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Bioscience Journal Original Article
Biosci. J., Uberlândia, v. 36, n. 3, p. 1008-1017, May/June 2020
http://dx.doi.org/10.14393/BJ-v36n3a2020-47769
SPATIAL-TEMPORAL OF FIRE FOCI IN THE STATE OF RIO DE JANEIRO,
BRAZIL
ESPAÇO-TEMPORALIDADE DOS FOCOS DE CALOR NO ESTADO DO RIO DE
JANEIRO, BRASIL
Givanildo de GOIS1; Welington Kiffer de FREITAS2; José Francisco de OLIVEIRA JÚNIOR3
1. Universidade Federal Fluminense, Pós-Doutorado do Programa de Pós-Graduação em Tecnologia Ambiental - PGTA, Volta
Redonda, RJ, Brasil. givanildogois@gmail.com; 2. Universidade Federal Fluminense, Programa de Pós-Graduação em Tecnologia
Ambiental – PGTA, Volta Redonda, RJ, Brasil. wkifferpgtauff@gmail.com; 3. Universidade Federal de Alagoas, Instituto de Ciências
Atmosféricas - ICAT, Maceió, AL, Brasil. jose.junior@icat.ufal.br
ABSTRACT: This study evaluated the space-time variability of fire foci via environmental satellites for the
State of Rio de Janeiro (SRJ) based on statistical procedures. The fire foci in the period of 2000 to 2015 were obtained
from the BDQueimadas fire database. Descriptive, exploratory, and multivariate statistical analyses were performed in
the software environment R i386 version 3.2.5. The north region had 6760 foci (21.11%), the south-central region had
3020 foci (9.43%), the Middle Paraíba had 6,352 foci (19.84%), the Metropolitan areas had 6671 foci (20.83%), and the
Green Coast region had 292 foci (0.91%). The cluster analysis identified three homogeneous groups of fire foci (G1, G2,
and G3) but did not include the municipality of Campos dos Goytacazes (NA). The G1 group (6.21 ± 0.01 foci, 57.61%)
included areas throughout the state and covered the coastal region and lowlands towards the north. The G2 group (6.21 ±
0.01 foci, 34.81%) included the northern, south-central, and coastal shallows regions. The G3 group (6.21 ± 0.01 foci,
9.78%) included the mountain ranges of the state. Environmental characteristics and socioeconomic are crucial in the
dynamics of fire foci in Rio de Janeiro.
KEYWORDS: Wildfires. Burned. Environmental satellites. Statistical methods. Meteorological
systems.
INTRODUCTION
In 1997, Brazil implemented an operational
system called the Database of Fires (BDQueimadas),
which was developed by the Center for Weather
Forecasting and Climate Studies (CPTEC) of the
National Institute for Space Research (INPE). The
objective is monitoring the foci of fires and predicting
the risk of fire in areas of vegetation (SETZER
SISMANOGLU, 2006, CPTEC, 2016). BDQueimadas
helps to prevent and minimize the environmental
impacts caused by forest fires, especially in the Protected
Areas in Brazil (CAÚLA et al. 2015; CLEMENTE;
OLIVEIRA JÚNIOR; LOUZADA, 2017).
In the last decades, data from environmental
satellites have been used to detect active fires and hot
spots and to map burned areas (ROY; LEWIS;
JUSTICE, 2002; CAÚLA et al., 2016). These data have
been effective in monitoring, preventing, and combating
fires (ROY et al., 2008; OLIVEIRA JÚNIOR et al.,
2017). Based on the data, information and estimates of
the location, period, and frequency of fires have been
generated, which can provide evidence of their spatio-
temporal dynamics (ANTUNES; RIBEIRO, 2000;
SILVA; ROCHA; ANGELO, 2013). Systematic
monitoring is essential for preventing and combating
fires, especially in Protected Areas (FERNANDES et al.,
2011; NUNES et al., 2015), as well as in planning,
control, and efficient management (BATISTA, 2003;
PIROMAL et al., 2008).
With the technological advancement of orbital
sensors and geotechnologies, it has become possible to
determine how anthropogenic actions interfere with the
environment and to help managers in making decisions
in relation to large forest fires (BAILING JÚNIOR;
MEYER; WELLS, 1992, COCHRANE, 2003,
COCHRANE; BARBER, 2009). The most advanced
orbital sensors are the Advanced-Very-High-Resolution
Radiometer (AVHRR) (SETZER; VERSTRAETE,
1994; GITAS; MITRI; VENTURA, 2004) and the
Moderate Resolution Imaging Spectroradiometer
(MODIS) (HUETE et al., 2002, CORREIA et al., 2006;
BOSCHETTI et al., 2008). MODIS provides data on
phenomena occurring on the Earth's surface, in the
oceans, and in the atmosphere (PIROMAL et al., 2008;
CAÚLA et al., 2016).
