Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data
Abstract
:1. Introduction
1.1. Remote Sensing for Forest Structural Property Modeling
1.2. Objectives
2. Materials and Methods
2.1. Study Site
2.2. Data
2.2.1. Field Datasets
2.2.2. Hyperspectral Data
2.2.3. Lidar Metrics
3. Methodology
3.1. Feature Selection and Dimensionality Reduction
3.1.1. Random Forest Implementation in Boruta
3.1.2. Principal Component Analysis
3.1.3. Simulated Annealing
3.1.4. Genetic Algorithm
3.2. Machine Learning Algorithm
3.2.1. Multivariate Adaptive Regression Spline
3.2.2. Extra Trees
3.2.3. Extreme Gradient Boosting
3.2.4. Support Vector Regression
3.3. Model Construction
3.4. Model Validation
4. Results
4.1. Feature Selection and Dimensionality Reduction
4.2. Model Performance
5. Discussion
5.1. Results Overview
5.2. Future Improvement
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | Statistical Analysis | |||||
---|---|---|---|---|---|---|
Min | Mean | Max | Standard Deviation | 5th Percentile | 95th Percentile | |
Height (H) (m) | 2.0 | 18.4 | 120.0 | 6.6 | 10 | 30 |
Diameter at Breast Height (DBH) (cm) | 10.0 | 20.9 | 152.5 | 13.1 | 10.4 | 48.5 |
Aboveground Biomass (AGB) (mg/ha) | 294.81 | 402.53 | 540.75 | 56.59 | 311.529 | 534.85 |
Number of Trees (count/ha) | 715 | 935.2 | 1074 | 75.78 | 743.5 | 1065 |
Sensors | Wavelength | Acronyms | Number of Bands | Full-Width Half Maximum (FWHM) |
---|---|---|---|---|
Eagle (E) | 400.7–450 nm | E401–E450 | 23 | 2.2 to 2.45 nm |
452.6–501 nm | E453–E501 | 22 | ||
503.3–580.4 nm | E50–E580 | 34 | ||
582.8–599.4 nm | E583–E599 | 8 | ||
601.9–680.7 nm | E602–E681 | 34 | ||
683–749.8 nm | E683–E750 | 29 | ||
752.4–999.2 nm | E752–E999 | 102 | ||
Hawk | 993.1–1396.7 nm | H993–H1397 | 61 | 6.22 to 6.32 nm |
1403–2497.37 nm | H1403–H2497 | 166 | ||
Total bands | 479 bands |
Acronyms | Statistical Metric Type | Number of Variables |
---|---|---|
LD01–LD06 | Maximum, mean, standard deviation, skewness, kurtosis, entropy | 6 |
LD07–LD08 | Height above mean and number of returns from the 2nd and above 2nd return, divided by the 1st return | 2 |
LD09–LD27 | Height percentile from 5% to 95% (5% interval) | 11 |
LD28–LD36 | Cumulative height based on nine breaks from minimum to maximum | 9 |
CHM | Pit-free canopy height metrics | 1 |
Total lidar statistical variables | 37 |
Variables | All Data | Hyperspectral | Lidar | ||||||
---|---|---|---|---|---|---|---|---|---|
BO | GA | SA | BO | GA | SA | BO | GA | SA | |
Eagle | 4 | 85 | 48 | 2 | 66 | 66 | |||
Hawk | 1 | 44 | 46 | 2 | 42 | 62 | |||
Lidar | 24 | 4 | 10 | 27 | 9 | 14 | |||
Total | 29 | 133 | 104 | 4 | 108 | 128 | 27 | 9 | 14 |
% from initial datasets | 5.62 | 25.78 | 20.16 | 0.84 | 22.55 | 26.72 | 72.97 | 24.32 | 37.84 |
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Arjasakusuma, S.; Swahyu Kusuma, S.; Phinn, S. Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data. ISPRS Int. J. Geo-Inf. 2020, 9, 507. https://doi.org/10.3390/ijgi9090507
Arjasakusuma S, Swahyu Kusuma S, Phinn S. Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data. ISPRS International Journal of Geo-Information. 2020; 9(9):507. https://doi.org/10.3390/ijgi9090507
Chicago/Turabian StyleArjasakusuma, Sanjiwana, Sandiaga Swahyu Kusuma, and Stuart Phinn. 2020. "Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data" ISPRS International Journal of Geo-Information 9, no. 9: 507. https://doi.org/10.3390/ijgi9090507