Amazon deforestation: simulated impact of Brazil’s proposed BR-319 highway project

Abstract

The scenario of deforestation in the Amazon may change with the reconstruction of Highway BR-319, a long-distance road that will expand the region’s agricultural frontier towards the north and west of the Western Amazon, stretches that until then have extensive areas of primary forest due to the hard access. We simulate the deforestation that would be caused by the reconstruction and paving of Highway BR-319 in Brazil’s state of Amazonas for the period from 2021 to 2100. The scenarios were based on the historical dynamics of deforestation in the state of Amazonas (business as usual, or BAU). Two deforestation scenarios were developed: (a) BAU_1, where Highway BR-319 is not reconstructed, maintaining its current status, and (b) BAU_2, where the reconstruction and paving of the highway will take place in 2025, favoring the advance of the deforestation frontier to the northern and western portion of the state of Amazonas. In the scenario where the highway reconstruction is foreseen (BAU_2), the results show that deforestation increased by 60% by 2100 compared to the scenario without reconstruction (BAU_1), demonstrating that paving would increase deforestation beyond the limits of the highway’s official buffer area (40 km). The study showed that protected areas (conservation units and indigenous lands) help to maintain forest cover in the Amazon region. At the same time, it shows how studies like this one can help in decision-making.

Full text

The text that follows is a PREPRINT.
O texto que segue é um PREPRINT.
Please cite as:
Favor citar como:
Santos, J.L., A.M. Yanai, P.M.L.A. Graça,
F.W.S. Correia & P.M. Fearnside. 2023.
Amazon deforestation:
Simulated impact of Brazil’s
proposed BR-319 highway
project. Environmental Monitoring and
Assessment 195: art. 1217.
https://doi.org/10.1007/s10661-023-11820-7.
ISSN: Electronic 1573-2959; Print 0167-6369
DOI: 10.1007/s10661-023-11820-7
Copyright: Springer
The original publication is available at
O trabalho original está disponível em:
https://doi.org/10.1007/s10661-023-11820-7
https://www.springer.com/journal/10661

1
Amazon deforestation: Simulated impact of Brazil’s proposed BR-319 highway project
Jerfferson L. Santos1,2, Aurora M. Yanai4, Paulo M. L. A. Graça4, Francis W. S. Correia2,3, and Philip M. Fearnside4
1Postgraduate program in Climate and the Environment, National Institute of Amazonian Research (INPA), Av. André
Araújo, 2936, CEP 69067-375, Manaus, Amazonas, Brazil.
2Postgraduate program in Climate and the Environment, State University of Amazonas (UEA), Av. Darcy Vargas, 1200,
CEP 69050-020, Manaus, Amazonas, Brazil.
3Laboratory of Terrestrial Climate System Modeling (LABCLIM), State University of Amazonas (UEA), Av. Darcy
Vargas, 1200, CEP 69050-020, Manaus, Amazonas, Brazil.
4 Department of Environmental Dynamics, National Institute of Amazonian Research (INPA), Av. André Araújo, 2936,
CEP 69067-375, Manaus, Amazonas, Brazil.
E-mail addresses: jlds.dcl19@uea.edu.br (Jerfferson L. Santos – Corresponding author), yanai@inpa.gov.br (Aurora
M. Yanai), pmlag@inpa.gov.br (Paulo M. L. A. Graça), fcorreia@uea.edu.br. (Francis W. S. Correa),
pmfearn@inpa.gov.br (Philip M. Fearnside).
1
2
3
4
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

2
Abstract
The scenario of deforestation in the Amazon may change with the reconstruction of the Highway BR-319, a long-
distance road that will expand the region's agricultural frontier towards the north and west of the Western Amazon,
stretches that until then have extensive areas of primary forest due to the hard access. We simulate the deforestation that
would be caused by the reconstruction and paving of Highway BR-319 in Brazil’s state of Amazonas for the period
from 2021 to 2100. The scenarios were based on the historical dynamics of deforestation in the state of Amazonas
(business as usual, or BAU). Two deforestation scenarios were developed: a) BAU_1, where Highway BR-319 is not
reconstructed, maintaining its current status and b) BAU_2, where the reconstruction and paving of the highway will
take place in 2025, favoring the advance of the deforestation frontier to the northern and western portion of the state of
Amazonas. In the scenario where the highway reconstruction is foreseen (BAU_2), the results show that deforestation
increased by 60% by 2100 compared to the scenario without reconstruction (BAU_1), demonstrating that paving would
increase deforestation beyond the limits of the highway's official buffer area (40 km). The study showed that protected
areas (conservation units and indigenous lands) help to maintain forest cover in the Amazon region. At the same time, it
shows how studies like this one can help in decision making.
Keywords: environmental modeling; land use change; Amazonia; arc of deforestation; human occupation.
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37

3
Credit Author Statement
Author contributions
Jerfferson Lobato dos Santos: Conceptualization, Data curation, Calibration, Validation, Analysis, Writing, Original
draft preparation.
Aurora Miho Yanai: Conceptualization, Methodology, Writing-Reviewing and Editing.
Paulo Maurício Lima de Alencastro Graça: Conceptualization, Methodology, Writing-Reviewing and Editing.
Francis Wagner Silva Correia: Conceptualization, Supervision, Writing-Reviewing and Editing.
Philip Martin Fearnside: Conceptualization, Supervision, Writing-Reviewing and Editing.
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54

4
1. INTRODUCTION
The Amazon basin covers an area of approximately 7 million km2, with 5.5 million km2 covered by forests, which
represents 40% of the global tropical forest area (Nobre, 2014; Weng et al., 2018). Amazon ecosystems host 15-20% of
the planet's species diversity (Lewinsohn & Prado, 2002) and store around 120 Gt of carbon (Saatchi et al., 2011). The
Amazon rainforest plays an important role in the regional and global climate system through the storage and absorption
of carbon (carbon cycle), transport of trace gases and aerosols, and through water cycling, which provides moisture that
is transported to other regions of the continent, and contributes to maintaining the hydrological regime at regional and
global scales (Rocha et al., 2015; Nobre et al., 2016; Marengo et al., 2018; Weng et al., 2018).
Deforestation, which is mostly for extensive cattle ranching, is a major contributor to greenhouse gas emissions and to
climate change at both the regional and global scales (Fearnside et al., 2009; Moutinho, 2009; Marengo et al., 2018;
Fearnside, 2022b). Deforestation in the Amazon has been monitored by satellite since 1988 and this monitoring is an
important tool for guiding public policies aimed at controlling the destruction of forests in the region (INPE, 2020).
Deforestation in the Amazon is one of the major problems that Brazil has been facing in recent decades, and the
reconstruction of highway BR-319 (Fig. 1a) is a major issue that has drawn the attention of environmentalists and
researchers This highway would facilitate access to a large area of preserved forest, which could change the current
scenario of deforestation in the Amazon (Fig. 1b) and cause substantial environmental and social impacts at the local,
regional, and global levels.
Fig. 1 a) Map of Highway BR-319, connecting the cities of Manaus, Amazonas and Porto Velho, Rondônia, showing the main federal
highways. b) Official highways and the spatial distribution of cumulative deforestation (1988 to 2021) with emphasis on the ‘arc of
deforestation.’ Map prepared by the authors. Data sources: IBGE, 2017; DNIT, 2021; INPE, 2020.
Highway BR-319 was built in 1972 and 1973 but was only inaugurated in 1976 (DNIT, 2016), a period of military
government. The highway was part of Brazil’s National Integration Program (PIN), under the motto “Security and
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73

5
Development,” uniting military concerns over perceived communist invasion with the developmental ideals promoted
by President Juscelino Kubitschek in the 1950s (Lessa, 1991; Kohlhepp, 2002; Oliveira-Neto, 2014; Facundes, 2019).
With the passage of time and lack of maintenance, the BR-319 became impassable in the late 1980s (DNIT, 2016) and
its reconstruction became the focus of various local movements and governments (MPOG, 2004).
It was in the 1970s that the most critical period of changes in the Amazon landscape started in Brazil, when
environmental impacts were intensified through colonization and development programs based on highways. These
highways still have an important role in the occupation of space, attracting people in search of cheap land and natural
resources and, consequently, increasing deforestation, fires, illegal logging, growth of cattle ranching, illegal mining,
speculation and land grabbing, armed conflicts, and disease outbreaks, among other effects (Lessa, 1991; Loureiro,
2002; Fearnside, 2003; Graça et al., 2007; Laurance & Balmford, 2013; Brito & Castro, 2018).
Barber et al. (2014) showed that 94% of all deforestation in the Brazilian Amazon occurred around official and
endogenous roads, demonstrating the role of highways as important drivers of deforestation. Reconstruction of
Highway BR-319 is therefore the subject of growing concerns, as disorderly occupation and environmental degradation
can extend the 'arc of deforestation' (Fig. 1b) advancing to the northern part of the state of Amazonas and to the state of
Roraima, reaching the border with Venezuela via Highway BR-174 (Manaus - Boa Vista) (Fearnside et al., 2009;
Fearnside & Graça, 2009; Barni et al., 2015). Planned roads associated with BR-319 would extend the impact to the
western portion of the state of Amazonas (Fearnside, 2018).
Even so, many politicians and enthusiasts for the reconstruction of Highway BR-319 have claimed that deforestation
would not occur, contrary to the warnings of scientists. However, it is a fact that the simple announcement of the paving
and improvement plans has already resulted in a disorderly pattern of occupation and an increase in deforestation and
fires along the middle stretch of the highway, with rampant illegal logging and invasion of public lands for real estate
speculation and extensive cattle ranching (Fearnside & Graça, 2009; Andrade et al., 2021; Ferrante et al., 2021).
The situation is made more worrisome by the current Brazilian scenario in which there is a tendency for deforestation to
increase, as can be seen in Fig. 2a. This trend is related to the economic pressures and political power of groups with
interests in land-related businesses and infrastructure projects in the Amazon, which has led to the weakening of the
Brazilian Forest Code (Supplementary Material, Appendix 1) and to other legislative changes that have been
progressively eliminating restrictions on deforestation since 2012 (Fearnside, 2022a). The 2019-2022 Jair Bolsonaro
presidential administration revoked many of the government’s internal norms that had been established to combat
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101

6
deforestation (Barbosa et al., 2021). At least 401 of these changes can be reversed in 2023 by the incoming Luiz Inácio
Lula da Silva administration (TALANOA, 2022). Legislative changes, however, will face a National Congress with a
new composition, indicating that it will be even more hostile to environmental protection than the Congress during the
Bolsonaro administration (ClimaInfo, 2022).
Fig. 2 Deforestation in the Brazilian Legal Amazon (a) and in the State of Amazonas (b) from 1988 to 2021 in 103 km². Source:
INPE (2022).
According to data from the Project for Monitoring Deforestation in the Legal Amazon by Satellite (PRODES), of the
National Institute for Space Research (INPE), the state of Amazonas resumed the increase of annual deforestation, from
523 km² in 2012 to 2306 km² in 2021, an increase of 440% (INPE, 2022), surpassing the historic record of 1995 (Fig.
2b). Furthermore, these data show that much of the deforestation in the state of Amazonas was concentrated in the
southern part of the study area, which is under the direct influence of BR-230 (Transamazon Highway) and BR-364
(Porto Velho– Rio Branco).
Thus, given the possibility of the reconstruction and paving of BR-319 and the possible changes in the pattern of land
use and cover, the question that the present study proposes to answer is: “What would be the impact of paving Highway
BR-319 on deforestation in the state of Amazonas in 2050 and 2100?”. The present study aims to evaluate the impact
of BR-319 and other highways planned in the study area.
2. MATERIAL AND METHODS
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116

