Illegal Selective Logging and Forest Fires in the Northern Brazilian Amazon - MDPI
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Article
Illegal Selective Logging and Forest Fires in the
Northern Brazilian Amazon
Tiago M. Condé 1,2, * , Niro Higuchi 2 and Adriano J. N. Lima 2
1 State University of Roraima (UERR), Department of Forest Engineering, 69373-000 Rorainópolis, Brazil
2 Forest Management Laboratory (LMF), National Institute of Amazonian Research (INPA),
69060-001 Manaus, Brazil; niro@inpa.gov.br (N.H.); adrianolmf@gmail.com (A.J.N.L.)
* Correspondence: tiagonafloresta@gmail.com; Tel.: +55-92-98102-3784
Received: 27 December 2018; Accepted: 11 January 2019; Published: 14 January 2019
Abstract: Illegal selective logging and forest fires occur on a large scale in the northern Brazilian
Amazon, contributing to an increase in tree mortality and a reduction in forest carbon stock. A total
of 120 plots of 0.25 ha (30 ha) were installed in transitional ecosystems or ecotones (LOt) between
the forested shade-loving campinarana (Ld) and dense-canopy rainforest, submontane (Ds), in the
National Forest (Flona) of Anauá, southern Roraima. Measuring the diameters at breast height
(DBH ≥ 10 cm) and the heights of 171 dead trees (fallen naturally, illegally exploited, and affected
by forest fires), enabled the estimation of carbon content from the application of a biomass equation
developed at Manaus, and the calculation of a correction factor, using the average height of the largest
trees. From 2015–2017, we mapped the real extent of illegal selective logging and forest fires across
the region with CLASlite and INPE/Queimadas. From measurements of 14,730 live and dead trees
across 30 hectares (491 ± 15 trees·ha−1 ), the illegal selective logging and associated forest fires, and
aggravation by severe El Niño droughts resulted in an 8.2% mortality of trees (40 ± 9 dead trees·ha−1 )
and a 3.5% reduction in forest carbon stock (6 ± 3 Mg·ha−1 ) in the short-term. The surface area or
influence of forest fires of very high density were estimated in the south-central region of Roraima
(8374 km2 ) and the eastern region of the Flona Anauá (37 km2 ). Illegal selective logging and forest
fires in forest areas totaled 357 km2 in the mosaic area, and 6 km2 within Flona Anaua. Illegal
selective logging and forest fires in the years of severe El Niño droughts threatened the maintenance
of environmental services provided by Amazonian forests.
Keywords: carbon; selective logging; fire; forest inventory; El Niño; tree mortality; Amazon forest
1. Introduction
Tropical forests account for more than half of the Earth’s biodiversity [1], and they are considered
to be important regulators of local and global climate through transpirational water flux, cloud
formation, atmospheric circulation, and carbon storage [2]. Observations at tower and plot-scales
(in situ), and satellite images have shown that tropical deforestation and forest degradation have
resulted in increased temperatures and drought conditions, at both local and global scales [3–5].
The radiation balance at a the local-scale is strongly affected by fires, suffering a net loss of up to 70%
of the photosynthetically active radiation at the surface, and directly influencing plant productivity [6].
The Amazonian hydroclimate is influenced by the textural roughness of the forest canopy [4],
where intense selective logging has resulted in the formation of large clearings and increased tree
mortality [7–9]. The remaining forest structure differs with regards to species composition and carbon
stock, depending on the intensity of selective logging and timber productivity [7–10].
Illegal selective logging in tropical forests does not occur when environmental and social
sustainability are prioritized, but only when short-term economic returns are sought. In contrast, legal
Forests 2019, 10, 61; doi:10.3390/f10010061 www.mdpi.com/journal/forests
Forests 2019, 10, 61 2 of 22
Forests logging
selective 2019, 10, x FOR
with PEER REVIEW
sustainable forest management or reduced-impact logging (RIL) focuses 2 of 22 on
the multiple use of timber and non-timber resources that are provided by forests, thereby preserving
legal selective logging with sustainable forest management or reduced-impact logging (RIL) focuses
environmental services [9,11,12]. The long-term viability of the timber trade in the Brazilian Amazon
on the multiple use of timber and non-timber resources that are provided by forests, thereby
depends on maintaining
preserving environmental an adequate volume of
services [9,11,12]. The legal timber viability
long-term extraction, of whilst maintaining
the timber trade in healthy
the
forests
Brazilian Amazon depends on maintaining an adequate volume of legal timber extraction,timber
[13]. Man’s greed for the rapid and selective harvesting of high-value tropical whilst as a
commodity
maintainingin the globalforests
healthy marketplace has greed
[13]. Man's accelerated
for thethe lossand
rapid of Amazonian habitatsofbyhigh-value
selective harvesting deforestation
and tropical
selectivetimberlogging as a[2,7,14],
commodity withincritical consequences
the global marketplacefor hasflora and fauna,
accelerated andofcontributions
the loss Amazonian to
habitats
severe climate by change.
deforestation and selective logging [2,7,14], with critical consequences for flora and fauna,
and contributions
The indiscriminate to severe
use ofclimate
fire bychange.
humans in areas of land-use conversion, from native forest to
The indiscriminate use
extensive ranching (cattle) and various of fire byagricultural
humans in areas uses,ofhas land-use conversion,
contributed from native
to an increase forest toareas
of burned
extensive ranching (cattle) and various agricultural uses, has contributed to an increase of burned
inside and outside of the native forests of Roraima. The fuel of forest fires in Amazonia are residues that
areas inside and outside of the native forests of Roraima. The fuel of forest fires in Amazonia are
are left by illegal selective logging, fragmentation, and burned pastures [15,16]. In the past few decades,
residues that are left by illegal selective logging, fragmentation, and burned pastures [15,16]. In the past
mega forest fires have been observed in the Amazon [3,17], specifically in Roraima [18–20], with strong
few decades, mega forest fires have been observed in the Amazon [3,17], specifically in Roraima [18–20],
interactions
with strong between the severity
interactions between of El
theNiño droughts,
severity and the
of El Niño occurrence
droughts, and ofthefires (drought-fire).
occurrence of fires
There is little
(drought-fire). scientific information on the impacts of illegal selective logging that are associated
with the There
forestisfires, on tree mortality
little scientific informationand carbon
on the impacts stocks in the
of illegal northern
selective Amazon
logging that areofassociated
Brazil. Fires
alterwith
forestthecharacteristics, species
forest fires, on tree diversity,
mortality structure,
and carbon stocksand composition,
in the and they
northern Amazon resultFires
of Brazil. in the strong
alter
forestofcharacteristics,
selection species diversity,
fire-adapted species. White-sand structure,
ecosystemsand composition,
represent theand they result
transitions in the strong
or ecotones between
selection of
campinaranas and fire-adapted species.inWhite-sand
dense rainforests ecosystems
southern Roraima, withrepresent the transitions
a high degree or ecotones
of flora endemism [21,22],
between campinaranas and dense rainforests in southern
and they are currently under strong pressure from the timber industry [23–25]. Roraima, with a high degree of flora
endemism [21,22], and they are currently under strong pressure from the timber industry [23–25].
