Next Article in Journal
Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth Monitoring: A Case Study in China
Next Article in Special Issue
A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery
Previous Article in Journal
Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study
Previous Article in Special Issue
An Analysis of the Early Regeneration of Mangrove Forests using Landsat Time Series in the Matang Mangrove Forest Reserve, Peninsular Malaysia
Article

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

1
Solved—Solutions in Geoinformation, Belém 66075-750, Brazil
2
Geoscience Institute, Federal University of Pará, Belém 66075-110, Brazil
3
Regional Center of the Amazon, National Institute for Space Research (INPE), São Paulo 12227-010, Brazil
4
Global Land Analysis and Discovery (GLAD) laboratory, Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
5
Instituto Tecnológico Vale (ITV), Belém 66055-090, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 808; https://doi.org/10.3390/rs11070808
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
Show Figures

Graphical abstract

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. https://doi.org/10.3390/rs11070808

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. https://doi.org/10.3390/rs11070808

Chicago/Turabian Style

Diniz, Cesar; Cortinhas, Luiz; Nerino, Gilberto; Rodrigues, Jhonatan; Sadeck, Luís; Adami, Marcos; Souza-Filho, Pedro W.M. 2019. "Brazilian Mangrove Status: Three Decades of Satellite Data Analysis" Remote Sens. 11, no. 7: 808. https://doi.org/10.3390/rs11070808

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop