Spatial Modeling of Fire Occurrence Probability in a Protected Area in Western Mexico
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DOI: 10.5152/forestist.2021.21008
Forestist 2021: XX(XX): 1-12 Original Article
Spatial Modeling of Fire Occurrence Probability in a Protected
Area in Western Mexico
Francisco Javier Sahagún-Sánchez1 , Abril Joaquina Méndez-García2 ,
Francisco Martín Huerta-Martínez3 , Marco Antonio Espinoza-Guzmán4
1
Departamento de Políticas Públicas, Universidad de Guadalajara, CUCEA, Guadalajara, Mexico
2
Licenciatura en Gestión y Economía Ambiental, Universidad de Guadalajara, CUCEA, Guadalajara, Mexico
3
Departamento de Ecología, Universidad de Guadalajara, CUCBA, Guadalajara, Mexico
4
Facultad de Biología, Universidad Veracruzana, Xalapa, Mexico
Abstract
Forest fires can generate economic losses, social vulnerability, and environmental damage. Therefore, more
knowledge about the causes and effects on the territory is needed. The present study aimed to develop a
spatial model to determine the influence of socio-economic and environmental variables on the probability
of fire in a protected area. The study was developed in La Primavera Flora and Fauna Protection Area, located
on the periphery of Guadalajara’s Metropolitan Area in western Mexico. We used remote sensing information
and field data of hot spots of fire from 2012 to 2018 to model forest cover's susceptibility to wildfires based
on a set of socio-economic and landscape variables in the Dinamica EGO software. We built and evaluated a
fire occurrence probability map (AUC = 0.88) and estimated that 20 970.90 ha of forests in the area are prone
to wildfires. Population density, communication routes, and agriculture were the variables with the highest
weight of evidence. The protected area presents different levels of fire occurrence probability due to factors
such as urban growth and land-use cover change. The model allows a spatially explicit visualization of the
sites most susceptible to fires, enhancing the prevention and management of forest fires in the study area.
Keywords: Conservation, management, protected area, urban settlements, weight of evidence, wildfire
Introduction
The surface area of forest affected by wildfires continues to increase globally, with an estimated level
Cite this article as:
of between 10 and 15 million hectares of forest lost every year (Castillo et al., 2003). These events
Sahagún-Sánchez, F. J.,
Méndez-García, A. J., have a socio-economic impact on public health, human losses, damage to infrastructure, and cause
Huerta-Martínez, F. M., & direct economic losses. In addition, it has ecological impacts in terms of air quality, climate, water
Espinoza-Guzmán, M. A. resources, soil, vegetation, landscape, biodiversity, and the provision of other ecosystem services
(2021). Spatial modeling of (González-Mathiesen et al., 2019; La Rosa et al., 2018; Sathaye et al., 2013).
fire occurrence probability
in a protected area in Fire is considered a type of disturbance that can occur on different temporal and spatial scales (Pickett
Western Mexico. Forestist,
August 27, 2021. DOI: et al., 1989). Like other forms of disturbance, fire plays an essential role in the dynamics and current
10.5152/ configuration, and structure of communities, ecosystems, and the landscape in general (Farfán et al.,
forestist.2021.21008. 2020). Historically, fire has been a part of traditional agricultural management strategies and an effec-
tive mechanism for the expansion of the agricultural frontier (Everson & Everson, 2016). Fires can origi-
Corresponding Author:
Francisco Javier Sahagún-Sánchez nate from different causes (natural and anthropic) that can become a risk situation for ecosystems and
e-mail: people when combined with favorable environmental and meteorological conditions. In recent years,
francisco.sahagun@cucea.udg.mx the impact of fires has been documented in the international arena, among which the fires in the USA,
Received: Brazil, and Australia stand out for their intensity and the damages caused (Kganyago & Shikwambana,
January 30, 2021 2020). In the western USA, the fires registered during 2018 caused around 100 human deaths and cost
Accepted: $24 billion in infrastructure. In 2019, the fires in Brazil were related to the expansion of agricultural activi-
May 12, 2021
Available Online Date: ties. They caused the loss of a large area of forests that affected biodiversity and the ecosystem services
August 27, 2021 provided by the Amazon and generated a conflict with the affected indigenous groups. In the case of
Content of this journal is licensed
under a Creative Commons Attribution- Australia, the 2019 fires were considered the worst since 1939 due to the decrease in rainfall and the
NonCommercial 4.0 International
Licence. impact of anthropic activities (Kganyago & Shikwambana, 2020; Lizundia et al., 2020; Whiteside, 2020).
