DOI: 10.

14393/SN-v32-2020-44054
Received: 17 August 2018 | Accepted: 13 February 2020

Papers

Land use changes and estimates of

anthropogenic CO2 emissions in a watershed
Jocy Ana Paixão de Sousa1
Elfany Reis do Nascimento Lopes2
José Carlos de Souza3
Roberto Wagner Lourenço4

Keywords: Abstract
Greenhouse gases Anthropogenic interference has always impacted the Earth's surface,
Environmental impacts with greater intensity in recent times due to land use changes, which
Transition matrix contribute to the emission of greenhouse gases, especially carbon dioxide.
In this sense, this study analyzes land use transitions and CO 2 emissions
resulting from these actions in a watershed. For this, land use mappings
were made in 2007, 2010, 2013, and 2016, along with estimates of the
emissions from transitions. The calculation of net CO 2 emissions included
data from the transitions that occurred, from past vegetation, and
pedological information. All procedures were performed with the aid of
geoprocessing and remote sensing techniques, resulting in matrices of
transitions and CO2 emissions, in addition to spatialized information.
The forest category showed the highest conversion to other types of land
use, with a loss of 208.86 ha between 2010 and 2013. In the observed
period of nine years, carbon emissions were higher than its sequestration
from the atmosphere, which shows the need for management and
planning to mitigate the impacts caused by intense land use changes in
the studied watershed.

1 Universidade Estadual Paulista (UNESP), Instituto de Ciência e Tecnologia, Sorocaba, SP, Brazil.
jocy.sousa@unesp.br
2 Universidade Federal do Sul da Bahia. Centro de Formação em Ciências Ambientais, Porto Seguro, BA, Brazil.

elfany@csc.ufsb.edu
3 Universidade Estadual de Goiás. Campus Cora Coralina. Cidade de Goiás, GO, Brazil. jose.souza@ueg.br
4 Universidade Estadual Paulista (UNESP), Instituto de Ciência e Tecnologia, Sorocaba, SP, Brazil.

roberto.lourenco@unesp.br

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

INTRODUCTION the occurrence of extreme events, highlighting

increased sea level, floods, drought, cyclones,
Anthropogenic interference with nature has storms, and extinction of fauna and flora
always caused environmental damage, but it species (BAUMERT et al., 2005; HOSHINO et
was in the middle of the 18th century, after the al., 2016; MOREIRA; GIOMETTI, 2008; REIS;
Industrial Revolution, that these SILVA, 2016).
environmental impacts reached a global scale. It is worth mentioning that the
Anthropogenic actions result in changes both in estimation of CO2 emissions resulting from
the terrestrial surface and in the atmospheric land use changes is of great relevance for
composition, contributing significantly to research on GHG reduction policies. Together
environmental and socioeconomic imbalances with the growing demand to estimate such
(CARVALHO et al., 2010; HOUGHTON et al., emissions arises the need to use geotechnology
2001). that improves the collection of these data,
Land use aimed at producing goods to highlighting Geographic Information Systems
supply human needs has proved to be a (GIS) and Remote Sensing (RS).
challenge, demanding a balance between the Geotechnologies allow updating data
rational use of natural resources and periodicity, greater processing in the amount of
productivity, both essential for human survival. data, and lower cost. Besides, they contribute to
How man interferes with nature reflects the acquisition of spatial information,
changes in the Earth’s surface and as these multitemporal analysis, and assist in the
changes intensify, environmental concern diagnosis and monitoring of the Earth’s
increases (SILVA; ROSA, 2016). surface. Therefore, they can be used in studies
Among the most significant that seek to estimate GHG (LEITE; FREITAS,
anthropogenic actions are land use changes, 2013; VAEZA et al., 2010).
which contribute to greenhouse gas (GHG) In this context, this study assesses land
emissions and influence the atmospheric use changes and estimates GHG emissions and
energy balance. Land use changes are reductions over nine years in a watershed in
considered to be one of the largest sources of southeastern Brazil.
carbon dioxide (CO2) emission in the world,
second only to fossil fuels (IPCC, 2014; MATA MATERIALS AND METHODS
et al., 2015).
Carbon dioxide (CO2) is a greenhouse gas The study was carried out in the Una river
originated both naturally and watershed, located in Ibiúna city, southeastern
anthropogenically. Studies have shown that Brazil (Figure 1).
intensification of the greenhouse effect The watershed has an area of
contributes to raising the temperature and to approximately 96 km² and stands out for being

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

inserted in a territory of high economic The area contributes significantly to the

development, with strong agricultural formation of important reservoirs, including
production, urban occupation, and landscape Itupararanga, considered the major regional
fragmentation, having different degrees of source of water supply (LOPES et al.; 2018),
disturbance due to anthropogenic activities highlighting Ibiúna, Sorocaba, Mairinque, and
(LOPES et al.; 2018; ROSA et al., 2014). Votorantim.

