Comparison of Forest Fire Profiles in Londrina, Brazil and Pisa, Italy

Abstract

The main aim of this study was to compare the historical profile of forest fires and to elaborate a risk zoning map for the regions of Londrina, Brazil, and Pisa, Italy in the period from 2005 to 2014. The records of fire occurrences were correlated with days of rain and temperature in the study areas. The results showed that 1,435 and 629 fires were recorded in the analyzed period, affecting areas of 3,220.4 and 1,550.8 ha in the regions of Londrina and Pisa, respectively. Data were then spaced in a risk zoning map. Fire occurrences and precipitation presented inverse correlation of 0.76 and 0.81 for Londrina and Pisa, respectively. Temperature showed direct correlation of 0.82 with fire occurrences for Pisa, and inverse correlation of 0.56 for Londrina. The analyzed data may serve as subside for planning fire prevention and combat activities.

Full text

Floresta e Ambiente 2019; 26(2): e20170607
https://doi.org/10.1590/2179-8087.060717
ISSN 2179-8087 (online)
Original Article
Silviculture
Creative Commons License. All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License.
Comparison of Forest Fire Proles in Londrina,
Brazil and Pisa, Italy
João Francisco Labres dos Santos1 , Alexandre França Tetto1 ,
Andrea Bertacchi2 , Antonio Carlos Batista1 , Ronaldo Viana Soares1 
1Universidade Federal do Paraná – UFPR, Curitiba/PR, Brasil
2Universitadegli Studi di Pisa Dipartimentodi Scienze Agrarie Alimentari e Agro-ambientali, Pisa/TO, Itália
ABSTRACT
The main aim of this study was to compare the historical profile of forest fires and to elaborate a
risk zoning map for the regions of Londrina, Brazil, and Pisa, Italy in the period from 2005 to 2014.
The records of fire occurrences were correlated with days of rain and temperature in the study
areas. The results showed that 1,435 and 629 fires were recorded in the analyzed period, affecting
areas of 3,220.4 and 1,550.8 ha in the regions of Londrina and Pisa, respectively. Data were then
spaced in a risk zoning map. Fire occurrences and precipitation presented inverse correlation
of 0.76 and 0.81 for Londrina and Pisa, respectively. Temperature showed direct correlation of
0.82 with fire occurrences for Pisa, and inverse correlation of 0.56 for Londrina. The analyzed
data may serve as subside for planning fire prevention and combat activities.
Keywords: precipitation, temperature, fire risk zoning, risk.