7
2.1. Study area
The study focuses on the federal highway BR-319, located in the interfluve between the Madeira and Purus Rivers,
connecting the cities of Manaus (Amazonas) and Porto Velho (Rondônia). The BR-319 is the main land access route to
the municipalities of Careiro, Manaquiri, Careiro da Várzea, and Autazes, as well as facilitating access to Humaitá,
Lábrea, and Manicoré. It provides the only land access to the communities of Vila Realidade (district of the
municipality of Humaitá) and Igapó-Açu (a district of the municipality of Borba). However, all of these locations are
accessible from the two ends of the highway without reconstructing the critical “middle stretch” that would give access
from the arc of deforestation to all areas connected to Manaus by road, including the state of Roraima.
The official road network in the state of Amazonas that connects to the 885 km of BR-319 corresponds to 1934 km,
comprising the federal highways BR-230 (827 km from Lábrea to the border between the states of Amazonas and Pará),
BR-174 (85 km, stretch BR-319 - Manicoré), and state highways AM-254 (94 km, BR-319 - Autazes) and AM-354 (43
km, BR-319 - Manaquiri). In addition, there are other planned projects by the government of the state of Amazonas to
build highways connecting BR-319 to other municipalities such as Borba (AM-356), Novo Aripuanã (AM-360),
Tapauá, Tefé and Juruá (AM-366) and Coari (AM-343). The last two roads (AM-366 and AM-343) would advance into
the vast area of forest to the west of the Purus River, facilitating deforestation in one of the most preserved forest areas
in Amazonia, known as the “Trans-Purus” region (Fearnside et al., 2020) (Fig. 3). Very little of the area that would be
accessed by these connecting roads is protected by designation as a “conservation unit.” (Fig. 3).
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

8
Fig. 3 Study area, Highway BR-319 and road network planned around BR-319, federal and state protected areas, indigenous lands,
federal settlement projects and military areas. Map prepared by the authors. Data sources: IBGE (2017), ICMBio, INCRA, FUNAI.
The official area of influence used in Brazil's environmental licensing processes for highways in the Amazon region is
40 km of buffer area, defined by Interministerial Ordinance 60 of 24 March 2015 (Brazil, 2015). However, considering
that the environmental impact of a paved highway in the Amazon can go beyond the minimum limit defined in the
interministerial decree, the present study considered the state of Amazonas as the total area for modeling the impacts of
deforestation, having as a 'backbone' Highway BR -319, as well as its connecting highways and roads, including both
existing and planned roads. The study area also includes a buffer zone of 20 km around the borders of the state of
Amazonas to represent the influence of adjacent areas, especially the highways present in the states of Acre, Rondônia,
Roraima, and Pará (Fig. 3).
2.2. Land-Use Modeling
Modeling deforestation was done using the environmental modeling platform DINAMICA-EGO (Environment for
Geoprocessing Objects) (Soares-Filho et al., 2002; Leite-Filho et al., 2020). DINAMICA-EGO can be applied to a
variety of types of studies, such as urban expansion modeling, economic ecological zoning proposals, and the
simulation of deforestation behavior (Soares-Filho et al., 2004; Rodrigues et al., 2007; Ramos et al., 2018; Santos et al.,
134
135
136
137
138
139
140
141
142
143
144
145
146

9
2021). In addition, the software is open access and has a user-friendly interface, which can be used by people unfamiliar
with programming languages such as R and Python. More details on the software can be found in the Supplementary
Material (Appendix 2, Fig. S1).
2.3. Deforestation modeling steps
The modeling process was carried out through the following steps: input data, calibration, validation, and simulation
(projection) of future deforestation. For the input and calibration data, the period from 2007 to 2013 was used. For
validation, the period from 2014 to 2021 was used, while the simulation scenarios were for the period from 2021 to
2100.
2.3.1. Input data
All input cartographic data were in raster format with a spatial resolution of 100 m. The mapping used the Brazil
Policonic Cartesian coordinate system, Datum SIRGAS 2000.
In addition to land-cover maps, maps of static and dynamic variables were used. Static variables are those for which the
value of the class of each cell (pixel) does not change over the course of a simulation. For this category, maps of
protected areas were used - indigenous lands (FUNAI, 2020), federal protected areas for integral protection and federal
protected areas for sustainable use (ICMBio, 2019), state protected areas for integral protection and state protected areas
for sustainable use (SEMA, 2021), and military areas (ANM, 2021). A map of settlement projects (INCRA, n.d) and
official hydrography or watercourses (INPE, 2020) were also used.
Dynamic variables are those whose values change over the course of a simulation. These included distance from official
and endogenous roads and distance from deforested areas. The Supplementary Material (Appendix 3) presents a
summary of the variables used in the configurations (Table S1) and the map of static variables (Fig. S2).
2.3.1.1. Regionalization of the study area
The model applied in this study used the regionalization approach, which consists of establishing different parameters
for each region and modeling the regional context that influences a given phenomenon (Leite-Filho et al., 2020). The
software uses a set of functors (tools or small subroutines) to divide a map into parts (i.e., regions) to process the dataset
of each region separately and then combine them. For this, a regionalized map of the study area was added as input to
the model.
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172

10
Thus, considering that the regionalization of the area makes it possible to individually parameterize each region, in the
present study the area was divided into nine regions (Fig. 4) that took into account the presence of highways (current
and planned), human clusters, land-use profile (contribution of social actors in deforestation) and hydrography. A
summary of the parameters used to divide the study area into regions is provided in the Supplementary Material
(Appendix 4, Table S2).
Fig. 4 Regionalized map of the study area.
2.3.2. Calibration
Calibration is the step of fitting the model parameters so that the simulation results are as similar as possible to the real
study case (Campos et al., 2022). Therefore, in this phase there is a continuous search to adjust these parameters until
the simulation result is as close as possible to the real one. In this study the reference period used to calibrate the model
was from 2007 to 2013, with the goal of performing a validation simulation round for the period from 2014 to 2021,
comparing the simulated map of 2021 with the satellite data for observed deforestation from the PRODES map for
2021.
173
174
175
176
177
178
179
180
181
182
183
184

11
Among the data needed to be applied in the simulation model are the weights of evidence of the variables, this being a
measure of influence that each variable has to cause a change, in this case the expansion of deforestation (Leite-Filho et
al., 2020). The weights-of-evidence applied in DINAMICA-EGO are based on a Bayesian method where the effect of a
spatial variable is calculated independently of any combination to produce maps that describe the most-favorable areas
for a change to occur (Soares-Filho et al., 2002, 2004; Leite-Filho et al., 2020).
To calculate the weights of evidence, a model was used in DINAMICA-EGO, which received the initial (2007) and
final (2013) landscape maps, in addition to the maps of static and dynamic variables, followed by calculating the ranges
and assigning transition-probability values for each variable used in the simulation model. An adjustment was necessary
to achieve the desired result, by defining the interval and distance of the weights of evidence at 100 m and 1500 m,
respectively, for the variables roads, deforestation, and hydrography. Such values were reached after several rounds of
adjustments and the validation test indicated that the best result was in this influence range. The table of parameters
used in the present study and a figure summarizing the calculation of the weights-of-evidence coefficients can be found
in the Supplementary Material (Appendix 5, Table S3, Fig. S3).
Considering that the only assumption for the weights-of-evidence method is that the input maps be spatially
independent, the next step is to analyze the correlation between the variable maps (Leite-Filho et al., 2020). After the
analysis of correlated pairs between variables using the Cramer's test and joint-uncertainty information, values above
0.5 were considered as dependent variables (Bonham-Carter, 1994). No dependent variables were observed in the
present study.
Another parameter used in the model is the transition rate, which is necessary to determine the number of cells that
transition between classes at each annual time step, in this case for forest to deforestation. The transition rate was
calculated using a sub-model in DINAMICA-EGO called “Determine Transition Matrix,” which uses maps of the initial
state (cumulative deforestation by 2007) and final state (cumulative deforestation by 2013). This tool generates two
matrices: the annual transition matrix (Multiple Step) and a global transition matrix (Single Step). “Multiple Step”
portrays the process of change between the classes that occurs each year, while “Single Step” portrays the change over
the whole analysis period (Leite-Filho et al., 2020). The simulation used the annual transition matrix (Multiple Step) ,
which reflects the average annual transition in the calibration period (2007 to 2013).
However, simply applying the deforestation rate provided in the annual transition matrix would result in a constant rate
across all model interactions. Thus, considering that deforestation rates actually fluctuate over time (increasing and
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212

12
decreasing), whether as a result of financial crises, conflicts, climatic events, political decisions and other factors, this
study included an increasing and reducing factor for deforestation rates, which was applied for interval periods of six
years (period equal to the reference period used to calibrate the model).
To represent increase in deforestation, an index was added to the transition rate (Multiple Step) that considered the
deforested area in the previous year plus the average percentage increase in all years in which deforestation increased in
the period from 2000 to 2014 in the state of Amazonas. This represented the increase in deforestation in the study area
by means of the following equation:
Ind.t = ((AD2-AD1) 100)/AD1) + Mdi (Eq. 1)
Ind.t = Transition Index
AD1 = Area deforested in Year 1 (km2)
AD2 = Area deforested in Year 2 (km2)
Mdi = Average annual deforestation during the years in which there was an increase (period from 2000 to 2014)
To represent reduction in deforestation, Equation 2 follows the same principle as Equation 1, using the average
percentage decrease in all years in which there was a reduction in deforestation during the period from 2000 to 2014.
Ind.t = ((AD2-AD1) 100)/AD1) - Mdd (Eq. 2)
Ind.t = Transition Index
AD1 = Area deforested in Year 1 (km2)
AD2 = Area deforested in Year 2 (km2)
Mdd = Average annual deforestation during the years in which there was a reduction (period from 2000 to 2014)
The increase and decrease factors (Mdi and Mdd) were calculated based on the average increase and decrease in
deforestation during the period from 2001 to 2014, to better represent the trends of increase and decrease over time,
which were defined as follows: 0.26 for increase and 0.20 for reduction. The years in which there were increases and
decreases in deforestation in the state of Amazonas are shown in the Supplementary Material (Appendix 5, Fig. S4), as
well as an example of the fluctuation of deforestation rates over time (Fig. S6). The present method allowed the
transition rates to fluctuate with each iteration of the model, which means that as there is a change in the landscape at
each time step, the (annual) transition rate is updated at each iteration in relation to the available forest area in each
region. A summary and the input data is shown in Appendix 5 and Table S4 of the Supplementary Material.
The spatial allocation functions for the new deforestation patches used in the model were Patcher and Expander, where
the Patcher function creates new areas (patches) of transition separate from the already deforested areas, while the
Expander function is responsible for enlarging already-deforested areas (Leite-Filho et al., 2020). In this study, several
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244

13
rounds of parameter adjustments were carried out and, in the validation test, the best result was found to be achieved
was using 30% as a value for the Expander function and 70% for the Patcher function. As for the size of the
deforestation patches, the average range of the size of the deforestation polygons of each region defined in the study
was calculated during the calibration period. The settings used to allocate deforestation patches through the Patcher and
Expander functions, including the percentages adopted, are available in the Supplementary Material (Appendix 6, Table
S6).
Considering that the model deals with the impact of roads on landscape change, the road builder module was coupled to
the model, using the map of official and endogenous roads as input. This module calculates the relative cost that a road
has in crossing a cell in the land-use map, depending on the destination given to the cell (protected lands, non-destined
forest areas, settlements, etc.). For this, we used an attractiveness map (which indicates the most favorable areas for
road construction) and a friction map (which indicates the areas with greater restrictions for road construction) (Leite-
Filho et al., 2020). The settings used in the road-builder module can be seen in the Supplementary Material (Appendix
7, Table S7).
2.3.3. Model validation
After calibration (2007 to 2013), a simulation model was used for the period from 2014 to 2021 in order to calculate the
change that occurred in this interval and validate the resulting map of the simulated model for 2021 by comparison with
the real map from PRODES 2021. For validation this study simulated a period that was different from the calibration
period in order to assess how good the model is at predicting changes in the landscape, based on the procedures used in
past studies (Siqueira-Gay et al., 2022).
The validation method applied in this study was the fuzzy similarity method (Hagen, 2003), adapted by Leite-Filho et
al. (2020). This method employs a constant decay function that measures the spatial adequacy between two maps
through multiple-window similarity analysis, that is, if the same number of change cells is found in the window, the fit
will be 1, regardless of their locations, and zero if the same number of change cells is not found (Leite-Filho et al.,
2020). Simply put, the model makes the comparison through window sizes, that is, with the number of cells
corresponding to the resolution used in the modeling. For example: in this study the resolution adopted was 100 m, so
window 1 (1 × 1) corresponds to 100 m × 100 m (0.01 km2), window 3 (3 × 3) = 300 m × 300 m ( 0.09 km2), and so on.
Because the comparison is made using both maps (simulated and observed), the results can generate rates with
minimum and maximum similarity values, which can vary from 0% to 100% (0% indicates that the maps are
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272

