Analyzing a forest conversion history da

299

Landscape Ecology 17: 299–314, 2002.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.

Analyzing a forest conversion history database to explore the spatial and
temporal characteristics of land cover change in Guatemala’s Maya
Biosphere Reserve
Daniel J. Hayes 1, Steven A. Sader 1,* and Norman B. Schwartz 2
1

Department of Forest Management, University of Maine, Orono, ME, USA; 2Department of Anthropology,
University of Delaware, Newark, DE, USA; *Author for correspondence (e-mail: Sader@umenfa.maine.edu;
phone: (207) 581-2845; fax: (207) 581-2875)
Received 17 July 2000; accepted in revised form 28 September 2001

Key words: Forest Clearing, Maya Biosphere Reserve, Remote Sensing, Socio-Economic and Biophysical Indicators, Swidden Agriculture
Abstract
We analyzed forest clearing and regrowth over a 23-year time period for 21 forest concession and management
units within the Maya Biosphere Reserve (MBR), Guatemala. The study area as a whole experienced a clearing
rate of 0.16%/year from 1974 through 1997. The overall clearing rate appears rather low when averaged over the

entire study area over 23 years because most of the reserve was inaccessible. However, despite the granting of
legal protection to the MBR in 1990, clearing rates continued to rise, with the highest rates occurring in the most
recent time period in the analysis, 1995 to 1997. Higher rates of clearing relative to regrowth occurred in newly
established communities and in the Reserve’s buffer zone, where the clearing of high forest was preferred for
pasture development. Exploratory models were built and analyzed to examine the effects of various landscape
variables on forest clearing. The different units in the analysis showed different relationships of forest clearing
with variables such as forest cover type and distance to access (roads and river corridors). Where available, socio-economic household survey data helped to explain patterns and trends observed in the time series Landsat
imagery. A strong relationship between forest clearing and distance to access was demonstrated. More clearing
occurred further from roads during later time periods as farmers moved deeper into the forest to find land to
clear. Communities inside the MBR that were less dependent on farming had forest clearing to regrowth ratios of
one:one or less. These communities used fallow fields in greater proportions than communities in the Reserve’s
buffer zone. General trends in clearing by forest cover type suggest a preference for clearing high forest (bosque
alto) types found on the higher elevation, better-drained soils, and fallow fields, and an avoidance of low-lying,
seasonally flooded terrain (bajos). Satellite remote sensing observations of forest clearing and regrowth patterns
can provide an objective source of information to complement socio-economic studies of the human driving
forces in land cover and land use change.
Introduction
Impacts on the global environment resulting from human activity are occurring at extraordinary magnitudes, rates, and spatial scales. The potential consequences of land cover and land use change are of
global concern, affecting climate and atmosphere, hydrology and nutrient cycles, biodiversity, productivity, sustainability, and the international economy

(Meyer and Turner 1994; Turner et al. 1994). Initiatives such as the Land-Use/Cover Change (LUCC)
project jointly created by the International Geosphere-Biosphere Programme (IGBP) and the Human
Dimensions of Global Environmental Change
(IHDP), as well as the U.S. National Aeronautics and
Space Administration’s (NASA) Land Cover and
Land Use Change (LCLUC) program, were born from
the global scientific community’s aim to better under-

300
stand the consequences of land cover and land use
change. These programs push a research agenda that
attempts to improve the modeling and projection of
indicators of environmental change (Turner et al.
1994). How human activities influence environmental change, the factors that drive these activities, and
the effects that environmental change has on both the
human and natural landscapes are the subjects central
to land cover and land use change research. The mission of these programs is to understand the linkage
among human activities, land use change, and environmental consequences, and thus provide a foundation for the sustainable use and management of global
resources (Rindfuss and Stern 1998).
Monitoring deforestation in the tropics

Crucial to achieving a better understanding of deforestation processes is improved knowledge of the magnitude and extent of the problem. Lambin (1994) defines deforestation monitoring as involving “the
collection, processing and interpretation of data to assess the nature, magnitude, and rates of changes in
forest cover.” Many aspects of land cover and land
use change occur at broad, landscape scales. Further,
processes of an ecological, social or economic nature
will have important spatial components (Turner and
Gardner 1991). Remote sensing technology provides
an excellent source of data for measuring and monitoring tropical forest change (Sader et al. 1990). Remotely sensed data are relatively inexpensive to collect, cover the globe, can be updated in a timely
manner, and are available at various spectral, temporal, and spatial resolutions. Methods to process these
data are being continually developed and are advancing our ability to assess the magnitude and rates of
tropical forest change. These data form an integral
part of current programs to assess forest resources and
monitor their changes, link the human dimensions of
forest change, and model the effects of change on the
global environment.
The spatial and temporal patterns as detected in
remote sensing imagery can reveal important impacts
on ecological and landscape processes as well as provide clues about land use and socio-economic system
indicators (Turner 1990; Lambin 1994; Rindfuss and
Stern 1998). For example, Fox et al. (1995) assessed