Figure 1. Location of the Una river watershed, Ibiúna, São Paulo, Brazil.

Org.: by the authors, 2018.

To obtain land use transitions and for 2016. The images used correspond to
estimates of net CO2 emissions, we used a November, except for the image from Landsat
sequence of methodological steps, as described 5, acquired for September.
below. The images were classified using visual
interpretation and multitemporal
Land use mapping retroanalysis. The method of visual
interpretation consists of the vectorization of
Land use maps were made using satellite the categories or classes identified in the study
images from Landsat 5 for the year 2007, Spot area through the identification of features by
5 for 2010, RapidEye for 2013, and Sentinel 2A their shape, tone, and texture (PANIZZA;

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

FONSECA; 2011). Soil map and soil carbon map under soil-
The land use categories adopted for the vegetation association
legends of the maps were adapted from the
guidelines of the Guide of Good Practices for A vector file of the pedological map of São Paulo
Land Use, Changes in Land Use, and Forest State (ROSSI, 2017) was clipped to the study
(“Guia de Boas Práticas para Uso da Terra, area, seeking to obtain the pedological classes
Mudanças no Uso da Terra e Floresta”) (IPCC, in the area. Due to low cartographic quality, the
2003) and the Technical Manual of Land Use soil texture was analyzed to detail the
(“Manual Técnico de Uso da Terra”) (IBGE, pedological characteristics and to identify more
2013). These categories are: Forest (Fo), specifically the soil carbon stock.
Reforestation (R), Field (F), Agriculture (A), Soil particle size was analyzed after the
Urban area (Ua), Flooded area (Fa), and collection of soil samples in 35 points
Pasture (P). irregularly distributed in the different land use
types. The collection was carried out using a
Past vegetation map soil auger at a depth of 0-20 cm, removing 500
grams of soil. All samples were packaged and
The past vegetation map was obtained by taken to the Water and Soil Laboratory of
clipping the vector file of the past vegetation Universidade Estadual Paulista (UNESP),
map of Brazil (IBGE, 2004) to the study area Institute of Science and Technology, Sorocaba
and by the construction of a Digital Elevation city.
Model (DEM), both used to determine the forest The samples were analyzed in the
formation that existed in the watershed. laboratory using the pipette method in thin air-
The DEM was generated by interpolating dried soil (TADS), according to the methodology
the contour lines and the rated points using the of the Agronomic Institute of Campinas (IAC,
Triangulated Irregular Network (TIN) method. 2009). After obtaining the percentages of silt,
The TIN consists of a vector structure with a sand, and clay, the texture was classified
node-arc topology, where for each of the three according to the Brazilian Soil Classification
vertices of each element of the triangle there System (EMBRAPA, 2006).
are coordinates and altitude information Soil and soil texture information tables
(SOUSA JUNIOR; DEMATTÊ, 2008). were combined using ArcGIS 10.3 (ESRI, 2014)
Subsequently, past forest physiognomies to define the soil groups in the watershed,
were classified in the vegetation categories according to Bernuox et al. (2002). The mapping
established by Bernuox et al. (2002). of the soil carbon stock under soil-vegetation
association was carried out based on the past
vegetation map and the soil groups identified.
The carbon values adopted were the same