14
completely different and 100% indicates they are identical). In this study we adopted the minimum similarity value as a
reference. We compared the simulation results with a null model, which uses the same maps and input rates but with
weights-of-evidence values set to zero. The null map was also compared with the observed map (PRODES 2021). To be
considered efficient, the proposed model must win in all comparisons made with the null model. Further details can be
found in the supplementary material (Appendix 8).
2.3.4. Projection of future scenarios
The current approach considers the trends in the expansion of territorial occupation by different local groups based on
the dynamics of historical deforestation for the Amazon (Business as Usual, or BAU), which reflects occupation
dynamics and conflicts that influence landscape change along highways (Castro et al., 2004; Brito & Castro, 2018;
Fearnside, 2022a). Thus, deforestation rates were not projected based on the perspective of improving the
environmental management in the area, such as strengthening and increasing the autonomy of public command-and-
control institutions, public policies aimed at sustainability or achieving the goal of reducing emissions stipulated in
international agreements, as this depends on the long-term commitment of state and federal governments.
Two environmental prognosis scenarios were developed for the period from 2021 to 2100: a) Scenario 1 (BAU_1) -
Highway BR-319 without paving (the current status with seasonal maintenance and with degradation in the rainy
season, with the pending reconstruction and paving project not approved); b) Scenario 2 (BAU_2) – Highway BR-319
with paving (the reconstruction and paving project is assumed to be authorized and started in 2025).
For the BAU_1 scenario, the averages of the historical transition rates from the calibration period (2007 to 2013)
obtained from each region of the study area were applied according to the methodology presented in the item 'model
validation', from 2021 to 2100. For the BAU_2 scenario, the transition rates followed the same principles as the BAU_1
scenario until the beginning of the paving of Highway BR-319 in 2025, when an increase in the deforestation rate
begins as a result of the migratory flow resulting from the road improvement and the expansion of the planned road
network until 2100. Post-paving rates were obtained from other regions within the study area itself, as defined below.
For the Scenario BAU_2, which considers Highway BR-319 to be paved from 2025 onwards, the rates found in
Regions 3 and 4 (where the sections of the BR-319 are located) take on present the same rates found in Region 1 (area
with a higher deforestation rate) Regions 3 and 4 would be new frontiers for expansion of ranching if BR-319 is paved,
and in Region 5 (Manaus), which will have the rate of Region 3, a region close to the capital of Rondônia (so that
Region 5 has a rate similar to that near a state capital in the 'arc of deforestation').
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300

15
After 2028, the transition rate found in Region 7 (providing Highway AM-366 is built as a result of the BR-319
highway), started to have the same rate as in Region 1 (same principle adopted to represent the Regions 3 and 4, if AM-
366 is built). The Region 1 rate was chosen because it represents a continuation of the expansion of deforestation
towards the western part of the study area due to the influence of migration to Amazonas from the states of Pará,
Rondônia, and Mato Grosso. We therefore chose Region 1 as a reference to represent the amount of deforestation.
Regardless of the applied rate, the model allows the use of weights-of-evidence coefficients from other regions that can
better simulate what is intended to be represented. Thus, the weights-of-evidence coefficients were also replaced to
better represent the influence of paved roads in the model, so Regions 3 and 4 (site of the BR-319 highway), and Region
5 (region with road connecting to BR-319, and therefore becoming a new agricultural frontier), started to have the same
weights-of-evidence coefficient as in Region 6 (which is a region with the paved Highway BR-364 in the 'arc of
deforestation').
Considering the construction plan for Highway AM-366 (without paving), Region 7 now has the same weight of
evidence as Region 1 (which is a region with the unpaved Highway BR-230 in the 'arc of deforestation' in the state of
Amazonas). In addition, to complement the analysis of the impact of deforestation, a paving plan was made for
Highway AM-366 for the year 2050, after which it started to change the weights-of-evidence coefficients to be more
similar to those of Region 6 (i.e., to resemble region with a paved highway: part of the Porto Velho–Rio Branco stretch
of BR-364).
The paving plan for Highway AM-366 is justified by the fact that the proposed road is located in a region planned for
oil and gas extraction, which may favor financing or raising funds for construction, in addition to a greater possibility of
political interference with the licensing body. However, it is worth noting that, considering the applied transition rates,
the result of the amount of deforestation does not change.
Patcher and Expander allocation followed the same principles as for the parameters used in road construction.
The plan for the construction and paving of the planned highways followed the principles of area availability and
occupation opportunity because, regardless of government plans for building a highway, when there is an available area
and opportunity, the illegal occupants of the area begin to follow the planned route of a highway, opening unofficial
roads and branches on the proposed official highway. This fact can be observed in an area in Region 4, where an illegal
road or “branch” is already being built on the route of the proposed Highway AM-366 (Fearnside, 2022b). Thus, for the
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327

16
present study, a three-year construction schedule (whether official or not) was adopted to start after the paving of BR-
319 (Table 1).
Table 1 The schedule for the construction and paving of the planned highways influenced by the implementation of BR-
319.
Road Segment Start
BR-319 Manaus– Porto Velho 2025*
AM-366 (Segment 1) Tapauá – AM-343 2028
AM-343 Coari - AM-366 2028
AM-366 (Segment 2) Entroncamento AM-366 - Tefé 2031
AM-366 (Segment 3) Tefé - Jutaí 2034
AM-356 BR-319 - Borba 2028
AM-360 BR-319 – Novo Aripuanã 2028
AM-366 (all segments) and AM-343 Tapauá – Coari - Jutaí 2050*
* Pavement estimate.
The application of transition rates in both scenarios followed the same methodology applied for the validation phase.
However, the values for the 'average of years in which there was an increase and decrease in deforestation' (Mdi and
Mdd) were adjusted in both scenarios to better represent the trends, using the average increase and decrease over the
period from 2000 to 2021. The value of 0.32 was adopted as an increase factor and 0.19 as a decrease factor, with
intervals of 6 years starting in 2021(Table S5, Supplementary Material).
3. RESULTS
3.1. Validation
The validation compared the 2021 simulated deforestation map with the 2021 deforestation obtained by the PRODES
mapping in 2021, which is considered as a reference for observed deforestation. This method considers the values of the
similarity index of 50% sufficient for model validation (Soares-Filho et al., 2013). The value of the minimum similarity
index obtained was 51% for the simulation model in a window of 11 × 11 cells.
In addition to the validation for 2021, the results were compared to a null model. In the null model the same input maps
and transition rates were used, but with the weights-of-evidence coefficients set to zero, producing the result shown in
Fig. 5.
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344

17
Fig. 5 Validation results for 2021 with minimum similarity and with the null model, using the constant decay method.
Regarding the comparison of the simulated deforestation, the validation showed a difference of -0.54% in relation to the
reference deforestation for the year 2021, resulting in a difference of -313.92 km2 (Supplementary Material, Appendix
8, Table S8). The results for each region are shown in the Supplementary Material (Fig. S8, Appendix 8).
3.2. Deforestation prediction for the years 2050 and 2100
In this section, the results of the scenarios will be presented, highlighting the simulated changes by 2050 and by 2100.
The results show that, for deforestation in BAU_1, there is an increase of 200.24% up to 2050 and 607.42% up to 2100,
in relation to that observed in the PRODES 2021 map. For BAU_2, there is an increase of 224.12% by 2050 and
711.33% up to 2100, for the entire modeled area, as shown in Fig. 6.
345
346
347
348
349
350
351
352

18
Fig. 6 Evolution of cumulative deforestation for the period from 2021 (A) to 2050 and 2100 in the BAU_1 (B and C) and BAU_2 (D
and E) scenarios. In this study, “non-forest” refers to those areas not considered by PRODES/INPE in the calculation of deforestation
in the Amazon (savannas, water, rocky outcrops, etc. - http://terrabrasilis.dpi.inpe.br/).
For the BAU_1 scenario in the Madeira-Purus interfluve (Regions 3 and 4) where the BR-319 Highway is located, there
were increases of 197.37% up to 2050 and 600.95% up to 2100 in Region 3 and increases of 241.08% up to 2050 and
762.04% up to 2100 in Regions 4. Especially for the northern stretch of Highway BR-319 (Region 4, which has more
D) B AU _ 2 - 2 050 - s imu la te d d ef or e st ed are a: 12 9,41 8.2 4 k m
2
E ) B AU _2 - 2 100 – s imu late d d ef or e st ed ar ea 41 0, 75 6.2 1 k m
2
A ) 2 021 - in itia l d efo re st ed ar ea : 57 ,74 5.1 5 km
2
B ) B AU _1 - 205 0 - s im ula te d d ef or este d a re a: 11 5,6 28 .48 k m
2
C) B AU _ 1 - 21 00 - s imu late d d ef o re st ed are a: 35 0,75 4.8 7 k m
2
353
354
355

19
area available for deforestation) after paving (BAU_2) there were increases of 260.08% up to 2050 and 843.65% up to
2100.
Another part of Amazonas that draws attention is the Trans-Purus region in the center of the state (Region 7). This is
due to the possible construction of Highway AM-366, which would connect to BR-319 (BAU_2). The BAU_2 scenario
shows an increase of 359.48% by 2050 and 1458.91% by 2100 (Fig. 7, panels D & E).
355
356
357
358
359

20
Fig. 7 Evolution of cumulative deforestation for the period from 2021 (A) to 2050 and 2100, in scenarios BAU_1 (B and C) and
BAU_2 (D and E) in Region 7 (Trans-Purus) as a result of the construction of Highways AM-366 and AM-343.
Region 5 (BR-174 from Manaus to the border with the state of Roraima) would have an increase of 225.36% by 2050
and 734.81% by 2100 due to the influence of the reconstruction of BR-319 (BAU_2). Thus, for the regions influenced
A) 2021 - Region 7 - Trans-Purus - Initial deforestation.
B) BAU_1 - 2050 – simulated deforested area region 7
(Trans-Purus).
C) BAU_1 - 2100 – simulated deforested area region 7
(Trans-Purus).
D) BAU_2 - 2050 – simulated deforested area region 7
(Trans-Purus).
E) BAU_2 - 2100 – simulated deforested area region 7
(Trans-Purus).
360
361

21
by Highway BR-319 (Regions 3, 4, 5 and 7) deforestation would have an increase of approximately 60% in BAU_2
(159,961.31 km2) in relation to BAU_1 (99,959.97 km2). The results for all regions are shown in Table 2.
Table 2 Increase in cumulative deforestation by region and percentage of increase in cumulative deforestation over the
simulated period in relation to 2021.
Region PRODES
2021
BAU_1
2050
(km2)
%
BAU_2
2050
(km2)
%
BAU_1
2100
(km2)
%
BAU_2
2100
(km2)
%
1 9,042.42 27,569.06 304.89 27,569.06 304.89 92,897.55 1,027.35 92,897.55 1,027.35
2 5,369.36 7,272.21 135.44 7,272.21 135.44 17,114.99 318.75 17,114.99 318.75
3 4,469.53 9,918.68 221.92 11,624.33 260.08 31,599.30 706.99 37,707.12 843.65
4 4,713.67 8,205.97 174.09 10,514.33 223.06 23,586.79 500.39 32,272.10 684.65
5 7,634.83 12,083.39 158.27 17,205.73 225.36 33,927.84 444.38 56,101.63 734.81
6 19,040.05 38,864.17 204.12 38,864.17 204.12 117,380.29 616.49 117,380.29 616.49
7 2,322.31 3,694.81 159.10 8,348.22 359.48 10,846.04 467.04 33,880.46 1458.91
8 3,327.30 5,046.21 151.66 5,046.21 151.66 14,387.02 432.39 14,387.02 432.39
9 1,825.68 2,973.98 162.90 2,973.98 162.90 9,015.05 493.79 9,015.05 493.79
Total 57,745.15 115,628.48 200.24 129,418.24 224.12 350,754.87 607.42 410,756.21 711.33
Roads played an important role in the distribution and dispersion of deforestation over time in the proposed model. Fig.
8 cuts out the study area to show how deforestation evolves around the simulated roads for the years 2050, 2060, 2070,
2080, 2090 and 2100. According to the model, a cluster of deforestation ends up attracting other deforestation, which
can occur on the banks of rivers without the presence of roads. However, a large part of the deforestation is conducted
along unofficial roads that branch off from the official roads (in Brazil, the pattern of these side roads is called the
“fishbone”). This pattern develops along roads connecting to riverside towns and cities, as can be seen in the evolution
of deforestation shown in Fig. 8, corroborating the studies by Castro et al. (2004), Nepstad et al. (2006), Barber et al.
(2014), dos Santos-Júnior, et al. (2018) and Fearnside (2022a,b).
362
363
364
365
366
367
368
369
370
371

