ArticlePDF Available


The present study sets a methodological framework to combine LiDAR derived data with fire behaviour models in order to assess fire risk at landscape level for forest management and planning. Two forest areas of the Model Forest in Urbión, Soria (Central Spain) were analyzed, covering 992.7 ha and 221.7 ha. The modelling phase was based in 160 field sample plots as ground data, and the LiDAR data had a density of first returns of 2 pulses/m 2 , which were used to construct 13 models for stand variables (e.g. basal area, stem volume, branch biomass). The coefficients of determination ranged from 0.167 for shrub cover, to 0.906 for dominant height. The modelled variables were used for a classification of fuel types compatible with the continuous data. The simulation phase was performed using the spatialized data on FlamMap in order to assess the potential fire behaviour resulting across the whole landscape for four scenarios of moisture and wind conditions. The results showed maps of fire intensity and prob-ability of fire occurrence, based on the simulation of 500 random ignition points, which allowed the anal-ysis of the spatial relation between the initial state and allocation of forest resources and their risk of fire. The methodology proposed, as well as the results of this research are directly applicable for operational forest planning at landscape level. Ó 2012 Elsevier B.V. All rights reserved.
Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR
José-Ramón González-Olabarria1,*, Francisco Rodríguez2, Alfredo Fernández-Landa3,4,
Blas Mola-Yudego5,6
(González-Olabarria, J.R., Rodríguez, F., Fernández-Landa, A., Mola-Yudego, B)
1 Centre Tecnològic Forestal de Catalunya. Ctra St. Llorenc de Munys, Km 2. ES-
25280, Solsona, Spain. Phone: +34-973-481752, Fax: +34-973-481392, e-mail:
2 Cesefor Foundation, Pol. Ind. Las Casas, calle C, Parcela 4, ES-42005 Soria, Spain
3 Agresta Sociedad Cooperativa, C/ Numancia 1, ES-42001 Soria, Spain
4 Universidad Politécnica de Madrid, Ciudad Universitaria, ES-28040 Madrid, Spain
5 University of Eastern Finland, School of Forest Sciences, PO Box 111, FI-80101
Joensuu, Finland
6 Department of Crop Production Ecology, Swedish University of Agricultural Sciences
(SLU), P.O. Box 7016, S-750 07 Uppsala, Sweden.
*Corresponding author
Research highlights
-Fire behaviour and occurrence are estimated with defined scenarios in a forest area
-We present a methodology that combines forest fire simulators and LiDAR data
-It provides 13 models for stand level variables and its spatialization
-A set of rules to classify fuel types compatible with the continuous data is presented
-The methodology is applicable for operational planning purposes at landscape level.
The present study sets a methodological framework to combine LiDAR derived data
with fire behaviour models in order to assess fire risk at landscape level for forest
management and planning. Two forest areas of the Model Forest in Urbión, Soria
(Central Spain) were analysed, covering 992.7 ha and 221.7 ha. The modelling phase
was based in 160 field sample plots as ground data, and the LiDAR data had a density
of first returns of 2 pulses/m2, which were used to construct 13 models for stand
variables (e.g. basal area, stem volume, branch biomass…). The coefficients of
determination ranged from 0.167 for shrub cover, to 0.906 for dominant height. The
modelled variables were used for a classification of fuel types compatible with the
continuous data. The simulation phase was performed using the spatizalized data on
FlamMap in order to assess the potential fire behaviour resulting across the whole
landscape for four scenarios of moisture and wind conditions. The results showed maps
of fire intensity and probability of fire occurrence, based on the simulation of 500
random ignition points, which allowed the analysis of the spatial relation between the
initial state and allocation of forest resources and their risk of fire. The methodology
proposed, as well as the results of this research are directly applicable for operational
forest planning at landscape level.
Keywords: Airborne LiDAR, Forest inventory, Fire Risk Assessment, Mediterranean
Model Forest
1. Introduction
The inclusion of fire risk into the planning of forest management is a recurrent research
topic since the 1980´s (e.g. Van Wagner, 1983; Reed and Errico, 1986). Wildfires have
an obvious effect on the outcomes of forest management through post-fire tree mortality
or value depreciation of surviving trees. At the same time, however, forest management
has the potential of modifying fire behaviour by changing the quantity and spatial
arrangement of forest fuels (Agee and Skinner, 2005; Peterson et al., 2005; Finney et
al., 2007). The way of considering the risk of fire into the process of planning forest
management has evolved from non-spatial approaches where the effect of fire was
defined as deterministic or as a stochastic quantity of timber losses, to the more recent
approaches were fire behaviour and its (spatially explicit) components are being
considered in order to assess the extent of fire induced damage and the influence of fuel
modification on fire behaviour (Bettinger, 2010).
In this sense, assessing adequately the current state of the forest is one of the first steps
required for planning the management of a forest area when the risk of fire is
considered. The assessment requires collecting precise data of the amount and
distribution of desirable resources and at the same time, estimates of the potential threat
that fire means to those resources. However, to estimate the risk of fire over a forest
landscape it must be considered that fire is a spatially explicit event, and varies its
behaviour depending on site-specific fuel conditions and the spatial arrangement of
different fuels (Finney, 2001). This spatial dimension, that entails information about the
state of the forest and the potential behaviour of fire across the landscape, it is required
in order to choose the most effective fuel treatments, in terms of type and allocation,
that would reduce the negative impact of fire on the forest (Agee et al., 2000; Finney et
al., 2007).
However, mapping fuel and forest stocking characteristics at a broad spatial scale is
often not feasible based on direct field measurements. But during the last decades, the
use of diverse types of remote sensors has become popular to acquire information about
the continuous distribution of fuel (Chuvieco and Congalton, 1989, Arroyo et al., 2008)
and forest resources at landscape level (Kirby and Hall, 1980; Boyd and Danson.,
2005), and among them, Light Detection and Ranging (LiDAR) has gained special
relevance. Airborne LiDAR can provide accurate information about the forest structure
in three dimensions over large areas (Andersen et al., 2005), and this information can be
used in the assessment of forest resources (Nelson et al., 1988; Næsset, 2002; 2004;
Holmgren et al., 2003; Hollaus et al., 2004; Hall et al., 2005; Maltamo et al., 2005) as
well as for modelling and mapping forest fuels. In fact, recent studies have
demonstrated the usefulness of LiDAR derived data e.g.: modelling surface fuels (Riaño
et al., 2007; Mutlu et al., 2008a), modelling canopy related variables to predict crown
fire activity (Riaño et al., 2003, 2004; Andersen et al., 2005; Morsdorf et al., 2004), or
both (Skowroski et al., 2007; Mutlu et al., 2008b).
A potential application of the fuel maps generated from LiDAR data would be their use
as inputs for fire simulation models, which can simulate the potential fire behaviour
across a large area. The combination of LiDAR derived information with these type of
simulation models, essentially FARSITE (Finney, 1998, 2004), has been referred to be a
significant improvement for planning fuel management operations (Riaño et al., 2003,
2004; Morsdorf et al., 2004; Andersen et al., 2005; Skowroski et al., 2007; Mutlu et al.,
2008a), even when a explicit application to estimate fire behaviour parameters on a
landscape level has only seldom been taken (Mutlu et al., 2008b). Additionally, the
potential value of combining spatially continuous inventory of forest resources and
information on fire behaviour, both derived from LiDAR data, has not been sufficiently
explored in order to integrate the spatial component of fire in the context of landscape-
level planning of forest management (Bettinger, 2009; Kim, 2009, Gonzalez-Olabarria
and Pukkala, 2011).