The objective of this research was to quantify the impacts of illegal selective logging and forest
The objective of this research was to quantify the impacts of illegal selective logging and forest
fires on tree mortality and carbon stock reduction in the National Forest (Flona) of Anauá in the
fires on tree mortality and carbon stock reduction in the National Forest (Flona) of Anauá in the
southern Roraima. The hypothesis tested in this study was that there was a considerable increase in
southern Roraima. The hypothesis tested in this study was that there was a considerable increase in
tree mortality
tree mortality andand carbon stock
carbon stockreduction
reductionwhen illegalselective
when illegal selectivelogging
logging is associated
is associated withwith forest
forest fires, fires,
particularly in years of severe El Niño droughts (2015–2017), in the northern
particularly in years of severe El Niño droughts (2015–2017), in the northern Brazilian Amazon. Brazilian Amazon.
2. Materials andand
2. Materials Methods
Methods
2.1. Study AreaArea
2.1. Study
OurOur
study area
study was
area thethe
was National
NationalForest
Forest (Flona) ofAnauá,
(Flona) of Anauá,located
located
in in southern
southern Roraima,
Roraima, in the
in the
northern
northern Brazilian
Brazilian Amazon
Amazon (Figure1).
(Figure 1).
Figure 1. Study
Figure area:
1. Study The
area: TheNational
NationalForest
Forest (Flona) ofAnauá
(Flona) of Anauáininthe
the northern
northern Brazilian
Brazilian Amazon.
Amazon.
Forests 2019, 10, 61 3 of 22
Forests 2019, 10, x FOR PEER REVIEW 3 of 22
The
The forest
forest Anaua
Anaua was was legally
legally declared
declared aa national
national forest
forest inin 2005,
2005, and
and itit covers
covers anan area
area of
of
approximately 2600 km 2 . It is an area protected by law [26] within the category of the sustainable use of
approximately 2600 km². It is an area protected by law [26] within the category of the sustainable use
natural
of naturalresources, whichwhich
resources, allowsallowsfor the for
legalthe
exploitation of timber by
legal exploitation of Sustainable
timber by Forest Management
Sustainable Forest
Plans (PMFS) after approval of the Management Plan. In 2018, this
Management Plans (PMFS) after approval of the Management Plan. In 2018, this Management Plan Management Plan had not yet
been completed. Consequently, illegal selective logging and associated forest
had not yet been completed. Consequently, illegal selective logging and associated forest fires has fires has been observed
more intensely in
been observed situ intensely
more in the eastern region,
in situ in thedue to its proximity
eastern region, due to to
agrarian reform settlement
its proximity to agrarianprojects,
reform
private rural properties, rural roads, and a federal highway (BR-174).
settlement projects, private rural properties, rural roads, and a federal highway (BR-174).
The
The history
history of
of changes
changes in in land
land use
use and
and land
land cover,
cover, and
and timber
timber exploitation
exploitation in in southern
southern Roraima
Roraima
(mosaic area: 66,928 km 2 ; State of Roraima: 224,396.8 km2 ) was spatially evaluated between 2011
(mosaic area: 66,928 km²; State of Roraima: 224,396.8 km²) was spatially evaluated between 2011 and
and
20162016
by theby the Deforestation
Deforestation Authorizations
Authorizations (ADs
(ADs == 103)and
103) andSustainable
SustainableForest
Forest Management
Management Plans
Plans
(PMFSs
(PMFSs = = 4)
4) issued
issued by by the
the environmental
environmental agencies:
agencies: the
the State
State Foundation
Foundation for for the
the Environment
Environment andand
Water Resources of Roraima (FEMARH) and the Brazilian Institute
Water Resources of Roraima (FEMARH) and the Brazilian Institute of Environment and Renewable of Environment and Renewable
Natural
Natural Resources
Resources(IBAMA)
(IBAMA) [27]. Monitoring
[27]. Monitoring of forest fires by
of forest firestheby
National InstituteInstitute
the National for Spacefor
Research
Space
(INPE)
Research from January
(INPE) fromtoJanuary
March in to 2016
Marchdemonstrated the penetration
in 2016 demonstrated of fire in Flona
the penetration of fireAnauá (Figure
in Flona Anauá1,
see Supplementary Materials: Figure
(Figure 1, see Supplementary Materials: Figure S1). S1).
2.2.
2.2. Data
Data Collection
Collection
A
A forest
forest inventory
inventory (FI)
(FI) was
was carried
carried out
out from
from 2014
2014 to
to 2017
2017 by
by aa team
team from
from the
the Forest
Forest Management
Management
Laboratory
Laboratory of the National Institute of Amazonian Research (LMF/INPA) (Figure 2). A total
of the National Institute of Amazonian Research (LMF/INPA) (Figure 2). A total of
of 120
120
plots of 0.25 ha (30 ha total) were installed. We measured the diameter at breast height
plots of 0.25 ha (30 ha total) were installed. We measured the diameter at breast height (DBH ≥ 10 cm)(DBH ≥ 10 cm)
of
of trees
treesinintransitional
transitionalecosystems,
ecosystems, ororecotones
ecotones(LOt), between
(LOt), forested
between shade-loving
forested campinarana
shade-loving campinarana(Ld)
and dense-canopy rainforest, submontane (Ds), within Flona Anauá, and classified
(Ld) and dense-canopy rainforest, submontane (Ds), within Flona Anauá, and classified them them according to
the Brazilian
according to Institute of Geography
the Brazilian Institute and Statistics (IBGE)
of Geography [28]. The (IBGE)
and Statistics ecotones[28].
referThe
to regions
ecotonesof refer
contact
to
between two different ecosystems.
regions of contact between two different ecosystems.
(a) (b) (c)
(d) (e) (f)
Figure 2. Forest inventory (FI) in Flona Anauá, northern Amazonia of Brazil: (a) Installation of plots
Figure
(0.25 ha)2.with
Forest inventoryand
a compass (FI)metric
in Flona Anauá,
tape; northern Amazonia
(b) Measurement of Brazil:of
of the diameters (a)trees
Installation
at breastofheight
plots
(0.25 ha)
(DBH ≥ 10 with
cm);a (c)
compass and of
Collection metric tape; (b) for
tree branches Measurement of the diameters
botanical identification; of trees
(d) The use ofat“peconha”
breast height
for
(DBH ≥collection;
branch 10 cm); (c)(e)Collection of tree
Measuring the branches
diametersfor botanical
(calipers: identification;
Haglöf) (d) The
and lengths use of "peconha"
or heights for
(metric tape)
branch
of dead collection; (e) Measuring
trees (naturally the diameters
fallen, illegally exploited,(calipers: Haglӧf)
and affected by and lengths
forest fires); or
(f) heights (metric tape)
Georeferencing of FI
of dead
plots andtrees
trees(naturally
positions.fallen, illegally exploited, and affected by forest fires); (f) Georeferencing of
FI plots and trees positions.