Remote sensing (RS) products are used as tools
for the detection of fire fire foci to understand the
patterns of the ecosystem response to fire events
(KEELEY; ZEDLER, 2009; CLEMENTE;
OLIVEIRA JÚNIOR; LOUZADA, 2017). Fires
produce changes in several types of ecosystems
(SETZER; SISMANOGLU, 2006), such as increases in
surface albedo, changes in carbon stocks, and decreases
Received: 04/04/19
Accepted: 20/12/19
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in the availability of water resources due to soil impact,
erosion, and deposition processes of sediments in
riverbeds (ANDERSEN et al., 2009; BROCK and
CARPENTER, 2010). Furthermore, the transport of
combustion products by smoke can spread materials that
are potentially dangerous for human health (SILVA et
al., 2012; SETZER; SISMANOGLU, 2006; CAÚLA et
al., 2015). Several studies show that there are direct
relationships between climate and forest fires, so the
trends and distributions must be observed and
considered in the development of management policies
(BAILING JÚNIOR; MEYER; WELLS, 1992;
BATISTA, 2003; NUNES et al., 2015; CAÚLA et al.,
2015; CLEMENTE; OLIVEIRA JÚNIOR;
LOUZADA, 2017).
Understanding the variability of fire foci in the
State of Rio de Janeiro (SRJ) constitutes a fundamental
strategy in the definition of public policies, especially in
areas with high risk of fires in the Atlantic Forest biome
(COURA et al.; 2010; FERNANDES et al.; 2011;
CLEMENTE; OLIVEIRA JÚNIOR; LOUZADA,
2017; CLEMENTE; OLIVEIRA JÚNIOR;
LOUZADA, 2017). However, there are few studies on
fire foci in SRJ with a methodological approach
involving statistical tools, geoprocessing, and RS
products (CAÚLA et al., 2016) in the search for
relational patterns of heat sources on the spatial and
temporal scale. Therefore, the objective of this study was
to evaluate the spatial-temporal variability of the fire foci
via environmental satellites through BDQueimadas for
the SRJ based on statistical procedures.
MATERIAL AND METHODS
Study Area
SRJ is located in the southeast region of Brazil
between latitudes of 20 ° 45 '54 and 23° 21' 57 "S and
longitudes of 40° 57' 59" and 44°53' 18 "W. It has an
area of 43,696,054 km² and borders Espírito Santo (ES)
in the northeast (NE), Minas Gerais (MG) in the north
and northwest (N-NW), São Paulo (SP) in the southwest,
and the Atlantic Ocean to the south and east. It has an
extensive coast that is approximately 635 km long.
Currently, the Brazilian Institute of Geography and
Statistics (IBGE) divides the geopolitical state into 92
municipalities (IBGE, 2018) and eight governmental
regions (Metropolitan, Northern, Northwest, Coastal
Flats, Mountainous, South-Central Fluminense, Middle
Paraíba, and Green Coast), as shown in Figure 1.
Figure 1. Location of meteorological stations in the State of Rio de Janeiro (Brazil) and subdivided in its eight regions of
Government with 92 municipalities, respectively.
Time series of fire foci
Data on fire foci were obtained from
BDQueimadas on the CPTEC website at the following
address: http://pirandira.cptec.inpe.br/queimadas
(CPTEC, 2016). Currently, CPTEC uses 31
environmental satellites with polar and geostationary
orbits in its South American observation network. The
environmental satellites include NOAA, GOES, AQUA
(EOS PM-1), TERRA (EOS AM-1), METEOSAT,
ATSR, and TRMM. These satellites perform orbital
imaging in Brazil. The study period was from 2000 to
2015.
Statistical analysis
Analyses were done based on the following
criteria: the application of descriptive statistics (DS) to
describe and understand the series of fire foci in the SRJ;
the measures of position (central tendency and
separatrix) and dispersion (absolute and relative); and the
annual accumulations of the fire foci subjected to an
exploratory analysis (boxplot) with the purpose of
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identifying the major/minor occurrence periods of fire
foci and outliers.
The annual cumulative time series of the fire
foci obtained by the homogeneous groups from the
cluster analysis (CA) were evaluated.
The CA technique was applied to the time series
of fire foci in order to separate objects into groups based
on the homogeneous characteristics that they have
(EVERITT; DUNN, 1991; MONTGOMERY;
RUNGER, 2007). The respective numbers of groups and
a dendrogram of the 92 municipalities were determined
based on the number of annual accumulated fire foci.
The number of groups adopted and the stratification of
fire foci were based on Ward's (1963) agglomerative
hierarchical method by means of the measure of
dissimilarity at the Euclidian distance (LYRA;
OLIVEIRA-JÚNIOR; ZERI, 2014; BRITO et al.,
2016). The Euclidean distance is given by equation 1:
(1)
Where dE is the Euclidean distance, are
quantitative variables j of individuals p, and are
quantitative variables j of individuals.
In Ward's (1963) method, the distance between
two clusters is the sum of the squares between the two
clusters made for all variables. This method minimizes
the dissimilarity or the total sum of squares within
groups. That is, the data are grouped by homogeneity
within each group and by heterogeneity outside each
group (LYRA; OLIVEIRA-JÚNIOR; ZERI, 2014;
BRITO et al., 2016).
