@MastersThesis{Ruiz:2022:UsMéFe,
author = "Ruiz, Isadora Haddad",
title = "Uso de m{\'e}tricas fenol{\'o}gicas calculadas de diferentes
{\'{\i}}ndices de vegeta{\c{c}}{\~a}o da
constela{\c{c}}{\~a}o de sat{\'e}lites PlanetScope para
classifica{\c{c}}{\~a}o de fitofisionomias do Cerrado",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2022",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2022-02-11",
keywords = "fenologia da vegeta{\c{c}}{\~a}o, planetScope, Cerrado,
constela{\c{c}}{\~a}o de sat{\'e}lites,
classifica{\c{c}}{\~a}o, ensemble metrics, land surface
phenology, random Forest, satellite constellation, Savannas.",
abstract = "O mapeamento da vegeta{\c{c}}{\~a}o nativa do Cerrado no Brasil
{\'e} desafiador, n{\~a}o havendo um consenso sobre a melhor
estrat{\'e}gia de sensoriamento remoto para lidar com a
variabilidade espacial de algumas fitofisionomias e a similaridade
espectral de outras. Neste estudo, avaliou-se o desempenho de 12
m{\'e}tricas fenol{\'o}gicas (Land Surface Phenology - LSP)
calculadas a partir de tr{\^e}s diferentes {\'{\i}}ndices de
vegeta{\c{c}}{\~a}o (IV) da constela{\c{c}}{\~a}o de
sat{\'e}lites PlanetScope (PS). As m{\'e}tricas foram usadas
como vari{\'a}veis de entrada para o algoritmo de aprendizado de
m{\'a}quina Random Forest (RF), visando classificar oito
fitofisionomias do Cerrado. A {\'a}rea de estudo foi a
Esta{\c{c}}{\~a}o Ecol{\'o}gica de {\'A}guas Emendadas (ESAE),
localizada na regi{\~a}o central do Brasil. Testou-se a
classifica{\c{c}}{\~a}o LSP na esta{\c{c}}{\~a}o de
crescimento 2017-2018 contra a classifica{\c{c}}{\~a}o IV na
esta{\c{c}}{\~a}o seca de 2017, usando um mapa
dispon{\'{\i}}vel de vegeta{\c{c}}{\~a}o como refer{\^e}ncia
para avalia{\c{c}}{\~a}o da precis{\~a}o dos resultados.
Al{\'e}m disso, analisou-se o desempenho do uso combinado (todos
os IVs ou m{\'e}tricas LSP em conjunto) e individual (cada IV ou
m{\'e}tricas LSP usadas separadamente) das vari{\'a}veis na
classifica{\c{c}}{\~a}o RF das fitofisionomias. Os resultados
mostraram que a acur{\'a}cia total (OA) da
classifica{\c{c}}{\~a}o RF usando 12 imagens PS adquiridas na
esta{\c{c}}{\~a}o seca de 2017, variou de 0,56 para o Green-Red
Normalized Difference (GRND) a 0,57 e 0,61 para o Enhanced
Vegetation Index (EVI) e Normalized Difference Vegetation Index
(NDVI), respectivamente. As m{\'e}tricas LSP, determinadas
durante a esta{\c{c}}{\~a}o de crescimento de 2017-2018,
produziram ganhos de 19,3% (EVI), 13,1% (NDVI) e 5,4% (GRND),
quando comparadas com o uso isolado de IVs da esta{\c{c}}{\~a}o
seca. Mantendo o EVI da esta{\c{c}}{\~a}o seca como
refer{\^e}ncia para compara{\c{c}}{\~a}o, o uso combinado dos
IVs (OA = 0,70) ou m{\'e}tricas LSP (OA = 0,73) produziu ganhos
na OA de 22,8% e 28,1%, respectivamente. As vari{\'a}veis mais
significativas para o modelo RF empregando conjuntamente as
m{\'e}tricas LSP foram obtidas principalmente do NDVI e EVI,
sendo elas: o valor m{\'{\i}}nimo (TRG) e m{\'a}ximo (PEAK) de
IV; o valor m{\'e}dio na primavera (MSP); o valor m{\'e}dio na
esta{\c{c}}{\~a}o de crescimento (MGS); e a taxa de verdejamento
na primavera (RSP). Os resultados mostraram a import{\^a}ncia de
se utilizar dados de alta resolu{\c{c}}{\~a}o espacial e
temporal da constela{\c{c}}{\~a}o de sat{\'e}lites PlanetScope
para classificar fitofisionomias de Cerrado, usando
informa{\c{c}}{\~o}es de fenologia da vegeta{\c{c}}{\~a}o.