In this context, the present study aims at providing a methodology that implements
LiDAR derived information, forest resources inventory, understory and canopy fuel
modelling, and fire behaviour simulation models in order to assess fire risk at landscape
level using spatially continuous information for forest management purposes in the
region of Soria (Central Spain).
2. Material and Methods
2.1. Description of the area of study
Two forest areas located within the Model Forest of Urbión in Soria (Central Spain)
were selected for the analysis, corresponding to the public utility forest numbers
MUP89 and MUP76, covering 992.7 ha and 221.7 ha, respectively. The Model Forest
selected is the most extensive forest mass of the Iberian Peninsula, with a forested area
of over 100 000 ha, and it combines diverse forest types and mountain pastures,
enclosed within the administrative borders of 35 municipalities (Segur, 2009). The
forest lands of Urbión are included in both the Mediterranean and Ibero-American
Commented [m1]: “as being” deja la frase sin sentido, no? mira
a ver
Model Forest Regions in the International Model Forest Network (Besseau et al., 2002).
The forest management of the two studied areas has being oriented towards the
production of a sustainable flow of certified timber, and the revenues and work derived
from the forest are shared by a large proportion of the local community.
In both cases, the forest lands are mainly dominated by pure stands of Pinus Nigra and
P. Pinaster, with a limited presence of Juniperus sp. The areas are located around 1000
- 1200 meters above sea level, and are generally influenced by Mediterranean climate,
although the high altitude and continentality of the region results in colder and longer
winters than most of Spain. As in most of the Mediterranean forests, wildfires are
considered an important risk for the production of timber, and should therefore be
considered in the forest management planning. For example, the studied area MUP89
was affected by a fire in 1970.
2.2. Description field data
In the studied area, 160 squared field sample plots of 500 m2, systematically distributed
in a square grid of approximately 250 x 250 m, were set as ground data, in order to
develop models for converting LiDAR data into forest information. Measurements of
dbh (diameter at breast height), height and canopy base height were recorded from
every tree in the field plots. Measurements of tree and shrub cover, and an estimation of
the mean shrub height were recorded at plot level. Finally, additional plot level
variables were calculated from the measurements using existing models: timber volume
using the models of Rodriguez et al. (2012); and biomass distribution in leaves,
branches, stem and roots, using the allometric functions of Ruiz-Peinado et al. (2011)
(Table 1).
2.3. Description data from LiDAR
The LiDAR data were provided by Stereocarto S.L. The data was based in 20 linear
flights of a Cesnna 402-C airplane, equipped with an ALS60 II LiDAR sensor. The
LiDAR system provided a density of first returns of 2 pulses / m2, and an overall
quantity of 4 height bins per first return. The LiDAR raw data was treated with the
FUSION system (McGaughey and Carson, 2003), from the Remote Sensing
Applications Center (USDA) in order to obtain structured statistical information about
the laser returns. A predefined height of 2 meters was used as a threshold to model
mature trees and understory vegetation. The LiDAR data at was aggregated in data sub-
sets using a square lattice of 500 m2 , matching the reference ground data (field sample
plots), for computing metrics at that spatial scale. From the pulses returned from the
aboveground vegetation (>2 meters from the ground), the metrics of the LiDAR pulses
generated by FUSION, and afterwards used as predictors in the models, corresponded to
different percentiles (1st, 5th, 10th , 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th,
95th, 99th) height and intensity of the pulses, the mean height and intensity values of
pulses, the interquartile range of the intensity values, and an estimation of the forest
canopy cover derived from the ratio between the number of first returns above 2 meters
and the total number of first returns. Additionally, an approximation of the shrub cover
was defined as the ratio between the number of first returns between 0.3 and 2 meters
and the number of first returns bellow 2 meters.
2.4 Integral methodology applied
The methodology applied aimed to provide a broad range of information about the state
of the forest at landscape level, from conventional data of forest resources to fire
behaviour and fire risk estimates, covering the whole spatial continuum of the studied
areas. The working methods consisted in the following steps (Fig. 1): 1) Acquisition of
LiDAR based data through flights over the study areas; 2) Implementation of field
inventories to be used as ground data and transformation of the raw LiDAR data into
plot size metrics (defined by 500 m2) using the FUSION software; 3) Combination of
the LiDAR based metrics and the ground data in order to develop models to predict
stand variables (stocking, fuel types, canopy characteristics…), that at the same time are
inputs for a classification of fuel types; 4) Spatializing the resulting information across
the whole landscape; 5) Implementation of the map layers in the FlamMap fire
behaviour model (Finney, 2006), including e.g.: topography, canopy cover, bulk
density, canopy base height, stand height and the defined fuel types.
2.3. Modelling stand variables
The data derived from the LiDAR measurements and the on field measured data was
used to construct models for predicting stand variables. The dependent variables
considered were: stand density (N), basal area (G), total volume (V), average quadratic
diameter (Dq), dominant height (H0), Lorey’s height (hL), height to the first branch
(hB), leaves biomass (FB), stem biomass (SB), branch biomass (BB), root biomass
(RB), canopy cover of the undercover vegetation (BC), and height of undercover
vegetation (hB). The variables used as predictors were constructed based on the
calculated LiDAR pulses’ metrics generated using the FUSION system.
The criteria used for constructing the models aimed at a high predictive power, based on
unbiased predictions and avoiding variable redundancy or higher co-linearity. Each
independent variable included in the models had to be significant at the 0.05 level. A
maximum of three variables was allowed for each model in order to avoid excessive
complexity. Different combinations of the variables as well as different model
alternatives were tested systematically, and the best performing model was finally
selected. The selected relationships between ground-based characteristics of the sample
plots (dependent variables) and laser-derived metrics were defined using nonlinear
regression analysis. Finally, an n-way cross-validation of each equation was performed
and the RMSE and the model efficiency (MEF, equivalent to the R2 of the fitting phase)
were calculated from the residuals.
2.4. Fuel type definition
Besides the stand variables, we proceeded to develop a relationship between the stand
characteristics and the standard Northern Forest Fire Laboratory (NFFL) fuel types
(Anderson, 1982). The fuel types were defined based on: an interpretation of the
description of the NFFL fuel types, a comparison of potential stand level variables and
the NFFL fuel types recorded in 431 plots dominated by P. nigra and P. sylvestris,
extracted from the 3rd Spanish National Forest Inventory for the same area (DGCN,
2006). The variables selected for the classification were based on the previously
constructed models derived from LiDAR data. The process of relating the stand
variables and the fuel types was undertaken by using the Chi-squares Automatic
Interaction (CHAID) (Kass 1980), decision tree technique in SPSS 19. The CHAID
technique is able to select the splitting criterions of the predictors (stand level variables)
for better classifying in between multiple dependent responses (fuel types). The best
suited splitting criteria for classifying a forest land regarding its fuel type were
transformed into a set of classification rules for an easy application into the whole area
In order to validate the process and to assess the predictive capability, the same set of
rules were applied to the 431 plots used for defining the classification criteria, and the
predicted fuel types were compared to the ones observed in field.
2.5. Generating fire scenarios in FlamMap
The FlamMap fire mapping and analysis system (Finney, 2006) is a computer program
that integrates a FARSITE defined landscape (it requires layers for: elevation, slope,
aspect, fuel model, canopy cover, canopy base height, stand height and canopy bulk
density), a defined weather scenario (wind direction and wind speed) and fuel moisture
conditions. It is then able to compute fire behaviour characteristics for all the points in
the landscape, including flame length, fireline intensity and crown fire activity (Fig. 2).
