mapping fuel loads and fire behavior from sentinel in...

Proceedings for the 6th International Fire Behavior and Fuels Conference April 29 – May 3, 2019, Albuquerque, New Mexico USA

Published by the International Association of Wildland Fire, Missoula, Montana, USA

Mapping fuel loads and fire behavior from Sentinel in Durango, NW Mexico.

Favián Flores-Medina, Daniel José Vega Nieva *, Norma Monjarás-Vega, Carlos Iván Briones-Herrera, José Javier Corral-Rivas

Facultad de Ciencias Forestales Universidad Juárez del Estado de Durango, Durango, Mexico *[email protected]; [email protected]; [email protected];

[email protected]; [email protected]; [email protected]

Eric Calleros, Pablito Marcelo López-Serrano Instituto de Silvicultura e Industria de la madera, Universidad Juárez del Estado de Durango, Mexico,

[email protected]; [email protected]

Ernesto Alvarado School of Environmental and Forest Sciences. University of Washington, USA, [email protected]

Armando González-Cabán

Pacific Southwest Research Station, US Department of Agriculture Forest Service, Riverside CA, USA, [email protected]

Diego Pérez-Salicrup

Instituto de Investigaciones en Ecosistemas y Sustentabilidad. Universidad Autónoma de México. Morelia, Mich, Mexico. [email protected]

Enrique Jardel,

Centro Universitario de la Costa Sur. UdeG. Autlán de Navarro, Jal. [email protected]

Introduction

The distribution and quantity of combustible materials have a direct influence on the behavior of the fire and is directly related to the risk of a forest fire (Hardy, 2005) .Information about the characteristics and properties of forest fuels is essential both for practical purposes of forest management and for research on the ecology of fire (Sandberg et al., 2001, Arnaldos Viger et al.,

2004, Morfín Ríos et al., 2012). It is important to quantify and classify forest fuels, since this information is the basis for making maps of priority areas of attention that can direct the work of fire prevention and the proper handling of fuels. Few studies have explored Sentinel images for fuel mapping (Arellano-Pérez et al,. 2018). The present work shows the results in modeling and mapping fuels and fire behavior from Sentinel images at a scale of 1:10 in the pine and oak forests of the Sierra Madre Occidental (SMO) of Durango México. Materials and methods

The sampling of fuels was carried out in plots of 2500 m2 (50 * 50m) belonging to the network of permanent forest and soil research sites. (Corral et al.,2009). This network of permanent sites has been utilized to map aboveground biomass from Landsat images in Durango (López-Serrano et al., 2016a, 2016b), but there are no previous studies attempting to map fuel loads from Sentinel in Mexico. A total of 267 plots were sampled along the Sierra Madre Occidental (SMO), in forests of pine and oak, which are under forest management. The inventoried sites were located in two ecoregions: PSA = Forest of pine (Pinus) of high mountain (temperate-cold to cold) and PTH = Forest of pine (Pinus) montane (temperate warm humid), according to the

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categorization of fuel beds of Jardel et al (2019). 146 and 121 plots were sampled for the PSA and PTH fuel types, respectively. At each plot, dead forest fuels (1h, 10h, 100h, 1000h) were measured under the sampling scheme shown in figure 1 following the technique of planar intersections of Brown et al (1982). For the estimation of accumulated litter, four quadrants of 1 m2 were established at a distance of 15 m from each sampling line (figure 1).

Figure 1. Location of the 267 sites of study (left figure) (Where: PSA = high mountain pine forest (temperate-cold to cold) and PTH = montane pine forest (temperate warm humid), and fuel sampling design (right figure).

Modeling and Mapping of forest fuel variables

A Sentinel-2 satellite mosaic(scenes from the month of May 2017) with a resolution of 10 m was utilized to calculate the Enhanced Vegetation Index (Liu et al., 2015). EVI=(B8-B4) /(5*B8+11.4*B4-11.1*B2) Linear and non linear models were tested to predict litter load + 1h dead fuels, % canopy coverage, canopy bulk density, and canopy base height from the Sentinel-calculated EVI. Predicted fuel characteristics were mapped at 10 m for the area of study. Fire behavior predictions.

