Development of a National Forest Fire Danger System for Mexico

Abstract

This presentation introduces the project "Development of a Forest Fire Danger System for Mexico" funded by the Mexican Forest Agency CONAFOR. The goal of the 3-year project is to develop an operational fire danger system for mapping daily and forecasted fire risk occurrence and fire propagation danger in Mexico, which will be online for decision-making on fire management by CONAFOR and fire management actors in Mexico. The presentation summarizes the project goals and structure and the results from the first year of the project, including: 1) The development of a fire occurrence risk module for mapping expected number of fires based on vegetation type, weather and satellite information and 2) The development of an online interface for daily mapping of fire risk and danger in Mexico.

Full text

Proceedings of the Fifth International Symposium on Fire Economics, Planning, and Policy:
Ecosystem Services and Wildfires
38
Development of a National Forest Fire
Danger System for Mexico1
Daniel J. Vega-Nieva2 *, María G.Nava-Miranda2, Erik Calleros-
Flores2, Pablito M. López-Serrano2, Jaime Briseño-Reyes2, Favian
Flores Medina2, Carlos López-Sánchez2, José J. Corral-Rivas2,
Armando González-Cabán3, Ernesto Alvarado-Celestino4, Isabel
Cruz5, Martín Cuahlte5, Reiner Ressl5, Albert Setzer6, Fabiano
Morelli6, Diego Pérez-Salicrup7, Enrique Jardel-Pelaez8, Citlali
Cortes-Montaño2, José A. Vega9, Enrique Jimenez8
Abstract
This presentation introduces the project "Development of a Forest Fire Danger System for
Mexico" funded by the Mexican Forest Agency CONAFOR. The goal of the 3-year project is
to develop an operational fire danger system for mapping daily and forecasted fire risk
occurrence and fire propagation danger in Mexico, which will be online for decision-making
on fire management by CONAFOR and fire management actors in Mexico. The presentation
summarizes the project goals and structure and the results from the first year of the project,
including: 1) The development of a fire occurrence risk module for mapping expected number
of fires based on vegetation type, weather and satellite information and 2) The development
of an online interface for daily mapping of fire risk and danger in Mexico.
Keywords: fire danger, fire risk, fuel dryness indices, online decision support system, Mexico.
Introduction
No operational fire danger system is currently available in Mexico. This in contrast
with countries such as USA, Canada or Brazil that have developed operational fire
risk systems based on temporal and spatial quantification of fuel greenness and
associated fire risk and danger (e.g. Deeming et al., 1977, Burgan et al., 1997, 1998,
1 An abbreviated version of the paper was presented at the fifth international symposium on fire
economics, planning, and policy: wildland fires and ecosystem services, Nov 14-182016, Tegucigalpa,
Honduras.
2 Facultad de Ciencias Forestales. Universidad Juárez del Estado de Durango (México). *corresponding
author. Email: danieljvn@gmail.com
3 Research Economist, Pacific Southwest Research Station. US Department of Agriculture Forest
Service.
4 School of Environmental and Forest Sciences. University of Washington.
5 Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO).
6 Instituto Nacional de Pesquisas Espaciais (Brazil).
7 Universidad Autónoma de México (México).
8 Universidad de Guadalajara (México).
9 Centro de Investigación Forestal – Lourizán, Xunta de Galicia (Spain).