Al{\'e}m disso, quando s{\'e}ries temporais densas n{\~a}o
estiverem dispon{\'{\i}}veis para calcular adequadamente as
m{\'e}tricas LSP, uma alternativa {\'e} o uso combinado de IVs
com sensibilidades diferentes aos par{\^a}metros
biof{\'{\i}}sicos da vegeta{\c{c}}{\~a}o. Isto {\'e}
v{\'a}lido especialmente para dados de sat{\'e}lite adquiridos
durante a esta{\c{c}}{\~a}o seca local, quando a frequ{\^e}ncia
de cobertura de nuvens {\'e} reduzida. ABSTRACT: Mapping of
savannas in Brazil is challenging since there is no consensus on
the best remote sensing strategy to deal with the spatial
variability of some physiognomies and the spectral similarity
among others. In this study, we evaluated the performance of 12
land surface phenology (LSP) metrics calculated from three
PlanetScope (PS) vegetation indices (VIs) for Random Forest (RF)
classification of eight savanna physiognomies. At the protected
Ecological Station of {\'A}guas Emendadas (ESAE) located in
central Brazil, we tested the LSP classification in the 2017-2018
growing season against the dry-season VI classification in 2017
using an available vegetation map for accuracy assessment.
Furthermore, we analyzed the performance of individual (each set
of VIs or LSP metrics used separately) and combined (all VIs or
LSP metrics used together) metrics for RF classification of the
savanna physiognomies. The results showed that the overall
accuracy (OA) of RF classification using 12 PS images acquired in
the 2017 dry season ranged from 0.56 for the Green-Red Normalized
Difference (GRND) to 0.57 and 0.61 for the Enhanced Vegetation
Index (EVI) and Normalized Difference Vegetation Index (NDVI),
respectively. The LSP metrics retrieved during the 2017-2018
growing season produced gains in OA of 19.3% (EVI), 13.1% (NDVI)
and 5.4% (GRND) when compared to the individual use of VIs in the
dry season. Keeping the dry-season EVI as a reference of
comparison, the combined use of VIs (OA = 0.70) or LSP metrics (OA
= 0.73) also generated gains in OA of 22.8% and 28.1%,
respectively. The most important ranked LSP metrics from the
combination of this type of variable were mainly calculated from
the NDVI and EVI: the minimum (TRG) and maximum (PEAK) VI values;
the mean Spring (MSP); the mean growing season (MGS); and the rate
of spring green up (RSP). The results show the importance of the
combined use of high spatial and temporal resolution data of the
Planets satellite constellation for the classification of
Brazilian savannas using the vegetation phenology information.
Besides that, when dense time series are not available for
retrieving the LSP metrics, an alternative is the combined use of
different VIs for satellite data acquired during the dry season
when the frequency of cloud cover is reduced.",
committee = "K{\"o}rting, Thales Sehn (presidente) and Galv{\~a}o, L{\^e}nio
Soares (orientador) and Breunig, F{\'a}bio Marcelo (orientador)
and Bourscheidt, Vandoir",
englishtitle = "Use of phenological metrics calculated from different vegetation
indices of the planetscope satellite constellation for classifying
Savanna physiognomies",
language = "pt",
pages = "71",
ibi = "8JMKD3MGP3W34T/46DD25B",
url = "http://urlib.net/ibi/8JMKD3MGP3W34T/46DD25B",
targetfile = "publicacao.pdf",
urlaccessdate = "28 mar. 2024"
}