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

used by CETESB (2012), which refer to carbon area demanded the knowledge of which
median data resulting from the soil-vegetation equation would be used.
association, as mentioned in the Reference Estimates of CO2 emissions and removals
Report - Carbon Dioxide Emissions and from soil carbon stock changes were performed
Removals by Soils due to Land Use Changes using Equation 1, as proposed by IPCC (2003).
and Liming (BRASIL, 2006). 𝑇
2
𝐸𝑆𝑖 = 𝐴𝑖 ∗ 𝐶𝑠𝑜𝑖𝑙 ∗ (𝑓𝑐(𝑡𝑜) − 𝑓𝑐(𝑡𝑓)) ∗ ( 20 ) [1]
For carbon determination, Brazil (2006)
used data from soils 0-30 cm deep. Then, carbon
Where:
estimates were made for each profile, obtained
ESi: net CO2 emission in the area in a given
by multiplying the apparent soil density with
period (tc);
the concentration and thickness of the horizon.
Ai: land use category area (ha);
Finally, carbon data were added to obtain
Csoil: soil carbon content resulting from the
carbon estimates in each location.
soil-vegetation association [tc.ha-1];
fc(to): factor of soil carbon change in the
Land use transition
previous year (dimensionless), referring to the
previous land use category;
Based on the land use maps obtained, analyses
fc(tf): factor of soil carbon change in the
of transitions between three periods (2007 to
following year (dimensionless), referring to the
2010, 2010 to 2013, and 2013 to 2016) were
following land use category;
performed using the Tabulate Area tool in
T: time interval (year).
ArcGIS 10.3. Transition matrices consist of
comparing the previous year with the following
Equation 2 was used to define the fc
year to detect changes in each category.
factor.

Estimation of CO2 emissions and removals

𝑓𝑐(𝑡𝑜) 𝑜𝑟 𝑓𝑐(𝑡𝑓) = 𝑓𝐿𝑢 ∗ 𝑓𝑀𝑔 ∗ 𝑓𝐼 [2]

The estimates of CO2 emissions and removals

Where:
refer to changes regarding land use and soil
fc(to): factor of soil carbon change in the
carbon stock in a given period.
previous year (dimensionless);
CETESB (2012) equations were used to
fc(tf): factor of soil carbon change in the
estimate CO2 emissions and removals from
following year (dimensionless);
land use changes, considering the transitions
fLu: factor of carbon change due to land use
that occurred from one year to another. In this
(dimensionless);
sense, a forest area converted to agriculture, for
fMg: factor of carbon change due to
instance, presents a specific equation.
management practices (dimensionless);
Therefore, each transition that occurred in an
fI: factor of carbon change due to use of

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

fertilizers (dimensionless). A positive result means that CO2 was

emitted into the atmosphere, while a negative
The values of the fc factor variables are result indicates CO2 removal from the
shown in Table 1. atmosphere. The values of tons of carbon were
converted to Gigagram (Gg) of carbon. Later,

Table 1. Factor of soil carbon change due to land

using Equation 4, these values were
use changes. transformed into CO2 Gigagram. The results
Land use fLu fMG fI fc
Field 1 - - 1 were presented in a matrix and specialized
Forest 1 - - 1 through ArcGIS 10.3.
Urban area 0 - - 0
Agriculture 0.58 1.16 0.91 0.612
Flooded area 0 - - 0 44
𝐸𝐶𝑂2 = 𝐸𝑐 ∗ ( ) [4]
Pasture 1 0.97 1 0.97 12
Reforestation 0.58 1.16 1 0.673
Source: Adapted from CETESB, 2012. Org.: by the
authors, 2018. Where:
ECO2: CO2 emission (GgCO2);
Net emission matrix Ec: Carbon emission (GgC);
44/12: Ratio between the molecular weights of
Equation 3 was used to calculate net emissions. CO2 and carbon.

𝑁𝐸 = ∑ 𝐸𝑅𝐿𝑈𝐶 + ∑ 𝐸𝑅𝑆𝐶 [3] RESULTS AND DISCUSSION

Where: Table 2 shows the quantification of the

ERLUC: Emission or removal from land use categories for the years 2007, 2010, 2013, and
change (tc); 2016. Figure 2 shows land use maps in the Una
ERSC: Emission or removal of soil carbon stock river watershed.
(tc).

Table 2. Quantification of land use categories for each year analyzed.

