@MastersThesis{Alves:2023:SeWeEx,
author = "Alves, Gabriel Koyama",
title = "Servi{\c{c}}o web para extra{\c{c}}{\~a}o de m{\'e}tricas
fenol{\'o}gicas para aplica{\c{c}}{\~o}es agr{\'{\i}}colas a
partir de grandes volumes de imagens de orbitais",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2023",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2023-08-25",
keywords = "vegeta{\c{c}}{\~a}o, sensoriamento remoto, s{\'e}ries
temporais, cubos de dados multidimensionais, vegetation, remote
sensing, phenological metrics, multidimensional data cubes.",
abstract = "O estudo dos padr{\~o}es de vegeta{\c{c}}{\~a}o sazonal
observados por sensoriamento remoto {\'e} chamado de Land Surface
Phenology (LSP). A partir de imagens de sensoriamento remoto,
{\'e} poss{\'{\i}}vel obter m{\'e}tricas usadas para o
monitoramento fenol{\'o}gico, que auxiliam no entendimento da
din{\^a}mica da vegeta{\c{c}}{\~a}o e na tomada de
decis{\~a}o. Existem diferentes m{\'e}todos presentes na
literatura para a extra{\c{c}}{\~a}o dessas m{\'e}tricas a
partir de imagens de sat{\'e}lites, como os baseados em limiares,
detec{\c{c}}{\~a}o de mudan{\c{c}}a e abordagens
emp{\'{\i}}ricas. No entanto, um dos desafios {\'e} a
extra{\c{c}}{\~a}o dessas m{\'e}tricas a partir dos grandes
volumes de imagens disponibilizadas atualmente por diferentes
provedores. Especialistas se deparam com limita{\c{c}}{\~o}es de
hardware para processar esse grande volume de dados em
computadores pessoais. Para isso, neste trabalho foi desenvolvido
um servi{\c{c}}o web, chamado Web Phenological Metrics Service
(WPMS), para extra{\c{c}}{\~a}o de m{\'e}tricas
fenol{\'o}gicas a partir de grandes volumes de imagens modeladas
como cubos de dados multidimensionais e de s{\'e}ries temporais
de {\'{\i}}ndices de vegeta{\c{c}}{\~a}o do projeto Brazil
Data Cube (BDC) do Instituto Nacional de Pesquisas Espacias
(INPE). Esse servi{\c{c}}o segue uma arquitetura
cliente-servidor, processando todo o dado do lado do servidor e
retornando para o cliente apenas o resultado do processamento.
Usando esse servi{\c{c}}o, um especialista pode extrair
m{\'e}tricas a partir de grandes volumes de imagens sem se
preocupar com limita{\c{c}}{\~o}es de processamento e com
instala{\c{c}}{\~o}es de pacotes e sistemas em seu computador
pessoal. Para este trabalho, foi feito um estudo que incluiu a
revis{\~a}o da literatura existente e an{\'a}lise de diferentes
ferramentas e softwares utilizados neste contexto, com o objetivo
de escolher aquele que melhor se adequasse na
constru{\c{c}}{\~a}o do servi{\c{c}}o. Durante os estudos e
an{\'a}lises, o pacote em R CropPhenology foi escolhido para a
extra{\c{c}}{\~a}o das m{\'e}tricas fenol{\'o}gicas. No
entanto, durante os testes nos dados de campo, identificou-se uma
limita{\c{c}}{\~a}o no pacote em rela{\c{c}}{\~a}o {\`a}
detec{\c{c}}{\~a}o de ciclos duplos de cultura em s{\'e}ries
temporais anuais. Em resposta a essa limita{\c{c}}{\~a}o, foi
necess{\'a}ria uma customiza{\c{c}}{\~a}o no pacote a fim de
detectar e distinguir os diferentes ciclos, resultando na
cria{\c{c}}{\~a}o do m{\'e}todo denominado Double
CropPhenology. O servi{\c{c}}o inclui tanto o endpoint para
extra{\c{c}}{\~a}o de m{\'e}tricas do pacote original quanto o
modificado. Por fim, os resultados obtidos pelo servi{\c{c}}o
WPMS e o sistema para visualiza{\c{c}}{\~a}o se mostraram
satisfat{\'o}rios e {\'u}til para o campo de
extra{\c{c}}{\~o}es de m{\'e}tricas fenol{\'o}gicas com foco
na agricultura, contribuindo para a tomada de decis{\~o}es mais
informadas. ABSTRACT: The study of seasonal vegetation patterns
observed by remote sensing is called Land Surface Phenology (LSP).
From remote sensing images, it is possible to obtain metrics used
for phenological monitoring, which help in understanding
vegetation dynamics and in decision making. There are different
methods present in the literature for extracting these metrics
from satellite images, such as those based on thresholds, change
detection and empirical approaches. However, one of the challenges
is extracting these metrics from the large volumes of images
currently available from different providers. Experts are faced
with hardware limitations to process this large volume of data on
personal computers. To this end, in this work a web service was
developed, called Web Phenological Metrics Service (WPMS), to
extract phenological metrics from large volumes of images modeled
as multidimensional data cubes and time series of vegetation
indices. of the Brazil Data Cube (BDC) project of the National
Institute for Space Research (INPE). This service follows a
client-server architecture, processing all data on the server side
and returning only the processing result to the client. Using this
service, a specialist can extract metrics from large volumes of
images without worrying about processing limitations and
installing packages and systems on their personal computer. For
this work, a study was carried out that included a review of
existing literature and analysis of different tools and software
used in this context, with the aim of choosing the one that best
suited the construction of the service. During the studies and
analyses, the R package CropPhenology was chosen to extract
phenological metrics. However, during testing on field data, a
limitation in the package was identified regarding the detection
of double crop cycles in annual time series. In response to this
limitation, it was necessary to customize the package in order to
detect and distinguish the different cycles, resulting in the
creation of the method called Double CropPhenology. The service
includes both endpoint for extracting metrics from the original
and modified packages. Finally, the results obtained by the WPMS
service and the visualization system proved to be satisfactory and
useful for the field of extracting phenological metrics with a
focus on agriculture, contributing to more informed
decision-making.",
committee = "Vinhas, Lubia (presidente) and Gomes, Karine Reis Ferreira
(orientadora) and Schultz, Bruno (orientador) and Adami, Marcos
and Antunes, Jo{\~a}o Francisco Gon{\c{c}}alves",
englishtitle = "Web service for extracting phenological metrics for agricultural
applications from large volumes of orbital images",
language = "pt",
pages = "72",
ibi = "8JMKD3MGP3W34T/49RNU3B",
url = "http://urlib.net/ibi/8JMKD3MGP3W34T/49RNU3B",
targetfile = "publicacao.pdf",
urlaccessdate = "17 maio 2024"
}