The vegetation cover map from Brazilian Institute of Geography and Statistics - IBGE
(https://portaldemapas.ibge.gov.br/; scale of 1:250,000) was used in FI planning (Figure 3). The
Forests 2019, 10, 61 4 of 22
ForestsThe
2019,vegetation
10, x FOR PEERcover map from Brazilian Institute of Geography and Statistics - IBGE (https:
REVIEW 4 of 22
//portaldemapas.ibge.gov.br/; scale of 1:250,000) was used in FI planning (Figure 3). The primary
primary units
units of the of the conglomerates
conglomerates were randomly
were randomly installed installed
within the within the areas
ecotone ecotone areas
(LOt), (LOt),
from from a
a mapped
mapped polygon
polygon vector based vectoronbased
Landsaton Landsat 8 (OLI) satellite
8 (OLI) satellite images. images.
This areaThis area covers
covers approximately
approximately 76,681
76,681
ha (767ha 2 ), occupying
km(767 km²), occupying
29% of the29%total
of the total
area area of
of Flona FlonaFour
Anauá. Anauá. Four conglomerates
conglomerates with a
with a standard
standard
“cross” "cross"
format format containing
containing 20 plots were20 installed.
plots were installed.
Three Three conglomerates
conglomerates with differentwith
formatsdifferent
were
formats
installed,were installed, butthe
but maintaining maintaining the plot's(tertiary
plot’s dimensions dimensions
units(tertiary unitsThe
of 0.25 ha). of 0.25
use ofha). The
0.25 hause of
plots
0.25 ha plots has been recommended for forest inventories in tropical ecosystems,
has been recommended for forest inventories in tropical ecosystems, due to a greater accuracy in due to a greater
accuracy
representing in representing the forest
the forest structure structure
[29–31]. [29–31].
An analysis An analysis
of variance (ANOVA)of variance (ANOVA)
for the density fortrees
of the the
density
(trees −1 )the
·haof andtrees (trees·ha
the forest −1) and the forest−carbon
carbon stock (Mg·ha 1 ) established
stock (Mg·ha −1) established that there are no
that there are no requirements (p > 0.05)
requirements
for a stratified(p > 0.05) for
sampling a stratified
design sampling
across the design across
two ecosystems the two
(Forested ecosystemscampinarana
shade-loving (Forested shade-
(Ld)
loving campinarana
and dense-canopy (Ld) and submontane
rainforest, dense-canopy rainforest, submontane (Ds)).
(Ds)).
Figure 3.
Figure Forest inventory
3. Forest inventory (FI)
(FI) done
done by
by sampling
sampling inin conglomerates
conglomerates (standard
(standard “cross” format) of
"cross" format) of Flona
Flona
Anauá in the northern Brazilian Amazon. Vegetation cover map
Anauá in the northern Brazilian Amazon. Vegetation cover map from IBGEfrom IBGE (https://portaldemapas.
ibge.gov.br/) with transparency (70%)
(https://portaldemapas.ibge.gov.br/) withoftransparency
image by Landsat 8 (OLI)
(70%) of imagefrom the period
by Landsat 8 (OLI)01/10/2017.
from the
Legend: Forested areas with large trees [28]: Dense-canopy rainforest, submontane (Ds);
period 01/10/2017. Legend: Forested areas with large trees [28]: Dense-canopy rainforest, submontane Forested
shade-loving campinarana (Ld); Ecotones (LOt), among others. Non-forest areas with the
(Ds); Forested shade-loving campinarana (Ld); Ecotones (LOt), among others. Non-forest areas with presence of
small trees, shrubs, herbs and grasses [28]: Grassy–woody shade-loving campinarana (Lg);
the presence of small trees, shrubs, herbs and grasses [28]: Grassy–woody shade-loving campinarana Shrubby
shade-loving
(Lg); Shrubbycampinarana
shade-loving(Lb); Treed shade-loving
campinarana (Lb); Treedcampinarana
shade-loving(La), among others.
campinarana (La), among others.
In the timber measurement (TM) stage, we measured the diameters and heights of 171 dead trees
In the timber measurement (TM) stage, we measured the diameters and heights of 171 dead trees
(naturally fallen, illegally exploited, and affected by forest fires) (Figure 4). The selection of trees was
(naturally fallen, illegally exploited, and affected by forest fires) (Figure 4). The selection of trees was
based on the results from the forest inventory, observing the classical pattern of the negative exponential
based on the results from the forest inventory, observing the classical pattern of the negative
distribution of tree diameters in native forests in the Amazon [32–34]. Species identifications were
exponential distribution of tree diameters in native forests in the Amazon [32–34]. Species
confirmed by the Herbarium of INPA (see Supplementary Materials: Table S1). DBH and dominant
identifications were confirmed by the Herbarium of INPA (see Supplementary Materials: Table S1).
height data were used to estimate forest carbon stocks from biomass equations.
DBH and dominant height data were used to estimate forest carbon stocks from biomass equations.
Forests
Forests 2019,
2019, 10,10,
61x FOR PEER REVIEW 5 5ofof2222
350
309 50
Forest Inventory - FI (30 ha) Illegally exploited
300
Density of trees - FI (trees ha-1)
Fallen naturally
40
Density of dead trees - TM
250 Affected by forest
32
fires
30 28
200
23
150
20
107
13
100 10
10 8
7 7
5 5 5 5 5
39 3 33
50 2 2 2
17 1 1 1
8 4 3 1 1 1 0
0 10 < 2020 < 3030 < 4040 < 5050 < 6060 < 7070 < 8080 < 90 90 < ≥ 100
10 < 20 20 < 30 30 < 40 40 < 50 50 < 60 60 < 70 70 < 80 80 < 90 90 < 100 ≥ 100 DBH classes (cm) 100
DBH classes (cm)
(a) (b)
Figure 4. Densities of trees on Flona Anauá, southern Roraima: (a) Mean tree density per diameter
class per hectare
Figure in theof
4. Densities forest
treesinventory
on Flona(FI); (b) Mean
Anauá, deadRoraima:
southern tree density
(a) per
Meandiameter class inper
tree density thediameter
timber
measurement (TM) in
class per hectare stage:
the Illegally exploited;
forest inventory Fallen
(FI); (b) naturally;
Mean dead Affected by forest
tree density perfires.
diameter class in the
timber measurement (TM) stage: Illegally exploited; Fallen naturally; Affected by forest fires.
