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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

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Author(s):
Wagner, Fabien H. [1] ; Sanchez, Alber [1] ; Tarabalka, Yuliya [2, 3] ; Lotte, Rodolfo G. [1] ; Ferreira, Matheus P. [1, 4] ; Aidar, Marcos P. M. [5] ; Gloor, Emanuel [6] ; Phillips, Oliver L. [6] ; Aragao, Luiz E. O. C. [1, 7]
Total Authors: 9
Affiliation:
[1] Natl Inst Space Res INPE, Remote Sensing Div, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] INRIA Sophia Antipolis, F-06902 Sophia Antipolis - France
[3] Luxcarta Technol, Parc Act Argile, Lot 119b, F-06370 Mouans Sartoux - France
[4] Mil Inst Engn IME, Cartog Engn Sect, Praca Gen Tiburcio 80, BR-22290270 Rio De Janeiro, RJ - Brazil
[5] Inst Bot, Dept Plant Physiol & Biochem, PB 4005, BR-01061970 Sao Paulo - Brazil
[6] Univ Leeds, Sch Geog, Ecol & Global Change, Leeds LS2 9JT, W Yorkshire - England
[7] Univ Exeter, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon - England
Total Affiliations: 7
Document type: Journal article
Source: REMOTE SENSING IN ECOLOGY AND CONSERVATION; v. 5, n. 4, p. 360-375, DEC 2019.
Web of Science Citations: 17
Abstract

Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U-net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView-3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red-green-blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U-net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km(2) region using WorldView-3 RGB bands pan-sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale. (AU)

FAPESP's process: 13/50533-5 - Understanding the response of photosynthetic metabolism in tropical forests to seasonal climate variations
Grantee:Luiz Eduardo Oliveira e Cruz de Aragão
Support type: Regular Research Grants
FAPESP's process: 16/03397-7 - Tools for satellite images management in array databases
Grantee:Alber Hamersson Sánchez Ipia
Support type: Scholarships in Brazil - Technical Training Program - Technical Training
FAPESP's process: 12/51872-5 - ECOFOR: Biodiversity and ecosystem functioning in degraded and recovering Amazonian and Atlantic Forests
Grantee:Carlos Alfredo Joly
Support type: BIOTA-FAPESP Program - Thematic Grants
FAPESP's process: 15/50484-0 - Functional diversity of intact and regenerating Amazon, Atlantic, and Cerrado systems using hyperspectral imagery
Grantee:Fabien Hubert Wagner
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 16/24977-1 - Measuring and mapping Atlantic Forest canopy foliar traits with hyperspectral images and radiative transfer modeling
Grantee:Matheus Pinheiro Ferreira
Support type: Scholarships in Brazil - Post-Doctorate