Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data
Abstract
1. Introduction
2. MAPL System
3. Data Collection and Processing
3.1. Data Collection
3.2. Processing of LiDAR Waveform Data
4. Decision-Tree-Based ML Classification
4.1. Decision Tree Classification
- if x1 < 99.14 then node 2 elseif x1 ≥ 99.14 then node 3
- if x3 < 45.17 then node 4 elseif x3 ≥ 45.17 then node 5
- class = Maple
- if x2 < 96.13 then node 6 elseif x2 ≥ 96.13 then node 7
- if x4 < 24.86 then node 8 elseif x4 ≥ 24.86 then node 9
- if x4 < 27.46 then node 10 elseif x4 ≥ 27.46 then node 11
- if x4 < 26.16 then node 12 elseif x4 ≥ 26.16 then node 13
- if x3 < 45.75 then node 14 elseif x3 ≥ 45.75 then node 15
- if x4 < 27.46 then node 16 elseif x4 ≥ 27.46 then node 17
- if x4 < 25.18 then node 18 elseif x4 ≥ 25.18 then node 19
- if x2 < 83.74 then node 20 elseif x2 ≥ 83.74 then node 21
- class = Blue Spruce
- class = Ash
- if x2 < 94.42 then node 22 elseif x2 ≥ 94.42 then node 23
- if x1 < 93.03 then node 24 elseif x1 ≥ 93.03 then node 25
- if x4 < 25.83 then node 26 elseif x4 ≥ 25.83 then node 27
- if x2 < 81.60 then node 28 elseif x2 ≥ 81.60 then node 29
- class = Austrian Pine
- class = Ash
- class = Ash
- class = Ponderosa Pine
- class = Austrian Pine
- if x1 < 92.15 then node 30 elseif x1 ≥ 92.15 then node 31
- if x2 < 93.56 then node 32 elseif x2 ≥ 93.56 then node 33
- if x2 < 92.71 then node 34 elseif x2 ≥ 92.71 then node 35
- if x1 < 92.15 then node 36 elseif x1 ≥ 92.15 then node 37
- if x4 < 27.13 then node 38 elseif x4 ≥ 27.13 then node 39
- class = Ash
- class = Ponderosa Pine
- class = Austrian Pine
- if x2 < 96.98 then node 40 elseif x2 ≥ 96.98 then node 41
- if x1 < 91.28 then node 42 elseif x1 ≥ 91.28 then node 43
- class = Ponderosa Pine
- if x2 < 90.15 then node 44 elseif x2 ≥ 90.15 then node 45
- class = Ponderosa Pine
- class = Austrian Pine
- if x3 < 45.75 then node 46 elseif x3 ≥ 45.75 then node 47
- class = Ash
- if x2 < 81.18 then node 48 elseif x2 ≥ 81.18 then node 49
- if x1 < 94.77 then node 50 elseif x1 ≥ 94.77 then node 51
- class = Ponderosa Pine
- class = Austrian Pine
- if x2 < 89.29 then node 52 elseif x2 ≥ 89.29 then node 53
- class = Austrian Pine
- class = Ponderosa Pine
- if x1 < 93.90 then node 54 elseif x1 ≥ 93.90 then node 55
- class = Ponderosa Pine
- class = Ash
- class = Ponderosa Pine
- if x1 < 93.90 then node 56 elseif x1 ≥ 93.90 then node 57
- class = Ponderosa Pine
- class = Austrian Pine
- if x2 < 91.86 then node 58 elseif x2 ≥ 91.86 then node 59
- if x2 < 93.56 then node 60 elseif x2 ≥ 93.56 then node 61
- class = Ponderosa Pine
- if x1 < 93.03 then node 62 elseif x1 ≥ 93.03 then node 63
- class = Ponderosa Pine
- if x2 < 91.00 then node 64 elseif x2 ≥ 91.00 then node 65
- class = Austrian Pine
- class = Austrian Pine
- class = Ponderosa Pine
- if x4 < 24.53 then node 66 elseif x4 ≥ 24.53 then node 67
- class = Austrian Pine
- if x4 < 24.53 then node 68 elseif x4 ≥ 24.53 then node 69
- class = Austrian Pine
- class = Ponderosa Pine
- if x2 < 95.27 then node 70 elseif x2 ≥ 95.27 then node 71
- class = Ponderosa Pine
- class = Austrian Pine
- class = Austrian Pine
- class = Ponderosa Pine
4.2. Model Performance Evaluation and Validation
- i.
- Fit the Decision Tree Model: Train the decision tree model using the training samples (in this case, a total of 2106 × 4 = 8424 waveforms), which include features and corresponding labels.
- ii.
- Make Predictions: Use the trained decision tree model to make predictions on the training datasets. Each dataset in the training dataset will be classified into a specific class by the decision tree model.
- iii.
- Compare Predictions with Actual Labels: The misclassification rate is the rate of incorrectly classified instances in the training dataset.
- iv.
- Calculate the Re-substitution Error Rate [34]:
5. Discussions
6. Conclusions
- Polarimetric measurement has been proven to be an effective method for target detection. Polarimetric diversity enhances measurement and provides more information on target characterization.
- The MAPL peak reflectance intensity data, at dual wavelength and dual polarization, is an effective and simple feature for classification purposes.
- The decision tree algorithm proves to be effective in this case as suggested by the re-substitution error and the k-fold cross-validation loss error.
- The method developed in this study can be extended to new data and other vegetation classification applications.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Tree Species | Dataset |
---|---|
Blue Spruce | 244 |
Ash | 277 |
Ponderosa Pine | 795 |
Austrian Pine | 318 |
Maple | 472 |
Ch1 | Ch2 | Ch3 | Ch4 | |
---|---|---|---|---|
Ch1 | 1.0000 | 0.9227 | 0.3258 | −0.4050 |
Ch2 | 0.9227 | 1.0000 | 0.3537 | −0.4343 |
Ch3 | 0.3258 | 0.3537 | 1.0000 | −0.3435 |
Ch4 | −0.4050 | −0.4343 | −0.3435 | 1.0000 |
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Hu, Z.; Tan, S. Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data. Electronics 2024, 13, 4534. https://doi.org/10.3390/electronics13224534
Hu Z, Tan S. Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data. Electronics. 2024; 13(22):4534. https://doi.org/10.3390/electronics13224534
Chicago/Turabian StyleHu, Zhong, and Songxin Tan. 2024. "Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data" Electronics 13, no. 22: 4534. https://doi.org/10.3390/electronics13224534
APA StyleHu, Z., & Tan, S. (2024). Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data. Electronics, 13(22), 4534. https://doi.org/10.3390/electronics13224534