Abstract
The Atlantic rainforest of Brazil is one of the global terrestrial hotspots of biodiversity. Despite having undergone large scale deforestation, forest cover has shown signs of increases in the last decades. Here, to understand the degradation and regeneration history of Atlantic rainforest remnants near São Paulo, we combine a unique dataset of very high resolution images from Worldview-2 and Worldview-3 (0.5 and 0.3m spatial resolution, respectively), georeferenced aerial photographs from 1962 and use a deep learning method called U-net to map (i) the forest cover and changes and (ii) two pioneer tree species, Cecropia hololeuca and Tibouchina pulchra. For Tibouchina pulchra, all the individuals were mapped in February, when the trees undergo mass-flowering with purple and pink blossoms. Additionally, elevation data at 30m spatial resolution from NASA Shuttle Radar Topography Mission (SRTM) and annual mean climate variables (Terraclimate datasets at ∼ 4km of spatial resolution) were used to analyse the forest and species distributions. We found that natural forests are currently more frequently found on south-facing slopes, likely because of geomorphology and past land use, and that Tibouchina is restricted to the wetter part of the region (southern part), which annually receives at least 1600 mm of precipitation. Tibouchina pulchra was found to clearly indicate forest regeneration as almost all individuals were found within or adjacent to forests regrown after 1962. By contrast, Cecropia hololeuca was found to indicate older disturbed forests, with all individuals almost exclusively found in forest fragments already present in 1962. At the regional scale, using the dominance maps of both species, we show that at least 4.3% of the current region's natural forests have regrown after 1962 (Tibouchina dominated, ∼ 4757 ha) and that ∼ 9% of the old natural forests have experienced significant disturbance (Cecropia dominated).
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Biodiversity*
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Conservation of Natural Resources*
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Environmental Monitoring*
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Neural Networks, Computer*
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Rainforest*
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Species Specificity
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Trees / classification
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Trees / growth & development*
Grants and funding
The research leading to these results received funding from the project BIO-RED ‘Biomes of Brazil -- Resilience, Recovery, and Diversity’, which is supported by the São Paulo Research Foundation (FAPESP, 2015/50484-0) and the U.K. Natural Environment Research Council (NERC, NE/N012542/1). F.H.W. has been funded by FAPESP (grant 2016/17652-9). A.S. acknowledges the support of the FAPESP (grant 2016/03397-7). M.P.M.A. has been funded by ECOFOR Project, BIOTA-FAPESP (grant number 2012/51872-5). A.L.C.R. has been funded by supported by the State of São Paulo Research Foundation/FAPESP as part of the project ECOFOR (Process number 2012/51872-5) within the BIOTA/FAPESP Program - The Biodiversity Virtual Institute (
www.biota.org.br) and co-supported by the British Natural Environment Research Council/NERC (NE/K016431/1). M.G.F acknowledges the support of Capes through a postdoctoral fellowship. Y.T. has been funded by the project EPITOME ANR-17-CE23-0009 of the French National Research Agency (ANR). L.E.O.C.A. thank the support of FAPESP (grant 2013/50533-5) and CNPq (grant 305054/2016-3). We also thank the Amazon Fund through the financial collaboration of the Brazilian Development Bank (BNDES) and the Foundation for Science, Technology and Space Applications (FUNCATE) no. 17.2.0536.1 (Environmental Monitoring of Brazilian Biomes). The funders (FAPESP, NERC, Capes, ANR and Luxcarta Technology) provided support in the form of salaries for authors F.W., A.S., Y.T. and M.G.F., but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.