@InProceedings{FerreiraZoZaFéShSo:2015:UsShIn,
author = "Ferreira, Matheus Pinheiro and Zortea, Maciel and Zanotta, Daniel
Capella and F{\'e}ret, Jean-Baptiste and Shimabukuro, Yosio
Edemir and Souza Filho, Carlos Roberto de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Institute
of Informatics, Federal University of Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil and Institute for Education Science and
Technology, Rio Grande, Brazil and Territoires, Environnement,
Teledetection et Information Spatiale, Montpellier, France and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and
Universidade Estadual de Campinas, Institute of Geosciences,
Campinas, Brazil",
title = "On the use of shortwave infrared for tree species discrimination
in tropical semideciduous forest",
booktitle = "Proceedings...",
year = "2015",
editor = "N. , Paparoditis and A. -M. , Raimond and G. , Sithole and G. ,
Rabatel and A. , Coltekin and F. , Rottensteiner and X. , Briottet
and S. , Christophe and I. , Dowman and S. O. , Elberink and G. ,
Patane and C. , Mallet",
pages = "473--476",
organization = "International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences (ISPRS Archives)",
keywords = "Hyperspectral remote sensing, tropical forests, classification.",
abstract = "Tree species mapping in tropical forests provides valuable
insights for forest managers. Keystone species can be located for
collection of seeds for forest restoration, reducing fieldwork
costs. However, mapping of tree species in tropical forests using
remote sensing data is a challenge due to high floristic and
spectral diversity. Little is known about the use of different
spectral regions as most of studies performed so far used
visible/near-infrared (390-1000 nm) features. In this paper we
show the contribution of shortwave infrared (SWIR, 1045-2395 nm)
for tree species discrimination in a tropical semideciduous
forest. Using high-resolution hyperspectral data we also simulated
WorldView-3 (WV-3) multispectral bands for classification
purposes. Three machine learning methods were tested to
discriminate species at the pixel-level: Linear Discriminant
Analysis (LDA), Support Vector Machines with Linear (L-SVM) and
Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF).
Experiments were performed using all and selected features from
the VNIR individually and combined with SWIR. Feature selection
was applied to evaluate the effects of dimensionality reduction
and identify potential wavelengths that may optimize species
discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved
the highest average accuracy (77.4%). Inclusion of the SWIR
increased accuracy to 85% with LDA. The same pattern was also
observed when WV-3 simulated channels were used to classify the
species. The VNIR bands provided and accuracy of 64.2% for LDA,
which was increased to 79.8 % using the new SWIR bands that are
operationally available in this platform. Results show that
incorporating SWIR bands increased significantly average accuracy
for both the hyperspectral data and WorldView-3 simulated bands.",
conference-location = "La grande Motte, France",
conference-year = "28 Sept. - 02 Oct.",
doi = "10.5194/isprsarchives-XL-3-W3-473-2015",
url = "http://dx.doi.org/10.5194/isprsarchives-XL-3-W3-473-2015",
isbn = "16821750",
label = "lattes: 9686528152912455 1 FerreiraZoZaF{\'e}ShSo:2015:UsShIn",
language = "pt",
organisation = "International Society for Photogrammetry and Remote Sensing",
targetfile = "1_ferreira.pdf",
url = "http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/473/2015/isprsarchives-XL-3-W3-473-2015.pdf",
volume = "40",
urlaccessdate = "02 maio 2024"
}