2/10 Santos JFL, Tetto AF, Bertacchi A, Batista AC, Soares RV Floresta e Ambiente 2019; 26(2): e20170607
1. INTRODUCTION
Forest fires dramatically transform ecosystems.
Several factors are related to the occurrence of fires
and climate elements are among them, which have
a decisive influence on start and propagation of
fire (Fimia, 2009). Understanding the historical
behavior of precipitation associated with historical
information on various aspects regarding the origin
and behavior of fires are important for developing
forest fire control plans. This offers opportunities
to visualize different prevention scenarios (Cabán,
2009).
According to Myers (2006), every single ecosystem
on Earth potentially has a fire regime, a history
that has in some way affected the structure and
composition of species. Seasonal changes in weather
conditions also influence the moisture content of
live and dead fuel materials (Lara, 2009). Therefore,
precipitation distribution is critical for the start,
duration and end of most hazardous fire seasons
(Soares & Batista, 2007).
According to Nunesetal. (2009), precipitation is
a limiting factor in both re ignition and propagation,
and there is strong correlation between the occurrence
of large res and prolonged drought periods. In a
recent study, Liu & Wimberly (2015) reported that
precipitation has greater inuence on the occurrence
of res compared to temperature and humidity.
Sampaioetal. (2006) found that the region of Londrina
presents precipitation near zero in the months of July
and August. Similar behavior was reported by Stefanini
(2008) for the region of Pisa.
Due to the importance of understanding statistical
information regarding re occurrences over time for
the elaboration of prevention and re control plans and
in order to help compare proles, the hypothesis that
precipitation has preponderant inuence in dierent
regions of the planet was tested.
e aim of this study was to compare the proles
of forest res and the inuence of precipitation on
re occurrences in Londrina, mid-northern region
of the state of Paraná, Brazil, and in the province of
Pisa, Tuscany, Italy, as well as to develop a risk zoning
map for both regions.
2. MATERIAL AND METHODS
2.1. Study area characterization
Two areas were selected to perform this study:
a) Four cities of the Metropolitan Region of Londrina,
state of Paraná, Brazil, as follows: Londrina,
Arapongas, Cambé and Rolândia, totaling an
area of 2,989.18 km2, located between coordinates
23°01’ and 23°56’ south latitude and 50°52’ and
51°35’ west longitude (Instituto Brasileiro de
Geograa e Estatísti ca – IBGE, 2015). According to
the Köppen classication, the region presents Cfa
climate characterized as temperate with year-round
rainfall and hot summer, with temperatures above
22°C in the warmer months and mean temperature
in the coldest months higher than or equal to
-3°C (Soaresetal., 2015), as shown in Figure1B.
Originally covered by Semi-deciduous Seasonal
Forest, this area is conditioned to a period of low
rainfall, causing the upper canopy of trees to lose
their leaves (Roderjanet al., 2002). According
to Hardestyet al. (2005), this is a re-sensitive
ecosystem;
b) The province of Pisa, Tuscany, Italy, with
2,444.72 km2 located at coordinates 43°43’ north
latitude and 10°25’ east longitude (Istituto Nazionale
di Statistica- ISTAT, 2015), is in Csa climate
region according to the Köppen classication,
seasoned with hot and dry summers, maximum
temperatures above 22°C, and temperatures
below -3°C in the colder months (Kotteketal.,
2006) (Figure1A). e vegetation of the region is
basically composed of coniferous and broadleaf
forests, in which the latter are divided into two
large systems: deciduous broadleaf trees, which
lose their leaves in winter and are not very drought
resistant; and perennial xerophilic sclerophytes,
which maintain their leaves in winter. Another
form of vegetation very common but mainly
present on the coast is “macchia mediterranea”,
generally formed by perennial shrubs, which are
resistant to dry climate (Regione Toscana, 1998).
It is a re-dependent ecosystem (Hardestyetal.,
2005).
2.2. Methodological process
Fire database was provided by the Fire Department
of the state of Paraná (Comando do Corpo de
Bombeiros- CCB), through the SysBM-CCB, and by
the Servizio Antincendi Boschivi (AIB) of the Province

3/10
Comparison of Forest Fire...Floresta e Ambiente 2019; 26(2): e20170607
Figure 1. Study areas and climographs of Londrina (A) and Pisa (B) in the period from 2005 to 2014. Sources: IBGE
(2015) and ISTAT (2015); Instituto Nacional de Meteorologia (INMET, 2015) and Servizio Agrometeorologico Regione
Toscana (ARSIA, 2015, prepared by the authors (2017).
of Pisa, Italy. Temperature and precipitation data were
provided by the Instituto Nacional de Meteorologia
(INMET) and Servizio Agrometeorologico Regione
Toscana (ARSIA). Alldata refer to the period from
01/01/2005 to 12/31/2014, totaling 10 years of
observation. Data processing was performed with
the help of Microsoft Excel 2016 and the Statgraphics
Centurion XV software.