2.3. Carbon from the Biomass Equation and the Use of the Correction Factor of the Dominant Height
2.3.The
Carbon from
carbon the Biomass
stored in trees Equation and the
was estimated in Use
threeofstages.
the Correction
Initially,Factor of the
estimates ofDominant
the total fresh
Height (the sum of the above-ground and below-ground biomasses) were estimated from equations
biomass
developed by the
3002The LMF/INPA
carbon stored infor Central
trees Amazonia
was estimated in at Manaus,
three stages.AM, for Ds,
Initially, methodofdescribed
estimates by
the total fresh
Silva in 2007 [30]. The correction factor for the average of the dominant height (H
biomass (the sum of the above-ground and below-ground biomasses) were estimated from equationsdom. ) of 20% of the
trees with theby
developed largest diameters [31]
the LMF/INPA were developed
for Central Amazonia according to the
at Manaus, equations
AM, for Ds,below:
method described by
Silva in 2007 [30]. The correction H
factor for the average of the dominant height (Hdom.) of 20% of the
dom.(Flona Anauá) 33.2
trees with the largest diameters =
FC = [31] were developed according = to
1.098023
the equations below: (1)
Hdom.(ZF−2/LMF/INPA) 30.2
.( á) .
FC = = = 1.098023 (1)
where FC is the correction factor of the dominant .( / /height
) .
in transitional ecosystems or ecotones (LOt)
between
where FC theisforested shade-loving
the correction factor ofcampinarana
the dominant (Ld) and in
height thetransitional
dense-canopy rainforest,
ecosystems submontane
or ecotones (LOt)
(Ds) on Flona Anauá. H
between the forested shade-loving
dom.(Flona Anauá) is the dominant height average of 20% of the
campinarana (Ld) and the dense-canopy rainforest, submontane largest trees in
TM on on
(Ds) Flona
FlonaAnauá;
Anauá.Hdom.(ZF-2/LMF/INPA)
Hdom.(Flona Anauá) is theis dominant
the dominant height
height average
average of 20%
of 20% of the
of the largest
largest trees
trees in in
TM
ZF-2/Manaus, AM.
on Flona Anauá; Hdom.(ZF-2/LMF/INPA) is the dominant height average of 20% of the largest trees in ZF-
In the second
2/Manaus, AM. stage, we estimated the dry biomass with the use of a correction factor based upon
the water content
In the secondin stage,
the trees
wedeveloped
estimated the by the
dryLMF/INPA.
biomass with We applied
the use of aa correction
factor of 0.582,
factorthe average
based upon
ofthe
thewater
factors developed for Ds [30] and Ld [31]. Finally, the carbon present in the
content in the trees developed by the LMF/INPA. We applied a factor of 0.582, the averagetrees was estimated
onofthe
thebasis of the
factors specificfor
developed carbon contents
Ds [30] and Ldof[31].wood, according
Finally, to LMF/INPA.
the carbon present inWe the applied
trees was a factor of
estimated
0.480%, an average of the factors developed for Ds [30] and Ld [35].
on the basis of the specific carbon contents of wood, according to LMF/INPA. We applied a factor of
0.480%, an average of the factors developed for Ds [30] and Ld [35].
2.4. Tree Mortality and Carbon Stock
2.4.Estimations
Tree Mortality and
of the CarboninStock
increase tree mortality and the decrease in forest carbon stock were made
in three stages. In the first, the forest inventory (FI) database was used to identify dead trees that
Estimations of the increase in tree mortality and the decrease in forest carbon stock were made
died through natural processes (naturally fallen) and dead trees affected by human process (illegally
in three stages. In the first, the forest inventory (FI) database was used to identify dead trees that died
exploited and affected by forest fires), excluding live trees (Figure 5). Subsequently, individual tree
through natural processes (naturally fallen) and dead trees affected by human process (illegally
carbon values were estimated from a biomass equation developed at Manaus, AM (LMF/INPA),
exploited and affected by forest fires), excluding live trees (Figure 5). Subsequently, individual tree
using a correction factor derived from the average of the dominant height of the trees in Roraima.
carbon values were estimated from a biomass equation developed at Manaus, AM (LMF/INPA),
Finally, we estimated the impact of illegal selective logging and forest fires on the increase of tree
using a correction factor derived from the average of the dominant height of the trees in Roraima.
mortality, and subsequent decreases in forest carbon stock in the Flona Anauá. Mean values and
Finally, we estimated the impact of illegal selective logging and forest fires on the increase of tree
95% confidence intervals (CI95% ) were estimated per hectare and population [36] (see Supplementary
mortality, and subsequent decreases in forest carbon stock in the Flona Anauá. Mean values and 95%
Materials: Equations S1; method described by Soares et al. in 2011, p. 163–174)). We assumed in this
confidence intervals (CI95%) were estimated per hectare and population [36] (see Supplementary
study that the reduction of the forest carbon stock arising through natural processes would be slow,
Materials: Equations S1; method described by Soares et al. in 2011, p. 163–174)). We assumed in this
study that the reduction of the forest carbon stock arising through natural processes would be slow,
Forests 2019, 10, 61 6 of 22
Forests 2019, 10, x FOR PEER REVIEW 6 of 22
changing the gas exchange of the forest ecosystem in the medium- and long-term. Dead trees that have
have naturally
naturally fallenfallen will keep
will keep their their
carboncarbon
storedstored
for a for a long
long time,time, without
without considerable
considerable losses
losses fromfrom
the
the forest. Trees that have naturally fallen are generally not removed from the forest
forest. Trees that have naturally fallen are generally not removed from the forest by loggers, because by loggers,
because
they has they has low commercial
low commercial value, duevalue, duedecomposition
to their to their decomposition state. In trees
state. In contrast, contrast, treesby
affected affected
illegal
by illegallogging
selective selectiveresults
logging
in results in anloss
an effective effective losscarbon
of forest of forest carbon
stock stock
in the in the short-term.
short-term. These treesThese
are
trees are immediately extracted, and processed at local sawmills. Forest fires alter the
immediately extracted, and processed at local sawmills. Forest fires alter the gas exchange of the forestgas exchange
of the forest
ecosystem ecosystem
in the in the
short term, short term,
reducing reducing
the forest carbon thestock
forest carbon stock
to different to different
degrees, accordingdegrees,
to fire
according to fire
incidence [15–17]. incidence [15–17].
Figure 5.
Figure 5. Flowchart
Flowchart of steps to
of steps to obtain
obtain estimates
estimates of
of tree
tree mortality
mortality and
and forest
forest carbon
carbon stocks
stocks in
in Flona
Flona
Anauá, northern
Anauá, northern Brazilian
Brazilian Amazon.
Amazon.
2.5.