2007 2010 2013 2016
Categories Area Area Area Area Area Area Area Area
(ha) (%) (ha) (%) (ha) (%) (ha) (%)
Field 741.28 7.69 673.27 6.98 592.11 6.14 598.67 6.21
Forest 4,242.5 44.00 4,122.5 42.75 3,938.08 40.84 3,828.19 39.70
Urban area 1,107.87 11.49 1,160.03 12.03 1,356.45 14.07 1,400.14 14.52
Agriculture 3,186.71 33.05 3,315.74 34.38 3,389.67 35.15 3,433.44 35.61
Flooded area 82.28 0.85 81.77 0.85 81.36 0.84 80.84 0.84
Pasture 91.70 0.95 92.20 0.96 93.35 0.97 97.75 1.01
Reforestation 190.43 1.97 197.26 2.05 191.75 1.99 203.74 2.11
Total 9,642.77 100.00 9,642.77 100.00 9,642.77 100.00 9,642.77 100.00
Org.: by the authors, 2018.

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

Figure 2. Land use map for the year 2007 (A), 2010 (B), 2013 (C) and 2016 (D) for the Una river
watershed.

Org.: by the authors, 2018.

There was an increase in the categories of vegetation and reduction of agricultural areas
agriculture, urban area, and pasture over nine due to rural exodus. In this regard, the
years (Table 2). The forest category was the one difficulty of using mechanized tools in areas
with the greatest loss of area. In a study in the with high declivity contributes to the
Atlantic forest area, Weckmuller et al. (2012) abandonment of these areas and consequent
also found an increase in the urban area, recovery of the Atlantic forest.
agriculture, and pasture, due to the reduction The decrease in natural vegetation shows
of forest areas. the level of anthropogenic exposure to which
In turn, Eckhardt et al. (2013) identified the watershed is subject, and the percentage of
opposite results, with the increase of natural vegetation loss over nine years (4.30%)

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

reinforces the need to preserve natural areas. information made it possible to classify soils
This preservation allows gene flow, the into five groups proposed by Bernuox et al.
formation of ecological corridors, natural (2002), as shown in Table 4.
regeneration, and the conservation of water As for the soil types, the watershed
resources. presents Oxisols, Ultisol, and Gleisols (Figure
The Una river watershed comprises the 3B) and the results of the carbon stock mapping
following plant physiognomies: dense for the soil-vegetation association are shown in
ombrophilous montane forest, seasonal Table 5.
deciduous forest, and seasonal semideciduous
forest (Figure 3A), which correspond to three Table 3. Vegetation types found for the Una river
watershed.
vegetation groups proposed by Bernuox et al. Vegetation Plant Physiognomy
(2002) for the Atlantic Forest biome, as shown Group
V3 Dense ombrophilous montane
in Table 3. forest
The textures found vary between clayey, V4 Seasonal deciduous forest
V5 Seasonal semideciduous
clayey-sandy loam, clayey-sandy, and clayey forest
loam. Together with the soil types, this Source: Adapted from Bernuox et al. (2002). Org.:
by the authors, 2018.

Figure 3. Vegetation types (A) and soil types (B).

Org.: by the authors, 2018.

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

Table 4. Soil groups found in the Una river S2) Oxisols with low activity clay, S3) Soils other
watershed. than Oxisols with low activity clay, S4) Sandy
Group Soil Category soils, S5) Hydromorphic soils. Source: Adapted
S1 High activity clay soils from CETESB, 2012. Org.: by the authors, 2018.
S2 Oxisols with low activity clay
S3 Soils other than Oxisols with low
activity clay
Tables 6, 7, and 8 show the transitions
S4 Sandy soils from 2007 to 2010, 2010 to 2013, and 2013 to
S5 Hydromorphic soils
Source: Adapted from Bernuox et al. (2002). Org.: 2016, respectively. In these tables, gray cells
by the authors, 2018. correspond to the transitions that occurred

Table 5. Soil carbon stock under the soil- from one period to the next; green cells
vegetation association of the Una river correspond to permanent land use; noncolored
watershed.
Soil (Kgc/m2) cells refer to lack of transition; and the
Vegetação

S1 S2 S3 S4 S5 Transition column refers to how much area was

V3 5.83 5.23 4.29 6.33 3.58
lost in each category. The lines represent the
V4 4.67 3.08 4.00 2.59 3.27
V5 4.09 4.43 3.74 2.7 5.36 previous year and the columns the following
Legend: V3) Dense ombrophilous montane forest, year (CETESB, 2011; 2012).
V4) Seasonal deciduous forest, V5) Seasonal
semideciduous forest, S1) High activity clay soils,