1
Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
In Mexico, an average of 8000 forest fires are registered per preservation and development of different species of wild
year (CONAFOR, 2013), most of them occur throughout spring, flora and fauna. In these PA, the inhabitants can use natural
when the driest atmosphere condition prevails and last until resources, observing the regulations established in the Ley
the beginning of the rainy season when the vegetation is stimu- General del Equilibrio Ecológico y la Protección al Ambiente
lated and begins its period of growth and development (Pérez- (LGEEPA) (SEMARNAT, 2021). The LPFFPA is part of the biologi-
Verdín et al., 2013). cal corridor that crosses the Jalisco state (Velasco, 2010). The
frequency and intensity of forest fires constitute one of the
Anthropogenic activities have altered fire regimes (Farfán et al., leading causes of degradation of the PA. Solely from 1998 to
2018), due to the proximity of forest areas to urban settlements, 2012, the LPFFPA saw 43 846 hectares damaged by forest fires,
where economic activity occurs (Cruz et al., 2017). The accel- generating a forest cover loss rate of 1.31%, which is higher
erated expansion of the agricultural frontier and the growth than the national average rate (Huerta-Martínez & Ibarra-
of both urban infrastructure and highways in combination Montoya, 2014).
with other socio-economic variables, like cattle raising, for-
est logging, tourist activities, land tenure, and land-use alloca- Diverse economic activities are undertaken in the immediate
tion, directly influence wildfires in many parts of Mexico and surroundings of the LPFFPA. Among which highlighted the real-
around the world (Ávila-Flores et al., 2010; Butler 2012; Dennis estate developments constructed near and even within the PA
et al., 2005; Salvati et al., 2014). According to the National Forest polygon, farming activities, and the establishment of industrial
Commission (CONAFOR, 2013), half of the forest fires occur- parks on the periphery. The preceding has generated land-use
ring in Mexico are caused by urbanization. The other half is the cover change processes, which have led to the transformation
result of agricultural and livestock activities, where the use of of the original vegetation cover (Ibarra-Montoya & Huerta-
fire for controlled burning is maintained as a cultural practice Martínez, 2016).
of agriculture and silvopastoral systems (Suryabhagavan et al.,
2016). On the other hand, forest fires can also be an important There is an increase in the surface dedicated to sugar cane
factor contributing to the establishment and development of production, which requires fire for controlled burning during
some species in the forest vegetation (Vargas & Campos, 2018), harvest, a practice exacerbating the likelihood of forest fires.
with fire forming part of a vital and an essential process for eco- Additionally, there is a significant influx of people into the
logical succession in forest ecosystems, thus maintaining their PA, who conduct recreational activities on its perimeter, thus
internal stability (Castillo et al., 2003). However, the uncontrolled increasing the risk of uncontrolled fires (Huerta-Martínez &
and non-programmed propagation of fire often involves risks to Ibarra-Montoya, 2016).
human life and property in wildland/urban interfaces (González-
Mathiesen et al., 2019). Although various policies and strategies have been formu-
lated in Mexico to safeguard forest ecosystems, fire damage
Fire is a severe threat for protected areas (PA), according to data continues every year (Merino-Pérez & Segura-Warnholtz, 2007;
published by the National Commission for Natural Protected SEMARNAT, 2015). Moreover, the economic and social pres-
Areas (CONANP). At the country level, forest fires are one of the sures on forest resources place management and conserva-
main pressures and threats to natural resources (CONANP, 2018). tion actions at a disadvantage in the face of the continuous
Recently, the Central Region of Mexico and the Trans-Mexican use and over-exploitation of these resources (Merino, 2018).