2.5. Mapping
Mapping of of Illegal
Illegal Selective
SelectiveLogging
Loggingand
andForest
ForestFires
Fireswith
withClaslite
Clasliteand
andINPE/Queimadas,
INPE/Queimadas,and
andRestrictions
due to Forest Physiognomy
Restrictions due to Forest Physiognomy
Using
Using aa combination
combination of of CLASlite
CLASlite [37][37] with
with INPE/Queimadas
INPE/Queimadas [38], [38], with
with restriction
restriction per
per forest
forest
physiognomy, we mapped the surface area or the influence (total area), and the
physiognomy, we mapped the surface area or the influence (total area), and the effective area ofeffective area of illegal
selective logginglogging
illegal selective associated with forest
associated withfires (Figures
forest 6–10). CLASlite
fires (Figures integratesintegrates
6–10). CLASlite a series ofaprocesses
series of
that take raw satellite imagery and that produce forest cover change
processes that take raw satellite imagery and that produce forest cover change images images [37]. These[37].
processes
These
can be summarized thus: (1) radiometric calibration and atmospheric correction of
processes can be summarized thus: (1) radiometric calibration and atmospheric correction of satellite satellite data; (2)
cloud, water, and shadow masking; (3) decomposition of image pixels into fractional
data; (2) cloud, water, and shadow masking; (3) decomposition of image pixels into fractional surface surface covers;
and (4–5)
covers; andclassification of the imagery
(4–5) classification into forest
of the imagery intocover,
forestdeforestation, and forest
cover, deforestation, anddisturbance (illegal
forest disturbance
selective logging logging
(illegal selective in this study).
in this Two scenes
study). Two(Mosaic) of the remote
scenes (Mosaic) sensor
of the remote Landsat
sensor8 Landsat
(OLI; Path: 232;
8 (OLI;
Rows: 059–060; area: 66,928 km 2 ; Period: 04 October 2015 to 10 January 2017) obtained from the USGS
Path: 232; Rows: 059–060; area: 66,928 km²; Period: 04 October 2015 to 10 January 2017) obtained from
website
the USGS (https://earthexplorer.usgs.gov/) were used
website (https://earthexplorer.usgs.gov/) byused
were CLASlite 3.2 for mapping
by CLASlite the totalthe
3.2 for mapping area of
total
illegal selective logging in southern Roraima, Brazil (Figure
area of illegal selective logging in southern Roraima, Brazil (Figure 6).6).
Forests
Forests 2019,
2019, 10,10,
61x FOR PEER REVIEW 77ofof2222
(a) (b)
(c) (d)
Figure 6. Flowchart of the steps with CLASlite for mapping illegal selective logging in southern
Roraima,
Figure 6.Brazil: (a) Radiometric
Flowchart calibration
of the steps and atmospheric
with CLASlite correction
for mapping illegalofselective
the satellite data in
logging (including
southern
cloud, water,
Roraima, and shadow
Brazil: masking);
(a) Radiometric (b) Decomposition
calibration of image
and atmospheric pixels into
correction fractional
of the satellitesurface covers
data (including
(photosynthetic vegetation—PV; non-photosynthetic vegetation—NPV and bare substrate—BARE);
cloud, water, and shadow masking); (b) Decomposition of image pixels into fractional surface covers (c)
Forest cover classification; (d) Forest change detection (deforestation and illegal selective
(photosynthetic vegetation—PV; non-photosynthetic vegetation—NPV and bare substrate—BARE); logging or
forest disturbance).
(c) Forest cover classification; (d) Forest change detection (deforestation and illegal selective logging
or forest disturbance).
CLASlite mapped without discrimination the illegal selective logging as any type of intensity of
forest disturbance,
CLASlite mapped differing only discrimination
without from deforestation (clear cut).
the illegal CLASlite
selective logginghasasitsany
own spectral
type library,of
of intensity
soforest
we created one restriction by forest physiognomy, derived of the vegetation cover
disturbance, differing only from deforestation (clear cut). CLASlite has its own spectral library, map from IBGE
(Scale 1:250.000; https://portaldemapas.ibge.gov.br/), to perform the overlapping
so we created one restriction by forest physiognomy, derived of the vegetation cover map from IBGE of CLASlite results
(Figure
(Scale 7a). Thus, https://portaldemapas.ibge.gov.br/),
1:250.000; we assume that in forest areas (large to trees), illegal
perform the selective logging
overlapping was quantified
of CLASlite results
as(Figure
being associated
7a). Thus, we assume that in forest areas (large trees), illegal selective logging (Ds),
or not with forest fires [28]: Dense-canopy rainforest, submontane forested
was quantified
shade-loving campinarana
as being associated or not(Ld),
withtransitional
forest fires ecosystems or ecotones
[28]: Dense-canopy (LOt), among
rainforest, others (Figure
submontane 7b,c).
(Ds), forested
Inshade-loving
non-forested campinarana
areas, with the(Ld),
presence of small trees, shrubs, herbs, and grasses, forest
transitional ecosystems or ecotones (LOt), among others (Figure disturbances
were quantified
7b,c). as beingareas,
In non-forested probably
withassociated
the presencewith of
forest fires,
small without
trees, shrubs,illegal selective
herbs, logging forest
and grasses, [28].
Non-forested
disturbances areas were represented
were quantified as beingby grassy–woody
probably associatedshade-loving
with forest campinarana
fires, without (Lg);
illegalshrubby
selective
shade-loving campinarana (Lb); treed shade-loving campinarana (La),
logging [28]. Non-forested areas were represented by grassy–woody shade-loving campinaranaamong others (Figure 7b,d).
(Lg);
Both forestshade-loving
shrubby disturbancescampinarana
were confirmed (Lb);bytreed
validations in field
shade-loving (in situ) observations.
campinarana (La), among others (Figure
7b,d). Both forest disturbances were confirmed by validations in field (in situ) observations.
Forests 2019,
Forests 10,10,
2019, 61 x FOR PEER REVIEW 8 of 22 22
8 of
(a) (b)
(c) (d)
Figure 7. Restriction by forest physiognomy (forest area/non-forest area) derived from the vegetation
cover map7.from
Figure IBGE to
Restriction byperform the overlapping
forest physiognomy of CLASlite
(forest results:area)
area/non-forest (a) Cover map
derived from
from theIBGE; (b)
vegetation
Restriction
cover map byfrom
forestIBGE
phytophysiognomy (forest areas/non-forest
to perform the overlapping areas); (c)
of CLASlite results: (a)Overlapping of CLASlite
Cover map from IBGE; (b)
results in forest
Restriction byareas:
forest forest disturbances and
phytophysiognomy deforestation;
(forest (d) Overlapping
areas/non-forest of CLASlite results
areas); (c) Overlapping in
of CLASlite
non-forest
results inareas:
forestforests
areas:disturbances and deforestation.
forest disturbances and deforestation; (d) Overlapping of CLASlite results in
non-forest areas: forests disturbances and deforestation.
