Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

Zhou, Tan; Popescu, Sorin C.; Lawing, A. Michelle; Eriksson, Marian; Strîmbu, Bogdan M.; Bürkner, Paul - Christian

doi:10.3390/rs10010039

Cited by 25 publications

(34 citation statements)

References 40 publications

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“…We think that tree matching is possible to be executed in real-time if SLAM algorithms [61][62][63] are used for 3D tree rendering instead of structure-from-motion algorithms [57,64,65]. Furthermore, accurate geo-referencing of tree stems could help improve species identification when analysis is executed data fusing spectral information with locational data [66][67][68].…”

Section: Discussionmentioning

confidence: 99%

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

2019

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Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure-from-motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.needed. Stand level information is particularly needed for intermediate cuts, when the decision is taken considering all the trees not only a portion of them. Although total height of each tree in a stand can be relatively easy estimated from point clouds, lidar or photogrammetric, there are at least two attributes that are difficult to estimate accurately and precisely for all trees: dbh and location. Many algorithms have been developed to estimate dbh from above canopy data (e.g., point clouds or images) with significant success [4][5][6][7]. However, while expected accuracy is in many instances achieved, the precision of estimating dbh from above canopy is limited, which restricts its operational usage. The effort placed in estimation of tree location mirror the one for tree size, but the success was clearly less impressive, as the accuracy is still measured in meters [8].Point clouds play a significant role in forest operations, particularly in road design and road maintenance [9,10]. Point clouds generated without an active sensor, such as lidar, or stereopsis are sometimes labeled photogrammetric point clouds [11] or phodar [12]. Photogrammetric point clouds (PPC) made inroads in harvesting, but they were employed similarly with aerial laser scanning, as the applications were based on t...

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Section: Discussionmentioning

confidence: 99%

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

2019

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“…The actual position of the trees was determined by visual interpretation of the UAV LiDAR point clouds. The average point spacing of the UAV LiDAR point clouds is 0.16 m. So, in most cases, individual tree or clump could be identified by inspecting the point clouds from different angles [57][58][59]. When compare the visual interpretation trees and clumps with the machine segmentation trees and clumps, we could obtain accuracy from spatial matching aspect.…”

Section: Individual Tree Detectionmentioning

confidence: 99%

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Qiu

Wang

Zou

et al. 2019

Forests

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To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%.(2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R 2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha −1 vs. 38.95 Mg ha −1 . However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

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“…Afterwards this voxelised data are used to derive terrain, canopy and other tree related metrics. The concepts of voxelisation (as explained below in Section 2.2.1 or with similar interpretations) have been used in forestry for handling both discrete [35][36][37][38][39][40] and waveform [41][42][43] data. In comparison to the discrete data, the waveform data are more likely to contain noise.…”

Section: Introductionmentioning

confidence: 99%

Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

Miltiadou

Αγαπίου

Aracil

et al. 2020

Forests

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In southern Australia, many native mammals and birds rely on hollows for sheltering, while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse Woody Debris—CWD) is a very different task from a classification perspective. This study focuses on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees have irregular shapes making delineation of them challenging. Additionally, since the study area is a native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods that will be resistant to those challenges. Previous study showed that detection of dead standing trees without tree delineation is possible. This was achieved by using single size 3D-windows to extract structural features from voxelised full-waveform LiDAR and characterise dead (positive samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the single 3D-windows approach and the new multi-scale 3D-windows approach were implemented for comparison purposes. The accuracy of the results was calculated using the precision and recall parameters and it was proven that the multi-scale 3D-windows approach performs better than the single size 3D-windows approach. This open ups possibilities for applying the proposed approach on other native forest related applications.

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Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

Cited by 25 publications

References 40 publications

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

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