deforestation in the brazilian amazon: a review of...
TRANSCRIPT
Deforestation in the Brazilian Amazon:
A review of estimates at the municipal level
By Pablo Pacheco
Draft for comments
Belém, Pará
June 2002
This paper constitutes part of a broader research initiative sponsored by The World Bank Group, Office in Brazil, to analyze the economics of cattle ranching and deforestation in the Brazilian Amazon. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the view of The World Bank. The author thanks Sergio Margulis, Sven Wunder, and Diógenes Alves for their useful comments and suggestions.
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Deforestation in the Brazilian Amazon:
A review of estimates at the municipal level
By Pablo Pacheco
1. Introduction
This paper constitutes part of a broader research process aimed at analyzing the
economics of cattle ranching, and the role of different social agents on driving deforestation in the
Brazilian Amazon. Some research conducted in the region has argued that the most important
proximate cause of deforestation in this regions constitute forest clearing for pasture expansion,
particularly driven by large-scale ranching operations (Hecht 1993, Geist and Lambin 2001).
There is still an ongoing debate about the underlying causes prompting the expansion of livestock
(Kaimowitz 2001), and it has been suggested that additional research is required to better
understand the economics of cattle ranching prompting pasture expansion (Margulis 2001).
The current debate about the spatial patterns and temporal trends of forest removal,
however, is constrained by the lack of detailed information about the location, magnitude and
pace of deforestation. The official estimates of deforestation in the Brazilian Amazon are too
aggregated, and the more detailed assessments of land-use/cover change undertaken since some
years ago are spatially and temporally fragmented. The two factors mentioned amplify the
uncertainties embedded in land-use change analysis, which tend to perpetuate because the
different estimates are difficult to be compared. Furthermore, the increasing interest within the
land-use/cover change research community to look for a more detailed scale of analysis has lead
to privilege the municipalities as the main unit of observation of such dynamics.
This paper has two main goals: 1) to assess critically the estimates of deforestation
currently available for the Brazilian Amazon at the municipal level, as well as to make explicit
some of their underlying assumptions, 2) to discuss some of the most recent dynamics of
deforestation, and the contribution of small- vs. large landholders in the light of such estimates.
Two are the methods often employed to estimate magnitude and rates of deforestation.
The first consists in the use of remote sensing information, which is often done through the use of
wall-to-wall techniques consisting in the analysis of the scenes covering a specific study area.
The second type of estimates comes from land use surveys and agricultural census data. Each of
the two methods mentioned has both advantages and shortcomings. The first offers a direct
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measure of actual forest cover, although it is often subject to classification uncertainties. The
second method allows gathering very rich land-use information, though the temporal resolution of
the data is bounded to the frequency of the census survey, and census data is subject to
inaccuracies due to reportage or underreporting problems (McConnell and Moran 2001).
There are only a few estimates of land-use change in the Brazilian Legal Amazon (BLA)1
at the municipal level. Three are the public institutions conducting land-use classification from
remote sensing data that will be considered here due to them constitute by far the most relevant
initiatives: INPE (National Institute of Spatial Research); IBAMA/CSR (Remote Sensing Center
of the Brazilian Institute of Environment), and FEMA – Mato Grosso (the state agency of
environment of Mato Grosso). In turn, since 1970s the IBGE (Brazilian Institute of Geography
and Statistics) conducts agricultural census every five years, excepting the one cancelled in 1990.
Each of the sources mentioned provides different figures of forest removal depending of
factor such as the definition of deforestation employed, the methods used to interpret remote
sensing data, as well as the spatial and temporal scales used for the estimation of forest clearing.
Although this paper’s main interest is to assess deforestation data at the municipal level,
some brief references to other well-known estimates of deforestation at both national and regional
level will also be provided. The official data of deforestation adopted for the Brazilian
government –coming from INPE- is provided at the federal state level, for the all the states
belonging to the BLA, and for the region as a whole. A more detailed assessment of those figures
is available elsewhere (e.g., Machado and Pasquis 2001, Faminow 1998).
This paper contains five parts including this introduction. The second part describes the
main sources of deforestation available at both the national and state level. The third presents the
deforestation estimates at the municipal level, and discusses their main methodological
assumptions and outcomes. The fourth compares the relative contribution to deforestation from
small- vs. large landholders based on information generated from INPE, CSR/IBAMA, and
IBGE. The conclusion summarizes our major arguments regarding the different datasets.
1 The Brazilian Legal Amazon (BLA) covers approximately 5 million square km. The BLA constitutes a political definition created by government decree in 1953. It covers the six “North” states (Acre, Amapá, Amazonas, Pará, Roraima and Rondônia), plus part of three others (Tocantins, north of the 130 parallel; Mato Grosso, north of the 160- parallel; and Maranhão, west of the 440 meridian) (Alves 2001a, Faminow 1998). The main reason for creation of the Legal Amazon was to define an area for the administration of economic development, rather than to describe a region according to a uniform ecosystem. That is the reason why the BLA includes, besides forested land, extensive areas of natural savanna (cerrado), and open forest in the transition zone between closed forest and cerrado (Faminow 1998:88).
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2. The deforestation estimates for the BLA
Brazil holds the largest continuous tropical forest in the world, but also loses the highest
amount of tropical forest among all tropical countries (Skole et al. 1994). The estimates regarding
the amount of original forest cover in the country, as well as the rates of which they are being
converted to other land-uses, are still subject of controversy (Machado and Pasquis 2001).
FAO’s Forest Resources Assessment (FRA) estimates the forest cover2 for the entire
country by 2000 at 543 million ha, and the rate of forest cover change at 2.3 million ha/year
between 1990-2000, equivalent to a loss of 0.41 %/year3. This value is a little higher to the
average observed in the other Amazonian countries (0.37%), and equivalent to the average for
Latin American countries as a whole (0.4 percent) (FAO 2001:157, Table 3).
The total forest cover estimates in the BLA vary among different sources. FAO estimates
it at 356 million ha (FAO 1981), whereas IBGE at 379 million ha (IBGE 1988). The INPE
estimate of the forest area in the BLA is about 419 million ha (quoted in Faminow 1988:88),
while the evaluation of Skole and Tucker (1993) is somewhat lower at 409 million ha.