4/10 Santos JFL, Tetto AF, Bertacchi A, Batista AC, Soares RV Floresta e Ambiente 2019; 26(2): e20170607
Comparison of proles followed some methodological
elements recommended by Rodríguez (1999) and
Tettoetal. (2012). Temporal analysis was carried out
considering the following variables: re occurrence and
burnt area related to year and month, and further relating
them to rainy days and temperature for the period. Rainy
days with rainfall that accumulated precipitation above
2.4mm were considered (Soares, 1972).
e zoning map was developed according to
methodology applied by Bovio & Camia (1997), in
which 10 square kilometer areas are delimited in cities
and the obtained historical data are standardized and
distributed into Basic Units (BUs). Each basic unit
represents a city of the analyzed regions. To better
spatialize and interpret occurrences in the chosen
period, six variables were determined based on the
frequency of res and the aected area, namely: number
of res/BU/10 km
2
/year; number of res > 30 ha;
percentage of years with re (%); mean burned area by
one re occurrence (ha); median of the burned area by
one re (ha); maximum burned area by one re (ha).
Subsequently, BUs were distributed into classes
through cluster analysis using the Ward (1963) method.
Determining the number of classes for each region
was based on the fusion coecient of the Euclidean
distances of each sample, respecting the limit of ve
risk levels (null, low, medium, high and very high)
(Bovio & Camia, 1997) with the aid of the Statgraphics
Centurion XV soware. Based on analyzed variables,
spacing was performed using the ARCGIS 10.3 soware
(Figure2).
Evaluating variable cause was not possible for
none of the regions, since it is not included in the
Fire Department System of Paraná, as remen do not
have attributes for such, as established in Article 45
of the State of Paraná Constitution (Paraná, 2006).
esituation is similar in the province of Pisa, in which
the Nuclei Investigativi Provinciali di Polizia Ambientale
e Forestale (NICAF) of the Corpo Forestale dello Stato are
responsible for the investigation (Corpo Forestale dello
Stato, 2015), while the Servizio Antincendi Boschivi is
responsible for monitoring, prevention and forecasting,
according to article 3 of law 353 from November 21,
2000 (Italia, 2000).
3. RESULTS AND DISCUSSION
3.1. Number of re occurrences and aected
areas
From 2005 to 2014, 1,435 re occurrences were
observed in Londrina, which aected 3,220.4 hectares.
During the same period, 629 re occurrences were
observed in Pisa, which aected 1,550.8 hectares
(Table1).
e average frequency of re records in Londrina
and Pisa was 143.5 and 62.9 res per year, respectively.
e highest values were observed from 2005 to 2008
and between 2010 and 2012 in Londrina, and 2011
and 2012 in Pisa. e lack of a well-dened public
policy for awareness in the use of controlled res in
agriculture contributes to the high number of occurrences
Figure 2. Composition of forest re risk zoning (FFRZ). Source: prepared by the authors (2017).

5/10
Comparison of Forest Fire...Floresta e Ambiente 2019; 26(2): e20170607
in Londrina (Vosgerauetal., 2006). eburning of
vegetable waste for agriculture purposes follows specic
norms in Tuscany, which carries out trainings and
campaigns for the population, especially for farmers
who use this practice. is along with determined
risk zones based on the history of the region allow
rationalizing resources for prevention, and are factors
that contribute to reduce number of re occurrences
(Regione Toscana, 2014).
The annual mean burned areas in Londrina
and Pisa were 322.0 and 155.1 ha, respectively, with
emphasis on years 2006 in Londrina and 2009 in Pisa
for burned hectares. e mean of the burned areas
for Londrina and Pisa were 1.67 and 2.05 hectares
per re, respectively, highlighting the years 2006 for
Londrina and 2009 for Pisa, with the highest number
of hectares burned per occurrence. ese values are
higher than those found by Rodríguezetal. (2013)
for Brazil and Cuba.
It was observed that August presented an average
of 2.8 rainy days for Londrina (Figure3A) and 2.7 days
for Pisa (Figure 3C). Average temperatures showed
dierent behavior, with months of June and July being
the coldest for Londrina, and June to August as the
hottest months for Pisa, which is explained by their
location in dierent hemispheres.
is precipitation behavior caused an increase
in the number of re occurrences and aected areas.
ForLondrina, 47.52% of forest res occurred during
the period from July to September, and 90.93% of areas
were also aected in this period (Figure3B). In Pisa,
67.57% of forest res with 83.09% of areas aected
occurred in the same period (Figure3D).
In this study, greater correlation was observed
between number of re occurrences and rainy days
(r = -0.76 for Londrina and r = -0.81 for Pisa). For the
region of Londrina, smaller inverse correlation of 56%
was observed between number of re occurrences and
temperature. On the other hand, direct correlation of
82% was found between days with re occurrences
and temperature for Pisa. As evidenced by Torresetal.
(2011), rainfall distribution and re occurrences show
inverse relationship for both areas and when associated
with high temperatures, as observed in Pisa, it is better
correlated with re occurrences.
According to Liu & Wimberly (2015), climatic
elements have weak inuence on the size of forest
res. is can be observed in the region of Londrina,
where the burned area presented low inverse correlation
with rainy days (r= -0.50) and temperature (r= -0.28).
However, correlation of r = -0.68 was observed between
burned area and precipitation in Pisa, and r= 0.70 when
related to temperature, showing that these variables,
especially temperature, inuence re propagation.
3.2. Historical zoning
Historical variables were grouped into 5 re risk
levels for the region of Pisa, while Londrina has 2 risk
levels, both at high risk of re. Table2 shows the mean
values for historical class for each region.
Table 1. Forest re occurrences in Londrina and Pisa in the period from 2005 to 2014.
Yea r
Londrina Pisa
Fire occurrences Area Mean Fire occurrences Area Mean
nº % ha % (ha/re) nº % ha % (ha/re)
2005 174 12.13 31.0 0.96 0.18 48 7.63 92.6 5.97 1.93
2006 218 15.19 2486.9 77.22 11.41 58 9.22 61.6 3.97 1.06
2007 211 14.70 181.6 5.64 0.86 69 10.97 52.6 3.39 0.76
2008 135 9.41 63.3 1.97 0.47 54 8.59 85.1 5.49 1.58
2009 65 4.53 58.7 1.82 0.90 75 11.92 642.8 41.45 8.57
2010 150 10.45 124.7 3.87 0.83 28 4.45 10.8 0.69 0.38
2011 186 12.96 143.0 4.44 0.77 114 18.12 201.7 13.01 1.77
2012 115 8.01 54.2 1.68 0.47 123 19.55 352.9 22.76 2.87
2013 98 6.83 47.7 1.48 0.49 44 7.00 39.7 2.56 0.90
2014 83 5.78 29.2 0.91 0.35 16 2.54 10.9 0.71 0.68
Tota l 1435 100.00 3220.4 100.00 - 629 100.00 1550.8 100.00 -
Mean 143.5 - 322.0 - 1.67 62.9 - 155.1 - 2.05
Source: CCB (2015) and AIB (2015), prepared by the authors (2017).

