Abstract

Mangroves play an essential ecological role in the maintenance of the coastal zone and are extremely important for the socioeconomics of coastal communities. However, mangrove ecosystems are impacted by a range of anthropogenic pressures, and the loss of this habitat can be attributed primarily to the human occupation of the coastal zone. In the present study, we analyzed the spatial patterns of land use in the mangrove of the Brazilian Amazon coast, and evaluated the anthropogenic drivers of this impact, using a remote sensing approach. We mapped the road network using RapidEye images, and human settlements using global data. The results of these analyses indicate that the Brazilian Amazon coast has a low population density and low rates of anthropogenic impact in most of the coastal microregions investigated, factors that contribute to the maintenance and conservation of the region’s mangrove. The study also revealed that the paved road network is one of the principal drivers of land use in the mangrove, whereas other factors, such as population density, urban centers, and the number of settlements are much less important. While the region has 2024 km of paved highways, unpaved roads (17,496 km) facilitate access to the mangrove, with approximately 90% of anthropogenic impact being recorded within a 3 km radius of these roads. While the network of paved highways is relatively reduced in extension, preventive measures are urgently required to impede any major shift in the current scenario, caused by the expansion of major development programs. The results of the study indicate that biophysical, economic, and political factors may also contribute to the reduction, stability, and development of one of the world’s largest areas of mangrove forest.

Highlights

  • Data 2015-07-15 2013-09-18 2015-09-20 composition 2012-09-16 composition 2013-01-04 2011-06-05 2011-06-05 2013-12-11 2011-06-06 2015-09-12 2015-09-12 2015-11-15 composition 2011/06/05 2011/06/05 2013/12/11 2015-10-23 2015-01-29 2012-10-24 2015-09-12 2015-11-15 2015-07-18 2014-09-27 2013-12-11 2013-12-24 2014-05-03 2015-07-18 2014-11-06 2014-09-27 2012-10-31 2011-10-15 2015-07-26 2011-10-27 2011-10-07 2015-08-08 composition 2015-07-18 composition 2012-10-06 2012-10-06 2015-07-07 2013-08-30 2015-07-22

  • Data 2013-10-22 2015-08-08 2013-05-31 2014-11-06 composition 2012-11-02 2015-08-07 2015-09-10 2015-09-10 2013-07-16 2015-08-08 31/10/2014 2015-10-07 2015-08-02 2015-10-25 2015-08-07 2015-08-07 2015-08-07 2014-10-13 2015-08-08 2014-10-13 2015-07-15

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Summary

Introduction

Results
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