Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. Study Area
2.1.2. Acquired Full-Waveform LiDAR
2.1.3. Field Data
2.2. Methodology
2.2.1. Voxelisation of Waveform Samples
- The space is divided into a 3D regular grid; each cube is named voxel
- Noise reduction by applying low level filtering—in other words ignore waveform sample with low intensity profile since they most probably contain noise
- Each waveform sample - whose position is calculated as explained in Section 2.1.2—is associated with the voxel that it lies inside
- Each voxel takes as value the average intensity of the waveforms that are associated with it
- The result is a discrete density volune (can be interpreted as a 3D grayscale image) with accumulated intensities of the waveforms
2.2.2. Extraction of Features from 3D-Windows
2.2.3. Random Forest
2.2.4. Weighted k-Nearest Neighbours Algorithm
2.2.5. Fusion of Probabilistic Field
2.2.6. Location detection
2.2.7. Cross Validation, Precision and Recall
- True Positives (): how many dead trees the system detected correctly
- False Positive (): how many locations the system wrongly labelled as dead trees
- False Negatives (): how many dead trees were not detected
- : is the number of locations that the classifier predicted that a dead tree exist
- : is the number of dead trees that existed within the field data
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
CHM | Canopy Height Model |
EU | European Union |
FN | False Negative |
FP | False Positive |
H2020 | Horizon 2020 |
k-NN | k Nearest Neighbour |
LiDAR | Light Detection and Ranging |
Ltd | Limited |
NSW | New South Wells |
TN | True Negative |
TP | True Positive |
SVM | Support Vector Machine |
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Multi-Scale 3D Windows Approach | Single Window Approach | Section |
---|---|---|
Voxelisation of waveform samples - Digital Terrain Model substracted during voxelisation | Voxelisation of waveform samples - Digital Terrain Model substracted during voxelisation | Section 2.2.1 |
Extraction of structural features characterising dead, live trees and testing data using 3D windows: selection of three sizes of 3D windows by observing the height histograms and create three sets of training and testing datasets, one for each 3D-window size. | Extraction of structural features characterising dead, live trees and testing data using 3D windows | Section 2.2.2 |
Usage of Random Forest to identify the most important features that are used to train the classifier | Usage of random forest to identify the most important features that are used to train the classifier | Section 2.2.3 |
K-Nearest Neighbour algorithm for creating a probabilistic field using positive (dead trees) and negative (alive trees) samples. Creation of one probabilistic field for each window size | K-Nearest Neighbour algorithm for creating a probabilistic field using positive (dead trees) and negative (alive trees) samples | Section 2.2.4 |
Fuse probabilistic fields created by the three multi-scale 3D-windows into one | Section 2.2.5 | |
Median filtering for noise reduction | Median filtering for noise reduction | Section 2.2.6 |
Threshold pixels containing information about ground from pixels containing tree using CHM | Threshold pixels containing information about ground from pixels containing tree using CHM | Section 2.2.6 |
Removal of pixels with low probability of been dead | Removal of pixels with low probability of been dead | Section 2.2.6 |
Median and Averaging filtering | Median and Averaging filtering | Section 2.2.6 |