The latter two performed an assessment of deforestation between 1978 and 19884. While
INPE found that the annual deforestation for the period was equivalent to 2.1 million ha/year,
Skole and Tucker (1993) place it at 1.5 million ha/year for the same period. Faminow (1998:88),
argues that such difference originates from INPE’s assumption that areas obscured by cloud cover
were in the same proportion as the measured areas of forest, deforested land and water, while
Skole and Tucker (1993) excluded those areas from the analysis.
2 FAO’s definition of forest includes natural forests and forest plantations. Forest corresponds to land with a tree canopy cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters (FAO 2001:137). 3 The FAO’s FRA employs a mixed of methods to estimate deforestation. While data for 1990 data is based in model predictions, data for the year 2000 is based on both expert guesses and limited sample of satellite observations. Different studies discuss the shortcomings of FAO’s model to estimate the country’s rates of deforestation (Faminow 1998, Rudel and Roper 1996, 1997). In turn, Tucker and Townshend (1999) have suggested that the use of samples of satellite images fails to estimate the distribution of deforested areas. As result, main critiques of FRA 2001 assessment are: 1) its results are distorted because of changes in methodology and/or base line data; 2) forest data for many countries is weak and reported in odd ways; and 3) differences between net and gross rates of change are not understood (Matthews 2001). 4 Both studies are based upon Landsat TM 1988 and Landsat MSS for 1978, though cloud cover forced to use some images taken either after or before such years. The data used covered the entire forested portion of the Brazilian Amazon Basin. Skole and Tucker (1993) digitized the deforested area with visual deforestation interpretation and standard vector GIS techniques. Then the digitized scenes were projected into equal-area geographic coordinates, edge matched, and merged in the computer to form a single database for the entire Brazilian Amazon.
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Since 1988 to date, INPE has been the single source providing annual estimates of
deforestation at both state and national levels (excepting 1993), conducted by a project labeled
PRODES, whose outcomes are adopted as the official estimate of deforestation by the Brazilian
government. INPE considers deforestation as “the conversion of areas of primary forest by
anthropogenic activity for the development of agriculture and cattle raising, detected from orbital
platforms” (INPE 2000:2). Under this approach, forest regrowth or areas in the process of
secondary succession are considered as deforested to the extent they were accounted as such in
the year they are first detected, and hence are included in gross deforestation estimation5.
The latter entails that: 1) if areas in secondary succession are re-cleared, they are not
accounted for in gross deforestation during the period they are re-cleared, and 2) if they remain
abandoned, they are not subtracted from gross deforestation to estimate net deforestation.
INPE estimates included a portion of a so-called “old deforestation” (prior to 1960) in
1978, its baseline period, comprising 9.1 million ha (Faminow 1998:99). A major part of this took
place in the states of Maranhão and Pará. The exclusion of secondary succession from forest
stocks is problematic in the sense that it overestimates net deforestation taking place in the BLA.
The area in secondary forest succession is significant. Skole and associates (1994)
reported 30% of the deforested area in Amazonia to be regenerating forest, a figure supported by
Lucas and colleagues (2000) who argue that one-third of the deforested area supports forest
regrowth, with more than a half of this forest estimated to be less than five years of age. Much of
the secondary succession, however, may be temporary fallows, and is not properly forest.
Furthermore, secondary forest succession differs significantly throughout the region (Moran et al.
1994). Therefore, the magnitude of such trends depends of the definition of forest employed.
According to INPE, the total deforested area in the BLA grew from 15.2 million ha in
1978 to 41.5 million ha in 1990, and was about 58.7 million ha in 2000 (INPE 2000). That figure
would be equal to 60.5 million ha in 2001 according to estimates from a linear projection based
on a sample from scenes located in the so-called “critical areas”6 (see Figure 1).
5 INPE uses Thematic Mapper (Landsat TM) which is processed to a scale of 1:250,000, which only allows for the identification of changes in forest cover areas larger than 6.25 ha (that size in ha corresponds to 1 mm2 in images at a scale of 1:250,000). Each scene is 184 X 185 km, and 229 scenes are required to give complete coverage of the BLA (INPE 2000). Once images are selected, they are printed to the mentioned scale. Then transparent overlays are prepared for each image and analysts mark all deforested areas by hand. The deforestation data are then converted to a digitalized map format by a scanner and introduced to a GIS. Yet, cloud cover creates many spaces without data. INPE, in such cases, assumes that areas under clouds are deforested at the same rate as the non-clouded part of the scene (Faminow 1998). 6 The critical area comprises a relatively small fraction of Landsat scenes of the region (some 44 scenes representing 20% of the total 229 scenes covering the region). Since 1996/97, based on the evidence that a high proportion of deforestation takes place on those scenes, the critical scenes are used to generate
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There is not a clear temporal pattern of deforestation because it follows an oscillatory
trend over time. So far, there has not been provided any explanation able to capture such complex
dynamic (Kaimowitz 2001). INPE estimates place the highest annual amount of deforestation
(excluding a dramatic increase in 1995) in the period 1978-1988 (2.1 million ha/year or 0.54%
year). The annual area of forest clearing was decreasing from the mid-1980s to the early 1990s. It
remained below 1.5 million ha/year during a major part of the 1990s (except for the “spike” in
1995), and it tends to increase in the late 1990s, though at level inferior to that of the early 1980s.
Deforestation was close to 1.8 million ha/year in 2000.
There is some debate about why deforestation rate grew so much in 1995. While some
argue that technical problems of cloud cover could have produced some effect of unregistered
deforestation in previous years was only captured in 1995. This, however, constitutes a dubious
argument supporting the idea that such series would have produced with some methodological
inconsistencies. In contrast, others consider that such increase could in fact be reflecting a “real
growth” in the forest clearing rates as a result of the policy shift in the mid-1990s (Plan Real)
aimed at economic stabilization (Lele et al. 2000). Nevertheless, none of the two arguments can
provisional estimates of gross deforestation for the entire Brazilian Legal Amazon. The interpolated deforestation for 2000/01 is equivalent to 1.7 million ha (INPE 2002).