In this study, we considered all selective logging from illegal sources for three reasons (Figure 8):
(a) Within the study,
In this Flona Anauá area, anyall
we considered selective logging
selective logging is from
illegal, because
illegal this conservation
sources for three reasonsunit (Figure
does
not yet have an approved Management Plan [26]; (b) Outside the Flona Anauá
8): (a) Within the Flona Anauá area, any selective logging is illegal, because this conservation unit area in agrarian
reform
does notsettlement
yet haveprojects and private
an approved rural properties
Management Plan [26]; with ADs, this
(b) Outside the environmental
Flona Anauá area license was
in agrarian
not used for deforestation (clear cut) for cattle ranching or agriculture implementation
reform settlement projects and private rural properties with ADs, this environmental license was not (as registered
in used
ADs),forbut spatial patterns
deforestation (clearcharacteristic
cut) for cattleof the illegal
ranching selective logging
or agriculture were still(as
implementation observed;
registered (c)in
Outside
ADs), butthe Flona
spatialAnauá area
patterns in PMFSs, observed
characteristic deforestation
of the illegal was associated
selective logging were stillwith illegal (c)
observed; selective
Outside
logging in spatial patterns that did not characterize either legal selective logging
the Flona Anauá area in PMFSs, observed deforestation was associated with illegal selective logging or RIL, and they
contained
in spatial associated
patterns deforestation. The findingeither
that did not characterize of these illegal
legal activities
selective was confirmed
logging or RIL, andbythey (in situ)
fieldcontained
observations of a complete absence of RIL techniques that are obligatory
associated deforestation. The finding of these illegal activities was confirmed by field (in when sustainable forest
situ)
management is authorized by competent environmental agencies in Brazil.
observations of a complete absence of RIL techniques that are obligatory when sustainable forest We detected extensive
forest degradation,
management excluding the
is authorized possibility of
by competent the selective extraction
environmental agencies in of Brazil.
legal timber. We performed
We detected extensive
anforest
accuracy assessment of the mapping by CLASlite per-control samples (CS
degradation, excluding the possibility of the selective extraction of legal timber. We = 400 total; validation
performed
in an
situ) (Figureassessment
accuracy 8d) by calculating the errorby
of the mapping matrix, andper-control
CLASlite the generalsamples
hits of the(CSuser (Producer),
= 400 and
total; validation
Kappa index
in situ) or Kappa
(Figure 8d) bycoefficient
calculating[39].
the error matrix, and the general hits of the user (Producer), and
Kappa index or Kappa coefficient [39].
Forests
Forests 2019,
2019, 10, 10,
61 x FOR PEER REVIEW 9 of
9 of 22 22
(a) (b)
(c) (d)
Figure 8. Analysis
Figure of of
8. Analysis thethe
spatial pattern
spatial of of
pattern illegal selective
illegal logging
selective in in
logging thethe
northern Brazilian
northern Amazon:
Brazilian Amazon:
(a)(a)
Within the Flona Anauá; (b) Outside the Flona Anauá area in deforestation authorizations
Within the Flona Anauá; (b) Outside the Flona Anauá area in deforestation authorizations (ADs);
(ADs);
(c) (c)
Outside
Outsidethethe
Flona Anauá
Flona Anauá area in Sustainable
area in SustainableForest Management
Forest Management Plans (PMFSs);
Plans (d)(d)
(PMFSs); Accuracy
Accuracy
assessment
assessmentof the mapping
of the mapping performed
performedby by
CLASlite
CLASliteperper
CS CS
(Landsat 8/OLI;
(Landsat Period:
8/OLI; 1010
Period: January 2017).
January 2017).
WeWe identified
identifiedthethe
locations
locations of of
fires onon
fires a monthly
a monthly basis
basis(burning
(burningspots = 76,018)
spots = 76,018) bybyusing thethe
using
program INPE/Queimadas [38] for the State of Roraima (area: 224,300.8 km 2 ; period: Period: 04
program INPE/Queimadas [38] for the State of Roraima (area: 224,300.8 km²; period: Period: 04
October
October2015
2015 to to
0404
January
January 2017;
2017; and
and satellites
satellitesAqua
Aquaand,and,Terra,
Terra,GOES-13,
GOES-13,NOAA-15,
NOAA-15NOAA-18,, NOAA-18,
NOAA-19, NPP) for generating the annual kernel density map after
NOAA-19, NPP) for generating the annual kernel density map after several tests (burning several tests (burning
spots =
spots = 68,296; Period: 04 January 2016 to 04 January 2017; cell size: 0.02 (6.25 km 2 ); search radius: 0.1;
68,296; Period: 04 January 2016 to 04 January 2017; cell size: 0.02 (6.25 km²); search radius: 0.1; mask:
mask:
StateState of Roraima;
of Roraima; satellites:
satellites: Aqua,
Aqua, Terra,
Terra, GOES-13,
GOES-13, NOAA-15,
NOAA-15 NOAA-18,NOAA-19,
, NOAA-18, NOAA-19,NPP), NPP), and
and to
to determine
determine thethe surface
surface area
area or
or influence (total area) of forests fires (Figure
influence (total area) of forests fires (Figure 9). 9).
Forests
Forests 2019,
2019, 10,10,
61x FOR PEER REVIEW 1010ofof2222
(a) (b) (c) (d)
(e) (f) (g) (h)
(i) (j) (k) (l)
(m) (n) (o) (p)
Figure 9. Monthly estimates and annual density of forest fires in the State of Roraima: (a) October 2015;
(b)Figure 9. Monthly
November estimates
2015; (c) December and2015;
annual density of
(d) January forest
2016; fires in the
(e) February State
2016; (f)of Roraima:
March 2016;(a)
(g)October
April
2015;
2016; (h)(b) November
May 2016; (i) 2015; (c) December
June 2016; 2015;(k)
(j) July 2016; (d)August
January2016;
2016;(l)(e) February2016;
September 2016;(m)
(f) March
October 2016; (g)
2016;
(n)April 2016; (h)
November May(o)
2016; 2016; (i) June2016;
December 2016;(p)
(j) Annual
July 2016; (k) August
density, 2016; (l)
2016/2017. September
Burning spots2016;
were(m) October
obtained
2016;
from the(n) November
program 2016; (o) December
INPE/Queimadas 2016; (p) Annual density, 2016/2017. Burning spots were
(http://www.inpe.br/queimadas/portal).
obtained from the program INPE/Queimadas (http://www.inpe.br/queimadas/portal).Forests 2019, 10, 61 11 of 22
Forests 2019, 10, x FOR PEER REVIEW 11 of 22
The fire
fireincidence
incidenceis aisreflection of the density
a reflection of the of fires mapped
density of firesby mapped
INPE/Queimadas, accumulated
by INPE/Queimadas,
in each region,
accumulated incalculated
each region, by the kernel density
calculated map (see
by the kernel Supplementary
density Materials: Equations
map (see Supplementary S2).
Materials:
The density
Equations S2).ofThe
thedensity
burning spots
of the was spots
burning classified as: Absent,
was classified Very low,
as: Absent, VeryLow, Moderate–Low,
low, Low, Moderate–
Moderate–Medium,
Low, Moderate–Medium, Moderate–High,
Moderate–High,High,High,
HighHigh
Intense, and and
Intense, VeryVery
High, thereby
High, combining
thereby the
combining
information generated by CLASlite with INPE/Queimadas, in order to map
the information generated by CLASlite with INPE/Queimadas, in order to map the surface the surface area or
influence (total area) and the effective area of illegal selective logging associated with forest fires in
southern Roraima (Figure 10).
Figure 10.