Volcanic Belt are the most affected, registering 56% of all forest It is necessary for developing new research that helps to esti-
fires nationally and affecting more than five thousand hectares mate the fire risk, given the relatively few studies of this type in
in the PA (CONANP, 2015). Mexico (Farfán et al., 2018; Ibarra-Montoya & Huerta-Martínez,
2016; Merino-Pérez & Segura-Warnholtz, 2007; Zuñiga-
In order to guarantee the environmental protection and restora- Vásquez et al., 2017). Therefore, spatially explicit models rep-
tion of the PA, CONANP developed a fire management strategy resent a viable alternative to obtain useful information on the
that includes detailed actions to prevent and control forest fires causes and trends in processes related to forest fires and allow
(CONANP, 2011). Among these highlighted the social and com- to estimate further damages and losses in regions that are fre-
munity participation in fire control and prevention, the genera- quently affected by wildfires (Aguejdad et al., 2017; Chuvieco
tion of technical and scientific information on fire management, et al., 2014; Farfán et al., 2020). The results may help define for-
the restoration and rehabilitation of ecosystems affected by est management and conservation actions more specifically
fire, and both general and specific research on the formation of (Galván & Magaña, 2020; Suryabhagavan et al., 2016; Zuñiga-
decision-making instruments (CONANP, 2018). Vásquez et al., 2017).
One of the most critical PA in western Mexico is La Primavera The aim of the present study was to develop a spatial model
Flora and Fauna Protection Area (LPFFPA), considered an for estimating forest fire probability in the LPFFPA, evaluating
essential environmental regulator for the Metropolitan Area the influence of socio-economic and environmental variables
of Guadalajara (MAG). According to current environmental to generate a fire occurrence probability map. The result may be
legislation in Mexico, Flora and Fauna Protection Areas are useful for designing fire control and prevention strategies in the
extensions of territory that contain essential habitats for the PA and other regions of interest.
2
Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
Figure 1.
Location of the Study Area La Primavera Flora and Fauna Protection Area (LPFFPA) and Its Influence Area.
Methods lithosol (8%) (FAO/UNESCO, 1976). The region has two types of cli-
mate, temperate subhumid S(w1)(w) and dry subhumid (A)C(w1)
Study area: The LPFFPA is located between the coordinates (w), with most rain in summer and only 5% rain in winter, annual
20°32′41″-20°43′39″ N and 103°27′18″-103°41′08″ W (Figure 1), rainfall ranging from 800 to 1 000 mm, and an average annual tem-
in the municipalities of Zapopan, Tala, and Tlajomulco de perature of 20.5°C (García, 2004). The LPFFPA’s area of influence
Zúñiga in the state of Jalisco, Mexico, federally protected under comprises two hydrological regions, Lerma–Chapala–Santiago
the decree published in the Official Gazette of the Federation in and Ameca, which supply the aquifers of the Atemajac-Tesistán,
March 1980 (Rodríguez et al., 2010). Toluquilla, and Etzatlán-Ahualulco valleys. There are approximately
20 permanent watercourses, 35 springs, and 64 wells in the PA,
most of which produce hot water (CONANP, 2000).
The LPFFPA comprises 30 500 hectares and is the forest
region closest to the MAG, for which it is the primary source
Oak-pine forest is the central vegetation cover found in the
of oxygen and regulates both its temperature and environ-
LPFFPA (CONANP, 2000). Fauna diversity is related to the vegeta-
mental humidity (Rodríguez et al., 2010).
tion type and the PA management program (CONANP, 2000),
registers 200 vertebrate species, which comprise 7 fish spe-
According to the CONANP, the LPFFPA is located at Sierra La cies, 19 amphibian and reptile species, 135 bird species, and
Primavera, which forms a part of the Sierra Madre Occidental and 29 mammal species, some of them are listed in risk categories
the Trans-Mexican Volcanic Belt, at an elevation range of 1 400-2 in the Official Mexican Standard NOM-SEMARNAT-059-2010
200 m. a. s. l. (CONANP, 2000). The PA soil types are regosol (92%) and (SIMEC, 2020).
3Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
Spatial Modeling (5) Distance to urban settlements; (6) Population density;
A spatial model was generated in Dinamica EGO software (CSR/ (7) Elevation and (8) Slope (Figure 2).