FlamMap, has not a temporal component, and does not simulate the growth and
evolution of individual fires, although it is able to simulate a number of random fires in
order to estimate the probability of a point in the landscape to get burned. This last
characteristic of FlamMap, together with the relatively small time computational
requirements, makes the system appropriate for planning forest management under the
risk of fire (Gonzalez-Olabarria and Pukkala, 2011).
The layers defining the FARSITE landscapes required to operate FlamMap were
obtained from LiDAR data, by applying the constructed models for predicting the stand
level variables or by using the predicted stand variables for selecting the accompanying
NFFL fuel models. For the required weather and fuel moisture information, we
considered four scenarios: the combination of two alternative fuel moistures and two
alternative wind speeds, based on our experience in the area. The fuel moistures defined
were: 1) normal expected conditions in Soria during typical summer midday and 2)
conditions expected in dry Mediterranean conditions. The wind speeds defined were: 1)
mild wind (16 km h-1) and 2) a strong wind (32 km h-1). The wind was considered to be
constant over the whole area, direction south-west (210º).
Finally, once the models were developed, the next step was to spatialize the predictions
across the area, using the LiDAR available, in order to map and visualize variables that
represent the state of the forest across the whole landscape.
3. Results
The modelling of the stand level variables resulted in 13 models, with coefficients of
determination from 0.167 for BC, to 0.906 for Ho (Table 2). The RMSE obtained in the
cross-validation phase was on average 1.05 times higher, ranging from 1.02 to 1.30
times, than those obtained in the fitting phase. The coefficients of determination
obtained in the fitting phase were on average 1.07 times higher, than those obtained in
the fitting phase. All parameter estimates included in the models were significant. No
significant biases were observed when examined as a function of the predicted variable
and predictors of the model.
The rules defined for classifying the forest into their corresponding fuel types (Table 3),
relied on cutting values of stand variables such as shrub and forest cover, the shrub and
tree height (HL), and the canopy biomass (FB + BB). The rules were found to classify
correctly 39% of fuels of the 431 NFI plots. However, when the fuel types were
grouped according their fire behaviour characteristics in pasture driven fuels (1, 2),
shrub driven fuels (5, 6, 7), and tree driven fuels (8, 9), the level of agreement between
the predicted and observed fuel type groups raised to 64.2% (Table 4).
The models allowed the spatialization of the stand level variables and the pre-defined
fuel types. In order to simplify the presentation of the results (Fig. 3), the maps
represent predictions for the whole area of the MUP89 (992.7 ha).
The potential fire behaviour was assessed by running FlamMap for the four scenarios
(alternative moisture and wind conditions) in the MUP89 forest. The predictions
resulting from the application of FlamMap are independent on the simulation of a single
fire event, and therefore are defined by the characteristics of each of the stands (map
cells of 500 m2), which allows to map fire related variables across the whole landscape
(Fig. 4) and evaluate the share of landscape surface belonging to a defined “fire
behaviour class” (Table 4).
In addition, FlamMap was also used to calculate the probability of fire occurrence,
obtained through the simulation of a number of fire events which served as a basis to
evaluate how many times a fire can potentially occur in a stand. We applied 500
ignitions randomly distributed across the landscape to identify areas more prone to be
affected by fire (Fig. 4). Finally, the results of the process are summarized in Table 4.
4. Discussion
This study presents a methodology that combines the acquisition of LiDAR data and
fire behaviour simulators in order to provide geo-referenced and spatially continuous
information of forest resources and potential fire behaviour. The type of information
provided through this methodology can be a valid tool for forest management purposes,
since, presented in maps, such information is a basis for the analysis of the spatial
relation between the initial state and allocation of forest resources, in the one hand, and
the risk that fire entails for those resources, in the other. Additionally, the availability of
information about the state of the forest, instead of merely parameters that influence fire
behaviour, brings the possibility of integrating the management of forest fuels within
the timber management process. This is an important advantage for planning fire
prevention measures over longer periods of time, especially if they are included into
forest management planning at landscape-level (González-Olabarria and Pukkala,
For the implementation of the methodology, the simulator FlamMap was chosen to
study the fire behaviour, as it presents some interesting characteristics: it provides fire
hazard information that can be easily applied to the studied landscape, it can estimate
fire occurrence probabilities for all the forest stands based on multiple fire ignitions and,
at the same time, the extent of the fires caused by those ignitions, and, finally, the
outputs provided are sound indicators of potential fire damage on trees (e.g. fire
intensity, flame length, crown fire activity). These indicators can be used in post-fire
tree mortality models which, combined with the initial state of the forest, provide
estimates of tree mortality and timber losses (Peterson and Ryan, 1986; Hely and
Bergeron, 2003; Rigollot, 2004; Fernandes et al., 2008), as has already been done for
planning forest management (Bettinger, 2009; Kim et al., 2009). The use of FlamMap
for forest planning purposes has the additional advantage (when compared to other
simulators e.g. FARSITE), that delivers an overall idea of the risk of fire over the whole
landscape under different climatic conditions, instead of simulating the growth of
specific fire events. This is especially important when there is limited historic
information on recurrent fires, or when there is no information available about the
conditions leading into their occurrence and impact.
Concerning the assessment of the initial state of the forest, the data retrieval used for the
models was based on tree and stand level variables derived from LiDAR-based
inventory (Falkowski et al., 2010). The results of this study can therefore be used for
short-term operational planning and are a promising stepping stone for improving forest
planning systems that optimize forest management at landscape level. However, when
the information is to be applied for medium to long term forest planning (i.e. tactical
and strategic forest planning) it would require to predict the evolution of both forest
resources and fire related variables, subject to the proposed forest management
operations (Gonzalez and Pukkala, 2011). This is an aspect not considered in the study,
as in order to estimate the evolution of the forest resources over a planning period, it
would be necessary to apply tree growth models adapted to the local conditions, in
terms of composition of species and site characteristics, being at the same time adapted
for responding to the management strategies to be considered in the planning process.
On the other hand, the assessment of the fire related variables would require models that
relate the future evolution of the forest to the dynamics of its associated understory
vegetation (Coll et al., 2010) and dead fuel build-up (Reinhardt and Crookston, 2003).
Finally, specific limitations of the study are the relatively small area used and the
absence of data available on the fire regime of the area, which entails the impossibility
of predicting the probability of fire occurrence based on a strong analysis of the
probabilities of fire ignition. Another aspect to be consider are the lower coefficients of
determination resulting from the models predicting understory characteristics (shrub
cover and shrub height). This was an expected result, possibly due to the interception of
a large number of laser pulses by the upper tree canopy. However, the derived models
provided satisfactory cross-validation results, and the modelled effects of the forest
canopy cover on the predicted variables are consistent with the role that light
interception by trees plays in controlling the structure of the shrub strata in previous
studies (Coll et al., 2010). Nevertheless, due to the importance that shrub strata play in
defining fuel types, additional efforts are required in future research, in order to better
model the understory shrub structure,
Concerning the application of the methodology in other zones, it must be stressed that
the area studied was characterized by the dominance of carefully managed even-aged
forest, which is not considered to be the most hazardous regarding the risk of fire
(Gonzalez et al., 2006, 2007). Therefore, future studies can be oriented to validate the
methodology in landscapes dominated by other types of forest, with special focus in
uneven-aged forest structures, in order to identify potential weaknesses, and to propose
modifications to the methodology, when required.