(2)
W is the homogeneity and intragroup
heterogeneity and is obtained by summing the square of
the deviations, n is the number of analyzed values, and
is the i-th element of the cluster.
The standard deviation (SD) and the standard
error (SE) of the annual fire foci were determined
according to equations (3) and (4).
(3)
(4)
Where is the i-th observed fire focus, is the
arithmetic mean of fire foci, and n is the sample size. All
statistical procedures used in the study were calculated in
the environment software R i386 version 3.4.2 R Core
Team (2017).
RESULTS AND DISCUSSION
Descriptive and exploratory statistics applied to
the series of fire foci in the government regions of
Rio de Janeiro
The annual averages of fire foci increased
significantly during the study period in SRJ. The
highlight of 2015 is the average of 250 foci per
government region. The North (6760 foci, 21.11%),
Metropolitan (6671 foci, 20.84%), Middle Paraíba
(6,352 foci, 19.84%), and south-Central (3020 foci,
9.43%) regions had the most fire foci. The high numbers
in the respective regions are due to several factors, such
as the quantity of factories, the complex relief (exposed
granite), disorderly urban occupation, and the burning of
waste and other associated human activities (mainly
deforestation and agricultural activities) (CAÚLA et al.,
2016; CLEMENTE; OLIVEIRA JÚNIOR;
LOUZADA, 2017). There may also be temperatures
above 47°C according to the orbital sensors of
environmental satellites (SETZER; SISMANOGLU,
2006; CPTEC, 2016; CLEMENTE; OLIVEIRA
JÚNIOR; LOUZADA, 2017).
There were 32,018 fire foci in SRJ, which had
high temporal variability and the statistical parameters
evaluated (Table 1). The Green Coast region had the
lowest mean and SE (18 ± 3.16 foci) compared to the
northwest (129 ± 4.86 foci) and Green Coast (127 ±
32.95 foci). However, similar results were obtained for
the SD of the regions. The minimum values obtained by
region were 0 foci (Green Coast), 1 focus (Coastal Flats),
4 foci (Metropolitan), and 8 foci (Central South). In the
north, northwest, Mountainous, and Middle Paraíba
regions, there were 62, 14, 27, and 25 foci, respectively.
The highest numbers were 41 foci in the Green
Coast region, 486 foci in coastal shallows, 2,825 foci in
Metropolitan areas, 939 foci in the south-Central region,
1,523 foci in the north region, 355 foci in the northwest
region, 1,383 foci in Mountainous, and 1,345 foci in
Middle Paraíba. The CV% was lower in the Green
Coast, northwest, north, and Middle Paraíba, in contrast
to the Mountainous, coastal shallows, southern, and
Metropolitan regions.
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The lower CV% values indicate low variability
of the fire foci between the regions. The high CV%
values represent significant changes in fire foci in SRJ
during the study period (CLEMENTE; OLIVEIRA
JÚNIOR; LOUZADA, 2017). The Ap coefficient
characterizes how distant the data distribution is from a
symmetric condition. The Ap coefficients for the eight
SRJ regions showed a predominance of high asymmetric
curves in the north (2.14), Mountainous (2.14), south-
Central (2.20), and Metropolitan (2.47) regions, followed
by (1.21), coastal shallows (1.59), and Middle Paraíba
(1.18). However, the Green Coast region (0.22)
presented a moderate positive asymmetry. In this case,
the right tail is more elongated than the left tail for means
greater than zero, which indicates the occurrence of high
values with low frequency (VELÁSQUEZ et al., 2013,
MACHIWAL; KUMAR; DAYAL, 2016). The K
coefficients obtained for all regions of SRJ revealed
leptokurtic distributions (K > 3, sharpest curve) of
annual heat accumulated foci for the north,
Mountainous, south-Central, and Metropolitan regions,
unlike the northwest regions, Middle Paraíba, Green
Coast, and coastal shallows, which had platykurtic
distributions (K < 3, a more flattened curve) – (Table 1).
The North and Metropolitan regions had the
highest annual amplitudes of 1,461 and 2,821 fire foci,
respectively. The Mountainous and the Middle Paraíba
regions had 1,356 and 1,345 foci, which were the lower
and upper limits, respectively. The highest medians and
SD occurred in the northern (334.00 ± 333.57 foci.years-
1), Metropolitan (171.00 ± 715.83 foci.years-1),
Mountainous (197.50 ± 329.04 foci.years-1), and Middle
Paraíba (273.00 ± 385.82 foci.years-1).
Table 1. Results of the descriptive statistics applied to the time series of accumulated fire foci in the period of 2000-2015.