UFMG, Brasil) (Soares et al., 2002) to evaluate socio-economic
and biophysical factors’ influence on the probability of fire in the We obtained the layers for the agriculture variables from the
LPFFPA. Dinamica EGO allows model complex processes, identi- land-use and vegetation cover information at scale 1:250
fies variables that promote change, calculates rates of change, 000 generated from the Series VI land-use survey of the National
and generates future scenarios; the software is considered a Institute of Statistics and Geography (INEGI, 2013, INEGI, 2016).
highly flexible tool (Soares-Filho et al., 2009). The layer for deforestation was obtained by calculating the
land-use cover changes in the forests of the region, based on
We used 618 hot spots of fires registered from visible infrared the land-use and vegetation cover layers from INEGI’s Series V
images obtained with a visible infrared imaging radiometer suite and Series VI land-use surveys from 2013 and 2016, using the
(VIIRS). The spatial resolution of the images is 250 m (VIIRS, 2020), TerrSet - Idrisi 18.07 Land Change Modeler software (Clark-Labs,
and field data obtained by Decentralized Public Organization MA, USA) (Clark-Labs, 2015; Eastman, 2017). This tool enabled
Bosque La Primavera from 2012 to 2018 to model the suscepti- a quantitative evaluation and a spatial representation of the
bility of forest cover to fires. VIIRS collects global satellite obser- dynamics of vegetation cover change (Sahagún-Sánchez &
vations from a National Aeronautics and Space Administration Reyes-Hernández, 2018).
satellite platform (Earthdata, 2020). Only the records of hot
spots obtained in the forest cover areas in the study area were In order to minimize the errors related to the affinity between
selected for the model, considering that they correspond to for- similar vegetation cover types (Mas et al., 2004), the layers used
est fires. The hot spots occurring in locations with other land- for the land-use cover change analysis were reclassified. The
use covers, such as agriculture or grassland, were segregated via types of original vegetation cover were added to the following
geographic information system (GIS) because that could cor- classes: (1) Irrigated agriculture; (2) Rain-fed agriculture; (3) Urban
respond to controlled fires for agricultural activities or another settlements; (4) Forest; (5) Water bodies; (6) Grassland and
source of the fire (Zuñiga-Vásquez et al., 2017). (7) Secondary vegetation. The INEGI layers for land-use and veg-
etation cover have been widely used as an official cartographic
The construction of the model considered the influence of the source at a national level, which is further used for reports for
eight causal variables listed below, which have been used as pro- organizations such as the United Nations Food and Agriculture
moters of land-use change in various studies (Ávila-Flores et al., Organization and others (Pérez-Vega et al., 2012). To comple-
2010; Cartus et al., 2014; Farfán et al., 2018; Sahagún-Sánchez ment the information on the change undergone by forest cover
et al., 2011): (1) Distance to rain-fed agriculture; (2) Distance to in the study area, we calculated the annual rate of change for the
irrigated agriculture; (3) Deforestation; (4) Distance to roads; period, based on that proposed by Puyravaud (2003):
Figure 2.
Spatial Representation of the Variables Included in the Fire Occurrence Probability Model for La Primavera Flora and Fauna
Protection Area (LPFFPA). (1) Distance to Rain-Fed Agriculture; (2) Distance to Irrigated Agriculture; (3) Deforestation; (4) Distance
to Roads; (5) Distance to Urban Settlements; (6) Population Density; (7) Digital Elevation Model; and (8) Slope.
4Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
R = [1/(t1-t2)] * 1n (A2/A1) probability of a transition i=≥j is expressed with the following
equation:
where r is the annual percentage of forest cover loss, and A1 and
A2 correspond to corrected estimations for said cover at points
in time t1 and t2, respectively.
P((i j | B C D N ) W e
N 1 W
e
N
where B, C, D … N corresponds to the values for k variables of
The distances to the highway network and urban settlements
influence with a given weight of evidence ( WN+ ) for each pixel
were calculated using a model in the Dinamica EGO software,
and each transition.
which enables the generation of layers with Euclidean distances
(Pijanowski et al., 2002), based on the maps of roads and urban
In the final model, the software calculates the probability of fire
locations obtained from INEGI (2016). The population density
occurrence employing the weights of evidence for the differ-
data were obtained via information from the population and
ent variables of influence used and generates a map of the area
housing census for neighboring locations (INEGI, 2010), through
most likely to be affected by fire in the study area. The map gen-
an inverse distance weighting interpolation procedure (Watson
erated by the model was classified into the following five cate-
& Philip, 1985).
