@Article{FragalSilvNovo:2016:ReHiFo,
author = "Fragal, Everton Hafemann and Silva, Thiago Sanna Freire and Novo,
Evlyn M{\'a}rcia Le{\~a}o de Moraes",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)}",
title = "Reconstructing historical forest cover change in the Lower Amazon
floodplains using the LandTrendr algorithm",
journal = "Acta Amazonica",
year = "2016",
volume = "46",
number = "1",
pages = "13--24",
month = "jan./mar.",
keywords = "Wetlands, flooded forest, land use change, monitoring, Landsat,
{\'A}reas {\'u}midas, florestas inund{\'a}veis, mudan{\c{c}}as
no uso da terra, monitoramento, Landsat.",
abstract = "The Amazon varzeas are an important component of the Amazon biome,
but anthropic and climatic impacts have been leading to forest
loss and interruption of essential ecosystem functions and
services. The objectives of this study were to evaluate the
capability of the Landsat-based Detection of Trends in Disturbance
and Recovery (LandTrendr) algorithm to characterize changes in
varzea forest cover in the Lower Amazon, and to analyze the
potential of spectral and temporal attributes to classify forest
loss as either natural or anthropogenic. We used a time series of
37 Landsat TM and ETM+ images acquired between 1984 and 2009. We
used the LandTrendr algorithm to detect forest cover change and
the attributes of {"}start year{"}, {"}magnitude{"}, and
{"}duration{"} of the changes, as well as {"}NDVI at the end of
series{"}. Detection was restricted to areas identified as having
forest cover at the start and/or end of the time series. We used
the Support Vector Machine (SVM) algorithm to classify the
extracted attributes, differentiating between anthropogenic and
natural forest loss. Detection reliability was consistently high
for change events along the Amazon River channel, but variable for
changes within the floodplain. Spectral-temporal trajectories
faithfully represented the nature of changes in floodplain forest
cover, corroborating field observations. We estimated
anthropogenic forest losses to be larger (1.071 ha) than natural
losses (884 ha), with a global classification accuracy of 94%. We
conclude that the LandTrendr algorithm is a reliable tool for
studies of forest dynamics throughout the floodplain. RESUMO: As
v{\'a}rzeas amaz{\^o}nicas s{\~a}o um importante componente do
bioma Amaz{\^o}nico, mas impactos antr{\'o}picos e
clim{\'a}ticos t{\^e}m levado {\`a} perda florestal e {\`a}
interrup{\c{c}}{\~a}o de processos e servi{\c{c}}os
ecossist{\^e}micos. O presente estudo teve como objetivos avaliar
a aplicabilidade do algoritmo Landsat-based Detection of Trends in
Disturbance and Recovery (LandTrendr) na detec{\c{c}}{\~a}o de
mudan{\c{c}}as na cobertura florestal de v{\'a}rzea no Baixo
Amazonas, e analisar o potencial de atributos espectrais e
temporais na classifica{\c{c}}{\~a}o das perdas florestais em
antr{\'o}picas ou naturais. Utilizamos uma s{\'e}rie temporal de
37 imagens Landsat TM e ETM+, adquiridas entre 1984 e 2009.
Aplicamos o algoritmo LandTrendr para detectar mudan{\c{c}}as na
cobertura florestal e extrair os atributos de dura{\c{c}}{\~a}o,
magnitude e ano de in{\'{\i}}cio das mudan{\c{c}}as, al{\'e}m
de NDVI ao final da s{\'e}rie. A detec{\c{c}}{\~a}o se
restringiu a {\'a}reas identificadas como cobertura florestal no
in{\'{\i}}cio e/ou final da s{\'e}rie. Os atributos derivados
da s{\'e}rie temporal foram classificados pelo algoritmo Support
Vector Machine (SVM), diferenciando as perdas florestais
antr{\'o}picas e naturais. A confiabilidade da
detec{\c{c}}{\~a}o dos eventos de mudan{\c{c}}a foi
consistentemente alta ao longo do rio Amazonas, e mais
vari{\'a}vel no interior da v{\'a}rzea. As trajet{\'o}rias
espectrais-temporais representaram fielmente os eventos de
mudan{\c{c}}a na cobertura florestal, com base em
averigua{\c{c}}{\~o}es em campo. A perda da cobertura florestal
por causas antr{\'o}picas foi maior (1.071 ha) do que por causas
naturais (884 ha), com exatid{\~a}o global de
classifica{\c{c}}{\~a}o de 94%. Conclu{\'{\i}}mos que o
algoritmo LandTrendr {\'e} uma ferramenta confi{\'a}vel para
aplica{\c{c}}{\~a}o em estudos de din{\^a}mica da cobertura
florestal de v{\'a}rzea.",
doi = "10.1590/1809-4392201500835",
url = "http://dx.doi.org/10.1590/1809-4392201500835",
issn = "0044-5967",
language = "en",
targetfile = "Fragal_reconstructing.pdf",
urlaccessdate = "27 abr. 2024"
}