Jose-Ramon González-Olabarria thanks the Ramon y Cajal program of the Spanish
Ministry for Economy for financial support and EFIMED, Cesefor and the
Mediterranean Model Forest Network for granting the additional support required for
visiting and working on the Model forest of Urbión. This study is conducted within the
CLAVE project “Parametrización y cartografía de especies vegetales en espacios
naturales de alto valor ecológico aplicando tecnologías de teledetección” co-funded by
Commented [m2]: mira a ver ahora
the Ministry for Economy and the private enterprises Agresta S. Coop. and Stereocarto
S.L. The authors greatly appreciate the technical and field support of David Lasala
Sanchez and the technicians of the Environment Department from Castilla y Leon.
Agee, J.K., Skinner, C.N., 2005. Basic principles of fuel reduction treatments. For.
Ecol. Manage. 211, 8396.
Andersen, H.E., McGaughey, R. J., Reutebuch, S, E., 2005. Estimating forest canopy
fuel parameters using LIDAR data. Rem. Sens. Environ. 94, 441-449.
Anderson, H.E., 1982. Aids to determining fuel models for estimating fire behavior.
General Technical Report INT-122, United States Department of Agriculture, Forest
Service, Intermountain Forest and Range Experiment Station, Ogden, UT. 26 p.
Andrews, P.L., 1986. BEHAVE: Fire behavior prediction and fuel modeling system.
Burn subsystem. Part 1. Rep. No. GTR INT-194. USDA Forest Service,
Intermountain Forest and Range Experiment Station, Ogden, UT.
Arroyo, L., Pascual, C., Manzanera. J., 2008. Fire models and methods to map fuel
types: The role of remote sensing. For. Ecol. Manage. 256, 1239-1252.
Besseau, P., Dansou, K., Johnson, F., 2002. The International Model Forest Network
(IMFN): Elements of Success. Forest. Chron. 78(5): 648-654.
Bettinger, P., 2009. A prototype method for integrating spatially-referenced wildfires
into a tactical forest planning model. Res. J. Forest. 3, 822.
Bettinger, P., 2010. An overview of methods for incorporating wildfires into forest
planning models. Math. Comput. For. Nat. Res. Sci. 2, 4352.
Boyd, D.S., Danson, F.M., 2005. Satellite remote sensing of forest resources: three
decades of research development. Prog. Phys. Geo. 29, 1 1-26.
Coll, L., González-Olabarria, J.R., Mola-Yudego, B., Pukkala, T., Messier, C., 2011.
Predicting understory maximum shrubs cover using altitude and overstory basal area
in different Mediterranean forest. Eur. J. For. Res. 130, 55-65.
DGCN. 2006. Tercer Inventario Forestal Nacional (20022004) Castilla y León: Soria,
Ministerio de Medio Ambiente, Madrid.
Falkowski, M.J., Hudak, A. T., Crookston, N.L., Gessler, P.E., Uebler, E.H., Smith,
A.M.S., 2010. Landscape-scale parameterization of a tree-level forest growth model:
a k-nearest neighbor imputation approach incorporating LiDAR data. Can. J. For.
Res. 40, 184-199.
Fernandes, P.M., Vega, J.A., Jiménez, E., Rigolot, E. 2008. Fire resistance of European
pines. For. Ecol. Manage. 256, 246-255.
Finney, M.A., 1998. FARSITE: Fire Area Simulator Model development and
evaluation. Research Paper RMRS-RP-4. USDA Forest Service, Rocky Mountain
Research Station, Ogden, UT, p. 47.
Finney, M. A., 2006. An overview of FlamMap fire modeling capabilities. In: Fuels
managementhow to measure success: conference proceedings. 2006 March 28-30;
Portland, Oregon. Proceedings RMRS-P-41. Fort Collins, CO: U.S. Department of
Agriculture, Forest Service, Rocky Mountain Research Station: 213-220.
Finney, M.A., Seli, R.C., McHugh, C.W., Ager, A.A., Bahro, B., Agee, J.K., 2007.
Simulation of long-term landscape-level fuel treatment effects on large wildfires. Int.
J. Wildland Fire. 16, 712727.
González, J.R., Palahí, M., Trasobares, A. and Pukkala, T. 2006. A fire probability
model for forest stands in Catalonia (north-east Spain). Ann. For. Sci. 63, 169-176.
González, J.R., Trasobares, A., Palahí, M., Pukkala, T., 2007. Predicting stand damage
and tree survival in burned forests in Catalonia (North-East Spain). Ann. For. Sci.
64, 733-742.
González-Olabarria, J.R., Pukkala, T., 2011. Integrating fire risk considerations in
landscape level forest planning. For. Ecol. Manage. 261, 278-297.
Hall, S.A., Burke, I.C., Box, D.O., Kaufmann. M.R., Stoker. J.M., 2005. Estimating
stand structure using discrete-return LiDAR: an example from low density, fire prone
ponderosa pine forests. For. Ecol. Manage. 208, 189-209.
Hély, C., Flannigan, M., Bergeron, Y., 2003. Modeling tree mortality following wildfire
in the southeastern Canadian mixed-wood boreal forest, For. Sci. 49, 566576.
Hollaus, M., Wagner, W., Maier, B., Schadauer, K., 2007. Airborne laser scanning of
forest stem volume in a mountainous environment. Sensors. 7, 1559-1577.
Holmgren, J., Nilsson, M., Olsson, H., 2003. Estimation of tree height and stem volume
on plots using airborne laser scanning. For. Sci. 49(3), 419-428.
Kass , G. V., 1980. An exploratory technique for investigating large quantities of
categorical data . Appl. Stat. 29 , 119 127.
Kim, Y.H., Bettinger, P., Finney, M., 2009.Spatial optimization of the pattern of fuel
management activities and subsequent effects on simulated wildfires .Eur. J. Oper.
Res. 197, 253265.
Kirby, C.L., Hall R.J., (Compilers). 1980. Practical applications of remote sensing to
timber inventory. Proceedings of a workshop held September 26-28, 1979 in
Edmonton, Alberta 169p
Maltamo, M., Packalen, P., Yu, X., Eerikainen, K., Hyyppa, J., Pitkanen, J., 2005.
Indentifying and quantifying structural characteristics of heterogeneous boreal
forests using laser scanner data. For. Ecol. Manage. 216, 41-50.
McGaughey, R. J., Carson, W.W., 2003. Fusing LIDAR data, photographs, and other
data using 2D and 3D visualization techniques. Proceedings of Terrain Data:
Applications and VisualizationMaking the Connection, October 2830, 2003 (pp.
Mutlu, M., Popescu, C.S., Stripling, C., Spencer, T., 2008a. Mapping surface fuel
models using LiDAR and multispectral data fusion for fire behavior. Rem. Sens.
Environ. 112 (1), 274285.
Mutlu, M., Popescu, S.C., Zhao, K., 2008b. Sensitivity analysis of fire behavior
modeling with LIDAR-derived surface fuel maps. For. Ecol. Manage. 256 (3), 289-
Næsset, E., 2002. Predicting forest stand characteristics with airborne laser using a
practical two-stage procedure and field data. Rem. Sens. Environ. 80, 88-99.
Næsset, E., 2004. Practical large-scale forest stand inventory using a small-footprint
airborne scanning laser. Scand. J. For.Res. 19, 164-179.
Nelson, R.F., Krabill, W., Tonelli, J., 1988. Estimating forest biomass and volume
using airborne laser data. Rem. Sens. Environ. 24 (2), 247267.
