@MastersThesis{Yi:1997:MaMoVe,
author = "Yi, Jos{\'e} Lu{\'{\i}}s Rodr{\'{\i}}guez",
title = "Mapeamento e monitoramento da vegeta{\c{c}}{\~a}o do estado do
Mato Grosso atrav{\'e}s de imagens AVHRR-NOAA",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "1997",
address = "Sao Jose dos Campos",
month = "1997-08-26",
keywords = "vegeta{\c{c}}{\~a}o, Mato Grosso, {\'{\i}}ndice de
vegeta{\c{c}}{\~a}o, {\'{\i}}ndice de vegeta{\c{c}}{\~a}o da
diferen{\c{c}}a normalizada, NDVI, monitoramento, radi{\^o}metro
avan{\c{c}}ado de resolu{\c{c}}{\~a}o muito alta, AVHRR,
vegetation, vegetation index, index of normalized difference
vegetation, monitoring radiometer advanced very high resolution.",
abstract = "A vegeta{\c{c}}{\~a}o reflete em primeira ordem a complexa
intera{\c{c}}{\~a}o do homem com o meio ambiente. Portanto de
seu conhecimento depender{\'a} a compreens{\~a}o dos
fen{\^o}menos que ocorrem em qualquer n{\'{\i}}vel. O objetivo
deste trabalho {\'e} apresentar a classifica{\c{c}}{\~a}o e
monitoramento da cobertura vegetal em escala regional atrav{\'e}s
de imagens AVHRR/NOAA-11. A {\'a}rea de estudo (estado de Mato
Grosso) foi selecionada devido {\`a} diversidade de tipos de sua
cobertura vegetal. Neste estudo foram utilizadas as imagens AVHRR
formato HRPT do per{\'{\i}}odo de agosto de 1992 a junho de 1994
e a partir das mesmas foram elaborados 6 mosaicos utilizando a
t{\'e}cnica do m{\'a}ximo valor do NDVI. Para a
classifica{\c{c}}{\~a}o da vegeta{\c{c}}{\~a}o foi escolhido o
mosaico de junho de 1993 por ser o melhor da s{\'e}rie em termos
de qualidade radiom{\'e}trica e menor cobertura de nuvens. Foram
mapeadas 8 classes de vegeta{\c{c}}{\~a}o atrav{\'e}s de
classifica{\c{c}}{\~o}es supervisionadas utilizando o algoritmo
MAXVER (para as bandas 1 e 2, e para as imagens fra{\c{c}}{\~a}o
{"}vegeta{\c{c}}{\~a}o{"}, {"}solo{"} e {"}sombra{"})e o
algoritmo {"}Bhattacharrya distance{"} por {"}regi{\~o}es{"} para
as bandas 1 e 2 segmentadas. Essas classifica{\c{c}}{\~o}es
foram avaliadas utilizando-se a estat{\'{\i}}stica Kappa, e como
refer{\^e}ncia o mapa de vegeta{\c{c}}{\~a}o do Brasil na
escala de 1:5.000.000 elaborado pelo IBGE-IBAMA. O maior valor de
Kappa (0,39) correspondeu {\`a} classifica{\c{c}}{\~a}o
supervisionada por {"}regi{\~o}es{"} das bandas segmentadas. Os
mosaicos das outras datas foram utilizados para a
avalia{\c{c}}{\~a}o das mudan{\c{c}}as sazonais das 8 classes
de vegeta{\c{c}}{\~a}o mapeadas. As varia{\c{c}}{\~o}es do
NDVI foram similares {\`a}s encontradas em outros trabalhos sobre
a rela{\c{c}}{\~a}o existente entre a precipita{\c{c}}{\~a}o e
o NDVI. Os resultados obtidos demonstraram a potencialidade das
imagens AVHRR/NOAA para o estudo e mapeamento da
vegeta{\c{c}}{\~a}o em escala regional. ABSTRACT: Vegetation
reflects in first order the complex interaction of human and the
environment. Therefore from its knowledge will depend the
understanding of the phenomenon in any leveI. The objective of
this work is to present the classification and monitoring of
vegetation cover, in a regional scale, using AVHRR/NOAA-11 images.
The study area, Mato Grosso state, was selected due to its high
diversity of vegetation cover types. The AVHRR/HRPT images
acquired in the period from August 1992 to June 1994 were used in
this study. Six mosaics using the maximum value NDVI composition
techniques were built. For vegetation classification, the mosaic
from June 1993 was selected because it was the best mosaic in that
period considering the radiometric quality and cloud cover. Eight
vegetation classes were mapped using supervised classification
techniques using: Maxver algoritm (for 1 and 2 AVHRR bands, and
for fraction images {"}vegetation{"}, {"}soil{"}, and {"}shade{"})
and Bhattacharrya distance {"}regions{"} algorithm for 1 and 2
segmented AVHRR bands. These classifications were evaluated using
Kappa statistics based on the vegetation map of Brazil in 1:
5.000.000 scale elaborated by IBGE-IBAMA as reference ({"}ground
truth{"}). The highest Kappa value (0,39) corresponded to
supervised classification using {"}regions{"} algorithm from
segmented bands. Other mosaics were used for the assessment of
seasonal changes for the eight vegetation classes mapped. NDVI
variations were in agreement with other works concerned to the
relation between NDVI and precipitation. The obtained results
demonstrated the potentiality of AVHRR-NOAA images for vegetation
mapping in a regional scale.",
committee = "Shimabukuro, Yosio Edemir (orientador/presidente) and Batista,
Get{\'u}lio Teixeira and Carvalho, Vitor Celso de and Miranda,
Evaristo Eduardo de",
copyholder = "SID/SCD",
englishtitle = "Classification and monitoring of vegetation using AVHRR-NOAA
images",
label = "7995",
language = "pt",
pages = "157",
ibi = "6qtX3pFwXQZ3r59YD6/GPi5D",
url = "http://urlib.net/ibi/6qtX3pFwXQZ3r59YD6/GPi5D",
targetfile = "publicacao.pdf",
urlaccessdate = "28 abr. 2024"
}