22
Fig. 8 Evolution of deforestation around the simulated roads over time in the BAU_2 scenario. The figure shows part of the region of
influence of AM-366 (Trans-Purus).
We can see that deforestation has increased in all protection categories (except for military areas, which have very low
deforestation). When comparing the deforestation of protected areas in relation to the total forest loss (inside and
outside protected areas) after 2021, an increase of deforestation in conservation units by 2,153.60 km2 up to 2050 can be
observed in the BAU_1 scenario, and 28,656.73 km2 up to 2100, corresponding to 3.72% and 9.78%, respectively, in
relation to total deforestation. In the BAU_2 scenario, deforestation in the protected areas was 1,960.65 km2 in 2050 and
34,612.13 km2 in 2100, corresponding to 2.73% and 9.80%, respectively, of the total deforested area.
In indigenous lands, projected deforestation after 2021 was 1,042.81 km2 in 2050 and 19,911.23 km2 in 2100 for the
BAU_1 scenario, corresponding to 1.80% and 6.79%, respectively, in relation to total deforestation. For the BAU_2
372
373
374
375
376
377
378
379

23
scenario, the total area of deforestation in indigenous lands was 964.44 km2 in 2050 and 21,079.15 km2 by 2100,
respectively, from which 1.34% and 5.97% of the total deforested area were after 2021. Regarding the total area of
protected areas, deforestation reaches 0.52% by 2050 and 7.91% by 2100, in the BAU_1 scenario and 0.48% by 2050
and 9.08% of the total area of conservation units and indigenous lands up to 2100 in the BAU_2 scenario. Fig. 9
presents the relationship between deforestation in protected and non-protected areas, showing the importance of
protected areas for the conservation of forests in the Amazon.
Fig. 9 Deforestation in protected areas (conservation units and indigenous lands) and non-protected areas (settlement projects are not
considered to be protected areas).
For settlement projects, according to the results of the projection for the BAU_1 scenario, the deforestation that
occurred after 2021 was 16,897.26 km2 by 2050 and 48,407.66 km2 by 2100, corresponding to 41.22% and 19.79% in
relation to the deforestation outside protected areas. For the BAU_2 scenario, deforestation after 2021 was 21,660.76
km2 by 2050 and 57,334.82 km2, which corresponds to 43.31% and 19.39% in relation to total deforestation (excluding
protected areas), respectively (Fig. 10). Regarding the total area of settlements, deforestation reaches 22.76% up to 2050
of the total area of settlements and 65.19% up to 2100 in the BAU_1 scenario, and it reaches 29.17% up to 2050, and
77.21% up to 2100 in the BAU_2 scenario.
380
381
382
383
384
385
386
387
388
389
390
391
392

24
Fig. 10 Deforestation in settlement projects and non-designated public land.
4. DISCUSSION
4.1 Simulated Deforestation
Although the method considers similarity index values above 50% to be enough to validate the model, which means that
the amount of change correctly predicted is greater than the sum of the various types of error (Pontius-Jr et al., 2007;
Soares- Filho et al., 2013), there is no general rule for calibration and validation in the land-use modeling process
(Rykiel, 1996; Mazzotti &Vinci, 2007). However, it is understood that the model must satisfactorily represent the spatial
dynamics of deforestation in the study area.
In the current study, the model reached 51% in the 11 × 11 window, which corresponds to the similarity in an area of
1.21 km2. Some studies carried out in smaller areas in Amazonia also found similarity starting at 50% in the 11 × 11
window or smaller, such as Yanai et al. (2012) in the 5×5 window, Maeda et al. (2011) in the 11 × 11 window, Barni et
al. (2015) in the 7 × 7 window, Roriz et al. (2017) in the 5 × 5 window, Ramos et al. (2018) in the 11 × 11 window; dos
Santos-Júnior et al. (2020) reached 49% in the 11 × 11 window, and Santos et al. (2021) reached 57% in the 7 × 7
window.
In addition, the accuracy was checked by comparison with a null model that, for the same window, reached 14%
similarity. According to Pontius-Jr et al. (2004), a model becomes more accurate than the null model when the spatial
resolution is increased, that is, the quality of the resolution scale influences the result of a predictive model when
compared to the null model. Considering the extent of the study area and the spatial resolution used, the validation
results achieved in this study can be considered satisfactory.
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410

25
In the model scenarios (BAU_1 and BAU_2) we sought to represent the current trend to increased deforestation rates in
the Amazon. After the large reduction in annual deforestation from 2004 to 2012, a gradual and consistent increase in
rates was observed beginning in 2012, when the Brazilian Forest Code was altered due to the strong political
representation of agribusiness in the National Congress (Fearnside, 2022a). Many environmental regulations were also
being revoked, especially during the 2019-2022 presidential administration of Jair Bolsonaro.
The results show that in both scenarios (BAU_1 and BAU_2) there is an evident increase in deforestation in the
southern part of the Amazon, influenced by roads, settlements, and the ‘arc of deforestation.’ Following this trend, the
results show increases in deforestation in all of the modeled area along Highway BR-319, as well as along connecting
highways such as AM-366, especially for the BAU_2 scenario due to the approval of the reconstruction and paving of
Highway BR-319. This corroborates the predictions of Fearnside et al. (2009) and dos Santos-Júnior et al. (2018), in
addition to models that considered projected road building in the Amazon region (Laurance et al., 2001; Soares Filho et
al., 2004, 2006; Aguiar, 2006, 2016).
Deforestation of protected areas and Indigenous Lands can also increase considerably, according to various studies
carried out in the region (Ferrante & Fearnside, 2019; Ferrante et al., 2021a,b). However, these areas continue to confer
a certain resistance to environmental degradation by deforestation, as demonstrated by the current deforestation data
available in the PRODES images from the National Institute for Space Research (INPE), as well as in the reports of the
programs of Ministry of Environment (MMA) to combat and control deforestation from the (MMA, 2016, 2018).
Therefore, it is important to create, implement, maintain, monitor, and inspect protected areas in the Amazon.
Regarding settlement projects, the study shows that there is a significant increase in all categories, indicating that
creating “sustainable-use settlements” in the region does not provide the desired protection (Yanai et al., 2017).
Settlements currently represent 15.66% of the deforestation in the study area, but for deforestation up to 2100 this
percentage rises to 65.19% in the BAU-1 scenario and 77.22% in the BAU_2 scenario. This corroborates the studies by
Yanai et al. (2017), who indicated that settlements play an important role in the dynamics of deforestation and future
carbon emissions in the Brazilian Legal Amazon region.
Simply giving the news of a settlement approval starts a race in search of legalized lands made available by the
government, according to the dynamics explained by Castro et al. (2004). This is exemplified by the Realidade
Sustainable Development Project (PDS) that was created in 2007 around the BR-319 in the municipality of Humaitá
(INCRA, 2015). The mere announcement of the approval of this PDS set off a race in search of land, promoting
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438

26
invasion of the land and dividing it into small lots for sale to new arrivals, with no interference from the |responsible
government agency (The National Institute for Colonization and Agrarian Reform, or INCRA). Thus, making logging,
agriculture, extensive livestock, speculation, and land grabbing grow in the settlement’s surroundings and along the
highway, as observed by Fearnside (2018), Andrade et al. (2021), and Ferrante et al. (2020, 2021) in studies carried out
in the region, demonstrating that the pattern of deforestation dynamics continues until the present day.
Another important issue is the proposed construction of State Highway AM-366, which would connect the BR-319
highway to the western part of the state of Amazonas (in this study represented by Region 7, see Fig. 5), one of the most
preserved areas in Amazonia, and essential for the environmental services that the forest offers (Fearnside, 2020;
Fearnside et al., 2020). An important source of impact would also be the advance of the ‘arc of deforestation’ towards
the north (Region 5) along the Federal Highway BR-174, which connects Manaus to Boa Vista and the border with
Venezuela (Fearnside & Graça, 2009; Barni et al., 2015).
Although the roads are considered strategic and important because they reduce the isolation of the population and
facilitate access, tourism and the flow of products, the development model based on the expansion of road axes in the
Amazon region is the main promoter of environmental degradation through its role in facilitating both the migration of
population to the region and the expulsion of population to more distant frontiers as smallholdings are bought up by
large cattle ranchers. The forest is lost in this process, with major environmental impacts. We can say that Brazil has
still not managed to find an action strategy that is efficient to reconcile the interests of the population that wants more
highways, with the preservation of the environment. The BR-163 (Santarém-Cuiabá) Highway serves as an example:
deforestation increased tremendously after the highway was reconstructed and paved, despite all attempts to develop
policies, plans and programs to reduce this environmental damage (Castro et al., 2004; Araújo et al., 2008; Brito &
Castro, 2018).
As observed in the maps generated by the model, the impact of deforestation goes beyond the official 40-km influence
area defined by Interministerial Ordinance 60, of 24 March 2015 for the environmental licensing processes of highways
in the Amazon region. This demonstrates that the environmental licensing process would benefit from modeling the
impact before defining the radius of influence in decision making. Fig. 11 shows the deforestation around Highway BR-
319 and the buffer area of 40 km (for the stretch where the Installation License for reconstruction of the highway is
being requested), and we can observe the continuous deforestation beyond the limits of the 40-km buffer.
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465

27
Fig. 11 Official 40-km area of influence defined by Interministerial Ordinance 60 of 24 March 2015) for environmental licensing of
highways in the Amazon region (a & b); the expansion of deforestation in the BAU_2 scenario is shown for 2100 (b) in relation to
the reference year (a).
Thus, a more comprehensive modeling study similar to the current one could be used to define the probable area of a
road project’s impact in the Amazon. This gives the environmental impact study more tools for decision making, which
makes it possible to define the best mitigation measures to reduce negative impacts and to have a more realistic
assessment of impacts for decisions on whether these highways should be built. While decisions on road building
should consider all possible impacts, it is understood that environmental licensing is limited in its ability to require that
the entrepreneur repair or mitigate the possible indirect impacts of an enterprise, such as the construction of connecting
highways by the local authorities or negative influence on other states.
It is therefore urgent for Brazil to adopt tools such as the strategic environmental assessment (Avaliação Ambiental
Estratégica = AAE), which is a planning and support instrument for strategic decision-making on the socio-
environmental impacts of the Brazilian government’s Policies, Plans and Programs (PPP) initiative (Partidário, 2001,
2003; Pellin et al., 2011), such as Avança Brasil 2000 and the 2004-2007 Pluriannual Plan, which included the
reconstruction of highways in the Amazon (Fearnside & Graça, 2009). Because, as we commonly see in the Amazon, a
simple PPP announcement for the installation of any large enterprise is capable of promoting migration and irregular
occupation of land by people in search of opportunities and cheap land, consequently leading to environmental
degradation such as what is occurring around BR-319.
5. CONCLUSION
The results presented in this study reflect the contribution of roads to advancing the agricultural frontier in Brazil’s state
of Amazonas, despite the limitations of environmental models in representing the complexity of the dynamics of
deforestation in the Amazon. Given the assumptions of our model, we conclude that by 2100 reconstruction of Highway
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484

28
BR-319 (BAU_2) would increase deforestation along the highway (Regions 3 and 4) and in the regions with roads
directly connected to BR-319 (Regions 5 and 7) by 60% in relation to deforestation in the projected scenario without
reconstruction (BAU_1).
In relation to protected areas (indigenous lands and conservation units), despite deforestation increasing over time, these
areas continue to play an important role in protecting the forest, and it is up to the government to increase protection,
monitoring, and inspection, as well as to create new areas, in view of the advance of deforestation in non-designated
public forests. Unlike protected areas, settlements do not provide environmental protection, regardless of their modality,
and it is the government’s responsibility to create environmental control mechanisms.
The results show that modeling the deforestation of a road enterprise can be part of the processes of environmental
licensing and strategic environmental assessment for the formulation and implementation of policies, plans, and
government investment programs in the Amazon region. Models of this type can better define the area of influence and
expansion of socio-environmental impacts, as well as provide information for measures to mitigate and control negative
impacts and to guide decision-making on whether or not to implement construction projects.
485
486
487
488
489
490
491
492
493
494
495
496
497