Peterson, D.L., Johnson, M.C., Agee, J.K., Jain, T.B., McKenzie, D., Reinhardt, E.D.,
2005. Forest structure and fire hazard in dry forests of the Western United States.
Gen. Tech. Rep. PNW-GTR-628. U.S. Department of Agriculture, Forest Service,
Pacific Northwest Research Station, Portland, OR.
Peterson, D.L., Ryan, K.C., 1986. Modeling postfire conifer mortality for long-range
planning. Environ. Manage. 10, 797808.
Reed, W.J., Errico, D., 1986. Optimal harvest scheduling at the forest level in the
presence of the risk of fire. Can. J. For. Res. 16, 266 - 278.
Reinhardt E, Crookston NL (2003) The Fire and Fuels Extension to the Forest
Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR- 116. USDA Forest Service,
Rocky Mountain Research Station, Ogden, UT, 209 p
Riaño, D., Meier, E., Allgöwer, B., Chuvieco, E., Ustin, S.L., 2003. Modeling airborne
laser scanning data for the spatial generation of critical forest parameters in fire
behavior modeling. Rem. Sens. Environ. 86, 177186.
Riaño, D., Chuvieco, E., Condes, S., González-Matesanz, J., Ustin, S.L., 2004.
Generation of crown bulk density for Pinus sylvestris L. from LiDAR. Rem. Sens.
Environ. 92, 345352.
Riaño, D., Chuvieco, E., Ustin, S.L., Salas, F.J., Rodríguez-Perez, J.R., Ribeiro, L.M.,
Viegas, D.X., Moreno, J.F., Fernández, H., 2007. Estimation of shrub height for fuel-
type mapping combining airborne LiDAR and simultaneous color infrared ortho
imaging. Int. J. Wildland Fire. 16, 341348.
Rigolot, E., 2004. Predicting postfire mortality of Pinus halepensis Mill. and Pinus
pinea L. Plant Ecol. 171, 139151.
Rodríguez, F. Lizarralde, I. Broto, M. Bravo, F. 2012. Merchantable volume equations
for the main species in the Spanish plateau. Manuscript.
Ruiz-Peinado, R., del Rio, M., Montero, G., 2011. New models for estimating the
carbon sink capacity of Spanish softwood species. For. Systems. 20 (1), 176-188.
Segur, M., 2009. Los bosques modelo y el modelo de Urbión. Montes. 98, 96-99.
Skowronski, N., Clark, K., Nelson, R., Hom, J., Patterson, M., 2007. Remotely sensed
measurements of forest structure and fuel loads in the Pinelands of New Jersey. Rem.
Sens. Environ. 108, 123129.
Van Wagner, C.E., 1983. Simulating the effect of forest fire on long term annual timber
supply. Can. J. For. Res. 13, 451-457.
Wagner, W., 1993. Prediction of crown fire behavior in two stands of jack pine. Can. J.
Forest Res. 23, 442449.
Table 1. Summary of the field plots used as ground data (N=160)
Parameter (unit)
N (stems ha-1)
G (m2 ha-1)
Dg (cm)
Ho (m)
hL (m)
CBH (m)
V (m3 ha-1)
FB (t ha-1)
SB(t ha-1)
BB(t ha-1)
RB(t ha-1)
BC (%)
hB (m)
N: stem number; G: basal area; Dg: quadratic mean diameter; Ho: dominant height; hL: Lorey’s mean
height; CBH: Crown base height; V: stem volume; FB: Foliar biomass; SB: Stem biomass; BB: Branch
biomass; RB: Root biomass; BC: Shrub Cover; hB: Mean Shrub Height;
Table2. Models, predictors, and models assessment for the variables at stand level.
Predictive model
N: number of stems; G: basal area; Dq: quadratic mean diameter; Ho: dominant height; hL: Lorey’s mean
height; CBH: Crown base height; V: stem volume; FB: Foliar biomass; SB: Stem biomass; BB: Branch
biomass; RB: Root biomass; BC: Shrub Cover; hB : Mean Shrub Height; LFCC: LiDAR Forest Canopy
Cover; LBC: LiDAR Shrub Cover; LH_20. LH_30. LH_40. LH_90. and LH_95: corresponding to the
20th, 30th, 40th, 90th and 95th percentile height of the canopy pulses respectively (in meters); LH_Mean:
arithmetic mean height of canopy pulses (in meters); LI_05. LI_10. LI_25. LI_95: corresponding to the
5th, 10th, 25th, and 95th percentile intensities of the canopy pulses; LI_Mean: arithmetic mean intensity of
canopy pulses; LI_IQ= interquartile intensity range of canopy pulses ;
Commented [MSOFFICE3]: Alguno de estos no es el MEF que
esta en el texto???
Table 3. Description of standard Anderson´s (1982) fuel models. and the stand
variables used to allocate them; FCC: Forest Canopy Cover; BCC: Shrub Cover; hL:
Lorey Height; hB: Mean Shrub Height; W: Crown biomass (FB + BB).
Fuel Model
Typical Fuel Complex
Canopy and/or Shrub
cover (%)
Heigh (m)
Grass and grass-dominated
Short grass
Timber (grass and
33<(FCC+BC) < 50
hB< 0.4
Tall grass
Chaparral and shrub fields
FCC >66
BC > 33
FCC > 25
BC >30
(FCC+BC) > 50
hB< 0.4
Dormant brush. hardwood
FCC < 25
BC >30
(FCC+BC) > 50
hB< 0.4
Southern rough
FCC > 25
BC > 30
hB> 0.4
Timber litter
Closed timber litter
FCC >50
BC < 30
W > 45
Hardwood litter
FCC >50
BC < 30
W < 45
Timber (litter and
Light logging slash
Medium logging slash
Heavy logging slash
Table 4. Predicted (based on the rules defined in Table 3),and observed fuel types for
431 plots of the national forest inventory.
Fuel type predicted
Fuel Matching
Fuel group
matching (%)
Fuel type observed in field
Table 5. Share of the MUP89 forest landscape (in ha) within a fire behaviour class.
Total area: 992.7 ha.
(km / h)
Crown fire activity
No fire
Surface Fire
Passive Crown Fire
Active Crown Fire
Heat per unit of area (KJ/m2)
<= 25000
25001 - 50000
50001 - 100000
> 100000
Burn probabilities
0.0001 - 0.0049
0.0050 - 0.0099
> 0.010
Flame length (m)
< 0.5
0.5 - 1
1.1 - 2
> 2
Figures’ Captions
Figure 1. Flowchart of the methods followed during the study
Figure 2: Input layers required to run FlamMap: a) elevation b) slope c) aspect d) fuel
model e) canopy cover f) canopy base height g) canopy bulk density and selected output
layers obtained from FlamMap: h) crown fire activity i) Flame length j) heat/Fireline
intensity k) burn probabilities
Figure 3. Mapping of some of the LiDAR derived variables in the MUP89 forest. From
left to right and top to bottom: a) Tree canopy cover; b) Stand dominant height (Ho); c)
Basal area; d) Marketable timber volume; e) Tree crown bulk density and f) NFFL Fuel
Figure 4: Example of fire behaviour variables obtained through FlamMap simulation
for the “extreme scenario” (dry fuel moisture and wind speed of 32Km/h). A: Flame
length in meters; B: Fire type (0 equals to no fire. 1 equals to surface fire. 2 equals to
passive crown fire. and 3 equals to active crown fire); C: Probabilities of fire
... At the same time, various vegetation indices have been used based on reflectance levels in Near-infrared (NIR) and shortwave infrared (SWIR) to measure fuel availability, such as the Normalized Difference Infrared Index (NDII) or the Vegetation Condition Index (VCI) (Chuvieco, 1999;Vallejo-Villalta et al., 2019); indices such as the normalized difference vegetation index (NDVI) that assesses vegetative density, vegetation susceptibility and changes in photosynthetic activity (Chéret & Denux, 2013;Wang et al., 2022), and other satellite products offered by the moderate resolution imaging spectroradiometer (MODIS) on board of Terra satellite (Probert et al., 2019). On smaller spatial scales, the use of LIDAR images has also been used to determine the density of vegetation covers (González-Olabarria et al., 2012;Hillman et al., 2021;Novo et al., 2020a). ...
