@MastersThesis{Amaral:1992:ReDaSi,
author = "Amaral, Silvana",
title = "Imagens do sistema sensor AVHRR/NOAA na dete{\c{c}}{\~a}o e
avalia{\c{c}}{\~a}o de desmatamentos na Floresta Amaz{\^o}nica:
rela{\c{c}}{\~o}es com dados do sistema TM/LANDSAT",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "1992",
address = "Sao Jose dos Campos",
month = "1992-09-17",
keywords = "vegeta{\c{c}}{\~a}o, Serra do Roncador (MT), Floresta
Amaz{\^o}nica, florestas, desmatamento, sat{\'e}lites NOAA,
mapeador tem{\'a}tico (Landsat), sat{\'e}lites Landsat, Sistema
de Informa{\c{c}}{\~a}o Geogr{\'a}fica, calibra{\c{c}}{\~a}o,
SIG, radi{\^o}metro avan{\c{c}}ado de resolu{\c{c}}{\~a}o
muito alta, imagens Landsat, imagens NOAA, desflorestamento,
estimativa, monitoramento, Advanced Very Resolution Radiometer
(AVHRR), deforestation, geometric rectification (imagery),
instrument errors, Landsat satellites, rain forest, statistical
analysis, thematic mappers (Landsat), calibration, geographic
information sytems.",
abstract = "Este trabalho avalia o uso de imagens AVHRR/NOAA para
detec{\c{c}}{\~a}o e avalia{\c{c}}{\~a}o de desmatamento na
Floresta Amaz{\^o}nica a partir de valida{\c{c}}{\~a}o e
calibra{\c{c}}{\~a}o com imagens TM/LANDSAT. Desenvolveu-se uma
metodologia piloto para regi{\~a}o da Serra do Roncador-MT, onde
o desmatamento e proveniente da instala{\c{c}}{\~a}o de projetos
agropecu{\'a}rios de grande escala. Utilizou-se uma imagem AVHRR
de 2 Km de resolu{\c{c}}{\~a}o espacial, corrigida
geometricamente, para classifica{\c{c}}{\~a}o de {\'a}reas
desmatadas e de floresta. A identifica{\c{c}}{\~a}o destas
classes, assim como a compara{\c{c}}{\~a}o dos resultados de
{\'a}rea estimada na interpreta{\c{c}}{\~a}o visual de imagens
TM/LANDSAT, baseou-se no uso de Sistema Geogr{\'a}fico de
Informa{\c{c}}{\~a}o. Os resultados AVHRR de {\'a}rea de
floresta e desmatamento foram comparados aos dados TM em analises
estat{\'{\i}}sticas, onde obteve-se forte correla{\c{c}}{\~a}o
e regress{\~a}o linear entre eles (R2 = 0,93). A
aplica{\c{c}}{\~a}o do modelo obtido na regi{\~a}o de S{\~a}o
Jos{\'e} do Xingu, MT, de padr{\~a}o de desmatamento semelhante
a primeira {\'a}rea, mostrou-se apropriada com erros m{\'e}dios
de 3% para {\'a}rea total de floresta. Os resultados obtidos
permitiram indicar a banda 3 AVHRR para a detec{\c{c}}{\~a}o e
monitoramento de altera{\c{c}}{\~o}es em {\'a}reas florestais.
Dados TM/LANDSAT s{\~a}o necess{\'a}rios para
calibra{\c{c}}{\~a}o das estimativas de area. ABSTRACT: This
work analyzes the use of AVHRR/NOAA images to detect and evaluate
deforestation in the Amazon Forest using TM/Landsat images for
validation and calibration of the results. A pilot methodology was
developed for the region of {"}Serra do Roncador{"}, MT, where
deforestation is caused by large agriculture and cattle projects.
An AVHRR image with spatial resolution of 2 km and geometrically
corrected was used to classify areas of deforestation and forest.
The identification of these classes and the comparison and
validation of the area estimates with the visual interpretation of
the TM/Landsat images were based and on the use of Geographical
Information System. The results of forest and deforestation areas
obtained from AVHRR were compared to TM data using statistical
analyses and strong linear correlation and regression were found
between the data sets (R2 = 0.93). An application of the model for
the region of S{\~a}o Jos{\'e} do Xingu, MT, with a
deforestation pattern similar to that found in the first region,
presented adequate results with average erros of 3% for the total
forest area. The results corroborated the use of AVHRR band 3 to
detect and monitor alterations in forestes areas. TM/Landsat data
are necessary to calibrate estimates of areas.",
committee = "Santos, Jo{\~a}o Roberto dos (presidente/orientador) and Setzer,
Alberto Waingort (orientador) and Batista, Get{\'u}lio Teixeira
and Assad, Eduardo Delgado",
copyholder = "SID/SCD",
englishtitle = "AVHRR/NOAA images to detect and quantify deforestation in Amazon
Forest: relation with TM/LANDSAT data",
label = "6558",
language = "pt",
pages = "197",
ibi = "6qtX3pFwXQZ3r59YD6/GP3np",
url = "http://urlib.net/ibi/6qtX3pFwXQZ3r59YD6/GP3np",
targetfile = "publicacao.pdf",
urlaccessdate = "2024, Apr. 29"
}