Table 6. Transition matrix for land use categories from 2007 to 2010 (ha).
Area
(ha) Land use in 2010
Total Transition
F Fo Ua A Fa P R
2007 2007-2010
Land use in 2007

F 628.86 0.24 12.15 82.49 0.06 0.60 16.88 741.28 112.42

Fo 38.93 4,112.50 26.99 62.57 1.51 4,242.50 130.00
Ua 0.56 3.66 1,102.60 1.05 1,107.87 5.27
A 3.37 0.66 12.98 3,167.81 1.89 3,186.71 18.90
Fa 0.06 0.45 0.06 81.71 82.28 0.57
P 0.34 3.16 88.20 91.70 3.50
R 1.21 5.38 1.70 1.76 180.38 190.43 10.05
Total
transition
280.71 ha

2010 673.27 4,122.50 1,160.03 3,315.74 81.77 92.20 197.26 9,642.77

Total

Legend: (Fo) Forest, (R) Reforestation, (F) Field, (A) Agriculture, (Ua) Urban area, (Fa) Flooded area, (P)
Pasture. Org.: by the authors, 2018.

Table 6 shows that the category with the areas, which corresponded to 89 ha.
greatest loss of area was the forest (130.00 ha), When comparing the transitions in Table
and that the largest conversion of natural areas 8 with previous periods, it is noted that the
for human use was from forest to agriculture least amount of transitions occurred in this
(62.57 ha). From 2010 to 2013 (Table 7), the period. However, the forest category still
forest category also accounted for the greatest accounted for the main changes.
loss of area, being converted mainly to urban

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

Table 7. Transition matrix for land use categories from 2010 to 2013 (ha).
Area
(ha) Land use in 2013
Total Transition
F Fo Ua A Fa P R
2007 2010-2013
Land use in 2010

F 513.89 6.45 75.76 61.47 0.15 3.63 11.92 673.27 159.38

Fo 48.24 3,913.64 89.00 63.01 0.49 6.52 1.60 4,122.50 208.86
Ua 1.21 0.91 1,155.03 2.82 0.06 1,160.03 5.00
A 24.39 8.57 19.47 3,256.12 0.24 3.62 3.33 3,315.74 59.62
Fa 0.40 0.43 0.52 80.42 81.77 1.35
P 0.96 0.45 10.97 0.24 79.58 92.20 12.62
R 3.42 7.66 5.79 5.49 174.9 197.26 22.36
Total
592.11 3,938.08 1,356.45 3,389.67 81.36 93.35 191.75 9,642.77

transition
469.19 ha
2013

Total
Legend: (Fo) Forest, (R) Reforestation, (F) Field, (A) Agriculture, (Ua) Urban area, (Fa) Flooded area, (P)
Pasture. Org.: by the authors, 2018.

Table 8. Transition matrix for land use categories from 2013 to 2016 (ha).
Area
(ha) Land use in 2016
Total Transition
F Fo Ua A Fa P R
2013 2013-2016
Land use in 2013

F 531.91 0.06 5.54 52.46 0.18 1.96 592.11 60.20

Fo 41.24 3,824.55 25.66 40.41 0.39 0.66 5.17 3,938.08 113.53
Ua 2.91 1.352.04 1.00 0.46 0.04 1,356.45 4.41
A 25.52 0.67 14.02 3,338.73 0.37 4.28 6.08 3,389.67 50.94
Fa 1.98 79.38 81.36 1.98
P 0.66 0.06 92.63 93.35 0.72
R 0.24 0.84 0.18 190.49 191.75 1.26
Total
598.67 3,828.19 1,400.14 3,433.44 80.84 97.75 203.74 9,642.77
transition
233.04 ha
2016 Total

Legend: (Fo) Forest, (R) Reforestation, (F) Field, (A) Agriculture, (Ua) Urban area, (Fa) Flooded area, (P)
Pasture. Org.: by the authors, 2018.