... The Analytic Hierarchy Process (AHP) is highlighted among them and is usually combined with Geographic Information Systems (GIS) (Gheshlaghi et al., 2019). Several studies also used satellite imagery and GIS (Kanga et al., 2014), fuzzy approaches (Erdin and Çağlar., 2014), artificial neural networks (Goldarag et al., 2016) and LIDAR data (González-Olabarria et al., 2012). The methodology presented in this work, is an integrated approach for fire risk assessment and management planning in peri-urban areas that are prone to forest fires, through machine learning techniques, geoinformatics and field observations. ...
O período entre 2018 e 2022 mostrou-nos que o problema dos incêndios à escala global não está a diminuir, antes pelo contrário. Parece que as consequências das alterações climáticas já estão a afectar a ocorrência de incêndios florestais em várias partes do Mundo, de uma forma que só esperaríamos que acontecesse vários anos mais tarde. Em muitos países do Sul da Europa, bem como em algumas regiões dos EUA, Canadá e Austrália, onde estamos habituados a enfrentar a presença de incêndios muito grandes e devastadores, continuamos a ter eventos que quebram recordes. Alguns países, como os da Europa Central e do Norte, que não estavam habituados a ter grandes incêndios, experimentaram-nos durante estes anos. Os anos anteriores foram muito exigentes para todo o Mundo, também noutros aspectos que nos afectaram a todos. Referimo-nos às restrições impostas pela pandemia que limitaram as nossas reuniões e viagens, afectando em muitos casos a saúde dos membros da Comunidade Científica Wildfire. Felizmente, conseguimos encontrar novas formas de comunicação, ultrapassar essas limitações e manter-nos em contacto uns com os outros. Durante semanas e meses, para muitos de nós, as reuniões pessoais e o trabalho de grupo foram substituídos por ligações em linha. Apesar da economia de dinheiro e tempo, e da facilidade de reunir uma grande variedade de pessoas que estas reuniões desde que nos apercebêssemos de que não substituem as reuniões presenciais, que trazem consigo outras dimensões inestimáveis, que fazem parte da comunicação pessoal e ajudam a construir uma comunidade científica.
... Remote sensing methods previously applied to fuel 85 type mapping include a wide range of techniques and input data, from medium (Palaiologou et al., 2013;Alonso-Benito et al., 2013;Marino et al., 2016;Aragoneses and Chuvieco, 2021) to high spatial resolutions (Arroyo et al., 2006;Mallinis et al., 2008). Both passive (Alonso-Benito et al., 2013;Aragoneses and Chuvieco, 2021) and active (Riaño et al., 2003;González-Olabarria et al., 2012) sensors have been used, as well as a combination of sensors (Mutlu et al., 2008;García et al., 2011;Palaiologou et al., 2013;Marino et al., 2016). ...
Full-text available
Accurate and spatially explicit information on forest fuels becomes essential to designing an integrated fire risk management strategy, as fuel characteristics are critical for fire danger estimation, fire propagation and emissions modelling, among other aspects. This paper presents the conceptual development of a new fuel classification system that can be adapted to different spatial scales and used for different purposes. The resulting fuel classification system encompasses a total of 85 fuel types, that can be grouped into six main fuel categories (forest, shrubland, grassland, cropland, wet and peat/semi-peat land and urban), plus a nonfuel category. For the forest cover, fuel types include two vertical strata, overstory and understory, to account for both surface and crown fires. Based on this classification system, a European fuel map at 1 km resolution, was developed within the framework of the FirEUrisk project, which aims to create a European integrated strategy for fire danger assessment, reduction, and adaptation. Fuels were mapped using land cover and biogeographic datasets, as well as bioclimatic modelling, in a Geographic Information System environment. The first assessment of this map was performed by comparing it to high-resolution data, including LUCAS (Land Use and Coverage Area frame Survey) data, Google Earth images, Google Street View images, and the GlobeLand30 map. This validation exercise provided an overall accuracy of 88 % for the main fuel types, and 81 % for all mapped fuel types. Finally, to facilitate the use of this fuel dataset in fire behaviour modelling, a first assignment of fuel parameters to each fuel type was performed by developing a crosswalk to the standard fuel models defined by Scott and Burgan (FBFM, Fire Behavior Fuel Models), considering European climate diversity.
... Por otro lado, para la caracterización de los combustibles de las copas, los trabajos de inventario de campo se centrarán en las principales masas arboladas de Andalucía, incluyendo las formaciones más representativas con especies de quercíneas, pinos y eucaliptos. Aunque existen estudios previos que aportan ecuaciones de estimación de los combustibles de las copas a partir de LiDAR aéreo, la mayoría se refieren a trabajos en coníferas no autóctonas o para formaciones arboladas peninsulares que no son del entorno mediterráneo (ANDERSEN, 2005;GONZALEZ-FERREIRO et al. 2014, 2017GONZÁLEZ-OLABARRIA et al. 2012). Algunos trabajos recientes aportan ecuaciones para especies de frondosas mediterráneas, pero están ajustados para inventarios realizados en zonas relativamente pequeñas, lo que limita su extrapolación a escala regional por la mayor variabilidad estructural de las masas arboladas sobre las que se deben aplicar (BOTEQUIM et al., 2019, FERRER PALOMINO y RODRIGUEZ Y SILVA, 2021). ...
Conference Paper
Full-text available
Las cartografías de alta resolución de los combustibles forestales se han convertido en una información espacial imprescindible para la simulación de incendios, herramienta básica tanto para la gestión preventiva como para la extinción. La generación y actualización de estas cartografías a escala regional supone un importante reto para las administraciones públicas al requerir no solo información detallada de la vegetación existente en todo el territorio en relación a su estructura y comportamiento potencial frente al fuego, sino a la necesidad de disponer de mecanismos ágiles de actualización de estos complejos de combustible. En el marco del proyecto CILIFO se está desarrollando una mejora de la caracterización de los combustibles superficiales y arbóreos presentes en la comunidad autónoma de Andalucía. Este trabajo incorpora tanto la generación de cartografías de calidad de modelos de combustible (clasificación UCO40 y Rothermel) y de combustibles de copas (altura de base de copa, carga disponible y densidad aparente) basadas en datos LIDAR del PNOA y datos de campo, como una herramienta para su revisión, actualización e integración en la Red de Información Ambiental de Andalucía (REDIAM) a través del “Módulo Seguimiento” de la plataforma SIPNA (Sistema de Información del Patrimonio Natural de Andalucía).
... Existe abundante literatura científica sobre 2/11 trabajos previos que estiman las principales variables descriptivas de los combustibles de copas a escala de rodal a partir de datos LiDAR aéreo. En general, las variables más estudiadas y con más modelos disponibles son la altura de la base de copa (CBH) y la carga de combustible disponible (CFL), con numerosos estudios en masas de distintas especies de pinos (GONZÁLEZ-OLABARRIA et al., 2012;GONZÁLEZ-FERREIRO et al., 2014HEVIA et al., 2016;BOTEQUIM et al., 2019;RUIZ et al., 2021). También existen referencias a modelos ajustados para la densidad aparente de copas (CBD) en pinares, pero estos estudios son mucho más escasos (RIAÑO et al., 2004;GONZÁLEZ-FERREIRO et al., 2014;HEVIA et al., 2016;BOTEQUIM et al., 2019). ...