29
Acknowledgments
We would like to thank the National Institute for Amazonian Research (INPA) and the State University of Amazonas
(UEA) for supporting the Postgraduate Program in Climate and Environment (CLIAMB). The first author thanks the
Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA) for its support. We thank the
Terrestrial Climate Systems Modeling Laboratory (LABCLIM/UEA) for the physical structure of numerical
simulations; the Amazonas State Research Support Foundation (FAPEAM) (Resolution 003/2019) and the Coordination
for the Improvement of Higher Education Personnel – CAPES (Finance Code 001) for institutional support. The PMF
research is supported by FINEP/Rede CLIMA (01.13.0353-00), Fundação de Amparo à Pesquisa do Estado de São
Paulo (FAPESP) (Process 2020/08916-8), FAPEAM
Declarations
Competitive Interests
We declare that the authors have no conflicting interests as defined by Springer, or other interests that could influence
the results and/or discussions reported in this article.
Availability of data and material
Datasets generated and/or analyzed during the current study are available from the corresponding author upon
reasonable request.
Funding
No funding was obtained for this study.
Third party material
All material is the property of the authors and no permissions are required.
Double Publication
The results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration by
another publisher.
Ethical responsibilities
All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of
Authors" as found in the Instructions for Authors.
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531

30
6. REFERENCES
Aguiar, A.P.D. (2006). Modeling Land Use Change in the Brazilian Amazon: Exploring Intra-Regional Heterogeneity
(Modelagem de Mudança do Uso da Terra na Amazonia: Explorando a Heterogeneidade Intrarregional). INPE,
São Jose dos Campos. Retrieved 30 November 2020, from http://mtc-m16b.sid.inpe.br/col/sid.inpe.br/MTC-
m13@80/2006/08.10.18.21/doc/publicacao.pdf
Aguiar, A.P.D., Vieira, I.C.G., Assis, T.O., Dalla-Nora, E.L., Toledo, P.M., Santos-Júnior, R.A.O., Batistela, M., Coelho,
A.S., Savaget, E.K., Aragão, L.E.O.C., Nobre, C.A. & Ometto, J.P.H. (2016). Land use change emission scenarios:
anticipating a forest transition process in the Brazilian Amazon. Global Change Biology 22, 1821–1840.
Andrade, M.B.T., Ferrante, L. & Fearnside, P.M. (2021). Brazil’s Highway BR-319 demonstrates a crucial lack of
environmental governance in Amazonia. Environmental Conservation 48(3), 161-164.
https://doi.org/10.1017/S0376892921000084
ANM (Agência Nacional de Mineração) (2021). SHP. Retrieved 18 May 2021, from
https://dados.gov.br/dataset/sistema-de-informacoes-geograficas-da-mineracao-sigmine
Araújo, R., Castro, E., Rocha, G., Sá, M.E., Matihs, A., Monteiro, M., Puty, C., Monteiro, R., Canto, O. & Bennati, J.
(2008). Estado e Sociedade na BR 163: desmatamento, conflitos e processos de ordenamento territorial. In: Castro,
E., Sociedade, Território e Conflitos: Br 163 em Questão. Belém: NAEA. 297 pp.
Barber, C.P., Cochrane, M.A., Souza, C.M. & Laurance, W.F. (2014). Roads, deforestation, and the mitigating effect of
protected areas in the Amazon. Biological Conservation 177, 203-209. https://doi.org/10.1016/j.biocon.2014.07.004
Barbosa, L.G., Alves, M.A.S. & Grelle, C.E.V. (2021). Actions against sustainability: Dismantling of the environmental
policies in Brazil. Land Use Policy 104, 105384. https://doi.org/ 10.1016/j.landusepol.2021.105384
Barni, P.E., Fearnside, P.M. & Graça, P.M.L.A. (2015). Simulating deforestation and carbon loss in Amazonia: Impacts
in Brazil’s Roraima State from reconstructing Highway BR-319 (Manaus-Porto Velho). Environmental
Management 55, 259–278. https://doi.org/10.1007/s00267-014-0408-6
Brazil (2015). Portaria Interministerial 60, de 24 de março de 2015. Retrieved 9 September 2022, from
http://portal.iphan.gov.br/uploads/legislacao/Portaria_Interministerial_60_de_24_de_marco_de_2015.pdf
Bonham-Carter, G.F. (1994). Geographic information systems for geoscientists: modelling with GIS. Pergamon,
Oxford. https://doi.org/10.1016/C2013-0-03864-9
Brito, R. & Castro, E.R. (2018). Desenvolvimento e conflitos na Amazônia: um olhar sobre a colonialidade dos
processos em curso na BR-163/Development and Conflict in the Amazon - a glimpse into the coloniality of on-
going processes in BR-163. REVISTA NERA, (42), 51–73. https://doi.org/10.47946/rnera.v0i42.5679
Campos, P.B.R., Almeida, C.M.d. & Queiroz, A.P.d. (2022). Spatial dynamic models for assessing the impact of public
policies: The case of unified educational centers in the periphery of São Paulo city. Land 11, 922.
https://doi.org/10.3390/land11060922
Castro, E.R., Monteiro, R. & Castro, C.P. (2004). Dinâmica de atores, uso da terra e desmatamento na rodovia Cuiabá-
Santarém. Paper do NAEA 179. 61 pp. http://dx.doi.org/10.18542/papersnaea.v13i1.11558
ClimaInfo (2022). Desmonte ambiental: Próximo Congresso será “mais boiadeiro” que o atual. ClimaInfo, 6 October
2022. https://climainfo.org.br/2022/10/06/desmonte-ambiental-proximo-congresso-sera-mais-boiadeiro-que-o-atual/
DNIT (Departamento Nacional de Infraestrutura de Transporte) (2016). BR-319/AM/RO Histórico do licenciamento
ambiental da rodovia e situação dos instrumentos celebrados para o atendimento às condições do licenciamento.
Retrieved 25 May 2020, from http://legis.senado.leg.br/sdleg-getter/documento/download/d3816d09-2e92-4f73-
bf18-18e2c37b0589
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572

31
DNIT (Departamento Nacional de Infraestrutura de Transporte) (2021). DNIT Geo. Retrieved 15 March 2022, from
http://servicos.dnit.gov.br/vgeo/
Facundes, F.S., Lima, R.A.P.L. & Santos, V. F. (2019). Expansion des réseaux routiers en Amazonie orientale -
Perimetral Norte, Amapá. CONFINS - Revista Franco-Brasileira de Geografia. No 42.
https://doi.org/10.4000/confins.23789
Fearnside, P.M. (2003). A Floresta Amazônica nas Mudanças Globais. Instituto Nacional de Pesquisas da Amazônia
(INPA), Manaus, Amazonas, Brazil. 134 pp. https://repositorio.inpa.gov.br/handle/1/4748
Fearnside, P.M. (2018). BR-319 e a destruição da floresta amazônica. Amazônia Real, 19 October 2018. Retrieved 25
May 2020, from, http://amazoniareal.com.br/br-319-e-destruicao-da-floresta-amazonica
Fearnside, P.M. (2020). TransPurus: Amazonia’s biogeochemical cycles depend on the fate of the region’s largest block
of intact forest. American Geophysical Union (AGU) Fall Meeting 2020. Paper Number: GC009-0015.
Fearnside, P.M. (2022a). Como sempre, os negócios: o ressurgimento do desmatamento na Amazônia brasileira. pp.
363-368. In: Fearnside, P.M. (ed.) Destruição e Conservação da Floresta Amazônica. Vol. 1. Editora do INPA,
Manaus, Amazonas, Brazil. 368 pp. https://repositorio.inpa.gov.br/handle/1/38899
Fearnside, P.M. (2022b). Uso da terra na Amazônia e as mudanças climáticas globais. pp. 21-38. In: Fearnside, P.M.
(ed.) Destruição e Conservação da Floresta Amazônica. Vol. 1. Editora do INPA, Manaus, Amazonas, Brazil. 368
pp. https://repositorio.inpa.gov.br/handle/1/38899
Fearnside, P.M. & Graça, P.M.L.A. (2009). BR-319: A rodovia Manaus-Porto Velho e o impacto potencial de conectar o
arco de desmatamento à Amazônia central. Novos Cadernos NAEA 12(1), 19-50.
http://dx.doi.org/10.5801/ncn.v12i1.241
Fearnside, P.M., Graça, P.M.L.A., Keizer, E.W.H., Maldonado, F.D., Barbosa, R.I. & Nogueira, E.M. (2009).
Modelagem do desmatamento e emissões de gases do efeito estufa na região sob influência da Rodovia Manaus-
Porto Velho (BR-319). Revista Brasileira de Meteorologia 24(2), 208-233. https://doi.org/10.1590/S0102-
77862009000200009
Fearnside, P.M., Ferrante, L., Yanai, A.M. & Isaac-Júnior, M.A. (2020). Trans-Purus: Brazil’s last intact Amazon forest
at immediate risk (commentary). Mongabay. Retrieved 22 May 2021, from
https://news.mongabay.com/2020/11/trans-purus-brazils-last-intact-amazon-forest-at-immediate-risk-commentary/
Ferrante, L. & Fearnside, P.M. (2019). Brazil’s new president and "ruralists" threaten Amazonia’s environment,
traditional peoples and the global climate. Environ. Conserv. 46(4), 261–263.
https://doi.org/10.1017/S0376892919000213
Ferrante, L., Andrade, M.B.T. & Fearnside, P.M. (2021a). Land grabbing on Brazil’s Highway BR-319 as a spearhead
for Amazonian deforestation. Land Use Policy 108, 105559. https://doi.org/10.1016/j.landusepol.2021.105559
Ferrante. L., Andrade, M.B.T., Leite, L., Silva-Júnior, C.A., Lima, M., Coelho-Junior, M.G., Silva-Neto, E.C.,
Campolina, D., Carolino, K., Diele-Viegas, L.M., Pereira, E.J.A.L. & Fearnside, P.M. (2021b). Brazil’s Highway
BR-319: The road to the collapse of the Amazon and the violation of indigenous rights. Die Erde - Journal of the
Geographical Society of Berlin 152(1): 65-70. https://doi.org/10.12854/erde-2021-552
FUNAI (Fundação Nacional do Índio). n.d. (no date information). Download de dados geográficos: Terra Indígena
(Regularizada, Homologada, Declarada, Delimitada e Área em Estudo). Retrieved 25 May 2020, from
http://www.funai.gov.br/index.php/shape
Graça, P.M.L.A., Maldonado, F.D. & Fearnside, P.M. (2007). Detecção de desmatamento em novas áreas de expansão
agropecuária no sul do Amazonas utilizando imagens CBERS-2. In: Anais XIII Simpósio Brasileiro de
Sensoriamento Remoto, pp. 917-924, Florianópolis, SC, Brazil. https://repositorio.inpa.gov.br/handle/1/31089
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614