6/10 Santos JFL, Tetto AF, Bertacchi A, Batista AC, Soares RV Floresta e Ambiente 2019; 26(2): e20170607
Figure 3. Rainfall distribution, temperature, re occurrence and burned areas per month in Londrina (A and B)
and Pisa (C and D) in the period from 2005 to 2015. Sources: INMET (2015), ARSIA, (2015), CCB (2015) and AIB
(2015), prepared by the authors (2016).
Table 2. Fire occurrence variables calculated for Londrina and Pisa.
No. of
BUs
Classes
Number of
res/BU/
10 km2/year
Number of
res> 30 ha
Percentage
of years with
forest res
(%)
Mean
burned area
in one re
occurrence
(ha)
Median of
the burned
area in
one re
occurrence
(ha)
Maximum
burned area
in one re
occurrence
(ha)
Mean
s2
Mean
s2
Mean
s2
Mean
s2
Mean
s2
Mean
s2
Londrina
3 4 0.74 0.81 0 0 100 0 0.77 0.56 0.11 0.16 30.7 15.14
1 5 0.58 3 100 11.26 0.05 2400
Pisa
1 1 0 0 0 0 0 0
1 2 0.09 0 10 7 7 7
27 3 0.22 0.17 0 0 52 25.62 1.37 1.17 0.37 0.55 10.1 14.03
3 4 1.45 0.4 0 0 100 0 0.37 0.55 0.05 0.38 18.7 15.96
4 5 0.37 0.43 1 0 60 0 10.04 14.03 0.38 1.74 123.3 0.49
Sources: CCB (2015) and AIB (2015), prepared by the authors (2017). Note: Number of BUs: number of provinces/municipalities in
Londrina and Pisa according to risk classes. Classes: 1 (null), 2 (low), 3 (medium), 4 (high) and 5 (very high).
For the region of Pisa, Class 2 presented irregular
distribution in the occurrences of res due to the low
number of years with res (10%) and the number of
forest res per basic unit.
Class 3 presented the highest number of cities and
mean and median values of burned areas, as well as
for the maximum burned area. is situation did not
occur in Londrina, which presented classes with high