Source: Adapted by the author, based on INPE (2000).
Figure 1. Deforestation in the BLA, 1978-2000
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be disregarded, and additional elements should also be taken into account to improve our
understanding of the size of land-use/cover change taking place at a regional level.
As shown in figure 2, annual deforestation started to decrease in all states from 1995 to
1997. In 1997, it approached a similar level to the early 1990s. In 1998, the annual deforested
area increased in all states relative to 1997. The trends of deforestation by state are surprisingly
similar among all states, though they tend to show some differences during the last three years.
There is no indication from such trends that deforestation will tend to slow down in the future.
The unequal spatial distribution of deforestation is relatively well explored due to an
explosion of remote sensing analysis and the use of GIS. The latter has enormously contributed to
improve our knowledge about the spatial distribution and patterns of frontier deforestation. Since
the early 1990s, several studies have reported that deforestation is a phenomenon relatively
concentrated in a few geographical areas (Alves 2002a, Skole and Tucker 1993). INPE (2000:20)
ratifies such view by mentioning that 76% of the mean gross deforestation took place in the BLA
Figure 2. Annual deforestation according to state (thousand ha/year), 1978-1999
Source: Adapted by the author, based on INPE (2000).
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1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
Amazonas Maranhos Mato Grosso Para Rondonia Others
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is located in only 49 Landsat “scenes”. The area where a major part of the deforestation is taking
place has due to its east - west going shape been labeled “arc of deforestation” 7.
The studies dealing with deforestation at larger scales support the quite obvious lesson
that it is problematic to generalize the processes and patterns of deforestation to the state level
because the dynamics of deforestation is highly heterogeneous when it is assessed at the
municipal level. Hence, though the concept of “arc of deforestation” may be instrumentally useful
to identify where the deforestation takes place, it constitutes a large area, comprising a third of the
entire BLA, where various processes of land-use change are interacting simultaneously.
Based on INPE estimates, the studies focus on typifying the spatial occurrence of
deforestation reinforce two main conclusions: 1) forest clearing occurs mostly around areas of
previous deforestation and near main roads (i.e., 87% of deforestation is located within 25 km of
pioneer occupation areas and almost half of it takes place within 25 km of the three major road
networks) (Alves 2002a: 102); and 2) deforestation is concentrated in a small number of states,
particularly in Mato Grosso, Pará and Rondonia, which together account for 76% of the total
gross deforestation in 1998, and 85% of the annual deforestation in 2000 (INPE 2001).
3. The assessments of deforestation at the municipal level
As was already mentioned, four institutions provide some estimates of deforestation at
the municipal level (INPE, IBAMA/CSR, FEMA-MT and IBGE). This section analyses the
procedures employed and assumptions made by each of them. Table 1 below summarizes the
temporal scale and vegetation types included in the four assessments, as well as the geographic
coverage and methods for them employed to perform land-use classification.
FEMA-MT provides the longest temporal series but its spatial extent is restricted to the
state of Mato Grosso. The IBAMA/CSR has produced series from 1996 to present but just for
some 197 municipalities, mostly located in the “arc of deforestation” 8. INPE has been processing
some data at the municipal level, derived from its state estimates, but it is constrained to the first
half of the 1990s; and more recent estimates have not yet been released. In turn, the most updated
IBGE’s estimates correspond to the land survey carried out in 1996.
7 The so-called “arch of deforestation” is constituted by a total of 249 municipalities embracing an area of about 170 million ha. A large proportion of the critical area earlier described is located within this portion of the BLA where there are higher pressures of land use change. 8 The CSR/IBAMA data covers 197 municipalities, or about 198 million ha, of which almost 75% are located in the states of Mato Gross, Pará and Rondônia. Hence, approximately 40% of the BLA total area is being monitored annually to detect forest conversion. Additionally, the CRS’s dataset covers approximately 85% of the “arc of deforestation”.
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Table 1. Assessments of deforestation in the BLA at the municipal level
Source Period Type of estimation
Geographic coverage
Method employed
INPE Assessment of deforestation for the periods 1992-92, 1992-94, and 1991-95.
Annual estimates of land cover change considering different various forest cover types (dense, open and deciduous)
Embraces the whole BLA region comprising 500 million ha
Color composite satellite images are processed to a scale of 1: 250,000. De-forested areas are scanned and integ-rated into a GIS
CSR / IBAMA
Annual estimates from 1996 to 2000 (2001 in process)
Annual estimates of deforestation including only forest cover
Covers an area from 170 to 190 million ha, mostly within the arc of deforestation
Deforested areas are digitalized based on visual interpretation of color composite satellite images
FEMA – MT
Annual estimates from 1992 to 1995, and bi-annual from 1995 to 2001
Annual estimates of land cover change (including forest, cerrado and transition areas)
Covers the entire state of Mato Grosso (90 million ha)
Deforested areas are digitalized based on visual interpretation of color composite satellite images
IBGE Estimates for the years 1970, 1975, 1980, 1985, 1995/96
Estimates of cleared area within agricultural estab-lishments including all types of existing vegetations
Covers an area of 120 million ha in the agricultural year of 1995/96
Census to establishments producing any plant or animal output
Source: Adapted by the author from FEMA (2001), INPE (2000), and Teixe ira (1999).
Fearnside (1993) notes that part of the confusion surrounding deforestation numbers is
the treatment of cerrado, or the way in which the estimates separate it from forest. Whereas INPE
and IBAMA/CSR estimates deal exclusively with forest cover types (including dense and open
forest, pioneer formations, deciduous forest, and mixed forest covers in transition zones, among
the most relevant types), FEMA does not differentiate land cover types in its municipal level
dataset, only for the state as a whole. The land survey, in turn, does not differentiate cleared areas
taking place either on forest or cerrado areas (Andersen et al. 2001).
The INPE dataset of deforestation
To the extent that the analysis undertaken at the state level does not reveal much about
the spatial dynamics of deforestation, increased attention has been paid to the analysis of land-
cover change in the municipal realm for the land-use/cover change research community. To date,
most research has employed, no matter its limitations, the dataset from INPE generated to analyze
land-cover change at a regional scale. INPE has made some efforts to disaggregate its dataset to
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the municipal level. Nevertheless, a factor limiting more progress is that researchers have only
limited access to such information because it is not open access information.