Conference Paper
Full-text available
La caracterización de la cantidad y distribución espacial de la biomasa aérea en masas arboladas es clave para evaluar el riesgo de incendio y su severidad potencial, así como para planificar actuaciones preventivas orientadas a reducir su impacto. La tecnología LiDAR permite generar cartografía de variables descriptivas de la estructura de los combustibles de copas en grandes superficies. Sin embargo, para obtener estimaciones fiables es necesario disponer de modelos adaptados a las particularidades de cada especie, calibrándolos adecuadamente con datos de campo. Este estudio utiliza información LiDAR de la segunda cobertura del PNOA (1 p/m2) para la modelización de la altura de la base de las copas (CBH), la carga de combustible disponible (CFL) y la densidad aparente de combustible de copas (CBD) en parcelas de Pinus sylvestris L. Se exploran distintas formulaciones, encontrando mejores ajustes para la variable CBH, seguida de CFL, siendo CBD la más difícil de estimar. Los modelos se emplearon para generar cartografía de alta resolución de los combustibles de copas en los pinares de Valsaín (Segovia), input requerido para simulación de incendios, comparando los resultados con modelos obtenidos a partir de datos LiDAR de la primera cobertura del PNOA (0,5 p/m2) en la misma zona.
... La teledetección ha sido ampliamente utilizada para estimar estas variables de combustible de la superficie y del dosel (González-Olabarria et al. 2012;González-Ferreiro et al. 2014). La combinación de la información LiDAR con las imágenes multiespectrales proporciona una poderosa herramienta para clasificar bosques con cobertura completa (Zhu et al. 2017). ...
Conference Paper
Full-text available
föco es un gemelo digital que utiliza Inteligencia Artificial y datos remotos para detectar zonas de riesgo de incendios en la interfaz urbano-forestal. A partir de mosaicos estacionales sin nubes de datos satelitales (Sentinel-2) y de datos capturados con dron (ortofoto y datos LiDAR), se ofrece una solución tecnológica sencilla, actualizada, rápida y de amplia cobertura. föco analiza toda la información remota existente de la siguiente manera: (i) Primero procesa el LIDAR de dron con una resolución espacial de 50cm, (ii) después realiza una segmentación basada en el modelo de altura de vegetación y en la ortofoto del dron, (iii) a continuación, calcula los estadísticos zonales de todas las variables remotas para cada segmento, (iv) después, construye la base de datos, y entrena algoritmos basados en Inteligencia Artificial al sistema, a partir de verdad terreno adquirida in situ, para finalmente (v) aplicar dichos algoritmos y crear un mapa de vegetación a nivel segmento. Una vez creados esos mapas, sobre cada segmento se aplican las normas vigentes de actuaciones en fajas contra incendios en el interfaz urbano forestal, basadas en el tipo de vegetación, su altura y su cobertura. Estas normas acostumbran a estar definidas en cada región. El resultado final se intersecta con la información catastral, de tal forma que cada propietario forestal puede recibir indicaciones de las actuaciones que debe realizar para incrementar su seguridad frente a incendios forestales.
... Most papers address only a specific place (such as a natural park) in a single country [14]- [18]. Nevertheless, 15 studies (7%), such as [19] and [20], compared experiences/results within more than one MCR. ...
Conference Paper
Full-text available
Wildfires are becoming an increasing concern worldwide, causing substantial social, economic, and environmental disruptions. This situation is especially relevant in Mediterranean-climate regions, present in all the five continents of the world, in which fire is not only a natural component of the environment but also perhaps one of the most important evolutionary forces. The rise in wildfire occurrences and their associated impacts suggests the need for identifying knowledge gaps and enhancing the basis of scientific evidence on how managers and policymakers may act effectively to address them. Considering that the main goal of a systematic map is to collate and catalog a body of evidence to describe the state of knowledge for a specific topic, it is a suitable approach to be used for this purpose. In this context, the aim of this study is to systematically map the research trends in wildfire management practices in Mediterranean-climate regions. A total of 201 wildfire management studies were analyzed and systematically mapped in terms of their: Year of publication; Place of study; Scientific outlet; Research area (Web of Science) or Research field (Scopus); Wildfire phase; Central research topic; Main objective of the study; Research methods; and Main conclusions or contributions. The results indicate that there is an increasing number of studies being developed on the topic (most from the last 10 years), but more than half of them are conducted in few Mediterranean countries (60% of the analyzed studies were conducted in Spain, Portugal, Greece, Italy or France), and more than 50% are focused on pre-fire issues, such as prevention and fuel management. In contrast, only 12% of the studies focused on "Economic modeling" or "Human factors and issues," which suggests that the triple bottom line of the sustainability argument (social, environmental, and economic) is not being fully addressed by fire management research. More than one-fourth of the studies had their objective related to testing new approaches in fire or forest management, suggesting that new knowledge is being produced on the field. Nevertheless, the results indicate that most studies (about 84%) employed quantitative research methods, and only 3% of the studies used research methods that tackled social issues or addressed expert and practitioner's knowledge. Perhaps this lack of multidisciplinary studies is one of the factors hindering more progress from being made in terms of reducing wildfire occurrences and their impacts.
Full-text available
Canopy fuel characterization is highly relevant for wildfire prevention, especially in the context of extreme events involving crown fires. Airborne laser scanning has been proven very useful to retrieve 3D forest structure at large scales, becoming freely available in many countries in the recent years which provide an opportunity to map fuel parameters that are critical for fire behaviour simulation. Previous studies on canopy fuel modelling predict canopy base height (CBH), fuel load (CFL) and bulk density (CBD) mainly in tree species from temperate conifer forests, with specific models still lacking for the main Mediterranean forest stands and especially deciduous species. This work presents first results of models obtained from low density airborne LiDAR data (1.5 p/m2) for canopy fuel characterization of critical structural variables (CBH, CFL and CBD) in the main Mediterranean forest stands existing in Andalusia region, including Pinus sp, Quercus sp and Eucalyptus sp tree species. The study is part of an ongoing project that include field inventory in 750 plots to characterize canopy fuels in 15 different forest stands representative of Mediterranean tree species, with a samplig design that consider structural heterogeneity in a wide study area (29000 km2). Different modelling techniques (linear regression and random foresst) were tested to assess the best formulation and input LiDAR metrics to be included in the models for each fuel parameter estimation, that will be used to generate high resolution maps of canopy fuels at regional scale. Preliminary models obtained from a set of 170 field plots in pure stands of Quercus ilex, Quercus suber, Pinus halepensis, Pinus pinea and Eucaliptus sp show promising results for canopy fuel characterization from low density airborne LiDAR data in these widely distributed species. However, our results also highlight a significant effect of the different modelling tecniques on the input metrics and accuracy of the models.