environmental change in northern Thailand on the basis of measured spatial patterns in multi-temporal
land cover classifications. Frohn et al. (1996) modeled deforestation trends in Brazil using contagion

and fractal dimension measures as landscape indicators. De Pietri (1995) developed spatial indices of
vegetation cover to indicate areas in Argentina currently and potentially at risk of environmental degradation from livestock grazing. Sader and Joyce (1988)
analyzed deforestation trends, from 1940 to 1983 in
Costa Rica, in relationship to ecological life zones,
soils, slope, and roads. They found distance to roads
to be the best explanatory variable for forest clearing
in 3 out of 4 time periods analyzed. Liu et al. (1993)
analyzed deforestation patterns in the Philippines and
found the distance to roads and the perimeter to area
ratio of forest patches to be good predictors of forest
clearing. Sader (1995) analyzed and compared the
size and shape of clearing and regrowth patches between two areas in northern Guatemala. Forest-clearing patch sizes were larger in areas where recent migration was occurring compared to those within a
longer-established community. Localized estimates of
deforestation in the Brazilian Amazon using Landsat
imagery in studies by Fearnside (1986) and Skole and
Tucker (1993) have demonstrated the spatial concentration of land cover change in certain political
boundaries and along significant migration (road) corridors.

Land Use and Environmental Change in
Guatemala’s Maya Biosphere Reserve
El Petén, Guatemala’s largest and northernmost department (Figure 1), covers 36,000 square kilometers
of mostly lowland tropical forests and wetlands. Together with adjacent forests in Mexico and Belize, the
area known as La Selva Maya constitutes the largest
contiguous tropical moist forest remaining in Central
America (Nations et al. 1998). Along with supporting
a rich diversity of plant and animal species, the Maya
Forest of the Petén also contains some of the world’s
most significant archeological sites as it provided the
biological foundation for one of the most developed
civilizations of the time, the Classic Maya period of
A.D. 250 to 900.
Today, the Petén supports a low but rapidly growing population relative to the more crowded and
heavily deforested sourthern highlands of Guatemala.
The traditional life of these people has included
mainly shifting cultivation agriculture and the harvest
of non-timber forest products (NTFPs), such as chicle, xate, and allspice. This “forest society” of the
Petén (Schwartz 1990), and the livelihood of its people, is inextricably linked to the fate of the forest – a

301

Figure 1. The location of the study area within the MBR along with the boundaries of the 21 discrete concession and management units and
their respective areas represented within the study area. The base image is Landsat TM band 5 (12 April 1997) and primary roads (as digitized from TM imagery) are shown as white lines.

forest that is being destroyed at an alarming rate (Sader et al. 2001). Forest is continually being cleared and

its resources greatly taxed as human migration and
the expansion of the agricultural frontier threatens the

302
people and environment of the northern Petén (Sader
et al. 1997). A thousand years ago, a postulated combination of factors including population growth, political instability and warfare, overuse of resources,
climate change, and environmental destruction may
have led to the collapse of the ancient Mayan civilization (Rice 1991; Culbert 1993). A similar combination of factors threatens the forest and its current inhabitants today, despite a much lower population and
a much shorter time frame (Sever 1998).
In the face of this population expansion and destruction of forests, the Maya Biosphere Reserve
(MBR) was established by congressional decree of
the Guatemalan government in 1990 to preserve the
remaining intact forest and the rich biological and

cultural resources that it holds. Spanning 1.6 million
ha, the MBR is a complex of designated management
units including five national parks, four biological reserves (biotopos), a multiple use zone (MUZ), and a
buffer zone. Within these management units, some
areas were further delineated into forest concessions
where management responsibilities (within the framework of the MBR) were delegated to the communities therein.
Background and objectives
Given the current concerns over forest clearing in the
MBR, a strong interest in obtaining landscape-level
forest change information for the region has developed among governmental agencies and the conservation community (Kristensen et al. 1997). In response, Sader and colleagues (e.g., Sader et al. (1994,
2001)) have monitored rates, trends, and patterns of
forest clearing in the MBR using Landsat Thematic
Mapper (TM) imagery. The objective of this research
is to advance the ongoing study of deforestation in the
MBR by bringing together an extensive archive of
time-series satellite data with spatial, biophysical, and
socio-economic determinants of land cover and land
use change. Specifically, we created a forest conversion history database, derived from satellite image
analysis, to monitor the rates and spatial extent of
forest change in several forest concessions within the

MBR, over a twenty-three year period. We used the
database to quantify rates of forest clearing and regrowth, as well as to analyze patch, landscape, and
proximity metrics. We compared the temporal and
spatial trends among several concession areas and
discussed in the context of socio-economic studies

conducted in some of the forest concession communities (Schwartz 1998).