7/10
Comparison of Forest Fire...Floresta e Ambiente 2019; 26(2): e20170607
means of burned area and maximum area burned per
re occurrence.
Class 5 represented cities that historically have
high risk of re; both Pisa and Londrina are similar
due to the high number of maximum burnt areas
(123.3 and 2400 ha). It was observed that only one
re occurrence with > 30 ha of burned area occurred
in Pisa, while three occurred in Londrina.
e region of Londrina presented the two highest
re risk levels (Figure4), which was due to the large
extensions of basic units and probably to the use of re
in agriculture. Basic unit that makes up class 5 represents
very high re risk level due to the occurrence of res
throughout the period and the maximum area burned
by a single re was 2400.0 ha. While carrying out a
risk zoning map according to demographic density,
Batistaetal. (2014) veried that the region of Cambé
was within the highest risk class.
Four basic units were grouped in Pisa with mean
burned area (10.04 ha) similar to that found for class5
in Londrina (11.26 ha) (Figure5).
e presence of Pisan Mountains in the city of Calci
contributes to frequent re occurrences (Di Renzoetal.,
2012), conguring it as a region of very high re risk.
e same occurs with Bientina and Santa Maria a
Monte, which comprise the area of the Montefalcone
State Nature Reserve, where Cerbaia hills are located
(Lisaetal., 2015). Another important factor in these
areas is the prevalence of “macchie sclerolle sempreverdi”
(Mediterranean vegetation of evergreen sclerophylls)
oen mixed with easily ignitable Pinus pinaster L.
(Pinaceae) species (Bertacchietal., 2004), which leads
to the occurrence of severe res with high propagation
rates (Viedmaetal., 2015). e central belt extending
from Pisa to Pomarance has medium risk level, which
may be related to the existence of pasture areas and
Figure 4. Historical risk zoning for Londrina in the period from 2005 to 2014. Sources: IBGE (2015) and CCB
(2015), prepared by the authors (2016). Note: 1 (Londrina), 2 (Arapongas), 3 (Rolândia) and 4 (Cambé).

8/10 Santos JFL, Tetto AF, Bertacchi A, Batista AC, Soares RV Floresta e Ambiente 2019; 26(2): e20170607
Figure 5. Historical risk zoning for Pisa in the period from 2005 to 2015. Sources: ISTAT (2015) and AIB
(2015), prepared by the authors (2016). Note: 1 (Bientina), 2 (Buti), 3 (Calci), 4 (Calcinaia), 5 (Capannoli),
6(Casale Marittimo), 7 (CascianaTerme – Lari), 8 (Cascina), 9 (Castelfranco Di Sotto), 10 (CastellinaMarittima),
11 (Castelnuovo Di Val Di Cecina), 12 (Chianni), 13 (Crespina – Lorenzana), 14 (Fauglia), 15 (Guardistallo),
16(Lajatico), 17 (Montecatini Val diCecina), 18 (Montescudaio), 19 (MonteverdiMarittimo), 20 (Montopoli In Val
D’arno), 21 (OrcianoPisano), 22 (Palaia), 23 (Peccioli), 24 (Pisa), 25 (Pomarance), 26(Ponsacco), 27 (Pontedera),
28(Riparbella), 29 (San Giuliano Terme), 30 (San Miniato), 31 (Santa Croce Sull’arno), 32 (Santa Luce), 33(Santa
Maria a Monte), 34 (Terriciolla), 35 (Vecchiano), 36 (Vicopisano) and 37(Volterra).
the use of re to clear the land (Lovreglioetal., 2012).
e provinces of Vecchiano, Terricciolaand Volterra
were grouped into the high and very high risk classes,
which is dierent from what is observed in practice,
and this dierence was due to the occurrence of large
res during the period. Such information opens
the possibility for further investigations. e city of
Casale Marittimo was the only with no records of
re occurrences during the study period; therefore,
it corresponds to null risk level.
4. CONCLUSIONS
e hypothesis that precipitation is a preponderant
factor in distinct regions has shown to be partially true
for both study areas.
i) Precipitation was a predominant factor for the
occurrence of res in Londrina; however, better
correlation was observed in Pisa when the dry
season was associated with elevated temperatures;
ii) It was possible to compare the historical prole
of forest res in both regions according to the
number of occurrences, burnt area, temperature
and precipitation;
iii) Londrina presented higher re risk as evidenced
by the zoning with higher risk classes, while the
Pisa region had distribution with greater number
of cities within moderate risk class.
ACKNOWLEDGEMENTS
To CAPES (Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior) for funding this research.
e authors would also like to thank UFPR (Universidade

