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Forest Ecology and Management 210 (2005) 9–23
A national forest resources assessment for Costa Rica
based on low intensity sampling
Christoph Kleinn a,*, Carla Ramırez b,1, Peter Holmgren c,Sonia Lobo Valverde d, Guido Chavez e
a Chair of Forest Assessment and Remote Sensing, Universitat Gottingen, Busgenweg 5, D-37077 Gottingen, Germanyb Consultant to the Pilot Inventory in Costa Rica, Biostatistics Section, CATIE, Turrialba 7170, Costa Rica
c Forest Resources Development Service, Forestry Department, Food and Agriculture Organization of the United Nations, FAO, Rome, Italyd SINAC—Ministry of Environment and Energy, Section of Forest Fires and Payment for Environmental Services, SanJose, Costa Rica
e Assistant to the Vice Minister of Environment and Energy, SanJose, Costa Rica
Received 7 May 2003; received in revised form 18 January 2005; accepted 7 February 2005
Abstract
A goal of a National Forest Inventory (NFI) is the provision of information which is relevant and required for national level
decision making and monitoring in forestry, but also for related sectors.
This paper presents and discusses a pilot study from Costa Rica where in 2000/2001 a low intensity sampling approach was
used to generate national level forestry information. On a 15 km � 15 km grid air photo plots were interpreted for forest and
land cover type. Readily available 1997 aerial photographs were used that were, however, only available for about 70% of the
country: of the 228 grid points for the whole country only 159 could be aerial photo interpreted. Out of the 15 km � 15 km
base grid of sample points, a 2 � 3 subset was selected for field assessment, resulting in a sample of 40 cluster plots,
each comprising of four elongated rectangular sub-plots of 150 m � 20 m located on the perimeter of a square of 500 m side
length.
Two novel components were integrated into the inventory: (1) the field plots were established on all lands, so that the tree
resource was not only tallied inside forests but also on all other tree-bearing lands outside forests. (2) In addition to the
biophysical information gathered on the traditional field plots, interviews were carried out with forest owners on the site of the
field plots, in order to obtain data on the use of the forest resource.
Field work was carried out by 6 field crews and took altogether about 3 months. Results were generated from the field samples
for the entire country. Aerial photo based area estimates were compared to the corresponding estimations from field sampling for
the same area. According to the field sampling the forest cover for Costa Rica in 2001 is estimated to be 48.4% (simple standard
error percent 9.3%). An estimated 8.2% of the total volume (dbh > 30 cm, all species) is outside forest.
* Corresponding author. Tel.: +49 551 39 3473; fax: +49 551 39 9787.
E-mail addresses: ckleinn@gwdg.de (C. Kleinn), cramzea@intelnet.net.gt (C. Ramırez), peter.holmgren@fao.org (P. Holmgren),
sonilobo@minae.go.cr (S.L. Valverde), guidocha@minae.go.cr (G. Chavez).1 Present address: National Coordinator of the National Forest Inventory in Guatemala, FAO-Guatemala, 12 calle 1-67 zona 14, ciudad de
Guatemala, Guatemala. Tel.: +502 363 55 60; fax: +502 263 55 50.
0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2005.02.023
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2310
This inventory took place with support from Food and Agriculture Organization (FAO) in the framework of FAO–Forest
Resources Assessment’s (FRA) Program Support to National Forest Assessments; it was carried out jointly by Sistema Nacional
de Areas de Conservacion (SINAC), the Costa Rican authority responsible for forestry issues, and Centro Agronomico de
Investigacion y Ensenanza (CATIE), an international agricultural research center. Experiences of the study were subsequently
used to implement similar inventories in three more countries (Guatemala, Cameroon, The Philippines).
# 2005 Elsevier B.V. All rights reserved.
Keywords: National forest inventory; Trees outside the forest; Forest cover estimation
1. Introduction
The principal goal of a National Forest Inventory
(NFI) is the provision of information which is
relevant and required for national level decision
making and monitoring in forestry, and also in related
sectors (Cunia, 1978). These large area exercises are
sometimes referred to as forest inventories at the
strategic scale (Schreuder, 2001), contrasting them to
smaller area forest inventories on a tactical scale such
as forest management inventories or operational
inventories. NFIs also provide data for sub-national
geographical or political units and are an input to
global forest assessments and other international
processes in the context of sustainable management
of the natural resources, such that there is a
considerable and generic international interest in
National Forest Inventories and the information they
generate.
Some countries have no or no reasonably up-to-
date NFI. Persson and Janz (1997) find that in many
NFIs the overall reliability is low in what refers to
results and approaches chosen. An interesting question
is what the reasons may be that various countries do
not yet have a comprehensive NFI in place although
among forest and natural resource managers the need
for up-to-date and high quality forest resource
information on the national level is usually clearly
recognized. Major reasons for not having NFIs
implemented are probably of budgetary nature and
insufficient political prioritization.
Field work is one of the major cost items of NFIs –
and a crucial element at the same time. Most of the
forestry-relevant variables can only be assessed with
reasonable accuracy in the field. Reducing the
intensity of field sampling while maintaining statis-
tical soundness is a step to make NFIs ‘‘affordable’’. If
the major goal is to generate and update forest
information on the national level, NFIs may be carried
out with a low intensity sampling strategy as presented
and discussed by Thuresson (2002). That approach
makes information available in a relatively short time
and at relatively low cost so that an NFI is not
prohibitive from a budget point of view.