32
Hagen, A. (2003). Fuzzy set approach to assessing similarity of categorical maps. Int. J. Geogr. Inf. Sci. 17, 235–249.
https://doi.org/10.1080/13658810210157822
IBGE (Instituto Brasileiro de Geografia e Estatística) (2017). Geociências. Retrieved 25 May 2020, from
https://www.ibge.gov.br/geociencias/downloads-geociencias.html
ICMBIO (Instituto Chico Mendes de Conservação da Biodiversidade) (2019). Limites das Unidades de Conservação
Federais (atualizado em julho de 2019): Unidades de Conservação Federais – SHP (SIRGAS2000). Retrieved 25
May 2020, from https://www.icmbio.gov.br/portal/geoprocessamento1/51-menu-servicos/4004-downloads-mapa-
tematico-e-dados-geoestatisticos-das-uc-s
INCRA (Instituto Nacional de Colonização e Reforma Agrária). n.d. (no update date information). Exportar shapefile.
Retrieved 25 May 2020, from http://certificacao.incra.gov.br/csv_shp/export_shp.py
INCRA (Instituto Nacional de Colonização e Reforma Agrária) (2015). Relatório de Assentamentos. Retrieved 25
September 2020, from https://painel.incra.gov.br/sistemas/Painel/
INPE (Instituto Nacional de Pesquisas Espaciais) (2020). PRODES – Amazônia. Retrieved 6 July 2022, from
http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes
INPE (Instituto Nacional de Pesquisas Espaciais). (2022). PRODES. Retrieved 25 May 2022, from
http://terrabrasilis.dpi.inpe.br/app/dashboard/deforestation/biomes/legal_amazon/rates
Kohlhepp, G. (2002). Conflitos de interesses no ordenamento territorial da Amazônia brasileira. Estudos Avançados
16(45). https://doi.org/10.1590/S0103-40142002000200004
Laurance, W.F., Cochrane, M.A., Bergen, S., Fearnside, P.M., Delamônica, P., Barber, C., D’Angelo, S. & Fernandes, T.
(2001). The future of the Brazilian Amazon. Science 291, 438-439. https://doi.org/10.1126/science.291.5503.438
Laurance, W.F. & Balmford, A. (2013). A global map for road building. Nature 495, 308-309.
https://doi.org/10.1038/495308a
Leite-Filho, A.T., Soares-Filho, B.S., Davis, J.L. & Rodrigues, H.O. (2020). Modeling Environmental Dynamics with
Dinamica EGO. Retrieved 20 October 2020, from https://www.csr.ufmg.br/dinamica/dokuwiki/doku.php?
id=guidebook_start
Lessa, R. (1991). Amazônia: as raízes da destruição. Ed. Atual. Edição 09.
Lewinsohn, T.M. & Prado, P.I. (2002). Biodiversidade brasileira: síntese do estado atual do conhecimento. Contexto,
São Paulo, SP, Brazil.
Loureiro, V.R. (2002). Amazônia: uma história de perdas e danos, um futuro a (re)construir. Estud. Avançados 16(4).
https://doi.org/10.1590/S0103-40142002000200008
Maeda, E.E., Almeida, C.M., Ximenes, A.C., Formaggio, A.R., Shimabukuro, Y.E. & Pellikka, P. (2011). Dynamic
modeling of forest conversion: Simulation of past and future scenarios of rural activities expansion in the fringes of
the Xingu National Park, Brazilian Amazon. International Journal of Applied Earth Observation and
Geoinformation 13(3), 435-446. http://dx.doi.org/10.1016/j.jag.2010.09.008
Marengo, J.A., Souza, C.M., Thonicke, K., Burton, C., Halladay, K., Betts, R.A., Alves, L.M. & Soares, W.R. (2018).
Changes in climate and land use over the Amazon region: Current and future variability and trends. Frontiers in
Earth Science 6, 228. https://doi.org/10.3389/feart.2018.00228
Mazzotti, F.J. & Vinci, J.J. (2007). Validation, verification, and calibration: Using standardized terminology when
describing ecological models. IFAS Extension, University of Florida, Gainesville, Florida, USA.
https://edis.ifas.ufl.edu/pdf/UW/UW25600.pdf.
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654

33
MMA (Ministério do Meio Ambiente) (2016). Plano de ação para prevenção e controle do desmatamento da Amazônia
Legal: pelo uso sustentável e conservação da floresta. 3ª fase (2012-2015). Casa Civil, Brasília, DF, Brazil.
Retrieved 6 July 2020, from https://www.mma.gov.br/ images/arquivo/80120/PPCDAm e PPCerrado - Encarte
Principal - GPTI _ p site.pdf
MMA (Ministério do Meio Ambiente) (2018). Balanço de execução 2018: PPCDAm e PPCerrado 2016-2020.
Retrieved 6 July 2020, from https://www2.camara.leg.br/atividade-legislativa/
comissoes/comissoes-temporarias/externas/56a-legislatura/políticas-para-integracao-meio-ambiente-e-economia/
expedientes-recebidos/ric-1577-2019-ministerio-do-meio-ambiente
Moutinho, P. (2009). Desmatamento na Amazônia: desafios para reduzir as emissões de gases de efeito estufa do
Brasil. Retrieved 10 June 2020, from http://www.fbds.org.br/IMG/pdf/doc-411.pdf. (accessed: 10 June 2020).
MPOG (Ministério do Planejamento Orçamento e Gestão). (2004). PPA 2004-2007 - Lista Geral de Projetos de Infra-
estrutura. Setembro 2004. MPOG, Brasília, DF, Brazil.
Nepstad, D.; Stickler, C.M. & Almeida, O. T. (2006). Globalization of the Amazon Soy and Beef Industries:
Opportunities for Conservation. Conservation Biology. https://doi.org/10.1111/j.1523-1739.2006.00510.x
Nobre, A.D. (2014). The Future Climate of Amazonia: Scientific Assessment Report. CCST-INPE, São José dos
Campos, SP, Brazil.
Nobre, C.A., Sampaio, G., Borma, L.S., Castilla-Rubio, J.C., Silva, J.S., Cardoso, M. & et al. (2016). The Fate of the
Amazon Forests: land-use and climate change risks and the need of a novel sustainable development paradigm.
Proc. Natl. Acad. Sci. U.S.A. 113, 10759–10768. https://doi.org/10.1073/pnas.1605516113
Oliveira-Neto, T. (2014). A Geopolítica rodoviária na Amazônia: BR-319. Revista de Geopolítica 5(2), 109-128.
Partidário, M.R. (2011). From EIA to SEA. chapter 14 In: Indovina, F. & Fregolent, L. (Eds). Environmental
Sustainability, Monographic issue, n. 71/72-2001, Archivio di Studi Urbani e Regionali, Venice, Italy.
Partidário, M.R. (2003). Avaliação de Impactes Ambientais de Políticas, Planos e Programas. Ambiente 21(8).
Pellin, A., Lemos, C.C., Tachard, A., Oliveira, I.S.D. & Souza, M.P. 2011. Avaliação ambiental estratégica no Brasil:
considerações a respeito do papel das agências multilaterais de desenvolvimento. Artigos Técnicos Eng. Sanit.
Ambient. 16(1).
Pontius-Jr, R.G., Boersma, W., Castella, J.-C., Clarke, K., de Nijs, T., Dietzel, C., … & Verburg, P.H. (2007).
Comparing the input, output, and validation maps for several models of land change. The Annals of Regional
Science 42(1), 11–37. https://doi.org/10.1007/s00168-007-0138-2
Pontius-Jr, R.G., Huffaker, D. & Denman, K. (2004). Useful techniques of validation for spatially explicit land change
models. Ecol. Model. 179(4), 445–461. https://doi.org/10.1016/j.ecolmodel.2004.05.010
Ramos, C.J.P., Graça, P.M.L.A. & Fearnside, P.M. (2018). Deforestation dynamics on an Amazonian peri-urban
frontier: Simulating the influence of the Rio Negro Bridge in Manaus, Brazil. Environmental Management 62(6),
1134-1149. https://doi.org/10.1007/s00267-018-1097-3
Rocha, V.M., Correia, F.W.S., Satyamurty, P., de Freitas, S.R., Moreira, D.S., da Silva, P.R.T. & Fialho, E.S. (2015).
Impacts of land cover and greenhouse gas (GHG) concentration changes on the hydrological cycle in amazon basin:
A regional climate model study. Revista Brasileira de Climatologia 15, 7-27.
https://doi.org/10.5380/abclima.v15i0.36386
Rodrigues, H.O, Soares-Filho, B.S. & Costa, W.L.S. (2007). Dinamica EGO, uma plataforma para modelagem de
sistemas ambientais. Anais XIII Simpósio Brasileiro de Sensoriamento Remoto, Florianópolis, Brasil, 21-26 abril
2007, INPE, São José dos Campos, SP, Brazil, pp. 3089-3096.
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695

34
Roriz, P.A.C., Yanai, A.M. & Fearnside, P.M. (2017). Deforestation and carbon loss in southwest Amazonia: Impact of
Brazil’s revised forest code. Environmental Management 60, 367–382. https://doi.org/10.1007/s00267-017-0879-3
Rykiel-Jr, E.J., (1996). Testing ecological models: the meaning of validation. Ecological Modelling 90, 229-244.
Santos, Y.L.F., Yanai, A.M., Ramos, C.J.P., Graça, P.M.L.A., Veiga, J.A.P., Correia, F.W.S. & Fearnside, P.M. (2021).
Amazon deforestation and urban expansion: Simulating future growth in the Manaus Metropolitan Region, Brazil.
Journal of Environmental Management 304(1), 114279. https://doi.org/10.1016/j.jenvman.2021.114279
dos Santos-Junior, M.A. dos, Yanai, A.M., Sousa-Junior, F.O., Freitas, I.S., Pinheiro, H.P., Oliveira, A.C.R., Silva, F.L.,
Graça, P.M.L.A. & Fearnside, P.M. (2018). BR-319 como Propulsora de desmatamento: Simulando o Impacto da
Rodovia Manaus-Porto Velho, Instituto de Desenvolvimento Sustentável da Amazônia (IDESAM). Manaus, AM,
Brazil. 56 pp. https://idesam.org/simula-desmatamento-br319/
Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E., Salas, W., Zutta, B., Buermann, W., Lewis, S., Hagen,
S., Petrova, S., White, L., Silman, M.A. & Morel. A. (2011). Benchmark map of forest carbon stocks in tropical
regions across three continents. Proc. Nat. Acad. Sci. U.S.A. 108(24), 9899-9904.
https://doi.org/10.1073/pnas.1019576108
SEMA (Secretaria do Meio Ambiente) (2020). UNIDADE DE CONSERVAÇÃO (atualizado em 2020). Retrieved 25
May 2020, from https://meioambiente.am.gov.br/unidade-de-conservacao/
Siqueira-Gay, J., Metzger, J.P., Sánchez, L.E. & et al. (2022). Strategic planning to mitigate mining impacts on
protected areas in the Brazilian Amazon. Nat Sustain. 5, 853–860. https://doi.org/10.1038/s41893-022-00921-9
Soares Filho, B.S., Pennachin, C. L. & Cerqueira, G. (2002). DINAMICA – a stochastic cellular automata model
designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling 154(3),
217-235. https://doi.org/10.1016/S0304-3800(02)00059-5
Soares Filho, B.S., Alencar, A., Nepstad, D., Cerqueira, G., Dias, M., Rivero, S., Solórzanos, L. & Voll, E. (2004).
Simulating the response of land-cover change to road paving and governance along a major Amazon highway: the
Santarém-Cuiabá corridor. Global Change Biology 10, 745-764. https://doi.org/10.1111/j.1529-8817.2003.00769.x
Soares-Filho, B.S., Nepstad, D., Curran, L., Voll, E., Cerqueira, G., Garcia, R.A., Ramos, C.A., Mcdonald, A., Lefebvre,
P. & Schlesinger, P. (2006). Modelling conservation in the Amazon basin. Nature 440, 520-523.
https://doi.org/10.1038/nature04389
Soares-Filho, B., Rodrigues, H. & Follador, M. (2013). Um método híbrido analítico-heurístico para calibrar modelos
de mudança de uso da terra. Ambiente. Modelo. Softw. 43, 80-87.
TALANOA. 2022. Reconstrução: 401 atos do Poder Executivo Federal (2019 - 2022) a serem revogados ou revisados
para a reconstituição da agenda climática e ambiental brasileira. Instituto Talanoa, Rio de Janeiro, RJ, Brazil. 169
pp. https://www.politicaporinteiro.org/2022/11/03/reconstruca o/
Weng, W., Luedeke, M.K., Zemp, D.C. & Lakes, T. (2018). Aerial and surface rivers: downwind impacts on water
availability from land use changes in Amazonia. Hydrology and Earth System Sciences 22(1), 911-927.
https://doi.org/10.5194/hess-22-911-2018
Yanai, A.M, Nogueira, E.M., Graça, P.M.L.A. & Fearnside, P.M. 2017. Deforestation and carbon stock loss in Brazil’s
Amazonian settlements. Environmental Management 59, 393-409. https://doi.org/10.1007/s00267-016-0783-2
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732

