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Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

Solved—Solutions in Geoinformation, Belém 66075-750, Brazil
Geoscience Institute, Federal University of Pará, Belém 66075-110, Brazil
Regional Center of the Amazon, National Institute for Space Research (INPE), São Paulo 12227-010, Brazil
Global Land Analysis and Discovery (GLAD) laboratory, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Instituto Tecnológico Vale (ITV), Belém 66055-090, Brazil
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 808;
Received: 25 January 2019 / Revised: 12 March 2019 / Accepted: 22 March 2019 / Published: 4 April 2019
(This article belongs to the Special Issue Remote Sensing of Mangroves)
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 Brazil’s mangrove cover is in the states of Maranhão, Pará, Amapá and Bahia. In terms of persistence, ~75% of the Brazilian mangroves remained unchanged for two decades or more. View Full-Text
Keywords: mangroves; machine learning; Google Earth Engine; spectral indices; Brazil; Landsat mangroves; machine learning; Google Earth Engine; spectral indices; Brazil; Landsat
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MDPI and ACS Style

Diniz, C.; Cortinhas, L.; Nerino, G.; Rodrigues, J.; Sadeck, L.; Adami, M.; Souza-Filho, P.W.M. Brazilian Mangrove Status: Three Decades of Satellite Data Analysis. Remote Sens. 2019, 11, 808.

AMA Style

Diniz C, Cortinhas L, Nerino G, Rodrigues J, Sadeck L, Adami M, Souza-Filho PWM. Brazilian Mangrove Status: Three Decades of Satellite Data Analysis. Remote Sensing. 2019; 11(7):808.

Chicago/Turabian Style

Diniz, Cesar, Luiz Cortinhas, Gilberto Nerino, Jhonatan Rodrigues, Luís Sadeck, Marcos Adami, and Pedro W.M. Souza-Filho. 2019. "Brazilian Mangrove Status: Three Decades of Satellite Data Analysis" Remote Sensing 11, no. 7: 808.

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