Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Data Collection
2.3. LiDAR Data and Processing
2.4. Above-Ground Biomass Modeling and Accuracy Assessment
3. Results
3.1. Field Based AGB Estimates
3.2. Correlation between AGB and Predictor Variables
3.3. Linear Regression (LR) Method for Biomass Estimation
3.4. Random Forest Method for Biomass Estimation
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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LiDAR Metrics | Metrics | Description |
---|---|---|
Height-related metrics | Percentile height zq5, zq10, zq15, zq20, zq25, zq30, zq35, zq40, zq45, zq50, zq55, zq60, zq65, zq70, zq75, zq80, zq85, zq90, zq95 | The percentiles of the height distributions (5th, 10th, 15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, 95th) of all points above 2 m |
Maximum height (zmax) | The maximum height above 2 m of all points | |
Mean height (zmean) | The mean height above 2 m of all points | |
The coefficient of variation in height (zcv) | The coefficient of variation in heights of all points above 2 m | |
Standard deviation (zsd) | The standard deviation of heights of all points above 2 m | |
zskew | The skewness of heights of all points above 2 m | |
zkurt | The kurtosis of the heights of all points above 2 m | |
zentropy | The entropy of height distribution | |
Density-related metrics | pzabove2 | Percentages of first returns above 2 m |
pzabovezmean | Percentage of returns > mean returns height | |
zpcum1 | Cumulative percentage of first returns in the lower 10% of maximum elevation | |
zpcum2 | Cumulative percentage of first returns in the lower 20% of maximum elevation | |
zpcum3 | Cumulative percentage of first returns in the lower 30% of maximum elevation | |
The relative shape of the canopy | CRR | Canopy relief ratio = (Height.mean − Height.min)/(Height.max − Height.min) |
Attributes | Mean | Minimum | Maximum | Standard Deviation |
---|---|---|---|---|
Density (trees/ha) | 462 | 39 | 2122 | 343 |
DBH (cm) | 24 | 6 | 101 | 14 |
Height (m) | 17 | 2 | 28 | 7 |
Basal area (m2) | 12 | 0.2 | 47 | 10 |
Volume (m3/ha) | 108 | 0.6 | 519 | 112 |
AGB (ton/ha) | 131 | 1 | 640 | 137 |
Model | Equation | R2 | RMSE (ton/ha) |
---|---|---|---|
AGB1 | ln(AGB) = 0.321 + 0.205 × zq95 | 0.721 | 91.67 |
AGB2 | ln(AGB) = 0.3211 + 0.205 × zq95 + 0.002 × zsd | 0.716 | 91.59 |
AGB3 | ln(AGB) = −0.073 + 0.197 × zq95 + 0.008 × zsd + 0.009 × pzabovezmean | 0.712 | 90.63 |
AGB4 | ln(AGB) = 0.520 +0.215 × Zq95 − 0.129 × zsd + 0.000 × pzabovezmean +0.186 × zpcum1 | 0.717 | 86.15 |
AGB5 | ln(AGB) = 0.623 + 0.207 × zq95 − 0.091 × zsd − 0.029 × pzabovezmean + 0.183 × zpcum1 + 2.609 × CRR | 0.715 | 85.91 |
Model | Training Data | Test Data | ||||
---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE (ton/ha) | MAE (ton/ha) | |
Linear regression | 0.72 | 91.75 | 63.2 | 0.65 | 105.88 | 75 |
Random forest | 0.92 | 41.53 | 25.27 | 0.85 | 60.9 | 39.7 |
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KC, Y.B.; Liu, Q.; Saud, P.; Gaire, D.; Adhikari, H. Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data. Land 2024, 13, 213. https://doi.org/10.3390/land13020213
KC YB, Liu Q, Saud P, Gaire D, Adhikari H. Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data. Land. 2024; 13(2):213. https://doi.org/10.3390/land13020213
Chicago/Turabian StyleKC, Yam Bahadur, Qijing Liu, Pradip Saud, Damodar Gaire, and Hari Adhikari. 2024. "Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data" Land 13, no. 2: 213. https://doi.org/10.3390/land13020213