Methods
Data acquisition and pre-processing
We acquired seven dates of Landsat imagery (MultiSpectral Scanner (MSS) and Thematic Mapper
(TM)), covering a span of 23 years, for the Worldwide Reference System (WRS) Path 20, Row 48
scene. This scene covers approximately 90% of the
land area of the MBR. The first two dates (1974, 79)
were represented by MSS data and the others (1986,
90, 93, 95, 97) were TM. The MSS data with 80m
pixel resolution were resampled to 30m to match the
spatial resolution of the optical bands of the TM data.
The seven images were subset to cover a large portion of the MBR containing all or parts of 21 discrete
management units and forest concession areas (Figure 1). This study area represents 32% of the area

within the MBR, located mostly in the MUZ and
buffer zone.
Each scene from each date was geographically referenced to a previously rectified 1995 Landsat TM
scene, in Universal Transverse Mercator Projection
Zone 16. Information in the visible red (RED, MSS
Band 2 and TM Band 3) and near infrared (NIR, MSS
Band 4 and TM Band 4) portion of the spectrum was
used for discrimination between forest and non-forest
areas of the imagery. These data were radiometrically
normalized using bright and dark target areas and
band-by-band linear regression techniques, to correct
for differences in the recording properties of the two
sensors and for seasonal and atmospheric differences
between dates (Eckhardt et al. 1990; Hall et al. 1991;
Jensen et al. 1995). Each date of imagery was masked
to eliminate clouds, water, non-forested wetlands, and
natural savanna through image classification procedures and Geographic Information System (GIS) editing. The analysis was based on the calculation of the
normalized difference vegetation index (NDVI) for
each date, according to the following formula:
NDVI ⫽

共NIR ⫺ RED兲
共NIR ⫹ RED兲

(1)

The NDVI has been found to be highly correlated
with crown closure, leaf area index, and other veg-

303
etation parameters (Tucker 1979; Sellers 1985; Singh
1986; Running et al. 1986).

tected for the first two time periods. Binary images
representing regrowth were created for 1986–90,
1990–93, 1993–95, and 1995–97.

Time-series change detection
An accurate and efficient change detection methodology, based on time-series classification of the NDVI,
was developed and tested (Hayes and Sader 2001 (in

press)). All image processing was performed with
ERDAS Imagine, v.8.3, software (ERDAS 1997). The
RGB-NDVI method (Sader et al. 2001) was used,
three dates at a time, on three data sets (1974, 79, 86;
1986, 90, 93; and 1993, 95, 97). By simultaneously
projecting each date of NDVI through the red, green,
and blue (RGB) computer display write functions,
major changes in NDVI (and hence forest cover) between dates will appear in combinations of the primary (RGB) or complimentary (yellow, magenta,
cyan) colors. Knowing which date of NDVI is coupled with each display color, the analyst can visually
interpret the magnitude and direction of forest cover
changes in the study area over the three dates. Automated classification was performed on three or more
dates of NDVI by unsupervised cluster analysis
(Hayes and Sader 2001 (in press); Sader et al. 2001),
using the ERDAS “isodata” routine, a minimum distance to the mean classifier (ERDAS 1997). Change
and no change categories are labeled and dated by interpreter analysis of the cluster statistical data and
guided by visual interpretation of RGB-NDVI color
composites. Clusters were segmented between change
and no change and then reclustered to reduce confusion and to better distinguish between clearing and
regrowth areas (Hayes and Sader 2001 (in press)).
Jensen (1996) referred to this technique as “cluster
busting”.
For each time period, each pixel was classified as
cleared, regrown, or no change. For each resulting
change detection image, on-screen digitizing and GIS
editing techniques were used to eliminate spurious
change due to natural fluctuations in vegetation and
soil moisture, changes in water level, clouds and
cloud shadows, and forest fires. Binary images representing forest clearing were created for each time period (i.e. clearing 1974–79, 1979–86, 1986–90, 1990–
93, 1993–95, and 1995–97). Binary images were also
created which represented areas of regrowth at each
time period. As a result of the low amount of clearing
activity from 1974–79 and 1979–86, along with the
limited temporal, spectral and spatial resolution of the
MSS data, no significant regrowth areas were de-

