@Article{DinizCoNeRoSaAdSo:2019:ThDeSa,
author = "Diniz, Cesar and Cortinhas, Luiz and Nerino, Gilberto and
Rodrigues, Jhonatan and Sadeck, Lu{\'{\i}}s and Adami, Marcos
and Souza Filho, Pedro Walfir M.",
affiliation = "{Solved—Solutions in Geoinformation} and {Solved—Solutions in
Geoinformation} and {Solved—Solutions in Geoinformation} and
{Solved—Solutions in Geoinformation} and {Solved—Solutions in
Geoinformation} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Universidade Federal do Par{\'a} (UFPA)}",
title = "Brazilian mangrove status: three decades of satellite data
analysis",
journal = "Remote Sensing",
year = "2019",
volume = "11",
number = "7",
month = "Apr.",
keywords = "mangroves, machine learning, Google Earth Engine, spectral
indices, Brazil, Landsat.",
abstract = "Since the 1980s, mangrove cover mapping has become a common
scientific task. However, the systematic and continuous
identification of vegetation cover, whether on a global or
regional scale, demands large storage and processing capacities.
This manuscript presents a Google Earth Engine (GEE)-managed
pipeline to compute the annual status of Brazilian mangroves from
1985 to 2018, along with a new spectral index, the Modular
Mangrove Recognition Index (MMRI), which has been specifically
designed to better discriminate mangrove forests from the
surrounding vegetation. If compared separately, the periods from
1985 to 1998 and 1999 to 2018 show distinct mangrove area trends.
The first period, from 1985 to 1998, shows an upward trend, which
seems to be related more to the uneven distribution of Landsat
data than to a regeneration of Brazilian mangroves. In the second
period, from 1999 to 2018, a trend of mangrove area loss was
registered, reaching up to 2% of the mangrove forest. On a
regional scale, ~85% of Brazils mangrove cover is in the states of
Maranh{\~a}o, Par{\'a}, Amap{\'a} and Bahia. In terms of
persistence, ~75% of the Brazilian mangroves remained unchanged
for two decades or more.",
doi = "10.3390/rs11070808",
url = "http://dx.doi.org/10.3390/rs11070808",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-11-00808.pdf",
urlaccessdate = "27 abr. 2024"
}