@InProceedings{WagnerSTLFAGPA:2018:UsCoNe,
author = "Wagner, Fabien Hubert and Sanchez Ipia, Alber Hamersson and
Tarabakla, Yuliya and Lotte, Rodolfo Georjute and Ferreira,
Matheus Pinheiro and Aidar, Marcos P. M. and Gloor, Manuel and
Phillips, Oliver L. and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz
de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and INRIA and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {} and {University of Leeds} and
{University of Leeds} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Using convolutional network to identify tree species related to
forest disturbance in a neotropical forest with very high
resolution multispectral images",
year = "2018",
organization = "AGU Fall Meeting",
abstract = "Mapping tree species at landscape scale to provide information for
ecologists and forest managers is a new challenge for the remote
sensing community. Here, we tested the potential of a recent deep
learning algorithm to identify and segment tree species associated
with forest disturbance in very high-resolution multispectral
images (0.3 m) from WorldView-3 satellite. The study was conducted
in a region of the critically endangered Brazilian Atlantic
Rainforest, which is a global priority for biodiversity
conservation due to its abundance of species of flora and fauna
occurring across an extremely fragmented and degraded landscape.
The convolutional network generated in this study for identifying
trees from different species was trained with about 1500
high-resolution true colour synthetic optical images and their
labelled masks for each species. Additionally, we created a new
framework for measuring disturbance levels within forest fragments
based on the spatial distribution of individual
disturbance-related trees. Our deep learning network segmented
tree species with overall accuracies of above 95% and Dice
coefficients of above 0.85. Then, the segmentation of tree species
was produced over a region >1000 kmē using WorldView-3 Red, Green
and Blue bands pan-sharpened at 0.3 m. We found that the crowns of
disturbance-related species covered between 1 and 5 % of the
natural forest canopies. Our results based on the trees
distribution shown that disturbance tends to increase with
fragment size and revealed information that were not accessible
from classical landscape fragmentation analysis, which is mainly
based on size and connection of the forest fragments. We are still
far from recognizing all the species, however, species that are
indicator of disturbance and early successional stage of forests
can be accurately mapped. Our work shows how deep learning
algorithm can support applications such as mapping tree species
and forest disturbance at the landscape scale from space.",
conference-location = "Washington, D. C.",
conference-year = "10-14 dec.",
language = "en",
targetfile = "wagner_using.pdf",
urlaccessdate = "27 abr. 2024"
}