Seed Growth Algorithm for segmenting/identifying unique segments of potentially dead trees | Seed Growth Algorithm for segmenting/identifying unique segments of potentially dead trees | Section 2.2.6 |
Assignment of predicted locations of dead trees | Assignment of predicted locations of dead trees | Section 2.2.6 |
Dis (m) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 | 5 | 5.5 | 6 | 6.5 | 7 |
Val1 | 0 | 0.51 | 2.04 | 5.61 | 7.14 | 9.18 | 11.73 | 14.8 | 17.86 | 21.94 | 26.53 | 35.71 | 38.78 | 41.84 |
Val2 | 0 | 0.5 | 2 | 4 | 7 | 10 | 14 | 17 | 19.5 | 24 | 30 | 37 | 41.5 | 45.5 |
Val3 | 0 | 1.1 | 1.66 | 5.52 | 7.73 | 10.5 | 12.15 | 14.92 | 18.23 | 20.99 | 25.41 | 32.04 | 33.7 | 38.12 |
Val4 | 0.98 | 0.98 | 3.41 | 7.8 | 9.76 | 14.63 | 20.49 | 24.88 | 29.76 | 35.12 | 40 | 47.32 | 51.22 | 53.66 |
Ave | 0.25 | 0.77 | 2.28 | 5.73 | 7.91 | 11.08 | 14.59 | 17.9 | 21.34 | 25.51 | 30.49 | 38.02 | 41.3 | 44.78 |
Dis (m) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 | 5 | 5.5 | 6 | 6.5 | 7 |
Val1 | 0 | 0.29 | 1.17 | 3.21 | 3.79 | 4.66 | 5.83 | 7.29 | 8.45 | 10.2 | 12.54 | 16.03 | 18.37 | 20.12 |
Val2 | 0 | 0.25 | 1 | 2.01 | 3.01 | 4.01 | 6.27 | 8.27 | 9.77 | 10.78 | 12.78 | 16.29 | 17.79 | 20.05 |
Val3 | 0 | 0.52 | 0.78 | 2.35 | 3.13 | 4.96 | 6.01 | 7.05 | 8.36 | 9.4 | 10.97 | 13.32 | 14.62 | 15.93 |
Val4 | 0.53 | 0.53 | 1.85 | 3.96 | 4.49 | 7.39 | 9.5 | 11.08 | 12.66 | 13.98 | 16.09 | 18.73 | 20.84 | 23.22 |
Ave | 0.13 | 0.4 | 1.2 | 2.88 | 3.61 | 5.26 | 6.9 | 8.42 | 9.81 | 11.09 | 13.1 | 16.09 | 17.91 | 19.83 |
Dis (m) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 | 5 | 5.5 | 6 | 6.5 | 7 |
Val1 | 1.53 | 5.1 | 10.71 | 14.8 | 22.45 | 29.59 | 33.67 | 40.31 | 47.45 | 54.59 | 59.18 | 61.22 | 63.78 | 70.92 |
Val2 | 1.5 | 4.5 | 7.5 | 11.5 | 20 | 29 | 37 | 43 | 49.5 | 56 | 60.5 | 63.5 | 68.5 | 70 |
Val3 | 0.55 | 3.87 | 8.84 | 13.81 | 20.99 | 25.97 | 29.28 | 38.12 | 44.75 | 52.49 | 59.67 | 63.54 | 65.75 | 67.4 |
Val4 | 0.98 | 5.85 | 9.27 | 13.17 | 19.51 | 27.32 | 34.63 | 44.39 | 53.66 | 60.49 | 66.34 | 69.27 | 73.17 | 79.02 |
Ave | 1.14 | 4.83 | 9.08 | 13.32 | 20.74 | 27.97 | 33.65 | 41.46 | 48.84 | 55.89 | 61.42 | 64.38 | 67.8 | 71.84 |
Dis (m) | 0.5 | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 | 5 | 5.5 | 6 | 6.5 | 7 |
Val1 | 0.35 | 1.04 | 2.08 | 2.77 | 4.62 | 6 | 7.97 | 10.05 | 12.12 | 13.97 | 15.82 | 17.67 | 19.4 | 22.06 |
Val2 | 0.35 | 1.05 | 1.76 | 2.81 | 4.68 | 6.91 | 9.02 | 11.01 | 12.88 | 15.46 | 18.03 | 20.37 | 23.42 | 24.59 |
Val3 | 0.12 | 0.83 | 2.02 | 3.33 | 4.88 | 6.06 | 6.9 | 8.92 | 10.58 | 13.08 | 15.46 | 17.24 | 18.67 | 20.57 |
Val4 | 0.24 | 1.22 | 2.08 | 2.82 | 4.28 | 5.39 | 7.47 | 10.28 | 11.87 | 14.44 | 15.67 | 16.52 | 18.36 | 20.2 |
Ave | 0.27 | 1.04 | 1.99 | 2.93 | 4.62 | 6.09 | 7.84 | 10.07 | 11.86 | 14.24 | 16.25 | 17.95 | 19.96 | 21.86 |
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Share and Cite
Miltiadou, M.; Agapiou, A.; Gonzalez Aracil, S.; Hadjimitsis, D.G. Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations. Forests 2020, 11, 161. https://doi.org/10.3390/f11020161
Miltiadou M, Agapiou A, Gonzalez Aracil S, Hadjimitsis DG. Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations. Forests. 2020; 11(2):161. https://doi.org/10.3390/f11020161
Chicago/Turabian StyleMiltiadou, Milto, Athos Agapiou, Susana Gonzalez Aracil, and Diofantos G. Hadjimitsis. 2020. "Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations" Forests 11, no. 2: 161. https://doi.org/10.3390/f11020161
APA StyleMiltiadou, M., Agapiou, A., Gonzalez Aracil, S., & Hadjimitsis, D. G. (2020). Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations. Forests, 11(2), 161. https://doi.org/10.3390/f11020161