Figure 10. Mapping
Mapping thethe surface
surface area
area or
or influence
influence (total
(total area)
area) and
and the effective area
the effective area of
of illegal
illegal selective
selective
logging with forest fires in southern Roraima, thereby combining the information generated
logging with forest fires in southern Roraima, thereby combining the information generated by CLASlite by
CLASlite with INPE/Queimadas: (a) The surface area or influence (kernel density—burning
with INPE/Queimadas: (a) The surface area or influence (kernel density—burning spots; spatial spots;
spatial resolution
resolution of 2472 of
m);2472 m); (b) Effective
(b) Effective area resolution
area (spatial (spatial resolution
of 30 m); of
(c)30 m);
The (c) The
surface surface
area area or
or influence,
influence,
and and thearea
the effective effective
withinarea
andwithin
outsideand outside
Flona Anauá.Flona Anauá.
associationofofillegal
The association illegal selective
selective logging
logging withwith
forestforest
fires infires
thisinstudy
this was
study was undertaken
undertaken to
to account
account
for for two important
two important facts foundfactsinfound in the
the field: (1)field: (1) we observed
we observed that after that after selective
illegal illegal selective
logginglogging
inside
inside
and and outside
outside Flona Anauá,
Flona Anauá, some areassomewereareas were burned,
burned, makingmaking it difficult
it difficult to trace to trace
these these activities,
activities, leaving
leaving
the the impression
impression that thesethat these
forest forest
fires were fires
thewere
only the
causeonlyof cause of this
this forest forest disturbance;
disturbance; (2) the
(2) the practice
practice
of burningof after
burning after the conversion
the conversion of forest
of forest (illegal (illegal
selective selective
logging andlogging and deforestation)
deforestation) to pasture and to
pasture andwas
agriculture agriculture
observedwas observed
in several ruralinproperties
several rural
aroundproperties around
Flona Anauá, Flona Anauá,
generating large generating
forest fires,
large forest
thereby fires, confirmation
providing thereby providing confirmation
of the fires of the
identified from fires identified from
INPE/Queimadas. INPE/Queimadas.
According to these rural
According this
producers, to these rural producers,
represents this represents
the least onerous the least onerous
way of undertaking way of undertaking
cattle ranching and agriculture cattle
in
ranching
the and agriculture in the Amazon.
Amazon.
3. Results
3.1. Forest Inventory (FI) and Timber Measurement (TM)
A total of 14,730 trees with DBH ≥ 10 cm were measured in the 30 hectares of plots used for the
forest inventory (FI) surveys between 2014 and 2017 in Flona Anauá, southern Roraima. The meanForests 2019, 10, 61 12 of 22
tree density was 491 ± 15 trees ha-1 (CI95% ), and the mean diameter (DBH) was 21 ± 0.3 cm (CI95% ),
with a range of 10–180 cm (Table 1). From timber measurements (TM), the mean tree diameter (DBH)
was different and ranged from 10–126 cm (Table 1). The total height (H) ranged from 8 to 54 m.
The dominant height (Hdom. ), which is defined as the average of 20% of the largest trees, ranged from
26–54 m. The correction factor for Flona Anauá based on Hdom. was estimated at FC = 1.098023.
Table 1. Mean tree density, diameter (DBH), total height (H), and dominant height (Hdom. ) in the forest
inventory (FI), and timber measurement (TM) in the northern Brazilian Amazon.
Forest Inventory (FI) Timber Measurement (TM)
Forest Phytophysiognomy 1
Density (trees·ha−1 ) DBH (cm) DBH (cm) H (m) Hdom. (m)
LOt 2 Forested shade-loving campinarana
491 ± 15 21 ± 0.3 37 ± 5 21 ± 1 33 ± 1
(Ld)/Dense-canopy rainforest submontane (Ds)
1Forest phytophysiognomy classified according to IBGE (2012); 2 Transitional ecosystems or ecotones; Mean values
and 95% confidence intervals (CI95% ).
3.2. Tree Mortality and Forest Carbon Stock
The forest of Flona Anauá had an average tree density of 491 ± 15 trees ha−1 (CI95% ),
corresponding to a mean forest carbon stock of 157 ± 5 Mg·ha−1 , and a total C stock (LOt: 76,681 ha)
of 12.1 ± 0.4 Tg (CI95% ). Illegal selective logging and forest fires resulted in a 48% tree mortality by
human process (illegally exploited and affected by forest fires; ranging from 0 to 248 dead trees·ha−1 )
and a 3.5% reduction in carbon stock (6 ± 3 Mg·ha−1 ) (Table 2). Tree mortality arising from natural
process (naturally fallen; ranging from 0 to 124 dead trees·ha−1 ) resulted in a 53% of tree mortality
(Table 2).
Table 2. Impacts of illegal selective logging and forest fires in tree mortality and forest carbon stock in
the northern Brazilian Amazon.
Tree Mortality Forest Carbon Stock
Description Density (Dead Total Dead Trees Percentage of Carbon Losses Percentage of
Trees·ha−1 ) in FI (30 ha) the FI (%) (Mg ha−1 ) the FI (%)
Natural Process 1 21 ± 3 638 4.3 - -
Human Process 2 19 ± 9 575 3.9 6±3 3.5
Total 40 ± 9 1213 8.2 6±3 3.5
1 Dead trees arising from natural processes (naturally fallen), where the reduction of the carbon stock will be slow,
was not quantified in the present study; 2 dead trees arising from human processes (illegally exploited and affected
by forest fires), where the reduction of the carbon stock was fast, changing rates of gas exchange of the forest
ecosystem in the short-term, proportional to the intensity of illegal selective logging and fire incidence. Mean values
and 95% confidence intervals (CI95% ).
3.3. Illegal Selective Logging and Forest Fires in the Southern Roraima
Illegal selective logging and forest fires in forest areas were detected in 357 km2 (0.5%) of the
mosaic area and 6 km2 (0.2%) within of Flona Anauá during the period 10/04/2015 to 01/10/2017
(Figures 7c and 8a–c; Table 3). This value was equivalent to 0.8% of the surveyed area (LOt). Forest
disturbances caused by forest fires in non-forest areas totaled 34 km2 (0.1%) in the mosaic area, and 0.2
km2 (0.01%) within of Flona Anauá. Deforestation in forest areas was estimated at 796 km2 (1.2%) in
the mosaic area, and 8 km2 (0.3%) within Flona Anauá. In non-forested areas, these values were much
lower. Regions closer to roads (rural and BR-174), agrarian reform settlement projects (PA/INCRA),
and private rural properties, where farmers and loggers with Authorizations of Deforestation (ADs)
are located, were the most affected.were much lower. Regions closer to roads (rural and BR-174), agrarian reform settlement projects
(PA/INCRA), and private rural properties, where farmers and loggers with Authorizations of
Deforestation (ADs) are located, were the most affected.
Table 3. Areas affected by illegal selective logging and forest fires, forest disturbances (in non-forest
Forests 2019, 10, 61 13 of 22
areas), and deforestation in the northern Brazilian Amazon.
Forest Areas (km²) Non-Forest Areas (km²)
Table 3. Areas affected by illegal selective logging and forest fires, forest disturbances (in non-forest
Description Flona Mosaic Roraima ² Flona Mosaic Roraima ²
areas), and deforestation in the northern
LOt ¹ Brazilian Amazon.
