensing Symposium (IGARSS)
publishDate 2020
url https://repositorio.inpa.gov.br/handle/1/19995
_version_ 1787143385944948736
score 11.755432

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