The comparison between transition factor, especially when considering the amount
matrices indicated that the forest showed of forests that have been suppressed and its
prominence in the conversion to anthropogenic consequences for the environment. This
areas. The highest numbers occurred from 2010 compromises one of the main ecosystem
to 2013, with 208.86 ha. Transitions have also services, which is CO2 sequestration or storage.
shown that urbanization has increased, When in excess in the atmosphere, CO2
although agricultural areas are more contributes to the intensification of the
prominent. greenhouse effect (RIBEIRO et al., 2009).
The greater conversion of the forest According to Baird and Cann (2011), a
category in the studied periods is a worrying large amount of CO2 is emitted into the

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

atmosphere when forests are cut down, with have values, the first because it corresponds to
deforestation accounting for about a quarter of the category in which there were no transitions
CO2 emissions. Regarding urbanization, from one period to the next, and the other
watersheds become vulnerable to rapid changes because there was no transition. In the
in natural conditions, influencing landscape Emission/Removal column, we have the total
quality and encouraging environmental net issue for each category. Lines represent the
degradation and irregular occupation previous year and columns the following year
(GUIMARÃES; PENHA, 2009). (CETESB, 2011; 2012).
Tables 9, 10, and 11 show the estimates of Table 9 shows that CO2 emission was
net CO2 emissions from 2007 to 2010, 2010 to greater than its removal, and the change from
2013, and 2013 to 2016, respectively. Gray cells forest to field was the one that most contributed
represent CO2 emissions (positive values) or to emissions.
removals (negative values) from one period to
the next. Green and noncolored cells do not

Table 9. Matrix of Estimates of net CO2 emissions (GgCO2) from 2007 to 2010.
GgCO2 Land use in 2010 Emission/
F Fo Ua A Fa P R Removal
Land use in 2007

F -0.0021 0.0025 0.0190 0.0001 -0.0002

0.0001 -0.0194
Fo 0.8383 0.4632 0.8100 0.0471
2.1586
Ua
-0.0516-0.0004 -0.0015 -0.0535
A 0.0012
-0.0004 0.0104 -0.0001 0.0111
Fa
-0.0006 -0.0001 -0.0007
0.0020 P 0.0017 0.0037
R
0.0039 -0.1030 0.0073 0.0060 -0.0858
Total = 2.0335
Legend: (Fo) Forest, (R) Reforestation, (F) Field, (A) Agriculture, (Ua) Urban area, (Fa) Flooded area, (P)
Pasture. Org.: by the authors, 2018.

Total net emissions were higher from to 2013. During this period, the estimated
2010 to 2013 (Table 10) compared to the period emissions were approximately twice those of
from 2007 to 2010, which was consistent with the 2007-2010 period and those of the 2013-
the number of transitions that occurred, mainly 2016 period. Although all categories present
of forests. From 2013 to 2016 (Table 11), in some type of transition that would emit this
turn, there was a decrease in emissions, but gas, forest conversions accounted for the largest
these were still higher than removals. emissions: 2.15, 4.26, and 2.11 for the periods of
When comparing Tables 9, 10, and 11, it 2007-2010, 2010-2013, and 2013-2016,
can be seen that the highest estimate of CO2 respectively.
emissions (4.1422 GgCO2) occurred from 2010

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

Table 10. Matrix of Estimates of net CO2 emissions (GgCO2) from 2010 to 2013.
GgCO2 Land use in 2013 Emission/
F Fo Ua A Fa P R Removal
Land use in 2010

F -0.0562 0.0535 0.0143 0.0011 -0.0044

-0.0032 -0.0115
Fo 1.2452 2.0147 0.8223 0.0086 0.1698
4.2628 0.0022
Ua -0.0015
-0.0186 -0.0048 -0.0249
A 0.0191
-0.0034 0.0184 0.0010 -0.0005 -0.0034 0.0312
Fa -0.0038 -0.0013 -0.0051
0.0009 P -0.0007 0.0087 0.0001 0.0090
0.0058 R -0.1670 0.0126 0.0210 -0.1276
Total = 4.1422
Legend: (Fo) Forest, (R) Reforestation, (F) Field, (A) Agriculture, (Ua) Urban area, (Fa) Flooded area, (P)
Pasture. Org.: by the authors, 2018.

