Sub-classification strategies for tree species recognition

2016 ◽  
Author(s):  
Rihab Ben Ameur ◽  
Lionel Valet ◽  
Didier Coquin
Keyword(s):  
Tree Species ◽  
Species Recognition

scholarly journals Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

2016 ◽  
Vol 49 (1) ◽  
pp. 239-259 ◽  
Author(s):  
Anton Kuzmin ◽  
Lauri Korhonen ◽  
Terhikki Manninen ◽  
Matti Maltamo
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Species Recognition

Tree species recognition system based on macroscopic image analysis

2016 ◽  
Vol 51 (2) ◽  
pp. 431-444 ◽  
Author(s):  
Imanurfatiehah Ibrahim ◽  
Anis Salwa Mohd Khairuddin ◽  
Mohamad Sofian Abu Talip ◽  
Hamzah Arof ◽  
Rubiyah Yusof
Keyword(s):  
Image Analysis ◽  
Tree Species ◽  
Species Recognition ◽  
Recognition System

An Overview of Tree Species Identification from T-LiDAR Data

2015 ◽  
pp. 342-359
Author(s):  
Alice Ahlem Othmani
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Species Recognition ◽  
Automatic Measurement ◽  
Lidar Data ◽  
3D Point Cloud ◽  
Terrestrial Lidar ◽  
Single Tree ◽  
Depth Images ◽  
Bark Texture

Due to the increasing use of the Terrestrial LiDAR Scanning (TLS also called T-LiDAR) technology in the forestry domain, many researchers and forest management organizations have developed several algorithms for the automatic measurement of forest inventory attributes. However, to the best of our knowledge not much has been done regarding single tree species recognition based on T-LiDAR data despite its importance for the assessment of the forestry resource. In this paper, we propose to put the light on the few works reported in the literature. The various algorithms presented in this paper uses the bark texture criteria and can be categorized into three families of approaches: those how combine T-LiDAR technology and photogrammetry, those based on depth images generated from T-LiDAR data and those based on raw 3D point cloud.

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

2019 ◽  
Vol 11 (11) ◽  
pp. 1338 ◽  
Author(s):  
Camile Sothe ◽  
Michele Dalponte ◽  
Cláudia Maria de Almeida ◽  
Marcos Benedito Schimalski ◽  
Carla Luciane Lima ◽  
...  
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Species Recognition ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Kappa Index ◽  
Species Classification ◽  
Tree Species Classification

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.

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