Development of forest clearing and regrowth
patches
Contiguous patches of pixels representing forest
clearing and regrowth at each time period were identified by employing the ERDAS “clump” command
to each binary image. In a raster image, this process
identifies and groups contiguous pixels of the same
class, based on all 8 neighboring pixels (ERDAS
1997). Clumped images were exported from Imagine
raster format to ARC (ESRI 1998) vector format. Resulting coverages were merged by time period so that
one coverage containing both clearing and regrowth
patches was created for all time periods. A vector
coverage containing polygons representing forest
concession areas of the MBR was obtained from Conservation International/ProPetén and added to the database so that each patch had a unit identifier. The
area of each patch was calculated and added as an
item in the database. Patches less than 1 ha in size
were merged with the adjacent polygon that shared
the longest border to avoid sliver polygons resulting
from spurious change and mis-registration.
Development of land cover history and distance
from access database
To explore the effects of forest type on land cover
change, the 1986 TM imagery was classified into forest and non-forest classes. An unsupervised approach
using the ERDAS “isodata” routine generated 50
spectrally distinct clusters from a 5-band data set. Input bands included TM bands 3 (visible red), 4 (near
infrared), and 5 (mid infrared), along with the NDVI
and the NDVI variance generated from a 3×3 pixel
moving window. Previous experience with this
dataset has shown that 50 clusters is more than sufficient to separate forest types at the general level desired here. Clusters were named by the analyst according to a simple classification scheme intended to
identify three levels of forest: 1) high forest (bosque
alto) occurring on hills and better-drained soils, 2)
transitional forest (bosque medio) found on mid
slopes and low-lying areas, and 3) seasonally inundated, shrub/scrub forest (bosque bajo). The original
50 clusters were grouped and consolidated according
to these three forest types, along with a non-forest

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Table 1. The attributes and levels of the time-series database.
Items

Levels

ID
74–79
79–86
86–90
90–93
93–95
95–97
86 over
97 use
unit
area
access

5,265 records
no change
cleared
no change
cleared
no change
cleared
regrow
no change
cleared
regrow
no change
cleared
regrow
no change
cleared
regrow
majority cover: alto, medio, bajo, wetland / savanna, non-forest
forest, guamil1, guamil2, non-forest1, non-forest2
21 concessions and management units, including the 4 communities of the socio-economic study
patch size, in hectares
minimum distance to roads/rivers, (km)

class (including both low biomass natural non-forest
as well as agricultural areas). A previously developed
mask was applied to separate wetland and savanna
from agriculture and other cleared forest. An attribute
was added to the existing land cover history database
that contained the majority 1986 land cover for each
patch.
Subsequently, the land cover of each patch at each
date was identified by updating the 1986 land cover
classification using the conversion history from the
time-series database. If a patch had not experienced
change through the following time periods, it was labeled as the cover type classified in 1986. Patches that
had regrown and were not cleared thereafter were
classified as second growth (guamil). In the same
way, cleared areas that had not been regrown were
labeled as non-forest. Age classes for regrowth or
clearing were determined from the patch change history in the database.
Roads and river corridors were digitized on-screen
from 1986 and 1997 TM color composite imagery
and saved in raster format. Based on analysis by local field experts, the 30m pixel resolution of TM imagery was sufficient for locating major roads and rivers. It should be noted that many smaller foot and
mule paths do exist and provide farmers access to
their fields, but were not digitized. However, the major roads and rivers that were detected are more important for direct access to forest for clearing as well
as for the transportation of goods to local markets. No
significant changes in the major road coverage of the
study area occurred between dates spanning the analysis, therefore only one road file was used. The distance to “access” (roads and rivers) was calculated in

kilometers and added to the database as an attribute.
The distance of each patch to access item calculates
the minimum distance of each patch to roads or rivers, whichever was closest. The final database structure, including all attributes and the levels or categories of each attribute, is shown in Table 1.
Database analysis
The information contained in the database allows for
the use of illustrative, descriptive, and statistical analyses for exploring the spatial and temporal characteristics of land cover and land use change. The analysis
included the investigation of the general trends in forest clearing and regrowth over time and between forest concession units. Subsequent analysis involved relating these statistics to measurable factors that are
hypothesized to affect land cover and land use change
patterns, such as distance to access, forest type, and
forest type availability.
The total area of forest cleared and regrown in
each time period was calculated for each concession
unit directly from the database. Annual rates of clearing (as a percent of remaining forest, per year) were
calculated by the following formula:
C t/F t
Yt

⫻ 100,

(2)

where C t is the area cleared during time period t; F t
is the area of forest remaining at the beginning of
time period t, and Y t is the number of years in time
period t. Concessions that showed little or no clear-