9/10
Comparison of Forest Fire...Floresta e Ambiente 2019; 26(2): e20170607
Federal do Paraná) and UNIPI (Università di Pisa),
for their support in carrying out this work, also the
Fire Department of the state of Paraná (Comando
do Corpo de Bombeiros - CCB) and the Servizio
Antincendi Boschivi (AIB) of the Province of Pisa,
for providing the database.
SUBMISSION STATUS
Received: 29 nov., 2016
Accepted: 12 mar., 2018
CORRESPONDENCE TO
João Francisco Labres dos Santos
Divisão de Ciências Agrárias, Universidade
Federal do Paraná – UFPR, Av. Pref. Lothário
Meissner, 900, CEP 80210-170, Curitiba, PR,
Brasil
e-mail: joabres@yahoo.com.br
REFERENCES
Batista AC, Tet t o AF, Deppe F, Grodziki L, Grassi JT.
Análise dos impactos das mudanças climáticas sobre o
risco de incêndios florestais no estado do Paraná. Scientia
Forestalis 2014; 42(104): 491-501.
Bertacchi A, Sani A, Tomei PE. La vegetazione del Monte
Pisano. Pisa: Felice; 2004.
Bovio G, Camia A. Land zoning based on fire history.
International Journal of Wildland Fire 1997; 7(3): 249-258.
http://dx.doi.org/10.1071/WF9970249.
Cabán AG. Planificación y análisis para la defensa contra
incêndios forestales. In: Vél e z R, editor. La defensa contra
incendios forestales: fundamentos y experiências. Madrid:
McGraw-Hill; 2009.
Comando do Corpo de Bombeiros – CCB. SysBM-CCB:
Cascavel [online]. 2015 [cited 2015 June 14]. Available
from: http://www.b ombeiroscas cavel.com.br/registroccb/
Corpo Forestale dello Stato. Organizzazione per la Tutela
Forestale, Ambientale e Agroalimentare [online]. 2015 [cited
2015 May 8]. Available from: http://www.carabinieri.it/
arma/oggi/organizzazione/organizzazione-per-la-tutela-
forestale-ambientale-e-agroalimentare
Di Renzo F, Fratini R, Marchi E. Stima dei danni da
incendi sui Monti Pisani. Sherwood 2012; (187): 9-14.
Fimia JCM. Fatores meteorológicos. In: Vél e z R, editor.
La defensa contra incendiosforestales: fundamentos y
experiências. Madrid: McGraw-Hill; 2009.
Hardesty J, Myers RL, Fulks W. Fire, ecosystems, and people:
a preliminary assessment of fire as a global conservation
issue. e George Wright Forum 2005; 22(4): 78-87.
Instituto Brasileiro de Geografia e Estatística – IBGE.
Brasil/Paraná/Londrina [online]. 2015 [cited 2015 May
15]. Available from: http://cidades.ibge.gov.br/xtras/perfil.
php?codmun=411370
Instituto Nacional de Meteorologia – INMET. BDMEP
- Banco de Dados Meteorológicos para Ensino e Pesquisa
[online]. 2015 [cited 2015 May 7]. Available from: http://
www.inmet.gov.br/portal/index.php?r=bdmep/bdmep
Istituto Nazionale Di Statistica – ISTAT. Superci Delle
Unità Amministrative a ni statistici. La supercie dei
Comuni, delle Province e delle Regioni italiane al 9 ottobre
2011 [online]. 2015 [cited 2015 May 7]. Available from:
http://www.istat.it/it/archivio/82599
Italia. Ministero Dell’Ambiente. Della Tutela del Territorio
e del Mare. Legge-quadro in materiadi incendi boschivi, 30
de novembro de 2000. Gazzetta Uciale, Roma, LZ (2000).
Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World
map of the Köppen-Geiger climate classification updated.
MeteorologischeZeitschri 2006; 15(3): 259-263. http://
dx.doi.org/10.1127/0941-2948/2006/0130.
Lara CH. El concepto de inflamabilidad. In: Vél ez R,
editor. La defensa contra incendiosforestales: fundamentos
y experiências. Madrid: McGraw-Hill; 2009.
Lisa C, Paffetti D, Nocentini S, Marchi E, Bottalico F,
Fiorentini Set al. Impact of wildfire on the edaphic
microarthropod community in a Pinus pinaster forest in
central Italy. IForest (Viterbo) 2015; 8(6): 874-883. http://
dx.doi.org/10.3832/ifor1404-008.
Liu Z, Wimberly MC. Climatic and landscape influences
on fire regimes from 1984 to 2010 in the Western United
States. PLoS One 2015; 10(10): 1-20. PMid:26465959.
Lovreglio R, Marciano A, Patrone A, Leone V. Le
motivazionidegli incendi boschivi in Italia: risultatiprel
iminaridiun’indagine pilota nelle Province a maggiorei
ncidenzadi incendi. Forest 2012; 9(1): 137-147. http://
dx.doi.org/10.3832/efor0693-009.
Myers RL. Convivendo com o fogo: manutenção dos
ecossistemas & subsistência com o manejo integrado do
fogo. Tallahassee: The Nature Conservancy; 2006.
Nunes JRS, Batista AC, Soares RV, Fier ISN, Santos CC.
Climatologia do comportamento da precipitação no
Distrito Florestal de Monte Alegre, PR, Brasil. Floresta 2009;
39(4): 783-792. http://dx.doi.org/10.5380/rf.v39i4.16313.
Paraná. Constituição do Estado 1989. Curitiba: Imprensa
Oficial; 2006.
Regione Toscana. La vegetazione forestale. Florença:
Edizioni Regione Toscana; 1998.
Regione Toscana. Piano operativo AIB 2014-2016 (art. 74
L.R. 39/00). Pistoia: Tipografica Pistoeise; 2014.