Governmental
Regions
Amount of
Fire Foci
(Foci. Year-1)
Accumulated
Annual
(%)
Annual
Accumulat
ed Foci
Fire
Average Medium Mode
Minimum
Value
Maximum
Value
Amplitude
Total
Limit
Bottom
Limit
Higher
(Focus. Year-1)
Northern 6760 21.11 422.50 334.00 62.00 62 1523 1461 11.00 735.00
Northwest 2062 6.44 128.88 99.50 14.00 14 355 341 66.63 290.38
Mountainous 4827 15.08 301.69 197.50 27.00 27 1383 1356 252.75 739.25
South-Central 3020 9.43 188.75 133.00 8.00 8 939 931 235.63 529.38
Coastal Flats 2034 6.35 127.13 84.50 99.00 1 486 485 104.13 302.88
Metropolitan 6671 20.84 416.94 171.00 4.00 4 2825 2821 209.00 655.00
Middle Paraíba 6352 19.84 397.00 273.00 25.00 25 1345 1320 416.13 1016.88
Green Coast 292 0.91 18.25 17.00 17.00 0 41 41 17.25 50.75
Total 32018 100 2001.13 1309.50 256.00 Standard
deviation
(SD)
Standar
d error
(SE)
Quartile Interqua
rtile
Range
(AIQ) Governmental
Regions
Coefficients
Bottom
(Q1)
Higher
(Q3)
Sampling
Variation
(%) (CV)
Asymmetry
(Ap)
Curtose
(K)
(Focus. Year-1)
Northern 78.95 2.14 + 4.57 leptokurtic 333.57 83.39 268.75 455.25 186.50
Northwest 77.15 1.21 + 0.45 platykurtic 99.43 24.86 67.25 156.50 89.25
Mountainous 109.07 2.14 + 4.41 leptokurtic 329.04 82.26 119.25 367.25 248.00
South-Central 120.33 2.20 + 4.58 leptokurtic 227.13 56.78 51.25 242.50 191.25
Coastal Flats 103.66 1.59 + 1.47 platykurtic 131.78 32.95 48.50 150.25 101.75
Metropolitan 171.69 2.47 + 5.22 leptokurtic 715.83 178.96 115.00 331.00 216.00
Middle Paraíba 97.21 1.18 + 0.24 platykurtic 385.92 96.48 121.25 479.50 358.25
Green Coast 69.21 0.22 + -1.18 platykurtic 12.63 3.16 8.25 25.25 17.00
This showed that the annual accumulated heat
outlets in all SRJ regions had an asymmetric distribution.
25% of accumulated fire foci occurred below the median
in the north, northwest, Mountainous, south-Central,
coastal shallows, Metropolitan, and Middle Paraíba
regions. The value of the first quartile (Q1) ranged from
48.50 to 268.75 foci.year-1 and was greater than 25% of
the values of the third quartile (Q3), which ranged from
150.25 to 455.25 foci.year-1. The interquartile range
(AIQ) ranged from 89.15 to 358.25 foci.year-1 in the
respective regions. The exception was the Green Coast
region, which presented the lowest median, and 25% of
the accumulated fire foci were lower than Q1 and or
higher than Q3 (between 8.25 and 25.25 foci.year-1). The
AIQ was 17.00 foci.year-1.
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The analysis of the sample CV% for the SRJ
government regions showed that the majority of the
regions presented values above 70%. This indicated high
temporal variability of annual heat accumulated in the
SRJ. The higher SD and higher AIQs indicated a high
degree of variability of annual accumulated fire foci
around the average in the north, northwest,
Mountainous, south-Central, coastal bays, Metropolitan,
and Middle Paraíba regions (Table 1). The temporal
variability in the detection of fire foci in SRJ can be
explained by the anthropic actions along with the
development of orbital sensors (AVHRR and MODIS)
and algorithms, such as the Wild Fire-Automated
Biomass Burning Algorithm (WF-ABBA) number
(currently 31) and the types of satellites (polar or
geostationary orbit), which are crucial factors in the
increase in foci. INPE used the NOAA-12 reference
satellite until August 2007 and then adopted the NOAA-
15 satellite. Currently, it uses the AQUA-MT and
TERRA-MT satellites (CPTEC, 2016).
According to Caúla et al. (2015), the AQUA-
MT satellite has a polar orbit, and it is more successful in
detecting fire foci per unit area in Brazil compared to
geostationary satellites and in relation to others with the
same orbit, such as the old reference satellites
mentioned. The influence of meteorological systems on
the time series of fire foci in SRJ, which in turn influence
rainfall patterns and the distribution of temperature and
humidity in the air, is also not ruled out. In this paper, we
present the results of a study of forest fires and general
fires (BATISTA, 2003; NUNES et al., 2015,
OLIVEIRA JÚNIOR et al., 2017)..
Homogeneous regions of fire foci
The CA technique identified three
homogeneous groups of fire foci (G1, G2, and G3) and
only the municipality of Campos dos Goytacazes did not
cluster (NA). Campos dos Goytacazes had more fire foci
and was an outlier in the time series. The CA technique
grouped all municipalities independently of the
government region (Figure 2). The highest number of
hot spots in Campos dos Goytacazes was due to the
large areas of pasture, climatic conditions (high
temperature and low rainfall) (BRITO et al., 2016,
SOBRAL et al., 2018), and the production and harvest
of sugarcane based on the practice and common use of
fires (CAÚLA et al., 2016; CLEMENTE; OLIVEIRA
JÚNIOR; LOUZADA, 2017). Furthermore, there is a
greater number of industries in the region (IBGE, 2018).