gories corresponding to the fire occurrence probability: (1) Very
low; (2) Low; (3) Medium; (4) High and (5) Very high; using the
The elevation was obtained from the digital elevation model method of natural breaks in the GIS. The surface area for each
(DEM) generated by the shuttle radar topography mission with case was then determined.
a resolution of 30 m (SRTM, 2011), while the percentage slope
was derived from the DEM in the GIS. Finally, the resulting map was evaluated via partial receiver oper-
ating characteristic (ROC) analysis in Dinamica EGO, according to
Likewise, the landscape layers for time 1 and 2 were built, for that proposed by Mas et al. (2013). ROC analysis is a quantitative
which a mosaic was generated using the layers for land-use method widely applied to evaluate the efficiency of the maps’
and vegetation cover of the INEGI V and VI series (INEGI, 2013, probability of occurrence. The construction of the ROC curve is
2016) and the information on the location of the fires on differ- an interactive process applied to the suitability threshold and is
ent dates. classified into two categories in order to obtain a binary model,
in which values for the original model that are below the thresh-
The final cut of the layers included an area of influence of 10 km old indicate a lack of suitability while those equal to or above
from the LPFFPA polygon to compare the fire occurrence proba- the threshold indicate suitability. These values are used to cal-
bility within the PA and outside it in the surroundings. All the layers culate sensitivity and specificity, which are reflected in the area
for the variables used were standardized to the WGS 84 coordi- under curve (AUC). The closer to a value of one in the AUC data,
nate reference system, with a UTM geographic projection for Zone the better the model (Ávila et al., 2014; Benito de Pando & Peñas
13 North in the raster format with a cell size of 30 × 30 m, and de Giles, 2007). The evaluation of the fire occurrence probability
processed in GIS ArcMap 10.3 (ESRI, CA, USA) (ESRI, 2011). map used the generated map, and the dataset included in the
layers with the records of fires detected in the analyzed land-
Statistical Analyses scape (Pontius & Schneider, 2001).
To determine the probability of fire occurrence, the coefficients
for the weights of the evidence in Dinamica EGO were calcu- Results
lated from the information of the defined socio-economic and
landscape variables. Different studies have shown the influ- A fire occurrence probability map was generated for the LPFFPA
ence of the proximal variables on land-use cover change and and its influence area (Figure 3), where the surface areas that
fire occurrence (Almeida et al., 2003; Geist & Lambin, 2002; may be affected by this type of event were spatially explicitly
Sahagún-Sánchez et al., 2011). To calculate the influence of the determined. The variables with a higher weight of evidence
selected variables, the software calculates the weight of evi- correspond to population density, distance to roads and agri-
dence of each variable for each transition, from which a transi- culture, followed by urban settlements, and deforestation, in
tion probability map can then be produced. descending order. Those variables have more influence on the
model result than landscape variables, such as slope or eleva-
The weights of evidence are obtained via a Bayesian method tion. The presence of urban settlements and deforestation, in
for conditional probability, which calculates the influence of a descending order, had the most influence than the variables for
spatial variable on an i=≥j transition with the following equation: landscape, such as gradient and altitude (DEM).
W+ = ln [P(C/D) / P(C/D)] Forest cover was found to have undergone losses accounting
for 454.86 ha. While the surface area occupied by urban settle-
where P(C/D) corresponds to the probability of the occurrence ments has expanded by 1329.30 ha, agricultural land use had
of event C, given a spatial pattern D, while W+ is the correspond- decreased, and the surface area with secondary vegetation and
ing weight of evidence. For a spatial dataset, the spatial post grassland cover had increased.
5Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
Figure 3.
Fire Occurrence Probability Map for La Primavera Flora and Fauna Protection Area (LPFFPA) and Its Influence Area.
The changes between the defined classes show that human and roads increased by 575.17 km in the zone of influence
settlements expanded and mainly replaced coverings with to the protected area during the period considered. The net
irrigated agriculture (227.97 ha) and rain-fed agriculture changes by class show that rain-fed agriculture decreased by
(1 142.73 ha). On the other hand, forest cover has been changed 2123 ha, irrigation agriculture by 265 ha, and forests by 455 ha.
to grassland (162.90 ha), secondary vegetation (195.57 ha), and On the other hand, human settlements increased their surface
rain-fed agriculture (89.01 ha). A large area changed from agri- by 1329 ha, grasslands by 688 ha, and secondary vegetation by
culture from temporary to secondary vegetation (1478.97 ha), 821 ha.
6Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
Table 1.
The Annual Rate of Land-Use/Cover Change in La Primavera Flora and Fauna Protection Area (LPFFPA) and Its Influence Area
from 2013 to 2016
Vegetation Cover Class Land-Use/Cover Area (ha) Surface Gain and Lost (ha) Deforestation Rate
2013 2016 2013–2016 r (% per year)
Rain-fed agriculture 32 588.64 32 323.68 −264.96 −0.27
Irrigated agriculture 67 162.59 65 039.67 −2 122.92 −1.07
Urban settlements 32 627.79 33 957.09 1 329.30 1.33
Forest 41 729.76 41 274.9 −454.86 −0.37
Water bodies 1 083.69 1 087.92 4.23 0.13
Grassland 13 906.08 14 594.4 688.32 1.61
Secondary vegetation 27 711.09 28 531.98 820.89 0.97
Total area 216 809.64 216 809.64
Table 2. The vegetation cover with the highest fire occurrence probabil-
Surface Areas with a Fire Occurrence Probability in La ity is forest, with 24 253.11 ha, followed by secondary vegetation,
Primavera Flora and Fauna Protection Area (LPFFPA) and with 5 713.11 ha. The AUC of the ROC curve for the model was
Its Influence Area 0.88, so it is considered a valid model that can adequately pre-
dict the fire occurrence probability in the studied area (Figure 4).
Surface
Fire Including Surface
Discussion, Conclusion and Recommendations
Occurrence Influence Inside the Percentage of
Probability Area (ha) PA (ha) the PA (%)
Forest fires tend to be recurrent in forest ecosystems. However,
Very high 6931.62 6921.63 22.58
the fire disturbance regime in the PA studied is being altered by
High 1754.82 1 90.47 5.51 anthropic activities. The prevalence of forest fire demands the
Medium 1954.98 1743.21 5.69 developing strategies supported by ever-more precise models
(Aguejdad et al. 2017; Muñoz et al. 2005).
Low 3537.09 2789.55 9.10
Very low 16 108.11 7826.04 25.53 The fire occurrence probability map generated was found to be
robust in terms of its predictive capacity (AUC = 0.88). The results
Grassland cover presented the highest annual rate of land-use suggest that the variables related to anthropogenic activities
cover change (1.61), followed by urban settlements (1.33), while significantly influence the prevalence of forest fire in the region,
rain-fed agriculture, which underwent a −1.07 change rate, is
the land-use type that lost the most surface area during the
period evaluated (Table 1).
The fire occurrence probability map generated shows an
intensification pattern in the central and northeast zone of
the LPFFPA (Figure 3). The surface with fire occurrence prob-
ability above the very low threshold comprised 30 286.62 ha,
which corresponds to 13.96% of the total study area, of which
20 970.90 ha are within the PA (Table 2).
The areas where the fire occurrence probability is higher include
large tracts of land belonging to the ejidos (community land) of
San Juan de Ocotán, Jocotán, General Lázaro Cárdenas, El Colli,
and Santa Ana Tepetitlán, all located in the municipality of
Zapopan.
Figure 4.
It was found that 68.40% of the total surface area of the PA may Receiver Operating Characteristics (ROC) Curve Showing the
be affected by fire, while the areas surrounding the PA, particu- Area Under the Curve (AUC) of the Fire Occurrence
Probability Map Generated for La Primavera Flora and Fauna
larly those with forest cover, present a moderate fire occurrence
Protection Area (LPFFPA).
probability, ranging from medium to low (Figure 3).
7Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
which coincides with the models built for other regions (Ávila- been documented by other authors (Nepstad et al., 2001;
Flores et al., 2010; Suryabhagavan et al., 2016; Vargas & Campos, Sparovek et al., 2019). It is essential to analyze the urbanization
2018). processes that produce these changes (Aguejdad et al., 2017),
as they generate an increase in activities related to people and
The generated map shows a higher intensity in the areas present- vehicles’ transit, which increase exposure and susceptibility to
ing a marked influence of the urban area of the MAG and the accidental or intentional fire in forest regions (Suryabhagavan
areas dedicated to agricultural and livestock activities in the sur- et al., 2016).
rounding ejidos, principally those of the municipality of Zapopan.