305
ing during the time period of this study were omitted
from further analysis.
For each time period and each concession unit, the
amount (area) of forest clearing was compared with
the amount of regrowth to report clearing:regrowth
ratios. Also for each time period and each concession
unit, the database was examined according to the
amount of clearing within each forest cover type
(alto, medio, bajo, and guamil) and the distance of the
cleared patch from road or river access. Forest clearing by cover type was also analyzed with respect to
the total area of each cover type available in each
concession unit during each time period.
The distance from access relationship to forest
clearing was analyzed using a distance from access
“zone” file. This file was created from a raster file
depicting roads and rivers, using the ERDAS “search”
routine (ERDAS 1997) that consolidated pixels in the
image into 1 pixel (0.03km) intervals (“zones”) from
access points. The number of observations was constrained to 254 pixels (7.62km) maximum from access, to keep within the limits of an 8-bit image of
30m pixel data (0–255, with 0 being adjacent to roads
or rivers and 255 representing those areas greater than
7.62km from access). Furthermore, it was found that
less than 0.5% of the clearing in the study area occurred greater than 7km from access points. Distance
from access (the “zone” file) was compared against
each date of clearing (the “class” file) using the ERDAS raster “summary” routine (ERDAS 1997). The
procedure produces cross-tabulation statistics comparing class values between two files, including the
area (ha) in common.
Total area cleared, all time periods combined, was
plotted against distance from access, in kilometers.
The relationship was tested by simple bivariate regression analysis using the correlation coefficients
and R-squared calculations between the independent
variable (distance from access) and the dependent
variable (forest clearing). Area cleared as a function
of the interaction between time and distance from access was also examined using regression analysis. As
with the regression analysis of total area cleared (all
time periods combined), area cleared for each time
period was summarized within 1 pixel intervals from
access, with 254 observations. The area cleared was
plotted against distance from access, using a separate
curve for each time period for comparison.
Having demonstrated the effect of distance to access on forest clearing, distance to access was added
to the analysis of clearing by cover type according to

cover type “availability”. To explore this relationship,
the percent of available area cleared in each cover
type was compared to the percent of total available
area represented by each cover type, at each 1 pixel
(30m) distance from access “zone”. The hypothesis
was that, under a circumstance of no preference or
avoidance among the forest cover types for establishing farm plots, that clearing within each type would
be proportional to the amount of that cover type that
was available. Thus, we expected the points to fall
along a line representing a 1:1 relationship of percent
cleared to percent available. For each cover type, the
distance (in percent) of each point from the expected
line was calculated, and the mean of these residuals
was calculated for each type. The set of residuals for
each cover type was tested for significance using a
one-sample t-test with a hypothesized mean of zero.

Results and discussion
The pre-processing techniques and multi-temporal
change classification methodology described above
resulted in an overall classification accuracy of 85%
(Hayes and Sader 2001 (in press)). The accuracy was
determined by error matrix analysis (Congalton and
Green 1999) using a random set of reference points
interpreted visually on the multiple dates of TM color
composite imagery (Cohen et al. 1998).
Forest clearing rates
Spanning all time periods in the study, the portion of
the study area contained in the buffer zone experienced the greatest forest loss, with 65.2% of the total
area cleared, and it had the highest rate of forest clearing in each time period (Table 2). Much of the forest
cleared outside the buffer zone was adjacent to the
road corridor connecting the towns of Carmelita and
Cruce Dos Aguadas. Several remote and inaccessible
concessions experienced little or no forest clearing
over the six time periods. The totals for each time period (over all units) show that little clearing, relative
to that of later time periods, occurred during the five
years between 1974 and 1979. Marked increases in
clearing took place over the next three time periods.
Total area cleared increased between 1995 and 1997
to the highest rate for any time period. Most individual concenssions followed this same general trend in
forest clearing over the six time periods. Notable exceptions include Carmelita (Figure 1, Table 2) which

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Table 2. Annual forest clearing rates (% of forest remaining cleared per year) over time for the concession units in the study area.
UNIT

Bioitza
Biotopo El Zotz
Buen Samaritano
Buffer Zone
Carmelita
CB La Danta
Centro Campesino
Cruce a la Colorada
Cruce Dos Aguadas
La Colorada
La Gloria
La Pasadita
Paso Caballos
Paxban
PN El Mirador
PN Laguna del Tigre
San Andres
San Miguel Palotada
ZUE Laguna del Tigre
ZUM Central
ZUM Centro Oueste
TOTAL