The results corroborate those from FERNANDES et al.
(2011), who classified SRJ into regions of fire
susceptibility. The North Fluminense was classified as a
region of high susceptibility due to the factors mentioned
and slopes with a high incidence of solar radiation.
Figure 2. Group Number (A) and Dendrogram (B) obtained from the cluster analysis of the fire foci of the municipalities
of the State of Rio de Janeiro, with their respective homogeneous groups (G1, G2, G3 and NA).
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The exploratory analysis technique was applied
for G1, G2 and G3 via boxplot (Figure 3), using the mean
values of the statistical parameters (mean, median,
maximum, minimum and 1st and 3rd quartile) of the fire
foci. The G1 group was represented by Figure 3 (a),
being the largest homogeneous group of fire foci,
consisting of 53 municipalities in the state (57.61%). In
group G1, outliers were registered for all years of the
dataset, the exceptions were 2003, 2005 and 2010. The
highlights were 2014 and 2015, which registered most
number of outliers, followed by average values (20.46
and 18.85 focus) greater than the median (14.00 and
12.50 focus). Similar to group G1, outliers were also
observed in group G2, again there were exceptions for
the years 2000, 2003 and 2007. Group G2 was the
second largest group consisting of 30 municipalities
(34.81%), with an average ranging from 81.43 to 66.00
and the medians from 49.07 to 35.50, and an SD of
27.89 fire foci. In group G3, there was a significant
reduction of outliers. Group G3 was the smallest
homogeneous group consisting of nine municipalities
(9.78%), ranging between 314.13 and 361.33 foci and
with a median of 204.50 and 76.00 foci and the largest
SD (196.75) compared to the other homogeneous
groups. NA did not show any outliers in the time series
although 2015 registered 1000 fire focus.
Figure 3. Boxplot of homogeneous groups G1 (A), G2 (B), G3 (C) and NA (D) of hot flashes from 2000 to 2015 in the
state of Rio de Janeiro.
The G1 group covered the coast and the Coastal
Flats region towards the northern part of the state. The
southern part extends from the northeast to southwest.
The G2 group had a similar distribution to group G1. The
highest concentrations of municipalities were observed
in the north (55.56%), Coastal Flats (45.45%), and
Middle Paraíba (41.67%) with five municipalities each
(Figure 4 ande Table 3).
Figure 4. Spatial distribution of the homogeneous groups of fire foci G1, G2, G3 and NA in the State of Rio de Janeiro in
the period 2000-2015.
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Table 3. Percentage (%) of fire foci by government region and homogeneous groups (G1, G2, G3 and NA) in the period of
2000-2015 in the State of Rio de Janeiro.
Governmental
Regions
G1
(%)
Governmental
Regions
(%)
Group
G2
(%)
Governmental
Regions
(%)
Group
G3
(%)
Governmental
Regions
(%)
Group
Northern 3 33.33 6% 5 55.56 17% 0 0.00 0%
Northwest 9 69.23 17% 4 30.77 13% 0 0.00 0%
Mountainous 8 53.33 15% 4 26.67 13% 3 20 33%
South-Central 4 44.44 8% 4 44.44 13% 1 11.11 11%
Coastal Flats 6 60 12% 4 40.00 13% 0 0 0%
Metropolitan 15 75 29% 4 20.00 13% 1 5 11%
Middle Paraíba 4 30.77 8% 5 38.46 17% 4 30.77 44%
Green Coast 3 100 6% 0 0.00 0% 0 0 0%
TOTAL 52
30
9
The G3 group had a smaller number of
municipalities in the Middle Paraíba (30.77%) and the
Mountainous region (20.00%). The entire G3 group was
concentrated along the mountain ranges of SRJ from
Serra do Mar, which is part of Serra dos Órgãos in the
Mountainous region, up to the Serra da Mantiqueira. The
southewest border is shared with the States of São Paulo
and Minas Gerais, where the rest of the Atlantic Forest is
located. The differences in the numbers of fire foci are
mainly due to local characteristics, such as vegetation,
proximity to the coastal environment, complex
topography, and multiscale meteorological systems that
affect the region's climate (CAÚLA et al. 2016,
FERNANDES et al., 2011; NUNES et al., 2015).
Human activities also contribute to the variation
(FERNANDES et al., 2011).
CONCLUSIONS
The statistical analysis has shown that the north,
south-Central, Middle Paraíba, and Metropolitan regions
stand out in relation to the temporal variability of fire
foci in SRJ, but not the Green Coast region. The applied
statistics clearly show that there is a considerable
increase of fire foci in Rio de Janeiro throughout the time
series, with emphasis on the high spatial-temporal
variability in the government regions. The number of
foci significantly increased in the analysis period due to
the anthropic actions of each government region, the
development of orbital sensors, and the increase of the
numbers and types of environmental satellites.
