scholarly journals Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 198 ◽  
Author(s):  
Kristina Koenig ◽  
Bernhard Höfle
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Full Waveform ◽  
Airborne Laser

scholarly journals Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data

2018 ◽  
Vol 51 (1) ◽  
pp. 336-351 ◽  
Author(s):  
Øivind Due Trier ◽  
Arnt-Børre Salberg ◽  
Martin Kermit ◽  
Øystein Rudjord ◽  
Terje Gobakken ◽  
...  
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Airborne Laser

scholarly journals Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning

2017 ◽  
Vol 9 (2) ◽  
pp. 108 ◽  
Author(s):  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
Paula Litkey ◽  
Harri Kaartinen ◽  
Mikko Vastaranta ◽  
...  
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Airborne Laser ◽  
Single Sensor

scholarly journals EVALUATING THE POSSIBILITY OF TREE SPECIES CLASSIFICATION WITH DUAL-WAVELENGTH ALS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1097-1103
Author(s):  
M. Pilarska ◽  
W. Ostrowski
Keyword(s):  
Spatial Data ◽  
Tree Species ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Tree Canopy ◽  
Species Classification ◽  
Airborne Laser ◽  
Simultaneous Acquisition

<p><strong>Abstract.</strong> Airborne laser scanning (ALS) plays an important role in spatial data acquisition. One of the advantages of this technique is laser beam penetration through vegetation, which makes it possible to not only obtain data on the tree canopy but also within and under the canopy. In recent years, multi-wavelength airborne laser scanning has been developed. This technique consists of simultaneous acquisition of point clouds in more than one band. The aim of this experiment was to examine and assess the possibilities of tree segmentation and species classification in an urban area. In this experiment, point clouds registered in two wavelengths (532 and 1064&amp;thinsp;nm) were used for tree segmentation and species classification. The data were acquired with a Riegl VQ-1560i-DW laser scanner over Elblag, Poland, during August 2018. Tree species collected by a botanist team within terrain measurements were used as a reference in the classification process. Within the experiment segmentation and classification process were performed. Regarding the segmentation, TerraScan software and Li et al.’s algorithm, implemented in LidR package were used. Results from both methods are clearly over-segmented in comparison to the manual segments. In Terrasolid segmentation, single reference segments are over-segmented in 28% of cases, whereas, for LidR, over-segmentation occurred in 73% of the segments. According the classification results, Thuja, Salix and Betula were the species, for which the highest classification accuracy was achieved.</p>

scholarly journals Classification of Typical Tree Species in Laser Point Cloud Based on Deep Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4750
Author(s):  
Jianchang Chen ◽  
Yiming Chen ◽  
Zhengjun Liu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Network Mapping

We propose the Point Cloud Tree Species Classification Network (PCTSCN) to overcome challenges in classifying tree species from laser data with deep learning methods. The network is mainly composed of two parts: a sampling component in the early stage and a feature extraction component in the later stage. We used geometric sampling to extract regions with local features from the tree contours since these tend to be species-specific. Then we used an improved Farthest Point Sampling method to extract the features from a global perspective. We input the intensity of the tree point cloud as a dimensional feature and spatial information into the neural network and mapped it to higher dimensions for feature extraction. We used the data obtained by Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAVLS) to conduct tree species classification experiments of white birch and larch. The experimental results showed that in both the TLS and UAVLS datasets, the input tree point cloud density and the highest feature dimensionality of the mapping had an impact on the classification accuracy of the tree species. When the single tree sample obtained by TLS consisted of 1024 points and the highest dimension of the network mapping was 512, the classification accuracy of the trained model reached 96%. For the individual tree samples obtained by UAVLS, which consisted of 2048 points and had the highest dimension of the network mapping of 1024, the classification accuracy of the trained model reached 92%. TLS data tree species classification accuracy of PCTSCN was improved by 2–9% compared with other models using the same point density, amount of data and highest feature dimension. The classification accuracy of tree species obtained by UAVLS was up to 8% higher. We propose PCTSCN to provide a new strategy for the intelligent classification of forest tree species.

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