Full-text available
This study aims to estimate the susceptibility of fire occurrence in the Qaradagh area of the Iraqi Kurdistan Region, by examining 16 predictive factors. We selected these predictive factors, dependent on analyzing and performing a comprehensive review of about 57 papers related to fire susceptibility. These papers investigate areas with similar environmental conditions to the arid environments as our study area. The 16 factors affecting the fire occurrence are Normalized Difference Vegetation Index (NDVI), slope gradient, slope aspect, elevation, Topographic Wetness Index (TWI), Topographic Position Index (TPI), distance to roads, distance to rivers, distance to villages, distance to farmland, geology, wind speed, relative humidity, annual temperature, annual precipitation, and Land Use and Land Cover (LULC). To extract fires that occurred between 2015 and 2020, 121 scenes of satellite images (most of them are scenes of Sentinel-2) were used, with the aid of a field survey. In total, 80% of the data (185,394 pixels) were used for the training dataset in the model, and 20% of the data (46,348 pixels) were used for the validation dataset. Conversely, 20% of these data were used for the training dataset in the model, and 80% of the data were used for the validation dataset to check the model’s overfitting. We used the logistic regression model to analyze the multi-data sites obtained from the 16 predictive factors, to predict the forest and vegetated lands that suffer from fire. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the accuracy of the proposed models. The AUC value is more than 84.85% in all groups, which shows very high accuracy for both the model and the factors selected for preparing fire zoning maps in the studied area. According to the factor weight results, classes of LULC and wind speed gained the highest weight among all groups. This paper emphasizes that the used approach is useful for monitoring shrubland, grassland, and cropland fires in other similar areas, which are located in the Mediterranean climate zone. Besides, the model can be applied in other regions, taking the local influencing factors into consideration, which contribute to forest fire mitigation and prevention planning. Hence, the mentioned results can be applied to primary warning, fire suppression resource planning, and allocation work. The mentioned results can be used as prior warnings of the outbreak of fires, taking the necessary measures and methods to prevent and extinguish fires.
Full-text available
Se presenta el concepto de bosque modelo, todavía reciente en su aplicación en España y en el área mediterránea, que busca avanzar en la sostenibilidad de los espacios forestales mediante la práctica del diálogo y la acción concertada. Para ello, se presentan a) las características del concepto y sus conexiones con los principales acuerdos internacionales en materia de desarrollo sostenible, b) las redes de bosques modelo, tanto la global como las regionales de interés para los gestores españoles (iberoamericana y mediterránea), y c) la aplicación local española existente en la actualidad, el Monte Modelo Urbión en Castilla y León.
Technical Report
Full-text available
Fire, in conjunction with landforms and climate, shapes the structure and function of forests throughout the Western United States, where millions of acres of forest lands contain accumulations of flammable fuel that are much higher than historical conditions owing to various forms of fire exclusion. The Healthy Forests Restoration Act mandates that public land managers assertively address this situation through active management of fuel and vegetation. This document synthesizes the relevant scientific knowledge that can assist fuel-treatment projects on national forests and other public lands and contribute to National Environmental Policy Act (NEPA) analyses and other assessments. It is intended to support science-based decisionmaking for fuel management in dry forests of the Western United States at the scale of forest stands (about 1 to 200 acres). It highlights ecological principles that need to be considered when managing forest fuel and vegetation for specific conditions related to forest structure and fire hazard. It also provides quantitative and qualitative guidelines for planning and implementing fuel treatments through various silvicultural prescriptions and surface fuel treatments. Effective fuel treatments in forest stands with high fuel accumulations will typically require thinning to increase canopy base height, reduce canopy bulk density, reduce canopy continuity, and require a substantial reduction in surface fuel through prescribed fire or mechanical treatment or both. Long-term maintenance of desired fuel loadings and consideration of broader landscape patterns may improve the effectiveness of fuel treatments.
Full-text available
Three decades have passed since the launch of the first international satellite sensor programme designed for monitoring Earth's resources. Over this period, forest resources have come under increasing pressure, thus their management and use should be underpinned by information on their properties at a number of levels. This paper provides a comprehensive review of how satellite remote sensing has been used in forest resource assessment since the launch of the first Earth resources satellite sensor (ERTS) in 1972. The use of remote sensing in forest resource assessment provides three levels of information; namely (1) the spatial extent of forest cover, which can be used to assess the spatial dynamics of forest cover; (2) forest type and (3) biophysical and biochemical properties of forests. The assessment of forest information over time enables the comprehensive monitoring of forest resources. This paper provides a comprehensive review of how satellite remote sensing has been used to date and, building on these experiences, the future potential of satellite remote sensing of forest resources is highlighted.
The technique set out in the paper, CHAID, is and offshoot of AID (Automatic Interaction Detection) designed for a categorized dependent variable. Some important modifications which are relevant to standard AID include: built-in significance testing with the consequence of using the most significant predictor (rather than the most explanatory), multi-way splits (in contrast to binary) and a new type of predictor which is especially useful in handling missing information.
The International Model Forest Network (IMFN) was announced by Canada at the Rio (UNCED) Summit ten years ago to pilot outside of Canada a promising participatory field-level approach to sustainable forest management then being developed in Canada through its national model forest network. The IMFN has since grown from three sites in two countries (outside of Canada) in 1994 to 19 sites in 11 countries, in addition to numerous additional sites proposed and at early stages of development. Now with the successor event to Rio, the World Summit on Sustainable Development, about to take place in Johannesburg, South Africa, there is an appropriate milestone at which to pause and consider its evolution and growth. Among the elements of success attributed to the growth of the network the authors consider the nature of the approach itself as being an innovative re-formulation of widely shared management values, its flexibility across borders and ecosystems, and the support provided in its development by the Canadian Model Forest Network and other domestic and international partners. The article looks at similarities and differences between the international and Canadian applications of this approach and describes some of the lessons learned and difficulties met in applying the approach internationally.
The technique set out in the paper, CHAID, is and offshoot of AID (Automatic Interaction Detection) designed for a categorized dependent variable. Some important modifications which are relevant to standard AID include: built-in significance testing with the consequence of using the most significant predictor (rather than the most explanatory), multi-way splits (in contrast to binary) and a new type of predictor which is especially useful in handling missing information.
The Fire and Fuels Extension (FFE) to the Forest Veg- etation Simulator (FVS) has been developed to assess the risk, behavior, and impact of fire in forest ecosys- tems. This extension to the widely-used stand-dynam- ics model FVS simulates the dynamics of snags and surface fuels as they are affected by stand management (of trees or fuels), live tree growth and mortality, and fires. It offers all the standard silvicultural options available in FVS as well as various fuel treatments and prescribed burns. In addition to the standard output provided by FVS, the FFE produces indicators of snag size and species distributions, fuel loading, and poten- tial fire behavior and effects. The extension can also simulate the effects of fires on various stand compo- nents, using user-provided weather conditions and model-predicted fuel loading. An overview of the model, including some sample results, is provided here.
A set of dynamic equations can be constructed that describe the evolution of a forest under the impact of harvesting and random fire. When fire is treated in a deterministic fashion, these equations can be used to formulate an optimal harvest scheduling problem that can be solved using linear programming. Examples using white spruce Picea glauca data for the Fort Nelson Timber Supply Area of British Columbia show that even modest rates of fire can have a dramatic impact and that present harvest scheduling models may be considerably overestimating projected forest harvest levels. -from Authors
A model is described that incorporates the effects of forest fire on long-term equilibrium timber supply. Its form is a computer simulation that burns and harvests specified proportions of a hypothetical forest with a given yield curve of volume over age. The primary result is the extent to which the equilibrium maximum sustainable annual harvest is depresssed by fire. This depression is always greater than the volume killed on the burned area. On the other hand, when the annual area cut is somewhat below the optimum level, the volume of harvest is relatively insensitive to the amount of fire. The results imply that the real impact of fire in managed forests is properly judged by the effect of the harvest, not from data on area burned and volume killed.