1
SUPPLEMENTARY MATERIAL
Reconstruction of Highway BR-319: Deforestation simulation in Brazil's state of Amazonas
List of figures
Figure S1: Conceptual diagram of the deforestation simulation model. Dashed line is where the looping occurs
adding the new deforestation and roads built in each time step (year), entering the transition probability
calculations, allocating new deforestation patches......................................................................................................3
Figure S2: Maps of static variables.............................................................................................................................5
Figure S3: Adjustment of the intervals and distance of the skeleton used to calculate the weights-of-evidence
coefficients in the model that best represented the real PRODES_2021 map in relation to the simulated one in the
validation model..........................................................................................................................................................8
Figure S4: Percentages of increase and decrease of deforestation in relation to the previous year for the state of
Amazonas. Where the average increase corresponds to 26.2% and the average decrease to 20.6% for the period
from 2000 to 2014.......................................................................................................................................................8
Figure S5: Percentages of increase and decrease of deforestation in relation to the previous year for the state of
Amazonas, where the average increase corresponds to 31.6% and average decrease to 19.5% for the period from
2000 to 2021................................................................................................................................................................9
Figure S6: Example applied to demonstrate the fluctuation of the deforestation rate over the intervening period of
6 years..........................................................................................................................................................................9
Figure S7: Map of attractiveness (a) and friction (b). These values are the result of the interaction between the
“land cover” and “protected areas” maps..................................................................................................................11
Figure S8: Projected deforestation by region in relation to 2021 deforestation........................................................12
List of tables
Table S1: Parameters used as input data for DINAMICA-EGO.................................................................................3
Table S2: Regionalization of the study area................................................................................................................5
Table S3: Parameters for calculating weights of evidence..........................................................................................6
Table S4: Data used to calculate annual deforestation rates for simulation from 2014 to 2021.................................8
Table S5: Data used to calculate annual deforestation rates for simulation from 2021 to 2100.................................9
Table S6: Patcher and expander allocation according to sub-region...........................................................................9
Table S7: Values assigned for the construction of attractiveness and friction maps used for road construction in the
model.........................................................................................................................................................................10
Table S8: Projected deforestation in relation to real deforestation............................................................................11

2
APPENDIX 1.
Law No. 12,651, of 25 May 2012 established general rules for the protection of vegetation, permanent
preservation areas (APPs) and legal reserve areas, forest exploitation, the supply of forest raw materials, control of
the origin of forest products and the control and prevention of forest fires; the law also foresees economic and
financial instruments to achieve its objectives. This law repealed and replaced Law No. 4771, of 15 September
1965 (the former Forest Code).
APPENDIX 2.
The DINAMICA EGO software was developed by the Center for Remote Sensing of the Federal University of
Minas Gerais (UFMG) to support multivariate and non-linear environmental modeling. It is based on cellular
automata, consisting of an array of n dimensions of cells according to their previous condition and the spatial
arrangement of neighboring cells through a set of transition rules, where each cell represents the possibility of
converting from one state to another in a given scenario (Soares-Filho et al., 2002, 2004, 2006; Lima, 2013;
Oliveira et al., 2019). The DINAMICA-EGO modeling environment (Fig. S1) involves a series of operators called
“functors” that can be understood as a process that acts on a set of input data on which a finite number of
operations is applied, producing as output a new dataset (Rodrigues, 2007; Lima, 2013).
Models must be built to answer: WHERE changes in land cover will occur; HOW MANY changes will occur each
year; and HOW the areas will be spatially distributed (Vitel, 2009).
Maps of static and
dynamics variables
Roads maps
Land use/
cover maps
Weights of
evidence
coefficient
Transition rates
Road builder
module
Transition
probability map
Allocation of
deforestation
Function performance
Expander
Function performance
Patcher
Attractiveness
and friction maps
Transition probability
Final road map
Final land use map
Fig. S1 Conceptual diagram of the deforestation simulation model. Dashed line is where the looping occurs adding the new
deforestation and roads built in each time step (year), entering the transition probability calculations, allocating new
deforestation patches.

3
APPENDIX 3.
Table S1 Parameters used as input data for DINAMICA-EGO.
Variables Source
Land cover: deforested area, forest, and non-
forest (savannas, water, rocky outcrops, etc.)
PRODES for 2007 and 2013
(INPE, 2020)
Static Variables
Protected Areas (integral-protection PAs;
sustainable-use PAs; indigenous lands; and
military areas)
ICMBIO (2019), SEMA (2020),
FUNAI (n.d), AMN (2021)
Settlement project (Agro-extractive
Settlement Project, or PAE; Sustainable
Development Settlement Project, or PDS;
Rapid Settlement Project, or PAR; Forest
Settlement Project, or PAF; Directed
Settlement Project, or PAD; and [traditional]
Settlement Project, or PA)
INCRA (n.d)
Oil and gas prospecting/research area Manual vectorization
Hydrography (watercourses) INPE (2020)
Dynamic variables
Highways and Roads (official and
endogenous)
DNIT (2013), plus manual
vectorization of endogenous roads
for the year 2013.
Deforestation PRODES for 2007 and 2013
(INPE, 2020)
In the state of Amazonas there are areas of vegetation that were suppressed for research and for prospecting for oil
and natural gas, which, in the land-cover map provided by INPE, appear as cumulative deforestation. Therefore, in
the model these areas can attract excessive allocation of deforestation around them and do not represent the
dynamics of deforestation in the Amazon region as a whole, which is dominated by the expansion of livestock,
agriculture, and mining in the vicinity of roads and previous deforestation. Therefore, a map, with a buffer of 1500
m of each oil and gas prospecting and exploitation area was prepared to serve as a “correction factor,” and these
areas were given a weight of evidence equivalent to a sustainable-use protected area, which creates friction against
the advance of deforestation in these areas but does not prevent deforestation if a planned road passes through the
area. This allows the model to allocate new deforestation in places that are more susceptible to land-use change.

4
a) Protected areas b) Settlement projects
c) Hydrography (watercourses)
Fig. S2 Maps of static variables.
APPENDIX 4.
Regionalization of the study area makes it possible to individualize each region, thus identifying specific
parameters such as transition rate and weights-of-evidence coefficients in the calibration, allowing the simulation
result to better represent reality. In addition, it can suggest how a particular region will behave if the variables in
play are different from those in other regions. This was the case when we used transition matrices and the weights-
of-evidence coefficients from another region to simulate a change based on what we wanted to represent (e.g.,
using weights-of-evidence coefficients from Region 6 used in Regions 3, 4 and 7 after the reconstruction and
paving of BR-319).
The regions used in this study are not official. They were defined by the authors, taking into account regional
characteristics such as the proximity and influence of the state capitals (Manaus and Porto Velho), hydrographic
limits, livestock expansion areas, protected areas, non-protected areas, fishing activity, wood industry, possibility
of expansion of the arc of deforestation, and the influence of paved roads and of regions that presented different
deforestation rates. This was needed to define the transition rates and weights-of-evidence coefficients for use in
the simulation. We divided some areas with similar characteristics and that had little or no influence from the BR-

5
319, such as Regions 8 and 9, to better represent the result in the final map without interfering with the desired
result. Table S2 shows the parameters used to define the regions in this study.
Table S2 Regionalization of the study area.
Step Justification
Region 1
Area to the east of the Madeira River that is under the influence of Highway BR-230, which is
the main highway connecting to BR-319 in the southern part of the state of Amazonas. It is
also influenced by the state of Pará to the east and the state of Mato Grosso to the south. It
contains some of the municipalities with the highest deforestation rates in the state of
Amazonas (Manicoré, Apuí, Novo Aripuanã, Humaitá), which stand out among the major
cattle production in the state. It has a large area of public land with non-designated public
forest, which is attractive for invasion and deforestation. It has strong activity in the wood
industry.
Region 2
Area of influence to the east of the Madeira River, on the right side of the Amazon River and
bordering the state of Pará. The region has livestock and lowland agriculture. It has low
population density and does not have a large extension of highways and endogenous roads.
Region 3
Southern portion of the interfluve between the Madeira and Purus Rivers in the state of
Amazonas. It is characterized by the influence of the BR-230, which connects the city of
Lábrea to Humaitá and southern part of the municipality of Canutama. Vila Realidade (a
district in the municipality of Humaitá) is located in this region, which, in recent years, has
shown large increases in deforestation, land invasion and logging. The region has great
influence from the state of Rondônia. It can be considered to be the region providing access
from the 'arc of deforestation' to the northern portion of the state. It has strong activity of the
wood industry.
Region 4
Northern portion of the Madeira-Purus interfluve in the state of Amazonas. It is heavily
influenced by the state capital (Manaus) and by Highway BR-174. It is a region with large
unprotected undesignated areas, as well as settlement projects that can attract migration.
Region 5
This region is characterized by the influence of the state capital, as a major consumer center.
The main locations where deforestation is expanding are those with access facilitated by
Highway BR-174 (Manaus - Boa Vista). The “Zona Franca Verde” of Manaus is present in
this region, which is a program focused on attracting investment for agriculture, livestock, and
tourist enterprises.
Region 6
Region of influence of the paved highways BR-364 and BR-317. The southern part of this
region has high rates of deforestation, especially in the districts of Extrema and Nova
California and the PA Monte and PA Antimary settlement projects. The region has a strong
tendency to initiate and expand livestock production areas, especially in the municipality of
Boca do Acre, the southern portion of Lábrea and in Guajara. It has strong activity of the wood
industry.
Region 7
Central portion of the state of Amazonas. This is the expansion area of the planned AM-366
state highway, which proposes connecting the BR-319 to the municipalities of Coari, Tefé and
Juruá. The region presents itself as an important producer of oil and natural gas. The region
has few protected areas and has large areas of non-designated public forests, which favors land
invasion and deforestation. The northern portion of the region has access and occupation from
the Solimões (Upper Amazon) River.
Region 8
Region of influence of the Rio Negro. This region has low population density and is
characterized by small towns, villages, and riverside communities. The region has extensive
indigenous lands and protected areas. The main economic activity is fishing and traditional
low-impact agriculture. It has an unpaved federal highway (BR-307), which connects the city
of São Gabriel da Cachoeira to the town of Cucui. The region borders Colombia and
Venezuela and has a strong presence of the Brazilian Army.
Region 9
Region of influence of rivers, cities, and riverside communities, indigenous lands, and
protected areas. The region borders Peru and has has low population density; economic
activity is mainly characterized by low-impact fishing and agriculture. It is located on the right
bank the Solimões (Upper Amazon) River and is influenced by the municipality of Tabatinga
on the border of Brazil with Peru and Colombia.

6
APPENDIX 5.
The figure below shows the parameters used to calculate the weights of evidence for the variables used in the
model. Categorical variables are those that have more than one category on the same map (e.g., protected areas
that have four categories: 1. Integral-protection PAs; 2. Sustainable-use PAs; 3. Indigenous lands; and 4. Military
areas). This is in contrast to variables that are not categorical and present only one item of information, without
subdivisions (road map, deforestation map, and hydrographic map).
Table S3 Parameters for calculating weights of evidence.
Identifier Categorical Increment Min. Delta Max. Delta Tol. Angle
distance_roads no 100 1 5,000,000 5.0
distance_deforestation no 100 1 5,000,000 5.0
static_var
distance_Hydrography no 100 1 5,000,000 5.0
Protected_areas yes
settlements yes
For the definition of weights of evidence, the DINAMICA-EGO model makes the calculations and defines the
distances based on the input maps. However, in the calibration stage, these data can be adjusted in order to achieve
the best representation of what is to be modeled (Soares-Filho et al., 2009). In the present study the interval was
adjusted and fixed at 100 m, based on the spatial resolution adopted in the study.
Regarding the definition of the influence distances of non-categorical variables, several tests were performed to
define the best result in the validation. The distance that best represented the similarity in the comparison of the
simulated deforestation map of 2021 with the real deforestation from PRODES in 2021 was 1500 m. Fig. S3
shows the non-categorical variables with an interval of 100 m and a distance of influence of 1500 m. It is worth
mentioning that in this study adjustment was only done for the intervals and distances of influence, with no
numerical adjustment of the weights-of-evidence coefficients.
Fig. S3 Adjustment of the intervals and distance of the skeleton used to calculate the weights-of-evidence coefficients in the
model that best represented the real PRODES_2021 map in relation to the simulated one in the validation model.