This municipality is one of those with the highest urban growth The software used in the present study (Dinamica EGO) enables
rate in the entire nation and, of the municipalities comprising the evaluation of the influence of the spatial and temporal vari-
Mexico’s second-largest metropolitan area, covers more territory ables on the processes presenting in the territory and has been
with a higher proportion of urban land (IMEPLAN, 2016). used to model the land-use change, the urban growth, and the
probability of fire, among other phenomena (Almeida et al.,
Susceptibility to fire is related to the proximity of the PA to dense 2003; Farfán et al., 2018; Sahagún-Sánchez & Reyes-Hernández
population centers, such as those found in the municipality of 2018). The models facilitate the projection of the possible eco-
Zapopan and the roads providing access to them, constitut- logical and socio-economic consequences of damage to these
ing a source of potential disturbance to the vegetation cover landscapes and constitute a valuable decision-making tool to
within the PA. Suryabhagavan et al. (2016) found increased fire formulate fire management and forestry alternative strategies
risk in sites near populations with modified vegetation, such as (Soares et al., 2002).
grassland or scrubland, which can facilitate the ignition of fires
that damage the forest. In this case, land-use change analysis Only a few spatial modeling studies, which help predict forest
precisely reveals the increased size of urban areas and those fires, have been conducted on a local scale (Farfán et al., 2020;
dedicated to grasslands, which have direct effects on the sus- Muñoz et al., 2005). Although Mexico is a country with diverse
ceptibility of the PA to wildfire. environmental and socio-economic conditions, this type of
model is useful as a basis for the prediction of fire in various
Recently, Ibarra-Montoya and Huerta-Martínez (2016) developed forest areas of interest for both forest management and forest
a spatial model for fires in the LPFFPA, using a maximum use. These models could be used to focus efforts and economic
entropy algorithm (MaxEnt) to determine an area with a high resources on preventing and mitigating forest fires in the coun-
probability of fire, comprising approximately 4 937 ha of forest try (Zuñiga-Vásquez et al., 2017).
in the PA (AUC = 0.83). The previous contrasts with the current
projection suggesting that almost 70% of the PA, correspond- The present study determined susceptibility to forest fire
ing to 20 970.90 ha, is susceptible to damage by a forest fire. based on the social and economic activities undertaken in the
The results obtained here indicate a very high probability of fire areas surrounding the LPFFPA; however, various authors have
in 6 921.63 ha and a high probability of 1 690.47 ha, for a total described how the characteristics related to the frequency
of 8 612.10 ha, representing nearly double of the total area pro- and severity of fires have been modified due to global climate
jected by the authors previously mentioned. change (De Groot et al., 2013; González et al., 2011).
Even though there is a higher amount of information available As in other cases (Farfán et al., 2018; Sufyabhagavan et al., 2016;
from spatial data derived from remote sensing on forest fires, Zuñiga-Vásquez et al., 2017), our developed model is a par-
few studies have used them to improve PA management pro- tial representation of reality and does not necessarily include
grams in Mexico. Farfán et al. (2018) developed a study for the all possible variables. It is necessary to obtain precise data on
Monarch Butterfly Biosphere Reserve (AUC = 0.71), in which variables such as precipitation, temperature, and humidity,
there is a significant surface area of forest cover and the occur- droughts, as well as the biomass and density of dead trees, as
rence of fires seems to be influenced by population density and these are significant factors in the ignition and behavior of fire
the fragmentation of the forest. As in other PAs, the pressure is (Ávila-Flores et al., 2010; Galván & Magaña, 2020; Ocampo-Zuleta
promoted by expanding the agricultural frontier and the extrac- & Beltrán-Vargas, 2018). For example, Joseph et al. (2019) found
tion of timber resources, which establishes unfavorable condi- that dryness and air temperature strongly predict extreme
tions for adequate fire management. wildfire probabilities, but housing density has a hump-shaped
relationship with fire occurrence, with more fires occurring at
The processes of land-use change are related to the variables intermediate housing densities. The increase in the area of for-
with the most significant influence on the probability of for- est fires in California during 1972–2018 is strongly related to the
est fire, therefore monitoring these changes is a top prior- increase in the vapor pressure deficit (VPD) in summer (Williams
ity. Distance to roads from PA constitutes a proximal factor et al., 2019). In Sierra Nevada, VPD and control difficulties were
that affects land-use change processes by facilitating access the most important factors for fire probability in higher eleva-
to resources (Brando et al., 2020; Geist & Lambin, 2002). The tion forest, while population density was comparatively more
increase in the traffic of vehicles and people in the vicinity of important in the lower elevation forest regions (Chen et al.,
areas with forest cover increases the possibility of fires, as has 2021). In other cases, rainfall anomalies due to climate change
8Sahagún-Sánchez et al. Spatial Modeling of Fire in a Protected Area
Forestist 2021: XX(XX): 1-12
have intensified the drought in the vegetation of the forests and necessary to pay attention to land-use change processes in the
tropical forest, becoming one of the variables that influenced municipality of Zapopan and review the territorial development
the large fires that recently occurred between 2019 and 2020 in plans to reduce the risks derived from expanding urban areas,
Australia, the Western United States, and the Amazon rainforest agricultural activities, and industrial parks in the surroundings
in Brazil (Kganyago & Shikwambana, 2020). of PA.