A pioneer work (Alves et al. 1997) made explicit some of the spatial patterns of
deforestation in the BLA based on data produced from INPE for two periods of time (1991-1992
and 1992-1994). This study found generally that deforestation was expanding at higher rates in
municipalities located in southern and eastern flanks of the BLA, as well as in the western portion
of Pará and Roraima. Furthermore, this work found that the likelihood of municipalities with high
rates of deforestation to continue deforesting at the same rate during a following period is very
high9. This study support the argument that deforestation is an inertial process by which the areas
most likely to be deforested are those located closer to the forest areas already intervened.
9 This work analyses 624 municipalities (22 in Acre, 15 in Amapá, 62 in Amazonas, 109 in Maranhão, 117 in Mato Grosso, 128 in Pará, 40 in Rondônia, 8 in Roraima and 123 in Tocantins). The municipalities’ total area is equivalent to 500 million ha of which four/fifth parts are forests. This study found that 90% of deforestation was concentrated in only 191 municipalities during the first period of analysis (1991-92), falling to 159 in the second one (1992-94). A total of 140 municipalities were present in the two periods. The clouded areas were excluded from the analysis, 6.6 million ha in the first period, and 7.2 million ha in the second one (Alves et al. 1997).
Figure 3. Accumulated deforested area in BLA by size of municipality, 1991-1995 (in %)
Source: Adapted by the author, based on Alves (2000).
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Alves (2000), using a similar approach, estimates the amount of forest removal at the
municipal level but looking at the accumulated deforested area during the period 1991-1995. This
purely descriptive study provides only a one-period view of the relative contribution of each
municipality to total deforestation during the mentioned period. To a high extent, it reiterates the
outcomes of the previous work about the location of deforestation (see Figure 3). An important
issue that arises from this work is that it is difficult to estimate annual estimates at the municipal
level because of varying imagery dates and corresponding periods of observation. Though images
are normalized for dates to allow comparison, such process tends to introduce some errors,
mainly for small areas, which tend to be averaged further at the municipal level.
Menezes (2001:108-109), based on INPE estimates, offers a static picture of the
accumulated deforestation per municipa lity by 199710. According to this source, 47 of 227
municipalities were responsible for 50% of deforestation in the states of Mato Grosso, Rondonia
and Pará. Moreover, 139 municipalities (covering an area of 123 million ha) contain 90% of
deforestation in those three same states or 77.4% of the total deforestation in the BLA.
Though the accuracy of these results may be questionable due to the coarse resolution of
the data used, this approach improves our knowledge about the spatial distribution of
deforestation. Yet, the main shortcoming of that it does not show the evolving trend of
deforestation over time due to the existence of just fragmented series.
The CSR/IBAMA data for the arc of deforestation
The CSR, an institution part of IBAMA, is developing another dataset at the rural
property level, as part of a larger system of environmental surveillance, licensing and monitoring.
A detailed description of this system can be found elsewhere (i.e., SCA 2001, Teixeira et al.
1999). The CSR determines annual deforested areas by a visual interpretation of Landsat +ETM
images. The CSR/IBAMA did not make explicit the definition of forest they use as part of its
classification procedures. They only perform a binary classification of forest and no forest from
which any vegetation like type is classified as forest 11.
10 Menezes et al. (2001), though based on INPE estimates of land cover change, uses a somewhat different estimate of deforestation. They took an INPE’s map of deforestation for 1996/97, and overlaid it to a map of municipal boundaries from IBGE. The result is a map of deforestation for each municipality by 1996/97. Yet, due to the problems of areas without data and cloud cover areas the sample is incomplete. 11 Areas with comparatively low reflectance values in the visible bands of the wavelength spectrum (band 1, 2, and 3), and high values in the near-infrared (band 4) area typically considered vegetation.
11
This data set consists of annual measurements of deforestation starting in 1996 for areas
greater than one ha. In 1996, forest secondary succession at advanced stages was considered as
forested land, and since then forest regrowth is no longer considered. In this regard, it is obvious
that for CSR/IBAMA the deforested area in 1996 will be lower than INPE’s one because it
includes intermediate and late stages of forest regrowth excluded from INPE analysis. The
treatment of secondary succession during the following years is similar to the one from INPE.
IBAMA/CSR covers a large part of the BLA, 80% of the municipalities monitored are
part of the “arc of deforestation”, while the rest is located in the southwest portions of the states
of Mato Grosso, Rondonia and Acre. This dataset should in theory capture a high proportion of
the deforestation in the BLA due to the fact that it covers over one third of the region, the portion
where most of the forest clearing is currently taking place. The major limitation of those data is
the missing information for some municipalities for some specific years due to the lack of
satellite images. Hence, in some scenarios of “no deforestation” it exists the doubt either if in
practice there was no deforestation on some municipality at some specific year or if merely
CSR/IBAMA did not perform an analysis of such area. The CSR/IBAMA is not explicit about it.
Figure 4. Accumulated deforestation by size of municipality (by 2000)
Source: Adapted by the author based on IBAMA/CSR.
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While spatial patterns reflecting how deforestation is distributed along places is easier to
determine with available data, it is more difficult to trace the evolution of deforestation over time
to more detailed scales (i.e., municipal level). Three situations of deforestation growth can be
identified from CSR/IBAMA data between 1996 to 1999: 1) municipalities with declining rates
of growth of deforestation, 2) municipalities with steady rates of growth, and 3) the dominant
situation is that where deforestation is growing at accelerated rates (see Figure 5).
Deforestation data from FEMA- Mato Grosso
Another source of deforestation data at the municipal level is the dataset being developed
by FEMA-MT, with the technical support of a consultancy company specialized in geo-
processing services. FEMA-MT has implemented a system as part of a larger pilot program of
environmental control and licensing supported by the PPG7 aimed at monitoring land-use
conversion at the rural property level (de Moura 2001). This system began to operate in 14
municipalities of the State of Mato Grosso, those with the highest rates of deforestation, and
Figure 5. Annual deforestation: percentage change 1996-97 and 1999-00
Source: Adapted by the author, based on CSR/IBAMA. Percentage of change calculations corresponds to the period 1996-97 and 1998-99 for Mato Grosso.
