Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing
<p>Our study area is a managed temperate forest dominated by Scots pine, read oak and European beech. Located at 51.7° N, 7.2° E (red dot) in densely populated North Rhine-Westphalia, Germany, it is surrounded by agriculture and industry, serving as a local recreation area. The map displays 215 surface fuel sampling locations, of which 30 were used to additionally sample canopy fuel characteristics. Wind properties were assessed at a nearby weather station.</p> "> Figure 2
<p>Wind direction and speed aggregated from hourly data in close proximity to the study area during months June, July, and August and from 2000 to 2021. The dominant wind direction was identified as southwest (240° N). Average wind speed is 2.1 m/s, whereas a maximum speed of 10.7 m/s was recorded.</p> "> Figure 3
<p>Wind direction (WD; (<b>A</b>)) in degrees north and wind speed (WS; (<b>B</b>)) in meters per second down-scaled and adjusted to the study area’s terrain using WindNinja software. Constant input values were assessed in an exploratory analysis of weather station data. WD was fixed at 240° N, WS at 10 m/s.</p> "> Figure 4
<p>Integrated fire hazard (FH) classification scheme proposed by the Interagency Fuels Treatment Decision Support System (IFTDSS) [<a href="#B53-fire-05-00029" class="html-bibr">53</a>]. It categorizes continuous fire behavior variables (CFL and CBP) to present a single intuitive metric. The highest FH is only reached in places where both CFL and CBP are very high.</p> "> Figure 5
<p>Boxplot of sampled surface fuel parameters for each fuel model. Dead and live fuel loadings are measured in [kg/m<sup>2</sup>], height of the fuelbed is in [m]. Beech and red oak produce slightly more 1 and 100 h fuels, while more live herbaceous biomass is found in pine stands. Higher-growing live fuels cause pine’s fuelbed depth to exceed that of the others.</p> "> Figure 6
<p>Surface fuel model prediction (<b>A</b>) and respective area of applicability (AOA; (<b>B</b>)). Spatial patterns originating from forest management are clearly visible. Pine is the most abundant species in the study area (51%) followed by beech (32%) and red oak (17%). Burnable pixels falling outside the AOA sum up to less than 1%.</p> "> Figure 7
<p>Boxplot of canopy fuel parameters from field sampling for each fuel model. Parameters include crown base height (CBH; (<b>A</b>)), canopy height (CH; (<b>B</b>)), diameter at breast height (DBH; (<b>C</b>)), and crown bulk density (CBD; (<b>D</b>)). CBD was calculated via FuelCalc using the other measures as inputs. Beech stands sampled in this study area are mostly young with low, yet dense canopy layers. Contrarily, red oak stands are old with open crowns and low CBH, resulting in low CBD. Pine stands are in between the others, with higher CBH leading to higher CBD than red oak.</p> "> Figure 8
<p>Spatial prediction of crown bulk density (CBD; (<b>A</b>)) and the respective area of applicability (AOA; (<b>B</b>)). Values strongly depend on the dominant tree species. Anomalies within stands are related to differences in forest structure. Pixels falling outside the AOA (20%) occur especially on slopes and are attributable to the low number of training samples for the predictive model.</p> "> Figure 9
<p>Landscape fire behavior outputs including flame length (FL; (<b>A</b>)) and rate of spread (ROS; (<b>B</b>)) for scenarios S1, 3, 5, and 7 (rows 1, 2, 3, and 4). Fire behavior depends more strongly on wind speed than on dead fuel moisture. Even if wind speed is low and dead fuel moisture is high, individual locations in pine stands and on steep slopes show FL > 6 m and ROS > 8 m/min. NB = Non-Burnable.</p> "> Figure 10
<p>Conditional flame length (CFL; column (<b>A</b>)) and conditional burn probability (CBP; column (<b>B</b>)) from MTT fire spread simulations for scenarios S1, 3, 5, and 7 (rows 1, 2, 3, and 4). CFL is presented as the mid-point of 20 FL classes weighted by probabilities. CBP was scaled by its maximum (0.013) and classified into five equal bins. Large continuous areas dominated by pine show the highest probability of burning if strong winds are present. NB = Non-Burnable.</p> "> Figure 11
<p>Integrated fire hazard (FH) as a product of conditional flame length (CFL) and conditional burn probability (CBP) for scenarios S1, 3, 5, and 7 (<b>A</b>–<b>D</b>). High and highest hazard throughout the study area can only be expected for strong winds and low fuel moisture. Higher fuel moisture significantly reduces high FH and limits it to large homogeneous pine stands. FH is medium to low when wind speed is low, while large pine stands and slopes bear more hazard than other areas. NB = Non-Burnable.</p> ">
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