YEARS

TOTALS

74–79

79–86

86–90

90–93

93–95

95–97

–
0.00
–
0.02
0.03
–
–
0.01
0.02
0.03
–
0.04
–
–
–
–
–
–
–
–
–
0.01

0.01
0.01
–
0.15
0.02
–
0.23
0.12
0.12
0.08
–
0.12
–
–
–
–
0.00
0.06
–
–
–
0.05

0.18
0.09
–
0.91
0.00
–
1.33
0.09
0.34
0.09
–
0.34
0.01
–
–
0.00
0.00
0.47
–
–
0.00
0.21

0.02
0.09
1.19
1.54
0.01
–
1.70
0.15
0.76
0.20
–
0.52
0.05
–
–
0.00
0.01
0.45
–
–
0.00
0.35

0.01
0.07
1.82
0.73
0.00
–
0.19
0.07
0.47
0.11
–
0.17
0.35
–
–
0.01
0.01
0.21
–
–
–
0.17

0.03
0.18
1.64
2.48
0.00
–
0.78
0.31
1.39
0.15
–
0.67
2.50
–
–
0.05
0.01
0.44
–
–
0.01
0.55

experienced its greatest amounts of forest clearing
during 1974–79 and 1979–86 and then showed a decrease in later time periods.
Forest clearing rates are reported by percent of
forest cleared per year for each concession in each
time period (Table 2). These rates provide a normalized perspective of forest clearing, as the time periods vary in length and the concessions encompass
very different extents of forest area. For example, a
similar area was cleared in Carmelita during 1974–79
(65.0 ha) as Buen Samaritano during 1995–97 (74.1
ha). The community of Carmelita covers a considerably larger area of forest than Buen Samaritano, and
the first time period spans five years as opposed to
two years in 1995–97. The annual percent of forest
cleared, then, is minimal for Carmelita during
1974–79 (0.03%/year) while significantly high for
Buen Samaritano during 1995–97 (1.64%/year). The
greatest annual clearing rate over the entire study area
occurred during the 1995–97 time period (0.55%/
year). The highest clearing rate reported for any concession and any time period was 2.50%/year in Paso
Caballos during 1995–97 and, during the same time

0.04
0.05
0.48
0.75
0.01
–
0.66
0.11
0.37
0.09
–
0.25
0.27
–
0.00
0.01
0.00
0.20
–
–
0.01
0.16

period, 2.48% of the forest in the buffer zone was
cleared annually. Over the entire 23-year span of the
study, the buffer zone had the highest annual clearing
rate (0.75%/year), followed by Centro Campesino
(0.66%/year). It should be noted that the community
of Centro Campesino exists within the buffer zone
(Figure 1), but is treated as a separate unit in this
analysis. In comparison, the study area as a whole
experienced a clearing rate of 0.16%/year from 1974
through 1997. The overall clearing rate appears rather
low when averaged over the entire study area because
most of the reserve was inaccessible.
Forest clearing to regrowth ratios
The relationship of forest cleared to regrowth for each
time period is given in Table 3. The study area as a
whole exhibited a ratio (including total area cleared
and total regrown over all time periods since 1986)
greater than one (2.05 overall, or approximately 2 ha
cleared for every 1 ha regrown). The highest total ratios were found for Paso Caballos (11.9) and Buen
Samaritano (5.8), two of the most recently established

307
Table 3. Area of forest cleared to area regrown ratios over time,
by concession unit.
UNIT

Biotopo El Zotz
Buen Samaritano
Buffer Zone
Carmelita
Centro Campesino
Cruce a la Colorada
Cruce Dos Aguadas
La Colorada
La Pasadita
Paso Caballos
PN Laguna del Tigre
San Miguel Palotada
TOTAL