Three homogeneous groups of fire foci were identified
based on cluster analysis (G1, G2 and G3), together with
the municipality of Campos dos Goytacazes (NA). This
was achieved using Ward’s method with the Euclidian
distance as a dissimilarity measure. Spatially, the G1
group was the largest in terms of the number of
municipalities, which were spread throughout SRJ and
covered the coastal and lowland regions towards the
north. The G2 group had a similar distribution to G1 but
with predominance in the north, south-central, and
coastal shallows regions. The G3 group was concentrated
along the mountain ranges of the state. The local
characteristics, proximity to the coast, complex
topography, and multiscale meteorological systems
contribute to the fire risk and are crucial factors in the
spatio-temporal variability of fire foci in the state.
ACKNOWLEDGMENTS
To Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior – CAPES - for the PNPD
scholarship, to the Postgraduate Program in
Environmental Technology - PGTA of the Universidade
Federal Fluminense.
RESUMO: Este estudo avaliou a variabilidade espaço-temporal de focos de calor via satélites ambientais para
o Estado do Rio de Janeiro (SRJ) com base em procedimentos estatísticos. Os focos de calor no período de 2000 a 2015
foram obtidos a partir do banco de dados de focos do BDQueimadas. Análises estatísticas descritivas, exploratórias e
multivariadas foram realizadas no ambiente de software R i386 versão 3.2.5. A região Norte tinha 6760 focos (21,11%),
a região Centro-Sul tinha 3020 focos (9,43%), o Médio Paraíba tinha 6,352 focos (19,84%), as áreas metropolitanas
tinham 6671 focos (20,83%) e a Costa Verde região teve 292 focos (0,91%). A análise de agrupamento identificou três
grupos homogêneos de focos de calor (G1, G2 e G3), mas não incluiu o município de Campos dos Goytacazes (NA). Em
que se observa no grupo G1 uma forte presença de outliers com valores atípicos, em todos os anos da série temporal,
sendo destaque para os anos de 2014 e 2015 que apresentam os maiores números de outliers seguidos dos valores das
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médias (20.46 e 18.85 focos) acima das medianas (14.00 e 12.50 focos). Comportamentos semelhantes foram
observados nos grupos G2 e G3, sendo o grupo G2 com média (81.43 mm e 66.00 focos) e medianas (49.07 e 35.50
focos) com um DP de 27.89 focos de fogo. O grupo G3 com média (314.13 e 361.33 mm) e mediana (204.50 e 76.00
focos) e o maior DP (196,75 focos) em comparação aos demais grupos. Características ambientais e socioeconômicas
são cruciais na dinâmica dos focos de calor no Rio de Janeiro.
PALAVRAS-CHAVE: Incêndios florestais. Queimadas. Satélites ambientais. Métodos estatísticos. Sistemas
meteorológicos.
REFERENCES
ANDERSEN, T.; CARSTENSEN, J.; HERNÁNDEZ-GARCÍA, E.; DUARTE, C. M. Ecological thresholds
and regime shifts: approaches to identification. Trends in Ecology & Evolution, Londres, v. 24, n. 1, p. 49–57,
2009. https://doi.org/10.1016/j.tree.2008.07.014
ANTUNES, M. A. H.; RIBEIRO, J. C. Uso de satélites para detecção de queimada e para avaliação do risco de
fogo. Ação Ambiental, Curitiba, v. 2, n. 1, p. 24-27, 2000.
BAILING JÚNIOR, R. C.; MEYER, G. A.; WELLS, S. G. Relation of surface climate and burned area in
Yellowstone National Park. Agricultural and Forest Meteorology, Boston, v. 60, n. 3-4, p. 285-293, 1992.
https://doi.org/10.1016/0168-1923(92)90043-4
BATISTA, A. C. Mapas de risco: uma alternativa para o planejamento de controle de incêndios florestais.
Floresta, Curitiba, v. 30, n. 1, p.45-54, 2003. https://doi.org/10.5380/rf.v30i12.2328
BOSCHETTI, L.; ROY, D.; BARBOSA, P.; BOCA, R.; JUSTICE C. A MODIS assessment of the summer
2007 extent burned in Greece. International Journal of Remote Sensing, Londres, v. 29, n. 8, p. 2433-2436,
2008. https://doi.org/10.1080/01431160701874561
BRITO, T. T.; OLIVEIRA JÚNIOR, J. F.; LYRA, G. B.; GOIS, G.; ZERI, M. Multivariate analysis applied to
monthly rainfall over Rio de Janeiro state, Brazil. Meteorology and Atmospheric Physics, Viena, v.129, n. 5,
p.469-478, out, 2016. https://link.springer.com/article/10.1007%2Fs00703-016-0481-x
CAÚLA, R. H. ; OLIVEIRA-JÚNIOR, J. F.; LYRA, G. B.; DELGADO, R. C. ; HEILBRON FILHO, P.F.L.
Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011.
