Tropical Tree Species 3D Modelling and Classification Based on LiDAR Technology

Over the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlapping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees.

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VALIDATING A WORKFLOW FOR TREE INVENTORY UPDATING WITH 3D POINT CLOUDS OBTAINED BY MOBILE LASER SCANNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-1163-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 1163-1168

Author(s):

J. Wang ◽

R. Lindenbergh

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Spatial Information ◽

Open Data ◽

Point Clouds ◽

Urban Trees ◽

Biomass Estimation ◽

Species Classification ◽

3D Point Clouds ◽

Tree Inventory

Urban trees are an important component of our environment and ecosystem. Trees are able to combat climate change, clean the air and cool the streets and city. Tree inventory and monitoring are of great interest for biomass estimation and change monitoring. Conventionally, parameters of trees are manually measured and documented in situ, which is not efficient regarding labour and costs. Light Detection And Ranging (LiDAR) has become a well-established surveying technique for the acquisition of geo-spatial information. Combined with automatic point cloud processing techniques, this in principle enables the efficient extraction of geometric tree parameters. In recent years, studies have investigated to what extend it is possible to perform tree inventories using laser scanning point clouds. Give the availability of a city of Delft Open data tree repository, we are now able to present, validate and extend a workflow to automatically obtain tree data from tree location until tree species. The results of a test over 47 trees show that the proposed methods in the workflow are able to individual urban trees. The tree species classification results based on the extracted tree parameters show that only one tree was wrongly classified using k-means clustering.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1097-2019 ◽

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

Abstract. 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&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.

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Classification of Tropical Forest Tree Species Using Meter-Scale Image Data

Remote Sensing ◽

10.3390/rs11121411 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1411 ◽

Cited By ~ 1

Author(s):

Matthew Cross ◽

Ted Scambos ◽

Fabio Pacifici ◽

Orlando Vargas-Ramirez ◽

Rafael Moreno-Sanchez ◽

...

Keyword(s):

Tropical Forest ◽

Tree Species ◽

Near Infrared ◽

Single Species ◽

Forest Tree ◽

Tropical Tree ◽

Forest Composition ◽

Species Classification ◽

Tropical Tree Species

Accurate classification of tropical tree species is critical for understanding forest habitat, biodiversity, forest composition, biomass, and the role of trees in climate variability through carbon uptake. The aim of this study is to establish an accurate classification procedure for tropical tree species, specifically testing the feasibility of WorldView-3 (WV-3) multispectral imagery for this task. The specific study site is a defined arboretum within a well-known tropical forest research location in Costa Rica (La Selva Biological Station). An object-based classification is the basis for the analysis to classify six selected tree species. A combination of pre-processed WV-3 bands were inputs to the classification, and an edge segmentation process defined multi-pixel-scale tree canopies. WorldView-3 bands in the Green, Red, Red Edge, and Near-Infrared 2, particularly when incorporated in two specialized vegetation indices, provide high discrimination among the selected species. Classification results yield an accuracy of 85.37%, with minimal errors of commission (7.89%) and omission (14.63%). Shadowing in the satellite imagery had a significant effect on segmentation accuracy (identifying single-species canopy tops) and on classification. The methodology presented provides a path to better characterization of tropical forest species distribution and overall composition for improving biomass studies in a tropical environment.

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FEATURE RELEVANCE ASSESSMENT OF MULTISPECTRAL AIRBORNE LIDAR DATA FOR TREE SPECIES CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-31-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 31-34 ◽

Cited By ~ 1

Author(s):

N. Amiri ◽

M. Heurich ◽

P. Krzystek ◽

A. K. Skidmore

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Lidar ◽

Species Classification ◽

Tree Species Classification ◽

3D Point Clouds ◽

Segmentation Approach ◽

Species Mapping ◽

Spectral Channels

The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three different wavelngthes. The available data were acquired in the summer 2016 at the same date in a leaf-on condition with an average point density of 37&thinsp;points/m2. For the purpose of classification, we segmented the combined 3D point clouds consisiting of three different spectral channels into 3D clusters using Normalized Cut segmentation approach. Then, we extracted four group of features from the 3D point cloud space. Once a varity of features has been extracted, we applied forward stepwise feature selection in order to reduce the number of irrelevant or redundant features. For the classification, we used multinomial logestic regression with L1 regularization. Our study is conducted using 586 ground measured single trees from 20 sample plots in the Bavarian Forest National Park, in Germany. Due to lack of reference data for some rare species, we focused on four classes of species. The results show an improvement between 4&ndash;10&thinsp;pp for the tree species classification by using MLS data in comparison to a single wavelength based approach. A cross validated (15-fold) accuracy of 0.75 can be achieved when all feature sets from three different spectral channels are used. Our results cleary indicates that the use of MLS point clouds has great potential to improve detailed forest species mapping.

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Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning

Frontiers in Plant Science ◽

10.3389/fpls.2021.635440 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dominik Seidel ◽

Peter Annighöfer ◽

Anton Thielman ◽

Quentin Edward Seifert ◽

Jan-Henrik Thauer ◽

...

Keyword(s):

Tree Species ◽

Classification Accuracy ◽

Laser Scanning ◽

Confusion Matrix ◽

Point Clouds ◽

Small Sample ◽

Training Data ◽

Computationally Efficient ◽

Species Classification ◽

3D Point Clouds

Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach.

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A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

Remote Sensing ◽

10.3390/rs13030353 ◽

2021 ◽

Vol 13 (3) ◽

pp. 353

Author(s):

Maja Michałowska ◽

Jacek Rapiński

Keyword(s):

Remote Sensing ◽

Tree Species ◽

Laser Scanning ◽

Discrimination Performance ◽

Point Clouds ◽

Airborne Lidar ◽

Support Vector ◽

Species Discrimination ◽

Species Classification ◽

Waveform Data

Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.

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