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limited to properties greater than 200 ha. Beginning 2002, the monitoring system has expanded to
the entire area of the state, and will include rural properties independently of size.
FEMA-MT has produced a series of vegetation cover changes including forest, cerrado,
and transition zones between forest and cerrado since 1992 to present for all the municipalities of
Mato Grosso (see Figure 6). The limitation is that information of annual cleared areas does not
differentiate it by type of vegetation being. Cleared areas disaggregated by vegetation types are
available only for the accumulated cleared area by 2000/01 (see Figure 7). Some 46% of original
cover removal took place in the cerrado, 39% in forests and 15% in transition zones.
The IBGE estimates from the agricultural census
The IBGE, as mentioned earlier, produced an estimate of cleared original cover based on
a land survey. The last agricultural census for which data is published is from the agricultural
year 1995/9612. This census provides detailed information on private land uses and, in theory,
12 The agricultural census is conducted every five years. The 1990 census was cancelled, and by 1995 IBGE decided to change the reference period from the calendar year to the agricultural year (August 1, 1995 to July 31, 1996). The change in reference period implied a change in the period in which the data
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Figure 6. Mato Grosso: Rate of total cleared area by size of municipality (by 2000/01)
Source: Adapted by the author, based on FEMA-MT.
Figure 7. Mato Grosso: Total cleared area by type of land-cover (by 2000/01)
46%
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14
includes all agricultural establishments in the BLA. The unit of analysis is the agricultural
establishment producing any plant or animal output during the time span under analysis, be it a
household or a farm, or any kind of rural residence (for a more detailed discussion about the
IBGE land survey see Andersen et al. 2001). The agricultural census groups all land into private
land and public land. There is no data for the use of public land.
In this dataset, it is not possible to estimate accurately deforestation because it is
unknown how much of the cleared area was originally forested. Yet, the IBGE estimates of
cleared area provide a good estimate about the intervened (or altered) areas for each of the
municipalities comprising the BLA (see Figure 8). The cleared area comprises all the areas under
either permanent or annual crops, areas in fallow, planted pasture, planted forest and unutilized
productive land (see Andersen et al. 2001, Menezes et al. 2001).
was gathered. Instead of collecting it in January the following year, as had been done for the 1970, 1975, 1980, and 1985 censuses, the gathering of data for the 1995/96 census began in August of 1996. Some researchers have suggested that this would produce a drop in agricultural establishments and agricultural workers in Brazil between 1985 and 1985, because a large portion of temporal establishments would have not been counted (Andersen 2001:53).
Figure 8. Total cleared area within agricultural establishments by 1995/96
Source: Adapted by the author based on IBGE, Agricultural Census 1995/96.
15
4. How much and why do the estimates differ?
This section seeks to compare the different estimates of land-use change described above,
but it is a difficult task due to different spatial and temporal scales, and the implicit definitions of
deforestation employed as part of the different analysis of deforestation. The latter leads to some
researchers doubt whether a comparison should be attempted.
The difference between the INPE and IBAMA/CSR figures is notorious. Due to the fact
that the CSR estimates cover most of the municipalities with high rates of deforestation, so it
should be possible to assume that those data capture a large portion of the BLA deforestation. In
practice the CSR numbers range between 33% and 44% of the INPE figures (see Table 2). That
difference is not just a matter arising from the different geographical coverage, but largely a
methodological issue. Nevertheless, a full comparison can be made in the case of Rondônia
exclusively, as the two sources cover the state’s entire area. Looking at both sources one can infer
that either INPE overestimates deforestation or IBAMA/CSR underestimates it, or both.
Table 2. Annual deforestation in BLA by state (thousand ha), 1996/97 – 1998/99 States 1996/97 1997/98 1998/99 1999/00 (d)
INPE (entire states) Mato Grosso 527.1 546.6 696.3 636.9 Rondônia 198.6 204.1 235.8 246.5 Pará 413.9 582.9 511.1 667.1 Others (a) 183.1 304.7 282.7 272.1
Total 1,322.7 1,638.3 1,725.9 1,822.6 CSR (197 municipalities) (b) Mato Grosso 262.6 274.3 244.6 N/A Rondônia 120.8 182.1 92.0 235.6 Pará 147.8 264.3 215.6 322.2 Others (c) 33.0 12.6 23.6 69.9 Total 564.2 733.3 575.8 627.6 Source: Adapted by the author based on INPE (2000) and data provided by IBAMA/CSR. Notes: a) Include the states of Acre, Amapa, Amazonas, Maranhao, Roraima and Tocantins; b) Some municipalities do not have information for some years. The number of municipalities including for monitoring are: 46 in Mato Grosso, 49 in Para, and 52 in Rondonia covering the whole state; c) Include all the states mentioned in note a. except Amapa and Roraima; d) By the moment this report was elaborated had not yet been released the INPE estimates of deforestation for 1999/00.
The coarse resolution employed by INPE lead it to both underestimate deforested areas in
cases where forest clearing occurs in small plots, and to overestimate deforestation in landscapes
with small forest patches. A more conclusive assessment can only be possible by looking at
specific situations. Indeed, a more detailed comparison undertaken for CSR/IBAMA shows that
16
issues of scale can be relevant to explain such differences between the two datasets, at least that
seems to be the case for Rondônia 13. Issues of forest definition and uncertainties linked to
classification, however, also constitute important factors to explain the datasets’ discrepancies.
Table 3 compares the datasets of CSR/IBAMA and the SCA (Secretary of the Amazon of
the Ministry of Environment) results based on INPE’s land-use map for 1997. It suggests that
CSR/IBAMA figures are lower respect to INPE because they classified secondary forest as forest.
Hence, whether CSR/IBAMA underestimate deforestation or INPE overestimate it depends
largely of what is defined as forest. Nevertheless, the latter argument is just valid for the
accumulated deforestation until 1996 (the CSR/IBAMA base line period), in reason to both
sources treat similarly forest regrowth during the following years. That ratifies that differences lie
on definitions of forest but also is an issue of scale. It is difficult separate both effects.