Table 11. Matrix of Estimates of net CO2 emissions (GgCO2) from 2013 to 2016.
GgCO2 Land use in 2016 Emission/
F Fo Ua A Fa P R Removal
Land use in 2013

F -0.0003 0.0120 0.0110 0.0180 0.0403 -0.0004

Fo 1.1130 0.4066 0.5470 0.0046 0.0296
2.1153 0.0145
Ua-0.0316 -0.0011 -0.0330 -0.0003
A
0.0202 -0.0005 0.0139 0.0016 -0.0005 0.0249 -0.0098
Fa 0.0000
P 0.0008 0.0007 0.0015
R 0.0060 0.0030 0.0189 0.0279
Total = 2.1769
Legend: (Fo) Forest, (R) Reforestation, (F) Field, (A) Agriculture, (Ua) Urban area, (Fa) Flooded area, (P)
Pasture. Org.: by the authors, 2018.

This greater conversion of forest to other conversions from forest to agriculture and from
categories can be justified by the fact that these forest to pasture, as well as from pasture to
areas have a high amount of carbon in their agriculture.
biomass. Thus, when vegetation is suppressed The estimated values of net emissions are
to meet the demand of other land use generally consistent with the dynamics of land
categories, the amount of CO2 emitted will be use in the watershed. Due to the size of the
greater than the capacity to sequester carbon in watershed, these emissions could not be
new uses. According to Don et al. (2011) and compared to the state or country level, since the
Kim and Kirschbaum (2015), forest suppression dynamics would be much greater in these
causes a rapid loss of carbon, especially if the locations. Notwithstanding, these results
biomass is burned, increasing atmospheric reinforce the importance of studying
CO2. watersheds, since they are conceived as basic
Carbon dioxide (CO2) emissions in the units of environmental planning and have
three periods indicate consistency with the data multiple uses.
reported in the study by Kim and Kirschbaum Looking for a comparison, the CETESB’s
(2015), who identified emissions in the inventory (2012) for São Paulo State identified

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

that conversion from agriculture to urban areas The Una river was shown to have
between 1994 and 2008 accounted for an anthropogenic interferences mainly in the
emission equivalent to 444.26 GgCO2. This central and northern portion, where most of the
value is much higher than that obtained in the emissions were found, indicating
watershed (0.0142 GgCO2), since they have environmental degradation in this location
different scales. (Figure 4).

Figure 4. CO2 flux due to land use change from 2007 to 2016 in the Una river watershed.

Org.: by the authors, 2018.

There is a trend of increased emissions if which compromises its water resources,

forest areas in this location are replaced by new biodiversity, and the well-being of the local
occupations, that is, the watershed is population.
susceptible to loss of environmental quality, Among the measures to be taken to

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SOUSA et al. Land use changes and estimates of anthropogenic CO2 emissions

reduce CO2 emissions is environmental periods studied, mainly in 2010-2013, in which

management and planning, which includes the converted area was 208.86 ha. Net CO2
preparing the Plans for the Recovery of emissions into the atmosphere were recorded in
Degraded Areas (PRAD), the Rural all periods analyzed. The period with the
Environmental Registry (CAR), and the highest emission was 2010-2013, totaling
Environmental Control Plan (PCA) for 4.1422 GgCO2.
activities with high environmental impact. The main contribution to CO2 emissions
Aided by proposals for the maintenance and in the Una river watershed consists of
restoation of natural areas with the use of vegetation suppression to meet the demand for
geotechnologies, these actions can minimize the agriculture and urbanization. Moreover, it was
impacts of land use change. shown that CO2 emissions were higher than its
Another effective measure would be to removals.
encourage small farmers to adopt the The study was effective in identifying
agrosilvopastoral system, since it associates anthropogenic interventions and the number of
agriculture and livestock with forest forest areas suppressed in the watershed. The
maintenance, providing an environment methodology adopted could serve as a
suitable for agricultural practices that also technical-methodological design in similar
benefits the environment. areas, seeking to diagnose the situation of CO2
Land use is considered an important emissions and removals from land use changes
factor when it comes to policies related to in areas of relevant water and environmental
climate change. For this reason, Rose et al. interest.
(2012) report that changes in the adopted
practices and technologies can reduce GHGs ACKNOWLEDGEMENTS
and, in the long run, turn into a low cost
mitigation strategy. This study was carried out with the support of
the Coordination for the Improvement of
FINAL CONSIDERATIONS Higher Education Personnel - Brazil (CAPES).
Financing code 001.
There was an increase in land use changes over
natural vegetation over nine years, equivalent
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