Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA
Abstract
:1. Introduction
1.1. Spatial Inputs to Fire Behavior Models
1.2. Lidar to Measure Forest Structure
1.3. Error Influence on Model Results
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition
2.2.1. Field Sampling
2.2.2. Lidar Data
2.3. Fire Behavior Model Inputs
2.4. Fire Behavior Model Simulations
2.4.1. Monte Carlo Simulations
2.4.2. Evaluation of Error Impact on Model Result
3. Results
3.1. Canopy Height and Canopy Base Height
3.2. Fire Behavior Model Results
3.3. Relationship Between Significant Difference in CBP and Model Input Variables
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Spatial Layer | Development Method |
---|---|
Topography (Elevation, Slope, Aspect) | Created directly from interpolated Lidar last return, and resampled from 1 m DEM |
Canopy Cover | Directly calculated with Lidar |
Canopy Height | Regression between field plot data and Lidar metrics |
Canopy Base Height | Regression between field plot data and Lidar metrics |
Canopy Bulk Density | Fire and Fuels Extension in Forest Vegetation Simulator, then regression with Lidar metrics |
Fuel Model | Regression between plot-measured surface fuels and Lidar metrics |
Weather | Remote Automated Weather Station (RAWS) |
Scott and Burgan (2005) Fuel Model | Description of Stands with Fuel Model Assigned | % of Study Area |
---|---|---|
SH3 (143) | Basal Area < 50 m2 ha−1, Canopy Cover < 40%, moderate fuel load dominated by shrubs and forest litter | 26 |
TU2 (162) | Basal Area 60 m2 ha−1, Canopy Cover > 30%, moderate fuel load dominated by shrubs and forest litter | 1 |
TU5 (165) | Basal Area 20–80 m2 ha−1, Canopy Cover > 40%, high fuel load dominated by shrubs and forest litter | 29 |
TL (189) | Basal Area 40–80 m2 ha−1, Canopy Cover > 30%, moderate to low site productivity | 52 |
SB2 (202) | Basal Area > 40 m2 ha−1, Canopy Cover > 40%, high site productivity, moderate fuel load with coarse fuels present | 10 |
Weather Input | Specific Parameter/Value | ||||
---|---|---|---|---|---|
Speed (km/h) | Direction (Degrees az.) | Relative Frequency | |||
Wind | 19 | 225 | 0.75 | ||
20 | 45 | 0.10 | |||
19 | 180 | 0.05 | |||
Fuel Type | 1 h | 10 h | 100 h | Live Herbaceous | Live Woody |
Moisture Content (%) | 2 | 3 | 5 | 30 | 60 |
Forest Variables | Mean (SD) | Minimum-Maximum | Regression Equation | R2 (RMSE) |
---|---|---|---|---|
Canopy Height (m) | 23.3 (10.1) | 0–51.9 | 4.73 − 0.82 × L25th + 1.88 × Lmean | 0.81 (4.12 m) |
Crown base height (m) | 3.7 (2.4) | 0–12.7 | 0.17 + 0.25 × L10th + 0.30 × L25th | 0.51 (1.62 m) |
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Kelly, M.; Su, Y.; Di Tommaso, S.; Fry, D.L.; Collins, B.M.; Stephens, S.L.; Guo, Q. Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA. Remote Sens. 2018, 10, 10. https://doi.org/10.3390/rs10010010
Kelly M, Su Y, Di Tommaso S, Fry DL, Collins BM, Stephens SL, Guo Q. Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA. Remote Sensing. 2018; 10(1):10. https://doi.org/10.3390/rs10010010
Chicago/Turabian StyleKelly, Maggi, Yanjun Su, Stefania Di Tommaso, Danny L. Fry, Brandon M. Collins, Scott L. Stephens, and Qinghua Guo. 2018. "Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA" Remote Sensing 10, no. 1: 10. https://doi.org/10.3390/rs10010010