Table 3. Total deforestation by 1996/97 in municipalities selected by CSR (thousand ha)
States No. FEMA (a) INPE (b) CSR/IBAMA Rondônia 52 4,498 4,585 Acre 22 681 850 Amazonas 12 57 191 Para 49 8,047 267 Tocantins 13 1,186 5 Maranhao 3 616 1 Mato Grosso 46 4,834 (c) 6,695 4,858 Total 197 21,780 10,756 Source: Adapted by the author based on data provided by FEMA, IBAMA/CSR and SCA/MMA. Note: a) Assumes that clearing is proportional to the distribution of land-cover types within the state; b) Corresponds to SCA/MMA estimates of accumulated deforestation at the municipal level for selected municipalities of the BLA based on INPE land-use map of 1997. This data is just referential because it was calculated based just in the 51.5% of the municipalities’ forest area, then it is unknown the portion of deforested areas that were left out of the analysis; (c) represents the total cleared area by 1996/97 multiplied by the proportion of forest cover existing in the state of Mato Grosso.
Let us review the IBGE’s census data to complete our comparison. It has been suggested
that a method to estimate the portion of cleared land that would have been forest originally
consists in multiplying the amount of cleared land with the share of naturally forested land in
each municipality under the assumption that clearing is randomly distributed across the
municipality (Andersen et al. 2001). This assumption is still likely to lead to an overestimation of
deforestation since people would tend to clear the most open areas first.
13 Nilson C. Ferreira, CSR/IBAMA, personal communication, April 2002.
17
Table 4 shows the outcome of such estimation. While INPE calculates that accumulated
deforestation represented about 10% of the total area of the BLA, it would be equivalent to
approximately 6% according to IBGE ‘s estimate of forest clearing.
The two mentioned datasets do not account for forest regrowth, though some land-use
categories of the agricultural census (such as areas in fallow or unutilized land could possibly be
under some type of secondary succession). The reasons underlying the differences seems, among
others: the census’ outcomes reflect only land-use within occupied (private) areas by
establishments (which could be undercounted), deforestation taking place elsewhere is not
considered, and abandoned deforested areas (though small in theory) are not part of the
calculations because they are considered as forested (Andersen et al. 2001).
Table 4. Total deforestation by federal state (thousand ha), 1995/96 States INPE (1995/96) Census data (1995/96)
Deforested Cleared Deforested (a) Mato Grosso 11,914 20,214 11,906 Rondônia 4,865 3,358 2,985 Pará 17,614 8,681 7,822 Others 17,314 15,506 6,977 Total BLA 51,707 47,760 29,690 % of total area of BLA 10.27 9.49 5.90 Source: Adapted by author based on Andersen et al. (2001), INPE (2000), IBGE (1998). Note: a. Under the assumption that clearing is randomly distributed across space.
The information provided by the land survey may constitute a good proxy of
deforestation at the municipal level under the assumptions noted above. For the reasons already
noted, the agriculture census outcomes will underestimate the amount of total deforestation.
Figure 9 provides a comparison of accumulated deforestation until 1996 in the state of Rondônia
for both IBGE and IBAMA/CSR datasets. The IBGE data distinguish deforested areas from other
types of land cover (such as transition zones or some areas of savanna) following the same
assumptions made above. As can be seen from the figure, even taking the total cleared area, the
IBGE estimates are systematically below to the ones from IBAMA/CSR.
18
Table 5. Summary of datasets’ strengths and shortcomings
Source Definition of forest
Main strengths
Main shortcomings
INPE Not explicit. Deforest-ation is considered as all conversion of primary forest by anthropogenic activity to other land-uses
Methodological consistency to produce estimates at a coarser resolution (federal state level)
Overestimates net deforestation since not consider forest regrowth, and probably overestimate gross deforestation.
CSR / IBAMA
Not explicit. Intermediate and advanced stages of secondary succession classified as forest
Visual interpretation at a more detailed scale of analysis makes of its classification results more reliable.
Limited geographical coverage. Underestimate net deforestation at its base line period due to the fact does not differentiate forest from different stages of regrowth.
FEMA – MT
Not explicit. Likely the same as CSR/IBAMA
Visual interpretation at a more detailed scale of analysis makes of its classification results more reliable.
Does not differentiate cover change by type of vegetation intervened as part of its annual estimates of deforestation.
IBGE Census data allows deriving cleared areas to different land uses.
Detailed identification of land uses within establishments from which it is likely to derive cleared areas from original vegetation.
Census may underestimate establishments, and deforestation taking place somewhere else.
