Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

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 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.Globally, mangrove forests are distributed in tropical and subtropical intertidal regions between approximately 30 • N and 30 • S [6]. In 2000, mangrove forests represented a total area of 137,760 km 2 , distributed in 118 countries and making up~1% of the tropical forests in the world [7]. Mangrove forests are an evergreen type of vegetation typically distributed from the mean sea level to the highest spring tide [8] and grow in extreme environmental conditions such as extreme tides, high salinity, high temperatures and muddy anaerobic soils [9].Mangrove systems play an essential role in human sustainability, providing a wide range of ecosystem services, including nutrient cycling, soil formation and wood production. They also provide fish spawning grounds and carbon (C) storage [10][11][12], being one of the most productive and biologically complex ecosystems on earth [13]. Mangroves and coastal wetlands sequester carbon at an annual rate two to four times greater than that of mature tropical forests and store three to five times more carbon per equivalent area than do tropical forests [10]. Despite its importance, this environment is still highly threatened due to population growth and urbanisation processes.Since the 1980s, mapping and change detection in mangrove areas at the global scale have been carried out [7,11,[14][15][16]. However, there are few studies in the current literature that include the systematic and continuous identification of mangroves and associated changes, whether on the global or regional scale. In Brazil, the first national mangrove map was published in 1991 [17], based on airborne real aperture radar data collected from 1972 to 1975. At that time, the national mangrove area was~13,800 km 2 . In the same period, Schaeffer-Novelli et al. [18] described the variability in the mangrove ecosystems along the Brazilian c...

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

“…In terms of the methods, prior studies use a range of classification techniques such as the iterative self-organizing data analysis (ISODATA) clustering, maximum likelihood classification (MLC), hybrid, random forest (RF), classification and regression trees (CART), support vector machine (SVM), and object oriented classification among others [16,17,34,[37][38][39][40][41]43,66]. With the advent of cloud computing platforms with free access to petabytes of geospatial data, such as Google Earth Engine (GEE), it has now become increasingly accessible and straight-forward to analyze enormous amounts of satellite imagery covering large regions [37,[67][68][69][70][71][72]. While GEE offers more than 15 classification techniques, most studies rely on machine-learning algorithms [37,[68][69][70][71][72], such as CART and RF, since these have proven to be some of the robust methods for land cover classifications.…”

“…With the advent of cloud computing platforms with free access to petabytes of geospatial data, such as Google Earth Engine (GEE), it has now become increasingly accessible and straight-forward to analyze enormous amounts of satellite imagery covering large regions [37,[67][68][69][70][71][72]. While GEE offers more than 15 classification techniques, most studies rely on machine-learning algorithms [37,[68][69][70][71][72], such as CART and RF, since these have proven to be some of the robust methods for land cover classifications. Such methods based on free data and robust algorithms can be particularly beneficial for regular monitoring, including tracking SDG indicators.…”

Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform

“…Blue band Band 1 (L5 and L7); Band 2 (L8) Annual, median OP [41] Green band Band 2 (L5 and L7); Band 3 (L8) Annual, median OP [41] Red band Band 3 (L5 and L7); Band 4 (L8) Annual, median OP [41] Near infrared (NIR) band Band 4 (L5 and L7); Band 5 (L8) Annual, median OP [41] Shortwave infrared (SWIR-1) band Band 5 (L5 and L7); Band 6 (L8) Annual, median OP [41] Shortwave infrared (SWIR-2) band Band 7 (L5 and L7); Band 8 (L8) Annual, median OP [41] NPV (non-photosynthetic vegetation fraction)…”

“…In order to implement the final MapBiomas land use and land cover maps for the Cerrado biome, the results of the Cerrado NV classification were integrated with other MapBiomas cross-cutting theme maps (e.g., pasture, agriculture, coastal zone, and urban infrastructure classes), which were independently developed [41,45]. This integration is hierarchical and follows prevalence rules to combine the classification results from all themes.…”

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

“…Recently, several machine learning algorithms have been designed for mapping and classifying land use land cover (LULC). Ensemble machine learning algorithms such as Random forest (RF) is widely used in LULC classification and mangrove mapping from Landsat images (Erftemeijer and Hamerlynck, 2005;Pal, 2005;Sesnie et al, 2008;Mountrakis et al, 2011), mangrove and sea grass mapping (Heumann, 2011;Hossain et al, 2015;Buitre et al, 2019;Diniz et al, 2019;Small and Sousa, 2019;Toosi et al, 2019), prediction in water resources (Zhao et al, 2012;McGinnis and Kerans, 2013;Naghibi et al, 2016;Naghibi and Dashtpagerdi, 2017), and prediction of land subsidence (Elmahdy et al, 2020a). Further studies combined image transformation and supervised classification to map and classify mangrove forests (Yokoya and Iwasaki, 2010;Ouerghemmi et al, 2018).…”