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Article

Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

1
Department of Remote Sensing, National Institute for Space Research (INPE), Av. dos Astronautas 1758, São José dos Campos SP 12227-010, Brazil
2
Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, TN 38010 San Michele all’Adige, Italy
3
Department of Forest Engineering, Santa Catarina State University (UDESC), Av. Luiz de Camões 2090, Lages SC 88520-000, Brazil
4
Department of Geography, Santa Catarina State University (UDESC), Av. Me. Benvenuta, 2007, Florianópolis SC 88035-001, Brazil
5
Department of Cartography, São Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1338; https://doi.org/10.3390/rs11111338
Received: 19 March 2019 / Revised: 16 May 2019 / Accepted: 28 May 2019 / Published: 3 June 2019
(This article belongs to the Special Issue UAV Applications in Forestry)
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. View Full-Text
Keywords: tree species mapping; tropical biodiversity; imaging spectroscopy; photogrammetry; support vector machine tree species mapping; tropical biodiversity; imaging spectroscopy; photogrammetry; support vector machine
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MDPI and ACS Style

Sothe, C.; Dalponte, M.; Almeida, C.M.d.; Schimalski, M.B.; Lima, C.L.; Liesenberg, V.; Miyoshi, G.T.; Tommaselli, A.M.G. Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data. Remote Sens. 2019, 11, 1338. https://doi.org/10.3390/rs11111338

AMA Style

Sothe C, Dalponte M, Almeida CMd, Schimalski MB, Lima CL, Liesenberg V, Miyoshi GT, Tommaselli AMG. Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data. Remote Sensing. 2019; 11(11):1338. https://doi.org/10.3390/rs11111338

Chicago/Turabian Style

Sothe, Camile; Dalponte, Michele; Almeida, Cláudia M.d.; Schimalski, Marcos B.; Lima, Carla L.; Liesenberg, Veraldo; Miyoshi, Gabriela T.; Tommaselli, Antonio M.G. 2019. "Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data" Remote Sens. 11, no. 11: 1338. https://doi.org/10.3390/rs11111338

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