Anauá Area (%) Anauá Area (%)
Forest Areas (km2 ) Non-Forest Areas (km2 )
Illegal selective logging and forest
Description
2.8 6 Flona 357Mosaic 0.16
Roraima 2
-Flona -
Mosaic -
Roraima 2
fires LOt 1
Anauá Area (%) Anauá Area (%)
Forest disturbances by forest fires - - - - 0.2 34 0.01
Illegal selective logging and forest fires
Deforestation 8 2.8 8 6 796 357 0.350.16 0.1 - 152 - 0.07-
Forest disturbances by forest fires - - - - 0.2 34 0.01
Total 10.8 14 1153 0.51 0.3 186 0.08
Deforestation 8 8 796 0.35 0.1 152 0.07
1Transitional
Total ecosystems or ecotones
10.8 (LOt 14
= Ld/Ds); 1153
2 Percentage of affected areas in the State of
0.51 0.3 186 0.08
Roraima.
1 Transitional ecosystems or ecotones (LOt = Ld/Ds); 2 Percentage of affected areas in the State of Roraima.
3.4. Fire Incidence in Roraima (2016 to 2017) and Flona Anauá
3.4. Fire Incidence in Roraima (2016 to 2017) and Flona Anauá
The surface area, or influence of forest fires of very high density (8,397 km²) were detected in the
south-central region
The surface of or
area, Roraima during
influence 2016fires
of forest and 2017 (Figure
of very 11a). The(8,397
high density eastern 2 ) wereofdetected
kmregion Flona Anauá
in the
experienced
south-centrala region
very high densityduring
of Roraima of forest fires
2016 and(37 km²)
2017 (Figure
(Figure 11b),
11a). Thewhile theregion
eastern south-central
of Flonaregion
Anauá
was the most aaffected
experienced by fires
very high in Roraima
density of forest(2016 andkm
fires (37 2 ) (Figure
2017) (Figure11b),
12). The
whileeffective area of the region
the south-central forest
fires
was was in 772affected
the most km² (1.2%) of the
by fires mosaic area,
in Roraima (2016and
and 8 km²
2017)(0.3%) within
(Figure Flona
12). The Anauá.area of the forest
effective
fires was in 772 km2 (1.2%) of the mosaic area, and 8 km2 (0.3%) within Flona Anauá.
60000 250
Estate of Roraima FLONA Anauá
49,472
201
50000
Surface or influence (Km²)
Surface or influence (Km²)
200
40000
150
30000
100
20000 68
12,156 47
8,397 50 34 37
10000 6,655
4,040 15
2,677
0 0
Very low Low Moderate - Moderate - High Very High Very low Low Moderate - Moderate - High Very High
Medium High Medium High
Density of forest fires - Period 2016 to 2017 Density of forest fires - Period 2016 to 2017
(a) (b)
Figure
Forests 10,Surface
2019,11. area REVIEW
x FOR PEER or influence of forest fires in Roraima (2016 to 2017): (a) State of Roraima; (b)14 of 22
Figure 11. Surface area or influence of forest fires in Roraima (2016 to 2017): (a) State of Roraima; (b)
Flona Anauá.
Flona Anauá.
Figure 12. Surface area or influence of forest fires in Roraima (2016 to 2017) in the eastern region of
Figure 12. Surface area or influence of forest fires in Roraima (2016 to 2017) in the eastern region of the
the Flona Anauá.
Flona Anauá.
3.5. Accuracy Assessment of Mapping Performed by CLASlite
The overall accuracy (86%) and Kappa coefficient (82%) of the mapping performed by CLASlite
were high (Table 4). The accuracy from the producer, which is the probability that the classifier
(CLASLite algorithm) has labeled an image pixel as C1 (deforestation), when the ground truthForests 2019, 10, 61 14 of 22
3.5. Accuracy Assessment of Mapping Performed by CLASlite
The overall accuracy (86%) and Kappa coefficient (82%) of the mapping performed by CLASlite
were high (Table 4). The accuracy from the producer, which is the probability that the classifier
(CLASLite algorithm) has labeled an image pixel as C1 (deforestation), when the ground truth identified
that deforestation had occurred at that site, was higher than C2 (illegal selective logging), but both
were considered accurate in mapping. Omission errors representing pixels that belonged to the ground
truth class (in situ), but which the classification technique by CLASlite had failed to classify into the
appropriate class, were higher for C2 (illegal selective logging) and C4 (non-forested areas).
Table 4. Accuracy assessment of the mapping performed by CLASlite in the affected areas for illegal
selective logging, deforestation, and forest fires in the northern Brazilian Amazon.
Validation 2 (Ground Truth - In Situ) Errors (%) Accuracy (%)
Class 1
C1 C2 C3 C4 Total Commission Omission Producer User
C1 89 8 0 14 111 20 11 89 80
CLASlite
C2 0 75 0 1 76 1 25 75 99
C3 5 12 98 2 117 16 2 98 84
C4 6 5 2 83 96 14 17 83 86
Total 100 100 100 100 400
Overall Accuracy = 86%
Kappa Index = 82%
1 C1 = Deforestation; C2 = Illegal selective logging; C3 = Forest areas; C4 = Non-forested areas; 2 Control samples.
4. Discussion
The increased occurrence and severity of climate change has been intensified by human activities,
contributing to changes in the Earth’s surface temperature, changes in the water levels of rivers and
oceans, increased greenhouse gases emissions, and a high mortality of fauna and flora [40]. Generally,
a decrease in the variability of species’ composition and alterations of forest structure have been
observed in tropical ecosystems subjected to fires [41,42]. A study with the MODIS sensor revealed
a 59% increase in the occurrence of forest fires in the Brazilian Amazon (2000–2007), in areas where
deforestation rates were being reduced, with severe implications for programs designed to reduce
emissions from deforestation and forest degradation (REDD) [43].
The increase in forest fires in the Amazon in recent decades is strongly related to the drought–fire
interaction, due to the high flammability of dry vegetation in the El Niño years, associated with
the indiscriminate use of fire by humans. The increase in selective logging in Amazonia [7], to the
detriment of the fall in deforestation rates (2004 to 2012) [3,44], has led to sawmills receiving an
alternative constant supply of unauthorized timber [23–25,45]. This has been possible, because it is
virtually invisible to low spatial resolution sensors (MODIS, Landsat, etc.) [7,13], and because this
illegal activity can be concealed in a criminal manner with the use of multiple indiscriminate fires,
resulting in large forest fires aimed at hindering the environmental monitoring of the origins of these
fires. Another factor that contributes to increased illegal selective logging is the large migration of
loggers and sawmills from the Brazilian states of Mato Grosso, Pará, and Rondônia, to Roraima.
Roraima has been reported as a “new gold woodland”, due to its large extent of forests, the low cost of
the land, and the low environmental control of deforestation and selective logging.You can also read