Environmental Earth Sciences (Print), Melbourne, v. 74, n. 2, p. 1497-1508, 2015.
https://doi.org/10.1007/s12665-015-4142-z
CAÚLA, R. H.; OLIVEIRA-JÚNIOR, J. F.; GOIS, G.; DELGADO, R. C.; PIMENTEL, L. C. G.; TEODORO,
P. E. Nonparametric statistics applied to fire foci obtained by meteorological satellites and their relationship to
the MCD12Q1 product in the state of Rio de Janeiro, Southeast - Brazil. Land Degradation & Development,
Londres, v. 28, n. 3, p. 1056-1067, 2016. https://doi.org/10.1002/ldr.2574
CLEMENTE, S. S.; OLIVEIRA JÚNIOR, J. F.; LOUZADA, M. A. P. Focos de Calor na Mata Atlântica do
Estado do Rio de Janeiro. Revista Brasileira de Meteorologia, São Paulo, v. 32, n. 4, p. 1-9, 2017.
http://dx.doi.org/10.1590/0102-7786324014
CLEMENTE, S. S.; OLIVEIRA JÚNIOR, J. F.; LOUZADA, M. A. P. Focos de calor do bioma Mata Atlântica
no estado do Rio de Janeiro: Uma abordagem de gestão e legislação ambiental. Revista de Ciências Agro-
Ambientais (Online), Alta Floresta, v. 15, n. 1, p. 158-174, 2017. https://doi.org/10.5327/Z1677-
606220172240
COCHRANE, M. A. Fire science for rainforests. Nature, Reino Unido, v. 421, p. 913-919, 2003.
http://dx.doi.org/10.1038/nature01437
1016
Spatial-temporal… GOIS, G. et al.
Biosci. J., Uberlândia, v. 36, n. 3, p. 1008-1017, May/June 2020
http://dx.doi.org/10.14393/BJ-v36n3a2020-47769
COCHRANE, M. A.; BARBER, C. P. Climate change, human land use and future fires in the Amazon. Global
Change Biology, Reino Unido, v. 15, n. 3, p. 601–612, 2009. https://doi.org/10.1111/j.1365-
2486.2008.01786.x
CORREIA, A. H.; FORMAGGIO, A. R.; SHIMABUKURO, Y. E.; DUARTE, V. Avaliação de índices de
vegetação MODIS para detecção de desmatamentos na Amazônia. Revista Ambiente e Água, Taubaté, v. 1, n.
2, p.52-64, 2006. https://doi.org/10.4136/ambi-agua.12
COURA, P. H. F.; SOUSA, G. M.; FERNANDES, M. C.; AVELAR, A. S. O uso de variáveis geomorfológicas
no estudo da susceptibilidade à ocorrência de incêndios no estado do Rio de Janeiro. Revista de Geografia,
Recife, v. 27, n. 2, p. 209-221, 2010.
CPTEC - Centro de Previsão do Tempo e Estudos Climáticos. Monitoramento de focos. Disponível em:
. Acesso em: 04 de fev. 2016.
EVERITT, B. S.; DUNN, G. Applied multivariate analysis. Edward Arnold, London, p. 400, 1991.
https://doi.org/10.1177/096228029300200109
FERNANDES, M. C.; COURA, P. H. F.; SOUSA, G. M.; AVELAR, A. S. Avaliação Geoecológica de
Susceptibilidade à Ocorrência de Incêndios no Estado do Rio de Janeiro, Brasil. Floresta e Ambiente,
Seropédica, v. 18, n. 3, p.299-309, 2011. http://dx.doi.org/10.4322/floram.2011.050
GITAS, I. Z.; MITRI, G. H.; VENTURA, G. Object-based image classification for burned area mapping of
Creus Cape, Spain, using NOAA-AVHRR imagery. Remote Sensing of Environment, Londres, v. 92, n. 1, p.
409-413, 2004. https://doi.org/10.1016/j.rse.2004.06.006
HUETE, A.; DIDAN, K.; MIURA, T.; RODRIGUEZ, E. P.; GAO, X.; FERREIRA, L. G. Overview of the
radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment,
Londres, v. 83, n.1-2 ,p.195-213, 2002. https://doi.org/10.1016/S0034-4257(02)00096-2
IBGE – INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Área Territorial Oficial e Censo
Demográfico Populacional do Brasil. Disponível em:
. Acesso em: 02 fev. 2018.
KEELEY, J. E.; ZEDLER, P. H. Large high intensity fire events in southern California shrublands: debunking
the fine-grained age-patch model. Ecological Applied, Rockville Pike-USA, v. 19, n. 1, p. 69–94, 2009.
https://doi.org/10.1890/08-0281.1
LYRA, G. B.; OLIVEIRA-JÚNIOR, J. F.; ZERI, M. Cluster analysis applied to the spatial and temporal
variability of monthly rainfall in Alagoas state, Northeast of Brazil. International Journal of Climatology,
Londres, v. 34, n. 13, p. 3546-3558, 2014. https://doi.org/10.1002/joc.3926
MACHIWAL, D.; KUMAR, S.; DAYAL, D. Characterizing rainfall of hot arid region by using time-series
modeling and sustainability approaches: a case study from Gujarat, India. Theoretical and Applied
Climatology, Londres, v. 124, n. 3-4, p. 593-607, 2016. https://doi.org/10.1007/s00704-015-1435-9
MONTGOMERY D. C., RUNGER, G. C. Applied Statistics and Probability for Engineers. 4ª Ed., Jonh
Wiley & Sons, Inc., 2007, 490p.