Based on the fuel maps, flame length and rate of spread were predicted and mapped at 10 m for the pine and pine-oak forests of the area of study in the SMO. Fire behavior predictions were carried out with Rothermel equation (1972), with a scenario of fuel moisture in dry conditions (3, 4 and 5% moisture for fuels of 1, 10 and 100 h, respectively), and wind speeds at 2m of 5 and 15 km / h. Results

The best fit models to predict the measured litter load + 1h dead fuels, canopy bulk density, canopy base height and % canopy cover from Sentinel EVI are summarized in table 1. Table 1. Models to predict fuel characteristics.

Model R2 RMSE

Litter + W1h = 1.88 + 82.33 * EVI 0.45 2.45 CBD= 0.11*exp (13.49 *EVI) 0.50 0.19 CC = 14.60 * exp (24.56 *EVI) 0.53 13.12 CBH= 1.91 *exp (8.13*EVI) 0.29 1.59

Where: Litter+W1h: load of litter and 1h dead fuels (Mg/ha), CBD: canopy bulk density (kg/m3), CBH: canopy base height (m), CC: % canopy cover.



The 10 m maps of litter loading + 1h, canopy bulk density, predicted flame length, surface rate of spread utilizing Sentinel images is shown in Figures 2 to 7.

Figure 2. Litter load + 1h (Mg / Ha) of the pine and pine-oak forests of the study area in the SMO. Scale: 1:10.

Figure 3. Canopy bulk density (Kg / m3) of the pine and pine-oak forests of the study area in the SMO. Scale: 1:10.

Figure 4. Flame length (m) (wind speed at 2m of 5 km / h) of the pine and pine-oak forests of the study area in the SMO. Scale: 1:10.

Figure 5. Flame length (m) (wind speed at 2m of 15 km / h) of the pine and pine-oak forests of the study area in the SMO. Scale: 1:10.

Figure 6. Surface rate of spread (m / min) (wind speed at 2m of 5 km / h) of the pine and pine-oak forests of the study area in the SMO. Scale: 1:10.

Figure 7. Surface rate of spread (m / min) (wind speed at 2m of 15 km / h) of the pine and pine-oak forests of the study area in the SMO. Scale: 1:10.



Maps for two detail windows of PTH and PSA of leaf litter and 1h fuel load, canopy bulk density, and predicted flame length and surface rate of spread are shown in figure 8 to 12

a) b)

Figure 8. Leaf litter load + 1h (Mg/ Ha) from windows of PTH (a) and PSA (b) Scale: 1:10. Dots represent sampled plots.at each area.

a) b)

Figure 9. Flame length (m) (wind speed at 2m of 5 km / h) from windows of PTH (a) y PSA (b). Scale: 1:10.

a) b)

Figure 10. Flame length (m) (wind speed at 2m of 15 km / h) from windows of PTH (a) and PSA (b). Scale: 1:10.



a) b)

Figure 12. Surface rate of spread (m / min) (wind speed at 2m of 5 km / h) from windows of PTH (a) and PSA (b) scale:10

a) b)

Figure 13. Surface rate of spread (m / min) (wind speed at 2m of 15 km / h) from windows of PTH (a) and PSA (b) Scale: 1:10.

Conclusions

Maps of fuel loads and canopy density were derived based on inventoried plots from Sentinel 2 images of 10 m pixel to support spatially explicit decision-making in the pine and pine-oak forests studied in the Sierra Madre Occidental. Based on these fuel maps, fire behavior predictions were generated for medium and high wind scenarios: flame length, surface rate of spread. These maps allow support for spatially explicit decision making of fire prevention and combat. Based on the expected behavior maps, preventive actions can be taken by making decisions about where physical fuel management treatments are most needed or the application of prescribed burns, based on the expected fire behavior in the current scenario and scenario of fuel management. Future work will focus on the modeling of fuel types and loads in other ecosystems in the SMO and Mexico from Sentinel and other sources of remote sensing information including other satellites and LIDAR.



References

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mapping fuel loads and fire behavior from sentinel in...

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