Figure 9. Accumulated forest clearing in Rondônia to 1996 (in thousand ha): Comparing municipal data from IBGE and CSR/IBAMA
-
50
100
150
200
250
300
Alta
Floresta d'oeste
Ariquem
esC
abixiC
acoalC
erejeirasC
olorado doO
esteC
orumbiara
Costa M
arquesE
spigao d'oesteG
uajara-mirim
JaruJi-paranaM
achadinho d'oesteN
ovaB
rasilandia d'oesteO
uro Preto
doO
esteP
imenta B
uenoP
ortoV
elhoP
residenteM
ediciR
ioC
respoR
olimd
eM
ouraS
antaLuzia d'oeste
Vilhena
Sao M
iguel doG
uaporeN
ovaM
amore
Alvorada d'oeste
Alto
Alegre
dosP
arecisA
ltoP
araisoB
uritisN
ovoH
orizonted
oO
esteC
acaulandiaC
ampo N
ovo deR
ondoniaC
andeiasd
oJam
ariC
astanheirasC
hupinguaiaC
ujubimG
overnadorJorgeTeixeira
Jamari
Ministro A
ndreazzaM
irante da Serra
Monte N
egroN
ovaU
niaoP
arecisP
imenteiras
doO
esteP
rimavera de
Rondonia
Sao F
eliped'oeste
Sao Francisco do
Guapore
Seringueiras
Teixeiropolis
Theobrom
aU
rupa
Vale do
Anari
Vale do
Paraiso
Forest removal (IBGE) Removal other cover types or transition zones (IBGE) Deforested (IBAMA/CSR)
-
50
100
150
200
250
300
Alta
Floresta d'oeste
Ariquem
esC
abixiC
acoalC
erejeirasC
olorado doO
esteC
orumbiara
Costa M
arquesE
spigao d'oesteG
uajara-mirim
JaruJi-paranaM
achadinho d'oesteN
ovaB
rasilandia d'oesteO
uro Preto
doO
esteP
imenta B
uenoP
ortoV
elhoP
residenteM
ediciR
ioC
respoR
olimd
eM
ouraS
antaLuzia d'oeste
Vilhena
Sao M
iguel doG
uaporeN
ovaM
amore
Alvorada d'oeste
Alto
Alegre
dosP
arecisA
ltoP
araisoB
uritisN
ovoH
orizonted
oO
esteC
acaulandiaC
ampo N
ovo deR
ondoniaC
andeiasd
oJam
ariC
astanheirasC
hupinguaiaC
ujubimG
overnadorJorgeTeixeira
Jamari
Ministro A
ndreazzaM
irante da Serra
Monte N
egroN
ovaU
niaoP
arecisP
imenteiras
doO
esteP
rimavera de
Rondonia
Sao F
eliped'oeste
Sao Francisco do
Guapore
Seringueiras
Teixeiropolis
Theobrom
aU
rupa
Vale do
Anari
Vale do
Paraiso
Forest removal (IBGE) Removal other cover types or transition zones (IBGE) Deforested (IBAMA/CSR)
19
The data here discussed show that INPE overestimate deforestation and CSR/IBAMA
underestimate it, and IBGE estimates are lower than the ones obtained from remote sensing
analysis. The latter is in part a result of resolution analysis (though coarser resolution analysis can
both underestimate deforestation taking place in small plots, and overestimate deforestation in
areas with remaining small patches of forest), as well as an issue of definition of forest with has
decisive influence on the classification outcomes. The INPE definition of forest, by which any
forest can regenerate, leads to overestimate net deforestation. By comparing it with CSR/IBAMA,
it is possible to argue that INPE may be also overestimating gross deforestation, though it remains
uncertain. In turn, CSR/IBAMA underestimate the accumulated deforestation by considering
intermediate and advanced states of forest regrowth in its definition of forest. Hence, whether a
source under or overestimate deforestation is a relative issue linked to its definition of forest.
The IBGE’s methodology is very consistent, though it can often undercount agricultural
establishments (particularly due to the change of time period that lead to not include many
temporal establishments), and probably there is some deforestation outside of occupied areas (i.e.,
some abandoned areas) that is not captured by the agricultural census. A major limitation of the
agricultural census data is that it does not allows measuring deforestation directly, and auxiliary
methods have to be employed by making some assumptions affecting the final outcome.
The data discussed here shows that deforestation has not followed a linear trend. It has
tended to increase systematically since 1996. Three states concentrate most of fourth/fifth parts of
deforestation (Para, Rondônia and Mato Grosso), and nothing makes to think that this trend will
revert, but Mato Grosso. According to FEMA the latter show some slow down in the
deforestation dynamics in 2000/01 respect to 1998/99 in about 32%. INPE suggest that
deforestation in the same state decreased in 9% from 1999 to 2000. Deforestation in the other two
states, though with slight oscillations, tended to rise in the last two years.
The deforestation rate in a large part of municipalities has increased in a large part of
municipalities, particularly of those located at the core of the deforestation arch. Nevertheless,
lack of information about the amount of original forest remaining in such municipalities makes
difficult to address if such trends will continue to the same rate during the future.
5. What does the size of deforested plots suggest about agents’ contribution?
To date, there are no reliable data about the contribution to deforestation made by large
farmers and ranchers as opposed to smallholder farmers, though large ranchers play a significant
role (Cattaneo 2000, Faminow 1998, Walker et al. 2000). Fearnside (1993) suggests that 70% of
20
deforestation is attributable to large-scale ranching operations, but Homma and colleagues (1995)
mention that half of deforestation in the Amazon is due to small slash-and-burn farmers. Chomitz
and Thomas (2000) claim that large establishments (those larger than 2,000 ha) account for about
half of all land converted from forest or cerrado to agricultural use. Walker and associates
(2000), conclude from an evaluation undertaken in three areas that there is much regional
variation due to different settlement history, and development interventions.
Since 1995 INPE has provided data on forest clearing by size of the deforested plots.
Although not explicitly linked to parcel boundaries, in the way in which this information is
processed, those data provide referential information of the contribution to deforestation by
different agents. The main constraint of INPE data, however, is the fact that it is not possible to
distinguish the contribution to forest clearing from areas smaller than 6.5 ha. This data has to be
taken cautiously because, for instance, some portion of deforestation mainly those of state
sponsored settlements can take place on adjacent plots. Conversely, medium and large
landholders can clear different plots in different parts of their farms.
Table 6. Average forest clearing by size of the deforested plot (in %), 1996-1999
Hectares 1996 1997 1998 1999 INPE (in %) (comprises the whole BLA) Less than 15 17.18 10.09 10.85 14.77 15 – 50 23.30 23.11 24.20 25.10 50 – 100 12.81 14.07 14.89 14.37 100 – 200 11.39 13.91 12.72 12.36 200 – 500 13.87 15.06 14.28 14.03 500 – 1000 8.87 9.42 9.46 8.44 More than 1000 12.58 14.34 13.60 10.93 Total 100.00 100.00 100.00 100.00 CSR (in %) (179 municipalities) (a) Less than 15 - 7.50 10.48 9.84 § 1 – 3 - 0.47 1.12 1.01 § 4 – 5 - 0.91 1.62 1.40 § 6 – 10 - 3.03 4.19 3.87 § 11 – 15 - 3.09 3.55 3.57
15 – 50 - 14.85 15.99 15.60 50 – 100 - 14.34 11.94 12.48 100 – 200 - 16.22 15.02 13.73 200 – 500 - 18.96 18.35 18.71 500 – 1000 - 14.25 12.66 12.38 More than 1000 - 13.89 15.57 17.25 Total - 100.00 100.00 100.00 Source: Adapted by author based on INPE (2000), and IBAMA/CSR (http://www2.ibama.gov.br/) Notes: a) 18 municipalities of the original CSR dataset have missing data for size of deforested plot.