NUNES, M. T. O.; SOUSA, G. M.; TOMZHINSKI, G. W.; OLIVEIRA JÚNIOR, J. F.; FERNANDES, M. C.
Variáveis Condicionantes na Susceptibilidade de Queimadas e Incêndios no Parque Nacional do Itatiaia.
Anuário do Instituto de Geociências (UFRJ. Impresso), Rio de Janeiro, v. 38, n. 2, p. 54-62, 2015.
https://doi.org/10.11137/2015_1_54_62
1017
Spatial-temporal… GOIS, G. et al.
Biosci. J., Uberlândia, v. 36, n. 3, p. 1008-1017, May/June 2020
http://dx.doi.org/10.14393/BJ-v36n3a2020-47769
OLIVEIRA-JÚNIOR, J. F.; SOUSA, G. M.; NUNES, M.T.O.; FERNANDES, M. C.; TOMZHINSKI, G. W.
Relationship between SPI and ROI in Itatiaia National Park. Floresta e Ambiente, Seropédica, v. 24, p.
e20160031, 2017. http://dx.doi.org/10.1590/2179-8087.003116
PIROMAL, R. A. S.; RIVEIRA-LOMBARDI, R. J.; SHIMABURURO, Y. E.; FORMAGGIO, A. R.; KRUG,
T. Utilização de dados MODIS para a detecção de queimadas na Amazônia. Revista Acta Amazonica,
Manaus, v. 38, n. 1, p. 77 – 84, 2008. http://dx.doi.org/10.1590/S0044-59672008000100009
R CORE TEAM (2017). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
ROY, D. P.; LEWIS, P.; JUSTICE, C. Burned area mapping using multi-temporal moderate spatial resolution
data – a-bi-directional reflectance model-based expectation approach. Remote Sensing of Environment,
Londres, v. 83, n. 1-2, p. 263–286, 2002. https://doi.org/10.1016/S0034-4257(02)00077-9
ROY, D. P.; BOSCHETTI, L.; JUSTICE, C. O.; JU, J. The Collection 5 MODIS Burned Area Product – Global
Evaluation by Comparison with the MODIS Active Fire Product. Remote Sensing of Environment, Londres,
v. 112, n. 9, p. 3690-3707, 2008. https://doi.org/10.1016/j.rse.2008.05.013
SETZER, A. W.; SISMANOGLU, R. A. Risco de Fogo – Resumo do Método de Cálculo (versão 5 – Fevereiro
- 2006). Disponível em: . Acesso em: 20 de out.
2014.
SETZER, A.W.; VERSTRAETE, M. M. Fire and glint in AVHRR’s channel 3: a possible reason for the non-
saturation mystery. International Journal of Remote Sensing, Londres, v.15, n. 3, p. 711-718, 1994.
https://doi.org/10.1080/01431169408954111
SILVA, L. S.; LANDAU, L.; MORAES, N. O.; PIMENTEL, L. C. G. Air quality photochemical study over
Amazonia Area, Brazil. International Journal of Environment and Pollution, Reino Unido, v. 48, n. 1, p.
194-202, 2012. https://doi.org/10.1504/IJEP.2012.049666
SILVA, T. B.; ROCHA, W.; ANGELO, M. F. Quantificação e análise espacial dos focos de calor no Parque
Nacional da Chapada Diamantina - BA. In: XVI Simpósio Brasileiro de Sensoriamento Remoto, 2013, Foz
do Iguaçu. Anais... São José dos Campos: Instituto Nacional de Pesquisas Espaciais (INPE), 2013. p. 6969-
6976.
SOBRAL, B. S.; OLIVEIRA JÚNIOR, J. F.; GOIS, G.; TERASSI, P. M. B.; MUNIZ JUNIOR, J. G. R.
Variabilidade espaço-temporal e interanual da chuva no estado do Rio de Janeiro. Revista Brasileira de
Climatologia, Curitiba, v. 22, n. 1, p. 281-308, 2018. http://dx.doi.org/10.5380/abclima.v22i0.55592
VELÁSQUEZ VALLE, M. A.; MEDINA GARCÍA, G.; COHEN, I. S.; OLESCHKO, L. K.; CORRAL, J. A.
R.; KORVIN, G. Spatial Variability of the Hurst Exponent for the Daily Scale Rainfall Series in the State of
Zacatecas, Mexico. Journal of Applied Meteorology and Climatology, Boston, v. 52, n. 1, p.2771-2780,
2013. https://doi.org/10.1175/JAMC-D-13-0136.1
WARD, J. H. Hierarchical grouping to optimize an objective function. Journal of the American Statistical
Association, Minnesota-USA, v. 58, n.1, p. 236-244, 1963. https://doi.org/10.1080/01621459.1963.10500845.