Proceedings of the Fifth International Symposium on Fire Economics, Planning, and Policy:
Ecosystem Services and Wildfires
39
Preisler et al., 2004, 2008, 2011, Riley et al., 2013, Van Wagner, 1987, Sismanoglu
and Setzer, 2012).
This lack of an operational fire danger system led the Forest National
Commission (CONAFOR in Spanish) and the National Research Agency
(CONACYT in Spanish) to fund the national scale project “Development of a Forest
Fire Danger System for Mexico”. The main objective of the study is the development
of an operational fire risk and danger mapping system based on satellite and weather
information for Mexico (Vega-Nieva et al., 2015). This document summarizes the
project goals and structure and the results from the first year of the project, including:
1) The development of a fire occurrence risk module for mapping expected number
of fires based on vegetation type, weather and satellite information and
2) The development of an online interface for daily mapping of fire risk and danger
in Mexico.
Goals of the Project “Development of a Forest Fire Danger
System for Mexico”.
In Mexico, a system for near real-time mapping of fire Hotspots has been implemented
by CONABIO (http://incendios1.conabio.gob.mx/), but no operational system for
prediction of Fire Risk (probability of fire occurrence) or Fire Danger (expected fire
behavior and difficulty of suppression) is currently available for Mexico. The Project
252620 in response to the call 3-C02-2014 by CONACYT-CONAFOR aims at
developing an operational Fire Risk and Danger System to be used by the Mexican
Government Forest Agency CONAFOR and relevant agents in decision making on fire
management in Mexico. The Project is being conducted by a consortium of researchers
from several institutions from Mexico, USA, Brazil and Spain.
The goals of the project are
1) To conduct a literature review of Fire Risk and Danger
2) To test existing Fire Risk and Danger systems for the prediction of fire occurrence
in Mexico.
3) To develop a Mexican Fire Risk System for the prediction of fire occurrence.
4) To develop a Fire Weather forecast system for Mexico.
5) To develop a module for mapping Fire Area in Mexico.
6) To test existing Fire Danger systems in Mexico against fire area records.
7) To develop a Mexican Fire Danger System
8) To develop and transfer to CONAFOR a online software for mapping of current
and forecasted Fire Danger in Mexico.

GENERAL TECHNICAL REPORT PSW-GTR-261
40
Modeling fire occurrence risk from monthly satellite fuel dryness
by vegetation type and region in Mexico.
Within this national project, a study was conducted by Vega et al. (2016) with the
goals of: 1) quantifying the monthly temporal trends of a MODIS satellite based fuel
greenness index, DR, and the temporal trends of fire density (FD) by vegetation type
and region in Mexico, 2) testing simple regression models for prediction of monthly
FD by vegetation type and region from monthly DR values in Mexico. The
methodology and the main results of this study are summarized below.
Methodology
Area of study
The area of study was the Mexican Republic. Figure 1 shows the vegetation types
present in the country according to the National Institute of Geography and Statistics
(INEGI in Spanish) most recent land use map (INEGI Land Use Map Series V,
1:25000 http://www.inegi.org.mx/geo/contenidos/recnat/usosuelo/) Four
geographical regions , Northwest (NW), Northeast (NE), Center (C), and South (S),
were established (figure 1), considering both the potential fire regimes zoning for
Mexico (Jardel et al. 2014), based on vegetation types and climatic zones (Holridge,
1996), together with a visual observation of the temporal and spatial patterns of
clustering in fire hotspots on the period of study.
Satellite hotspots and fuel dryness indices.
Considering the availability of MODIS fire hot spots information for Mexico we
selected the period of 2003-2014 for our study. We compiled monthly MODIS fire
hotspots for the 12 years of the study period from CONABIO
(http://incendios1.conabio.gob.mx/ ).
The monthly NDVI composite images with a spatial resolution of 1 x 1 km
(MODIS product MOD13A3) from the study period were downloaded from
http://modis.gsfc.nasa.gov/data/dataprod/mod13.php.
Following Burgan et al. (1998), Dead Ratio (DR) values were calculated for
each pixel based on the values of NDVI for each monthly image, on the maximum
and minimum NDVI values for each pixel and on the absolute maximum and
minimum NDVI observed values in the area of study for the whole study period.
Dead ratio is an empirical index representing the fraction of fuel that is not live (DR=
100- Live Ratio), reaching 100 in a fuel that is completely cured with no live
biomass, and with lower values representing fuels with a higher fraction of live
biomass (Burgan et al., 1998).