YEARS

TOTAL

86–90

90–93

93–95

95–97

0.96
0.00
5.24
0.15
7.35
0.49
1.17
0.83
1.86
–
–
14.68
2.82

0.60
–
1.83
0.37
1.56
0.76
1.52
1.10
1.15
–
1.09
0.77
1.57

1.24
26.20
0.83
0.79
0.22
1.02
0.75
2.69
0.52
–
–
0.62
0.88

2.28
2.14
4.05
–
1.30
7.17
6.36
1.94
3.90
10.08
3.65
3.28
4.10

1.02
5.80
2.38
0.28
1.82
1.03
1.68
1.21
1.49
11.87
4.16
1.60
2.05

– = no regrowth

communities. The concession units showing ratios
closest to one, since 1986, are Cruce a la Colorada
(1.03) and Biotopo El Zotz (1.02), the latter a core
area of the reserve. Carmelita, a community in which
the population does not rely heavily on farming (Table 4), had a greater area regrown than area of forest
cleared since 1986 (total ratio of 0.3). The buffer zone
portion of the study area, where agriculture and
ranching provide the major sources of income
(Schwartz 1990), shows a total ratio of almost two
and a half hectares of forest cleared for every hectare
regrown (2.4), including a ratio of 4.1 in 1995–97.
Over all concessions, there was a greater area regrown than cleared only in 1993–95 (0.9). All other
time periods had greater amounts of clearing than regrowth over the entire study area, the largest ratio occurring in 1995–97 (4.1).
Land management and adaptive strategies, time
since establishment of communities, and changes in
population immigration and emigration are likely to
affect the amount of forest cleared relative to the
amount regrown during a period of time (Schwartz
1998). The more recently established communities
have had much higher rates of clearing than regrowth
corresponding to the immigration of families and the
subsequent establishment of farm plots (e.g. Paso Caballos in 1995–97 and Buen Samaritano in 1993–95).
During the first few years of establishment, little regrowth is expected as fields are within the first crop
cycle. Depending on the crop to fallow ratios used, it

will be two or more years before significant regrowth
can be observed. Inhabitants of Carmelita, a community in which the amount of regrowth since 1986 has
exceeded the amount of clearing over the same time
(total ratio of 0.3), are known to rely more on NTFP
harvesting than agriculture (Table 4). Carmelita, furthermore, is the oldest established community in the
study area and has a declining population (Schwartz
pers. comm.). Biotopo El Zotz, on the other hand, is
a designated core area of the Reserve, and the clearing of forest is illegal. The clearing that has occurred
in this area appears to be a result of farmers moving
over the border from Cruce Dos Aguadas to establish
farm plots. Communities in the buffer zone, including Centro Campesino (Centro Campesino plus the
rest of the buffer zone equal a total clearing to regrowth ratio of 2.1 from 1986 to 1997), are also long
established. However, in contrast to Carmelita, these
communities generally have higher populations,
NTFP harvesting is not a major source of income
(Table 4), and the establishment of pasture for raising
cattle is more prevalent than elsewhere in the study
area (Schwartz 1998).
Forest clearing by cover type
An interesting question to explore is whether or not
there is a preference, or avoidance, of certain cover
types for establishing a farm plot. Further, by analyzing the area of forest clearing by cover type, the degree to which fallow fields are used for establishing
farm plots can be examined. For example, since 1986,
farmers and ranchers in Centro Campesino established 69.5% of their plots by clearing high forest
(bosque alto). No low forest (bosque bajo) was
cleared and the re-clearing of fallow fields (guamil)
accounted for 11.2% of the total area cleared since
1986. By contrast, the majority of clearing in Buen
Samaritano (66.7%) occurred in transition forest
(bosque medio), and only 0.6% of the area cleared
was in guamil. Biotopo El Zotz (13.5%), Cruce Dos
Aguadas (6.1%), and Carmelita (5.9%) had the highest proportion, among all concessions, cleared from
bajo forest. It should be noted, however, that the
cleared hectares representing Carmelita are much
lower than the other concession units. The higher proportions of bajo clearing in Cruce Dos Aguadas may
be partially explained by the occurrence of some
Q’eqchi families planting a third farm plot (payapak)
in low-lying areas (i.e. bajos) during the dry season.
These are planted in addition to the dry and wet sea-

308
Table 4. Socio-economic survey respondent information regarding land use (from Schwartz (1998)).
Most important source of income (%)
Farm
NTFPs 1
Buen Samaritano
Carmelita
Centro Campesino
Cruce Dos Aguadas
1

100.0
15.4
100.0
85.3

Other

73.1

11.5

10.8

1.0

Median area
cropped (ha)

Average milpa
size (ha)

–
2.8
4.9
5.6

5.0
2.7
3.5
4.8

non-timber forest products.

son plots that are normally planted (Schwartz 1998).
As previously mentioned, Biotopo El Zotz is adjacent
to Cruce Dos Aguadas and may have experienced
clearing by farmers from the concession of Cruce Dos
Aguadas crossing the border into the EL Zotz reserve
area. Furthermore, a large bajo runs through these
two units. The highest proportion of clearing from
guamil occurred in Carmelita (43.9%). Of all forest
cleared since 1986 in the entire study area, the majority was from alto forest (55.4%), the least from
bajo (3.0%), and 11.5% was from guamil.
Statistics illustrating the percent of clearing by
cover type can be misleading because those types
cleared for establishing farm plots might be constrained by the area available in each type. If clearing
by cover type were proportional to what is available
in each concession unit, it would be expected that the
percent of total cleared area in each cover type should
equal the proportional area represented by each cover
type (other factors being equal). Over the entire study
area, 55.4% of clearing from 1986–1997 occurred in
alto forest, compared to 41.5% available in 1986.
3.0% of bajo forest was cleared, compared to 16.6%
available. A substantially greater proportion of
cleared alto forest than the available proportion occurred in the buffer zone (59.9% to 33.1%) and Centro Campesino (69.5% to 38.7%). Results of the
household survey by Schwartz (1998) indicate a preference among respondents in Centro Campesino for
using areas of high forest to create pasture for grazing cattle. This may also explain the low proportion
of clearing in the bajo and guamil types found in the
buffer zone and Centro Campesino. Most concessions
showed a greater amount of clearing in guamil compared to the proportional availability of that type, notably Carmelita (43.8% of clearing area to 2.0% available) and San Miguel Palotada (32.6% to 9.9%
available). The one exception to this trend is the
buffer zone (9.5% of clearing area to 14.5% available). Schwartz (1990, 1998) suggests the lack of land
tenure and political instability as major disincentives

