Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Field Data
3.1.1. Surface Fuels
3.1.2. Canopy Fuels
3.2. Remote Sensing Data
3.2.1. Sentinel-1 and -2
3.2.2. LiDAR
3.3. Wind
3.4. Fuels Prediction
3.4.1. Surface Fuels
3.4.2. Crown Bulk Density
3.5. Fire Behavior and Hazard Modeling
4. Results
4.1. Surface Fuels
4.2. Crown Bulk Density
4.3. Fire Behavior and Hazard
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Name | Unit | Reference | n |
---|---|---|---|---|
LiDAR | ||||
height | ||||
canopy height model | m | [27,33] | 1 | |
crown base height | m | [31] | 1 | |
maximum height | m | [27] | 1 | |
mean height | m | [27] | 1 | |
height standard deviation | m | [27] | 1 | |
height coefficient of variation | m | [27] | 1 | |
height inter-quartile range | m | [27] | 1 | |
height skewness | - | [27] | 1 | |
height kurtosis | - | [27] | 1 | |
height entropy | - | [27] | 1 | |
height percentiles | m | [27] | 18 | |
cumulative height percentiles | m | [27] | 9 | |
mean height grass, shrubs, trees | m | [32] | 3 | |
vertical tree-shrub height gap | m | [32] | 1 | |
percent of returns above | % | [27] | 1 | |
percent of returns above 2 m | % | [27] | 1 | |
cover | ||||
C | vegetation cover | % | [34] | 1 |
percent ground returns | % | [27] | 1 | |
cumulative vertical profile | % | [37] | 21 | |
cover of grass, shrubs, trees | % | [35] | 3 | |
density | ||||
Rumple index | - | [36] | 1 | |
N | total number of returns | - | [27] | 1 |
D | density 1st returns in canopy | % | [29] | 1 |
terrain | ||||
elevation | m | [27] | 1 | |
terrain slope | ° | [28] | 1 | |
terrain aspect | ° | [28] | 1 | |
Sentinel-1 | ||||
VV polarization t. c. | dB | [24] | 3 | |
VH polarization t. c. | dB | [24] | 3 | |
VV/VH ratio t. c. | - | [24] | 3 | |
Sentinel-2 | ||||
ultra blue band t. c. | SR | 3 | ||
blue band t. c. | SR | 3 | ||
green band t. c. | SR | 3 | ||
red band t. c. | SR | 3 | ||
red edge 1 band t. c. | SR | 3 | ||
red edge 2 band t. c. | SR | 3 | ||
red edge 3 band t. c. | SR | 3 | ||
NIR 1 band t. c. | SR | 3 | ||
SWIR 1 band t. c. | SR | 3 | ||
SWIR 3 band t. c. | SR | 3 | ||
SWIR 4 band t. c. | SR | 3 | ||
vegetation index t. c. | - | [38] | 3 |
S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | |
---|---|---|---|---|---|---|---|---|
FMS | D1L1 | D1L1 | D1L1 | D1L1 | D3L1 | D3L1 | D3L1 | D3L1 |
Wind speed [m/s] | 10 | 10 | 2 | 2 | 10 | 10 | 2 | 2 |
Air temp. [°C] | 35 | 25 | 35 | 25 | 35 | 25 | 35 | 25 |
Fuel Loadings [kg/m2] | ||||||
---|---|---|---|---|---|---|
Species | 1-h | 10 h | 100 h | Live Herb | Live Shrub | Fuelbed Depth [m] |
Beech | 1.60 | 0.62 | 0.23 | 0.00 | 0.10 | 0.47 |
Red Oak | 1.41 | 0.62 | 0.40 | 0.06 | 0.42 | 0.52 |
Pine | 1.17 | 0.58 | 0.20 | 0.20 | 0.43 | 1.06 |
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Heisig, J.; Olson, E.; Pebesma, E. Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing. Fire 2022, 5, 29. https://doi.org/10.3390/fire5010029
Heisig J, Olson E, Pebesma E. Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing. Fire. 2022; 5(1):29. https://doi.org/10.3390/fire5010029
Chicago/Turabian StyleHeisig, Johannes, Edward Olson, and Edzer Pebesma. 2022. "Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing" Fire 5, no. 1: 29. https://doi.org/10.3390/fire5010029