7
Regarding the methodology for applying the deforestation rate, it was decided to survey the average increase and
decrease (Mdi and Mdd) in the period from 2000 to 2014 to better represent the trends of increase and decrease in
the simulation from 2014 to 2021, as can be seen in Fig. S4. For the increase, the average of all the years in which
deforestation was positive in relation to the previous year was calculated. The corresponding average was
calculated for the decreases.
The same principle was used to simulate the scenarios from 2021 to 2100, with the goal of updating the index to
better represent the trend of increase and decrease up to 2021, which was the year of the beginning of the scenario
simulation (Fig. S5). However, it was decided to maintain the input transition rates used in the calibration,
considering that the model presented a satisfactory result in the validation, as shown in Table S5.
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
-60
-40
-20
0
20
40
60
80
100
4
40
76
-21
-37
2
-23
-1
-33
47
-16
4
11
-14
Deforestation (%)
Fig. S4 Percentages of increase and decrease of deforestation in relation to the previous year for the state of Amazonas. Where
the average increase corresponds to 26.2% and the average decrease to 20.6% for the period from 2000 to 2014.
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
-60
-40
-20
0
20
40
60
80
100
4
40
76
-21
-37
2
-23
-1
-33
47
-16
4
11
-14
42 59
-11
4
37
5
53
Deforestation (%)
Fig. S5 Percentages of increase and decrease of deforestation in relation to the previous year for the state of Amazonas, where
the average increase corresponds to 31.6% and average decrease to 19.5% for the period from 2000 to 2021.

8
Tabela S4 Data used to calculate annual deforestation rates for simulation from 2014 to 2021.
Regions Average
Rates
of
Transition
2007 - 2013
Index of
Transition
(%)
Deforestation
cumulative up
to 2007
(km2)
Deforestation
cumulative up
to 2013
(km2)
Forest area
available in
2007
(km2)
Forest area
available
in 2013
(km2)
Region 1 0.0008937 50.59 4,168.97 5,194.10 198,443.96 197,418.83
Region 2 0.0005672 29.39 4,948.60 5,116.53 42,958.80 42,790.87
Region 3 0.0007987 38.30 2,931.67 3,292.34 75,413.77 75,053.10
Region 4 0.0007379 32.37 4,103.92 4,365.23 59,124.14 58,862.83
Region 5 0.0004074 30.74 6,984.21 7,315.11 135,521.33 135,190.43
Region 6 0.0008912 36.43 12,626.69 13,943.21 246,743.18 245,426.66
Region 7 0.0001413 30.85 2,045.33 2,144.46 116,939.81 116,840.68
Region 8 0.0000469 30.08 3,050.72 3,175.21 442,870.26 442,745.77
Region 9 0.0000608 31.13 1,596.85 1,678.69 224,457.00 224,375.16
Table S5 Data used to calculate annual deforestation rates for simulation from 2021 to 2100.
Regions Average
Transition
Rates
2007 - 2013
Index of
Transition
%
Deforestation
cumulative up
to 2014
(km2)
Deforestation
cumulative up
to 2021
(km2)
Forest area
available in
2014
(km2)
Forest area
available in
2021
(km2)
Region 1 0.0008937 102.41 5,345.78 9,109.74 197,267.15 193,503.19
Region 2 0.0005672 35.01 5,147.07 5,302.04 42,760.33 42,605.36
Region 3 0.0007987 66.16 3,331.46 4,469.53 75,013.98 73,875.91
Region 4 0.0007379 39.42 4,388.07 4,713.67 58,839.99 58,514.39
Region 5 0.0004074 35.85 7,352.00 7,634.83 135,153.54 134,870.71
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
0,001
0,002
0,003
0,004
0,005
0,006
0,007
0,008
0,009
Rate
Year
Defo restatio n rate
Fig. S6 Example applied to demonstrate the fluctuation of the deforestation rate over the intervening period of 6 years.

9
Region 6 0.0008912 65.34 14,279.09 19,040.05 245,090.78 240,329.82
Region 7 0.0001413 38.42 2,182.20 2,322.31 116,802.94 116,662.83
Region 8 0.0000469 36.20 3,193.15 3,327.30 442,727.83 442,593.68
Region 9 0.0000608 40.05 1,689.40 1,825.68 224,364.45 224,228.17
APPENDIX 6.
Table S6 Patcher and expander allocation according to sub-region.
Region From To Mean_Patch_Size
(ha)
Patch_size_Variance
(ha) Patch_Isometry
1 Forest Deforestation 11 34 1.5
2 Forest Deforestation 5 15 1.5
3 Forest Deforestation 8 24 1.5
4 Forest Deforestation 6 18 1.5
5 Forest Deforestation 5 15 1.5
6 Forest Deforestation 7 21 1.5
7 Forest Deforestation 5 15 1.5
8 Forest Deforestation 5 15 1.5
9 Forest Deforestation 5 15 1.5
APPENDIX 7.
To guide the construction of roads in the model, it was necessary to insert an attractiveness map (with areas that
are favorable to building roads) and a friction map (with resistance to building roads). For this, a sub-model of
DINAMICA-EGO was used that multiplies the values assigned to the classes of each input map (Land cover 2013
and Land categories 2013) and, as a result, friction and attractiveness maps were obtained (Table S7).
Considering that the model’s focus is deforestation, “non-forest” (savannas, water, rocky outcrops, etc.) and
“deforestation” (previously deforested area) were assigned a value of zero in both maps (a zero value does not
generate a road). The higher the value for attractiveness, the greater the possibility of the model building roads;
therefore, value 1 was assigned to “forest” in the “land cover” map, and 5 for “non-protected areas” in the map of
“land categories,” while the other classes were kept with the value 1 (Table S7). The highest value (5) makes
unprotected areas highly attractive to road construction (Fig. S7a); however, roads will only be built in these areas
in the model if the value for the land category is non-zero, that is, if the area is in forest.
Friction is based on the same principle, and the higher the friction value for a land-cover class, the greater the
resistance for road construction (Table S7). “Military areas” and “indigenous lands” were assigned the highest
friction value (10,000) “Integral-protection protected areas” were given a friction value of 8000, and “sustainable-
use protected areas” received a value of 6000, while “non-protected areas” received a value of 1000. These values
can be assigned by the modeler, representing what the modeler considers to be the relative difficulty of building
roads in areas of different land categories. For example, it is easier to build a road in a sustainable-use
conservation unit than in an indigenous land. We arrived at these values after they gave the best result in several

10
validation tests. The combination between the maps made the model define where to allocate the roads based on
the highest value of attraction and lowest value of friction (Fig. S7b).
Table S7 Values assigned for the construction of attractiveness and friction maps used for road construction in the
model.
Map Map component Attractiveness Friction
Land cover 2013 Non-forest 0 0
Deforestation 0 0
Forest 1 1,000
Land categories 2013 Non-protected area 5 1
Sustainable-use protected areas 1 6
Integral-protection protected areas 1 8
Indigenous lands 1 10
Military area 1 10
a) Attractiveness map b) Friction map
Fig. S7 Map of attractiveness (a) and friction (b). These values are the result of the interaction between the “land cover” and
“protected areas” maps.
APPENDIX 8
Null Model - Simply put, the model makes the comparison through window sizes, that is, with the number of cells
corresponding to the resolution used in the modeling. For example: in this study the resolution adopted was 100
m, so window 1 (1 × 1 ) corresponds to 100 m × 100 m (0.01 km2), window 3 (3 × 3) = 300 m × 300 m ( 0.09
km2), and so on. Because the comparison is made using both maps (simulated and observed), the results can
generate rates with minimum and maximum similarity values, which can vary from 0% to 100% (0% indicates
that the maps are completely different and 100% indicates they are identical). In this study we adopted the
minimum similarity value as a reference. We compared the simulation results with a null model, which uses the
same maps and input rates but with weights-of-evidence values set to zero. The null map was also compared with
the observed map (PRODES 2021). To be considered efficient, the proposed model must win in all comparisons
made with the null model.

11
Table S8 Projected deforestation in relation to real deforestation.
km2Difference % Difference in
km2
Cumulative
Deforestation
in the study
area
up to 2021
km2
Simulated deforestation
study area in 2021 57.431,23 -0,54 -313,92 57,745.15
REFERENCES
ANM (Agência Nacional de Mineração) (2021). SHP. Retrieved 18 May 2021, from
https://dados.gov.br/dataset/sistema-de-informacoes-geograficas-da-mineracao-sigmine
DNIT (Departamento Nacional de Infraestrutura de Transporte) (2016). BR-319/AM/RO Histórico do
licenciamento ambiental da rodovia e situação dos instrumentos celebrados para o atendimento às condições
do licenciamento. Retrieved 25 May 2020, from
http://legis.senado.leg.br/sdleg-getter/documento/download/d3816d09-2e92-4f73-bf18-18e2c37b0589
DNIT (Departamento Nacional de Infraestrutura de Transporte) (2021). DNIT Geo. Retrieved 15 March 2021,
from http://servicos.dnit.gov.br/vgeo/
FUNAI (Fundação Nacional do Índio). n.d. (no date information). Download de dados geográficos: Terra
Indígena (Regularizada, Homologada, Declarada, Delimitada e Área em Estudo). Retrieved 25 May 2020,
from http://www.funai.gov.br/index.php/shape
IBGE (Instituto Brasileiro de Geografia e Estatística) (2017). Geociências. Retrieved 25 May 2020, from
(https://www.ibge.gov.br/geociencias/downloads-geociencias.html
ICMBIO (Instituto Chico Mendes de Conservação da Biodiversidade) (2019). Limites das Unidades de
Conservação Federais (atualizado em julho de 2019): Unidades de Conservação Federais – SHP
(SIRGAS2000). Retrieved 25 May 2020, from https://www.icmbio.gov.br/portal/geoprocessamento1/51-
menu-servicos/4004-downloads-mapa-tematico-e-dados-geoestatisticos-das-uc-s
1
2
3
4
5
6
7
8
9
0,00 2.000,00 4.000,00 6.000,00 8.000,00 10.000,00 12.000,00 14.000,00 16.000,00 18.000,00 20.000,00
9,042.42
5,369.36
4,469.53
4,713.67
7,634.83
19,040.05
2,322.31
3,327.30
1,825.68
8,593.83
5,631.99
4,327.03
5,053.85
8,179.25
17,815.50
2,430.94
3,502.94
1,895.90
Validation 2021 Real 2021
Deforestation (Km²)
Regions
Fig S8 Projected deforestation by region in relation to 2021 deforestation.

12
INCRA (Instituto Nacional de Colonização e Reforma Agrária). n.d. (no date information). INCRA Geo.
Retrieved 25 May 2020, from http://certificacao.incra.gov.br/csv_shp/export_shp.py
INPE (Instituto Nacional de Pesquisas Espaciais) (2020). PRODES – Amazônia. Retrieved 6 July 2022, from
http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes
Lima, T.C.; Guilen-Lima, C.M.; Oliveira, M.S.; Soares-Filho, B.S. (2013). DINAMICA EGO e Land Change
Modeler para simulação de desmatamento na Amazônia brasileira: Análise comparativa. In: Simpósio
Brasileiro de Sensoriamento Remoto, 16. (SBSR), 2013, Foz do Iguaçu. Anais... São José dos Campos: INPE,
2013. pp. 6379-6386.
Oliveira, P.C.S.S., Santos, A.M. & Ferreira, N.C. (2019). Modelagem dinâmica do desmatamento no sul da
Amazônia ocidental. Bol. Geogr., Maringá 37(3), 188-206.
Soares Filho, B.S., Pennachin, C. L. & Cerqueira, G. (2002). DINAMICA – a stochastic cellular automata model
designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling,
154(3), 217-235.
Soares Filho, B.S., Alencar, A., Nepstad, D., Cerqueira, G., Dias, M., Rivero, S., Solórzanos, L. & Voll, E.
(2004). Simulating the response of land-cover change to road paving and governance along a major Amazon
highway: the Santarém-Cuiabá corridor. Global Change Biology 10, 745-764.
Soares-Filho, B.S., Rodrigues, H. & Costa, W.L.S. (2009). Modeling Environmental Dynamics with Dinamica
EGO. Retrieved 20 October 2020, from https://csr.ufmg.br/dinamica/dokuwiki/doku.php?id=tutorial:start.
Soares-Filho, B.S., Nepstad, D., Curran, L., Voll, E., Cerqueira, G., Garcia, R.A., Ramos, C.A., Mcdonald, A.,
Lefebvre, P. & Schlesinger, P. (2006). Modeling conservation in the Amazon basin. Nature 440, 520-523.
Vitel, C.S.M.N. (2009). Modelagem da Dinâmica do Desmatamento de uma Fronteira em Expansão: Lábrea,
Município do Estado do Amazonas. Dissertação de mestrado em Ciências de Florestas Tropicais, Manaus,
Amazonas, Brazil: Instituto Nacional de Pesquisas da Amazônia (INPA), 121 pp.