Proceedings of the Fifth International Symposium on Fire Economics, Planning, and Policy:
Ecosystem Services and Wildfires
41
Figure 1. Map of vegetation types and regions considered in the analysis. Where: TFOR:
Temperate Forest, SHV: Shrubland Vegetation, SHSV: Shrubby Secondary Vegetation,
PTROPF: Perennial Tropical Forest, PAS: Pastureland, DTROPF: Deciduous Tropical Forest,
ARBSV: Arboreous Secondary Vegetation, AG: Agriculture, NV: No Vegetation; and NW:
North West, NE: North East, C: Centre, S: South regions. Source: INEGI land use map (series
V)
Fire Density Index.
For each of the 28 vegetation types and regions considered, monthly Fire Density
(FD) was calculated by dividing the number of fires in the area by the surface (km2)
of the vegetation/region considered. Monthly FD values for each vegetation type and
region were scaled to a Fire Density Index (FDI) as follows:
FDI= Number of fires / Surface (km2) x 5000
The FDI index is defined so that a FD of 0.01 fires /km 2 – e.g. 1 fire / 100 km 2
– is equivalent to an FDI value of 50. Accordingly, a FD of 2 fires / 100 km 2 is
equivalent to an FDI value of 100, which might be considered an indicator of a high
fire density.
Modeling monthly FDI from DR.
Fire season concentrated on the first 6 months of the year for all vegetation types
considered. Consequently, all land uses were modeled for the period January-June.
We tested linear and nonlinear power equations as regression models. Table 1
summarizes the equations tested. Each month or group of months was allowed to
have distinct coefficients by multiplying the observed DR by a dichotomous variable
(0 or 1) so that each month or group of months would obtain an individual parameter,
both in the lineal and nonlinear models (eqs. 1 and 7, table 1). After observing the
coefficients obtained in this approach, several groups of months were tested as
candidates for grouping with the same coefficients (eqs. 2-6 8-12). Statistical and
graphical analyses were used to evaluate the performance of the equations. The
goodness-of-fit of each model was evaluated using the adjusted coefficient of
determination (R2) and root mean squared error (RMSE).

GENERAL TECHNICAL REPORT PSW-GTR-261
42
Table 1. Equations tested for prediction of monthly Fire Density Index from Dead Ratio values. Where:
FDI: monthly Fire Density Index, DR: monthly Dead Ratio, a and b are model coefficients, J: January,
F: February, M: March, A: April, My: May, Ju: June, Jl: July, Ag: August, S: September, O: October,
N: November, D: December.
Eq.
Num.
Fit
type
Grouped
months
Equation
1 Linear -
)( JuJuMyMyAAMMFFJJ DRbDRbDRbDRbDRbDRbaFDI ++++++=
2 Linear J&F
)( JuJuMyMyAAMMJFJF DRbDRbDRbDRbDRbaFDI +++++=
3 Linear J,F&M
)(
JuJuMyMyAAJFMJFM
DRbDRbDRbDRbaFDI ++++=
4 Linear
J,F&M,
A&My
)( JuJuAMyAMyJFMJFM DRbDRbDRbaFDI +++=
5 Linear
J,F&M,
A&Ju
)( MyMyAJuAJuJFMJFM DRbDRbDRbaFDI +++=
6 Linear
J,F,M&A,
My&Ju
)( MyJuMyJuJFMAJFMA DRbDRbaFDI ++=
7
Non
linear -
b
JuJuMyMyAAMMFFJJ DRaDRaDRaDRaDRaDRaFDI )( ++
+++=
8
Non
linear
J&F
b
JuJuMyMyAAMMJFJF
DRaDRaDRaDRaDRaFDI )( ++++=
9
Non
linear
J,F&M
b
JuJuMyMyAAJFMJFM
DRaDRaDRaDRaFDI )( +++=
10
Non
linear
J,F&M,
A&My
b
JuJuAMyAMyJFMJFM
DRaDRaDRaFDI )( ++=
11
Non
linear
J,F&M,
A&Ju
b
MyMyAJuAJuJFMJFM
DRaDRaDRaFDI )( ++=
12
Non
linear
J,F,M&A,
My&Ju
b
MyJuMyJuJFMAJFMA
DRaDRaFDI )( +=
13 Linear -
)
(
DDNNOOSSAgAgJlJl
JuJuMyMyAAMMFFJJ
DRbDRbDRbDRbDRbDRb
DRbDRbDRbDRbDRbDRbaFDI
++++++
++++++=
14 Non
linear -
b
DDNNOOSSAgAgJlJl
JuJuMyMyAAMMFFJJ
DRaDRaDRaDRaDRaDRa
DRaDRaDRaDRaDRaDRaFDI
)
(
++++++
+++++=
15
Non
linear
All but
My
b
MyMySONDJFMAJuJlAuSONDJFMAJuJlAu
DRaDRaFDI )( +=
Results and discussion
Table 2 shows the models that better fitted the data for each vegetation type and
region and the goodness of fit statistics for the best models. With the exception of the
deciduous and perennial tropical forests of the NE, the nonlinear models described
better the data than linear models for all vegetation types and regions, suggesting that