for sustainable forestry, agriculture and soil conservation practices, especially in the buffer zone.
Distance from access to forest clearing
In examining the use of the different forest types for
establishing farm plots, the relationship of clearing to
cover type proportion is helpful in illustrating trends.
However, the relative amount of the various forest
types in each unit does not completely explain availability. For example, forest closer to access corridors
(roads and rivers) is considered to be “more available” than forest further from roads. The proximity
relationship of clearing to points of access is an important factor in the analysis of forest type clearing.
First, it is necessary to test the general pattern of forest clearing in proximity to access.
Total area cleared, all time periods combined, was
plotted against distance from access, in kilometers. A
curvilinear relationship is apparent in the data (Figure 2), with area cleared decreasing exponentially as
distance increases. Regression analysis indicates that
distance from access is significantly and negatively
associated with area cleared (coefficient = −29.98),
with an R-square of 0.734 (Figure 2a). Distance
squared was also significant to area cleared, with a
negative coefficient and an R-square of 0.539. The
strongest relationship was obtained by using the log
transform of area cleared against distance from access
(km), resulting in a coefficient of −0.53 and an Rsquare of 0.951 (Figure 2b).
Relationship of clearing and access among forest
concessions
This relationship of the cumulative percent of area
cleared to distance from access (roads and rivers) was
also explored by individual forest concession unit
(Table 5). Of all clearings in the study area since
1986, 96.1% were within 6 kilometers from access,
with over 75% within 3 kilometers. Of all units, Buen

309

Figure 2. The relationship of area cleared logarithm to distance from access within 1 pixel (0.03 km) intervals from roads and rivers. The
bottom plot (b) depicts the linear regression line fit to log-transformed area. The regression statistics for three models are given in (c).

Samaritano had the greatest percentage of clearing
concentrated within 1 km of access (61.8%) and all
forest cleared was within 3 km. High percentages of
clearing within 1 km of access were also found in
Centro Campesino (53.6%) and the portion of the
buffer zone included in the study area (42.8%). Cruce
Dos Aguadas had the lowest concentration of cleared
area within 1 km from access (23.3%). The lower
concentration of clearing closer to access in Cruce
Dos Aguadas may be due in part to findings of the
socio-economic study. For example, Schwartz (1998)
reported that survey respondents from Q’eqchi households in Cruce Dos Aguadas tend to go further from
roads to establish their farm plots than respondents
from the Ladino households. Cruce Dos Aguadas had
a higher Q’eqchi population than the other communities included in the socio-economic survey. However,
similar data are not available for the other communities for comparison with Cruce Dos Aguadas.

The regression analysis demonstrates the strong relationship between clearing and distance from access
(Figure 2). Among the concession units, differences
in the amount of clearing within the 1, 2, and 3 km
intervals from access (Table 5) may be, in part, due
to the mode of transportation and type of agriculture
used at varying distances from roads. It is expected
that permanent agriculture and pasture would be located closer to roads for efficiency (Schwartz 1998).
On the other hand, the major form of transportation
to farm plots is walking (Schwartz (1990, 1998)) and
farm plots will be established deeper into the forest
as land for cropping and shifting cultivation becomes
scarce closer to roads. Communities in the buffer
zone, including Centro Campesino, tend to make
more pasture than other communities (Schwartz
1998), which would account for the high percentage
of clearing within 1km of access in these two units.
Under this assumption, the smaller concentration of
clearings within 1 km of access in Cruce Dos Agua-

310
Table 5. Cumulative percent of total forest cleared, by 1km intervals from access (roads and rivers) and concession unit.
UNIT

Biotopo El Zotz
Buen Samaritano
Buffer Zone
Carmelita
Centro Campesino
Cruce a la Colorada
Cruce Dos Aguadas
La Colorada
La Pasadita
Paso Caballos
PN Laguna del Tigre
San Miguel Palotada
TOTALS

Distance (km)