The degree of influence of the complex suite of biophysical and The model developed enables the explicit spatial visualization
human drivers of fire remains controversial and incompletely of the most susceptible sites to fire, improving forest fires' pre-
understood (Clarke et al., 2020). Juárez et al. (2017) conclude vention and control in the PA. However, it is essential to con-
that socio-economic drivers, deforestation, and global climate sider the future inclusion of other variables in the models, like
change should be considered to gain a better understanding of vapor pressure deficit, rainfall anomalies, droughts, and others,
the occurrence of forest fires. More knowledge about the influ- to improve the results obtained.
ence of variables, distribution, and behavior of fire regimes is
required to understand the associated phenomena and their This type of study will allow a better understanding of the causal
relationship with the anthropogenic activities undertaken both factors that influence fires in the area. It is necessary to improve
inside and around the areas of interest for the conservation and fire management strategies according to the sites’ particular
use of forest resources (Chuvieco et al., 2014; Zuñiga-Vásquez environmental and socio-economic conditions where forest
et al., 2017). fires occur. Reduce potential fire occurrence probability is key
to maintain the integrity and ecosystem services provided by
In recent years, research on forest fires around the world has the PA.
concentrated on the use of data derived from remote sen-
sors (Boschetti et al., 2015), which favors real-time monitoring Peer-review: Externally peer-reviewed.
of the sites of potential forest fires, thus facilitating the man-
agement and containment actions of field technicians and Author Contributions: Concept – F.J.S.S.; Design – F.J.S.S., A.J.M.G.,
officials. Moreover, the availability of large quantities of data F.M.H.M., M.A.E.G.; Supervision – F.J.S.S., F.M.H.M., M.A.E.G.; Materials –
F.J.S.S., A.J.M.G., F.M.H.M., M.A.E.G.; Data Collection and/or Processing –
enables predictions to be made about the possible occurrence
A.J.M.G.; Analysis and/or Interpretation – F.J.S.S., A.J.M.G., F.M.H.M.,
of fire in economically or environmentally essential regions.
M.A.E.G.; Literature Search – A.J.M.G.; Writing Manuscript – F.J.S.S.,
Detailed real-time information on this type of problem would A.J.M.G., F.M.H.M., M.A.E.G.; Critical Review – F.J.S.S., F.M.H.M., M.A.E.G.
enable the generation of policies for the protection and con-
servation of ecosystem goods and services, at both a local and Acknowledgments: We thank the Decentralized Public Organization
national level to guarantee the protection of urban settlements Bosque La Primavera and the non-governmental organization
(CONAFOR, 2019). The previous information enables the mod- Incidencia y Gobernanza Ambiental A.C. for the information and sup-
els' predictive capacity improvement and will facilitate deci- port provided.
sion-making processes for adequate fire management (Vargas
& Campos, 2018). Conflict of Interest: The authors have no conflicts of interest to declare.
Financial Disclosure: The authors declared that this study has received
Identifying the variables that mainly affect the occurrence
no financial support.
of fires would guide the policy around the establishment of
conditions to regulate socio-economic activities in the region.
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