21
INPE’s figures show that about one sixth of the total deforestation take place in plots less
than 15 ha in size, and that a larger proportion falls between 50 and 500 ha. The IBAMA/CSR
numbers tell us a slight different history because they record the size of smaller deforested plots.
The latter source indicates that the contribution of plots from one to three ha represent around one
percent of the total forest clearing of the 179 municipalities with higher deforestation rates in the
BLA. In contrast, this same source suggests that the contribution of larger deforested plots (more
than 500 ha) would explain almost a half of the total deforestation (see Table 6).
The data here presented confirms the common notion in the debate about land-use/cover
change in the BLA that a major part of deforestation is driven by large-scale operations. In this
regard, what really matters is their relative contribution to forest removal. Though the data of size
of deforested plot is a proxy to identify the patterns of deforestation, they say little about the real
contribution of different agents to deforestation due to the reason earlier mentioned. More
research is needed linking deforestation analysis to land tenure in specific locations. Neither
remote sensing analysis nor census data interpretation census can resolve that issue by its own.
Therefore, the debate about the contribution of different agents to deforestation continues
to be relevant, and no definitive conclusions can be drawn based on the available data.
Table 7. Size of the annual deforested plots by state (in %), average 1997-1999 Hectares Rondonia Para Mato Grosso Others (a) Total
Less than 15 16.11 10.53 5.09 15.34 9.48 § 1 – 3 1.32 0.99 0.46 2.23 0.89 § 4 – 5 2.31 1.54 0.65 2.38 1.34 § 6 - 10 6.60 4.27 1.96 5.63 3.78 § 11 - 15 5.87 3.73 2.01 5.09 3.47 15 - 50 25.44 8.92 10.88 18.68 13.99 50 - 100 17.61 13.08 10.98 13.70 13.11 100 – 200 15.68 14.98 14.95 16.15 15.19 200 - 500 14.25 19.91 21.08 16.05 19.00 500 - 1000 6.63 11.70 17.82 8.71 13.29 More than 1000 4.28 20.86 19.20 11.37 15.94 Total 100.00 100.00 100.00 100.00 100.00 No. of municipalities 52 46 39 42 179 Source: Adapted by author based on IBAMA/CSR (www2.ibama.gov.br) Notes: a) Includes the states of Acre, Amazonas, Tocantins and Maranhão.
Many of the agents’ drive for deforestation is place-specific related, as result of factors
such as the settlement history, land prices, profitability, infrastructure, and the evolution of the
land tenure. Hence, the proportion of total deforestation of small-size plots is higher in states such
as Rondonia or Acre, in opposition to Mato Grosso, where has a much higher concentration of
22
land (see Table 7). Ratifying the above trends, Fearnside (2002:3) based on FEMA –MT dataset,
found that 2% of cleared areas were smaller than 6.5 ha from 2000 to 2001 in Mato Grosso.
Alves (2002b), in an analysis of forest clearing of different sizes between 1991-1997
found that cleared areas larger than 100 ha increased their relative contribution during such period
from 17.7% to 24.4%, while clearing smaller than 50 ha decreased their contribution. He argues
that the relative increase of the largest forest clearings is an effect of the successive connection of
cleared areas in regions where deforestation tends to be concentrated. This work also ratifies the
fact that the relative importance of different clearing sizes varies from region to region.
6. Conclusions
Different land-use studies with different methodologies come up with highly variable
deforestation estimates for the Brazilian Amazon. Definitive conclusions about the reliability of
these different datasets would have to come up from the comparison of the primary data used to
produce such datasets, and looking at individual cases. This paper merely attempts to compare the
data based on a global assessment of their methods and their results at the aggregated and the
municipal level. A full-fledged with an explanation and decomposition of quantitative disparities
is a more difficult task because spatial and temporal scales as well as forest definitions vary
across the different sources.
The advantage of the INPE data is its methodological consistency over a prolonged time
period. Due to the fact this methodology has been developed to produce estimates at the state
level, it becomes hard to derive adequate estimates at the municipal level, although this has partly
been attempted. It would be important to make additional efforts to disaggregate such information
to the municipal scale, and to disseminate the results to the LUCC research community. The
INPE estimates are overestimating net deforestation, and perhaps even gross deforestation, as
indicated by the estimates from CSR/IBAMA.
Though the CSR/IBAMA data could constitute an interesting dataset to assess more
recent deforestation trends, its geographical coverage is limited. It also may be underestimating
deforestation on some municipalities due to its forest definition used to measure accumulated
deforestation at its baseline period. Yet, the methodology employed to produce such estimates,
based on visual interpretation at a more detailed scale of analysis, makes its annual estimates
more reliable.
The FEMA dataset shares many of the attributes of the IBAMA/CSR dataset, and above
all its apparent methodological consistency. In order to allow better comparisons, it would be
23
interesting to know the proportion of the different cover types cleared to other uses within each
municipality. In other words, it would be important to separate forest from cerrado land-cover
changes in the FEMA dataset to be compared to another dataset for this state.
The available estimates coming from the IBGE’s agricultural censuses constitute a good
source of information of clearing for agricultural use, though it is not possible to differentiate
deforestation from other natural vegetation clearing without auxiliary assumptions. The IBGE
estimates are consistently lower than the ones provided by remote sensing analysis. The reasons
for that are diverse, mainly: the census covers only land-use within areas occupied by agricultural
establishments (which are likely to be underestimated, particularly in the 1995/96 census), and
deforestation taking place somewhere else is not considered, and abandoned areas are not part of
the calculations. The latter areas, however, are probably quite small.
The institutions performing land-use change analysis, and hence producing deforestation
statistics should be aware that as important as to deliver estimates of deforestation is to make
explicit for the users the methodologies that were employed to produce such estimates.
Otherwise, comparison of such datasets becomes a difficult task, and interpretations of such
results may distort the real dynamics of land-use change taking place in the real world.
24
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