Proceedings of the Fifth International Symposium on Fire Economics, Planning, and Policy:
Ecosystem Services and Wildfires
43
the relationship of DR with fire occurrence is not linearly proportional –e.g. fire
occurrence risk increases very rapidly with increasing DR.
Table 2. Coefficients and goodness of fit of the best fit equations for the prediction of monthly Fire
Density Index from Dead Ratio values for each vegetation type and region. Where: Veg_Reg:
Vegetation and region; Eq: best fit equation from table 1, a and b are model coefficients, J: January,
F: February, M: March, A: April, My: May, Ju: June, Jl: July, A: August, S: September, O: October,
N: November, D: December coefficients for the corresponding month or group of months. RMSE: Root
Mean Standardized Error; R2adj: Adjusted R2; TFOR: Temperate Forest, PAS: Pastureland,
PTROPF: Perennial Tropical Forest, ARBSV: Arboreous Secondary Vegetation, SHSV: Shrubby
Secondary Vegetation, DTROPF: Deciduous Tropical Forest, NV: No Vegetation; and NW: North
West, NE: North East, C: Centre, S: South regions.
Veg_Reg Eq a JF M JFM A JFMA My AMy Ju AJu MyJu b RMSE R2ADJ
TFOR_C 8 0.019 0.021 0.024 0.026 0.023 7.771 33.3 0,75
TFOR_NE 8 0.016 0.018 0.019 0.021 0.019 10.438 15.4 0,62
TFOR_NW 12 0.015 0.017 11.371 32.4 0,62
TFOR_S 9 0.016 0.016 0.015 0.014 25.706 11.7 0,68
PAS_C 8 0.019 0.022 0.028 0.032 0.027 5.276 18.2 0,95
PAS_NE 8 0.019 0.021 0.024 0.026 0.023 5.668 9.3 0,86
PAS_NW 8 0.011 0.011 0.012 0.012 0.013 13.729 3.3 0,60
PAS_S 8 0.059 0.110 0.178 0.197 0.082 2.243 92.3 0,79
PTROPF_C 9 0.024 0.034 0.041 0.034 4.817 35.7 0,79
PTROPF_NE 3 -
102.96 2.278 2.794 3.388 2.266 0.000 19.1 0,67
PTROPF_NW 12 0.015 0.020 5.662 9.1 0,67
PTROPF_S 8 0.019 0.021 0.023 0.026 0.023 7.018 7.5 0,70
DTROPF_C 11 0.023 0.052 0.048 3.234 19.5 0,91
DTROPF_NW 9 0.013 0.017 0.024 0.022 5.245 9.8 0,89
DTROPF_NE 2 -
219.92 3.503 3.882 4.767 5.615 5.368 0.000 78.6 0,46
DTROPF_S 8 0.037 0.044 0.050 0.049 0.036 3.923 28.0 0,76
ARBSV_C 8 0.019 0.024 0.031 0.035 0.028 5.361 26.5 0,90
ARBSV_NE 8 0.022 0.025 0.028 0.034 0.028 6.131 10.7 0,94
ARBSV_NW 9 0.016 0.024 0.030 0.027 4.706 28.6 0,70
ARBSV_S 8 0.034 0.059 0.080 0.080 0.034 2.956 37.0 0,79
SHSV_C 8 0.017 0.020 0.026 0.030 0.025 5.590 17.8 0,93
SHSV_NE 10 0.017 0.022 0.019 7.257 14.6 0,73
SHSV_NW 9 0.012 0.014 0.015 0.015 11.690 18.9 0,65
SHSV_S 8 0.033 0.048 0.060 0.061 0.033 3.722 59.4 0,79

