@Article{SotheDASLLMT:2019:TrSpCl,
author = "Sothe, Camile and Damponte, Michele and Almeida, Cl{\'a}udia
Maria de and Schimalski, Marcos Benedito and Lima, Carla Luciane
and Liesenberg, Veraldo and Miyoshi, Gabriela Takahashi and
Tommaselli, Antonio Maria Garcia",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Research
and Innovation Centre, Fondazione E. Mach and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Universidade Estadual de Santa
Catarina (UDESC)} and {Universidade Estadual de Santa Catarina
(UDESC)} and {Universidade Estadual de Santa Catarina (UDESC)} and
{Universidade Estadual Paulista (UNESP)} and {Universidade
Estadual Paulista (UNESP)}",
title = "Tree species classification in a highly diverse subtropical forest
integrating UAV-based photogrammetric point cloud and
hyperspectral data",
journal = "Remote Sensing",
year = "2019",
volume = "11",
number = "11",
pages = "1--25",
note = "Setores de Atividade: Atividades dos servi{\c{c}}os de tecnologia
da informa{\c{c}}{\~a}o, Pesquisa e desenvolvimento
cient{\'{\i}}fico.",
keywords = "tree species mapping, tropical biodiversity, imaging spectroscopy,
photogrammetry, support vector machine.",
abstract = "The use of remote sensing data for tree species classification in
tropical forests is still a challenging task, due to their high
floristic and spectral diversity. In this sense, novel sensors on
board of unmanned aerial vehicle (UAV) platforms are a rapidly
evolving technology that provides new possibilities for tropical
tree species mapping. Besides the acquisition of high spatial and
spectral resolution images, UAV-hyperspectral cameras operating in
frame format enable to produce 3D hyperspectral point clouds. This
study investigated the use of UAV-acquired hyperspectral images
and UAV-photogrammetric point cloud (PPC) for classification of 12
major tree species in a subtropical forest fragment in Southern
Brazil. Different datasets containing hyperspectral
visible/near-infrared (VNIR) bands, PPC features, canopy height
model (CHM), and other features extracted from hyperspectral data
(i.e., texture, vegetation indices-VIs, and minimum noise
fraction-MNF) were tested using a support vector machine (SVM)
classifier. The results showed that the use of VNIR hyperspectral
bands alone reached an overall accuracy (OA) of 57% (Kappa index
of 0.53). Adding PPC features to the VNIR hyperspectral bands
increased the OA by 11%. The best result was achieved combining
VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa
index of 0.70). When only the CHM was added to VNIR bands, the OA
increased by 4.2%. Among the hyperspectral features, besides all
the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF
features and the textural mean of 565 and 679 nm spectral bands
were pointed out as more important to discriminate the tree
species according to Jeffries Matusita (JM) distance. The SVM
method proved to be a good classifier for the tree species
recognition task, even in the presence of a high number of classes
and a small dataset.",
doi = "10.3390/rs11111338",
url = "http://dx.doi.org/10.3390/rs11111338",
issn = "2072-4292",
label = "lattes: 1861914973833506 3 S{\"o}theDASLLMT:2019:TrSpCl",
language = "en",
targetfile = "remotesensing-11-01338.pdf",
url = "https://www.mdpi.com/2072-4292/11/11/1338",
urlaccessdate = "28 abr. 2024"
}