10/10 Santos JFL, Tetto AF, Bertacchi A, Batista AC, Soares RV Floresta e Ambiente 2019; 26(2): e20170607
Roderjan CV, Galvão F, Kunyioshi YS, Hatschbach GG.
Unidades fitogeográficas do estado do Paraná, Brasil.
Ciência & Ambiente 2002; (24): 75-92.
Rodríguez MPR, Soares RV, Batista AC, Tetto AF, Becerra
LWM . Comparação entre o Perfil dos Incêndios Florestais
de Monte Alegre, Brasil, e de Pinar Del Río, Cuba.
Floresta 2013; 43(2): 231-240. http://dx.doi.org/10.5380/
rf.v43i2.27650.
Rodríguez MPR. Bases metodológicas para el perfeccionamiento
de la prevención de los incendios forestales [tese] Pinar del
Río: Universidad de Pinar delRío; 1999.
Sampaio SC, Longo AJ, Queiroz MMF, Gomes BM, Vilas
Boas MA, Suszek M. Estimativa e distribuição da precipitação
provável no estado do Paraná. Acta Scientiarum: Human
and Social Sciences 2006; 28(2): 267-272.
Servizio Agrometeorologico – ARSIA. Regione Toscana
[online]. 2015 [cited 2015 May 6]. Available from: http://
agrometeo.arsia.toscana.it/
Servizio Antincendi Boschivi – AIB. Consorzio Lamma
[online]. 2015 [cited 2015 May 14]. Available from: http://
www.lamma.rete.toscana.it/aib/frameset.html
Soares RV, Batista AC, Tet t o AF. Meteorologia e climatologia
orestal. Curitiba: FUPEF; 2015.
Soares RV, Batista AC. Incêndios orestais: controle, efeitos
e uso do fogo. Curitiba: FUPEF; 2007.
Soares RV. Determinação de um índice de perigo de incêndio
para a região centro-paranaense, Brasil [dissertação].
Turrialba: Centro Agronomico Tropical de Enseanza e
Investigacion; 1972.
Stefanini MC. Analisi dendrocronologiche su esemplari
dell’Orto Botanico di Pisa. Attidella Società Toscana de
Scienze Naturalidi Pisa 2008; 115(1): 99-105.
Tetto AF, Batista AC, Soares RV. Ocorrência de incêndios
florestais no estado do Paraná, no período de 2005 a 2010.
Floresta 2012; 42(2): 391-398. http://dx.doi.org/10.5380/
rf.v42i2.22516.
Torre s FTP, Ribeiro GA, Martins SV, Lima GS. Correlações
entre os elementos meteorológicos e as ocorrências
de incêndios florestais na área urbana de Juiz de Fora,
MG. Revista Árvore 2011; 35(1): 143-150. http://dx.doi.
org/10.1590/S0100-67622011000100017.
Viedma O, Quesada J, Tor r e s I, De Santis A, Moreno JM.
Fire severity in a large fire in a Pinus pinaster forest is
highly predictable from burning conditions. Ecosystem
2015; 18(2): 233-250. http://dx.doi.org/10.1007/s10021-
014-9824-y.
Vosgerau JL, Batista AC, Soares RV, Grodzki L. Avaliação
dos registros de incêndios florestais do estado do Paraná
no período de 1991 a 2001. Floresta 2006; 36(1): 23-32.
http://dx.doi.org/10.5380/rf.v36i1.5608.
Ward JH Jr. Hierarchical grouping to optimize an objective
function. Journal of the American Statistical Association
1963; 58(301): 236-244. http://dx.doi.org/10.1080/0162
1459.1963.10500845.