GENERAL TECHNICAL REPORT PSW-GTR-261
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Different patterns of FDI and DR relationships were observed for different
vegetation types and regions, agreeing with observations that point to a variety of fire
regimes resulting from combinations of climatology and fuel types in the country
(e.g. Rodríguez et al., 1996, 2008, Morfin et al., 2007, 2012, Avila et al., 2010, Jardel
et al., 2009, 2014, Perez-Verdin et al., 2014). Derived model coefficients for months
and groups of months may offer information about the patterns of timing of fire
season and their relationships with DR patterns in different vegetation types and
regions. Most of the vegetation types in the south and center region showed an earlier
start of fire season (1 month earlier) compared to the NW region, suggesting that
either longer periods of accumulated drought in that latter region are required for fire
to start, or perhaps reflecting different patterns of agricultural burns timing in the
different regions of the country. Within regions, tropical forest showed latter starts of
fire season compared to other vegetation types in the same region (1 or 2 more
months in the NW), suggesting that longer accumulated drought periods are required
in those more humid ecosystems for fire to start.
Development of an online interface for the Mexican Fire
Danger System
In the first year of the project, UJED programmed an online test interface for the
Forest Fire Danger System of Mexico, freely available online at the link:
http://fcfposgrado.ujed.mx/incendios/inicio/index.php
The interface includes several layers for current situation (figure 2) and a
section with evolution of fuel dryness and risk indices. (figure 3), available at:
http://fcfposgrado.ujed.mx/incendios/inicio/historicos_animaciones.php
The layers included in the GIS interface for current situation include observed daily
layers for fire hotspots, fuel dryness index, and fire occurrence risk (figure 2).
A number of thematic layers in included in the GIS interface, including:
CONAFOR fire priority areas, Regional Fire Management Centers, Type of land
cover, Natural Protected Areas, Limits of States, Municipalities and Forest
Management Units (figure 1). A base map containing towns, roads and topography
from three online sources (Bing Maps, ArcGis Online 1, ArcGis Online 2) is also
included .The user can zoom in/off using base maps as a spatial reference. The user
can turn on/off any layer in the GIS interface, including the possibility of
simultaneously visualizing a combination of layers (e.g. fire risk and a
topography/roads map from Bing maps) by regulating the layers level of
transparency.

Proceedings of the Fifth International Symposium on Fire Economics, Planning, and Policy:
Ecosystem Services and Wildfires
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Figure 2. Online interface of the Mexican Forest Fire Danger System: current situation. Top
figure shows current fuel dryness index and observed fire hotspots (in blue) in October 2016.
Colors represent fuel dryness, with green being very wet fuel and red and light pink being dry
and very dry fuel conditions. Bottom figure shows the predicted fire occurrence risk map and
observed fire hotspots (in bright pink) in May 2016. Colors represent risk of fire occurrence,
with green meaning low probability of fire occurrence and red and dark red representing high
and very high fire occurrence risk. http://fcfposgrado.ujed.mx/incendios/inicio/index.php
Figure 3. Examples of Fuel Dryness Index (left figure, monthly fuel dryness for 2011) and Fire
Ocurrence Risk maps (right figure, march to june 2011). Animations of fuel dryness and fire
risk for historic years in Mexico can be consulted at the link:
http://fcfposgrado.ujed.mx/incendios/inicio/historicos_animaciones.php

GENERAL TECHNICAL REPORT PSW-GTR-261
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Summary and conclusions.
The Project “Development of a Forest Fire Danger System for Mexico”, funded by
the Forest National Commission (CONAFOR in Spanish) and the National Research
Agency (CONACYT in Spanish) aims at developing an operational fire risk and
danger mapping system based on daily satellite and weather information, to be used
by the Mexican Government Forest Agency CONAFOR and relevant agents in
decision making on fire management in Mexico. During the first year of the project,
several weather and satellite based indices have been tested, with first results for
prediction of fire occurrence risk based on a satellite fuel dryness index for Mexico.
Future work in the project will include the development of probabilistic fire risk
based on daily weather-based fire danger indices together with spatial factors such as
distance to roads and locations. These daily fire risk models will be included in the
online platform which will provide daily assessments of fuel drought and expected
fire risk occurrence. This operational tool will be used for improving the planning of
fire extinction and for strategic fire management decision making in Mexico.
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