The Forest Resources Assessment Program (FRA)
of the United Nation’s Food and Agriculture
Organization (FAO) has proposed to investigate
the virtues of this approach in the context of
their initiative to assist countries to gather reliable
national forest information (Saket, 2002). A corre-
sponding pilot study was carried out in 2000/2001 in
Costa Rica where details of the approach, its
implementation and design were developed and
tested. This paper describes the approach and
presents some experiences, results and conclusions
of this pilot study. The complete report of the results
is in FAO (2001b).
2. Some forest information for Costa Rica
Costa Rica has an area of about 51,000 km2 and a
very diverse topography and vegetation, and was once
almost 100% forested (Keogh, 1984). The period of
most severe deforestation was between 1950 and
1980, and principal causes were reported to be the
demand for land rather than for wood (Hartshorn et al.,
1982). Costa Rica was at that time among the
countries with the highest deforestation rate world-
wide: Leonard (1986) reported a deforestation rate of
3.9% per year for 1950–1984. Today, Costa Rica has
an efficient conservation system in place, and about
25% of the national territory are protected areas, most
of it covered by forest.
Much has been published on the forest area of
Costa Rica. FAO (2000a) compiled an annotated
bibliography on forest cover change in the country.
Kleinn et al. (2002) compared published forest cover
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–23 11
figures from the past 60 years (Fig. 1): while a clear
trend is visible, the high variation around the trend line
is striking. Those forest cover figures are published
and cited figures, yet only few figures originate from
original studies, and many are citations and modifica-
tions of earlier studies. Some publications did not
reveal clearly where the figures came from. In most
cases the underlying definition of ‘‘forest’’ was not
given nor were the forest types considered specified.
Statistically based forest inventory data for the entire
national territory were published for the reference year
1967 under an FAO project (Sylvander, 1981). For the
1980s very low forest cover figures were reported
(e.g Sader and Joyce, 1988), down to less than 20%.
In later studies, forest cover was determined with
considerably higher values.
The more recent studies are satellite imagery based
forest mapping studies. A recent one was carried out
with Landsat 7 TM imagery for the year 2000 by
EOSL, CCT and FONAFIFO (2002). There, a forest
cover of 46.3% was found, which includes plantations
and mangrove forests, and a forest definition with a
minimum crown cover of 80% was used.
In the 10-year forest development plan for Costa
Rica, enacted in March 2001, the relevance of up-to-
date information on the state of the forests is
recognized and the development of an information
system recommended (MINAE et al., 2001). The
Fig. 1. Forest cover figures for Costa Rica as published in 54 sources (mod
Academic Publishers). The dotted straight line comes from figures publishe
until about to the mid-1980s. After that an upward trend is visible, though w
published figures and are not necessarily original figures or figures based
Forest Resources Assessment Program of FAO offered
to assist implementing a pilot study for a national level
forest inventory.
3. Methods: the inventory design
3.1. General characteristics—population and
sampling frame
The population of interest for the biophysical
inventory was all tree cover in Costa Rica. The
topographic map of Costa Rica was used as a
preliminary area sampling frame, updated by more
recent aerial photography for most areas (see below).
The islands were not included as they represent a very
small percentage of the national territory.
The population was deliberately chosen to be not
only forests, but also including the tree resource
outside forests (TOF) which is increasingly recog-
nized as an important landscape element and a
resource providing many environmental and economic
services and benefits (FAO, 2000b; Kleinn, 2000;
Sadio et al., 2002). This meant that, unlike in
traditional forest inventories, field plots and aerial
photo interpretation did not stop at the forest boundary
but extended into all other land uses, except those
where natural conditions do not allow tree growth
ified from Kleinn et al., 2002, Fig. 2 with kind permission of Kluwer
d in the FORSTAT database of FAO. There is a clear downward trend
ith a relatively high variability. It should be noted that the figures are
on original forest inventories.
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2312
(such as waters, barren land, areas above the timber
line).
A second feature relatively novel for a tropical large
area forest inventory was the inclusion of interviews
with land owners. The objective of this component was
to obtain information about past, present and projected
future use of the forest and tree resources that go beyond
what can be directly observed in the field. The
corresponding population of interest was defined for
this study as the ‘‘community’’ of land owners in Costa
Rica. This population constitutes only a part of the
forest users, with a specific expectation towards their
forest areas (the ‘‘owner’s perspective’’); however, an
expansion of the interviews to the group of all forest
users was not feasible in this study: while the population
of land owners is relatively easily defined, it would be a
much larger and methodologically very complex task to
design and implement an interview survey for all groups
of users. Interviews, workshops, meetings and group
discussions are common techniques in many commu-
nity forestry projects; interviewers may spend con-
siderable time with the stakeholders, build up
confidence and get a direct insight into the dynamics
of the community. This is one of the major differences
in large area forest assessments and a major imple-
mentation challenge at the same time: interviews must
be more focused and usually restricted to a short visit.
Field sampling was a major component of the
inventory providing direct and ‘‘first-hand’’ observa-
tions on a series of relevant forest variables with a
measurable (estimable) precision. To increase preci-
sion of area estimations of forest types and land use
classes, aerial photograph interpretation was inte-
grated. Aerial photography from 1997 was available
from a flight campaign (Proyecto Terra) carried out to
compile new topographic maps. Because of permanent
cloud cover in the northern part of Costa Rica, this
most recent available aerial photography covered only
about 70% of the country’s area. This study did not
have the resources to commission new imagery; so, 3–
4 years old photographs were used, even though it
would always be preferable to obtain imagery closer to
the inventory date. Though it was not possible to
derive estimates for the entire country from this
imagery, the aerial photograph component was
integrated into this pilot study for methodological
reasons, and the results obtained were compared to the
results of the corresponding field plots.
3.2. Land use classes
The system of land use classes used (Table 1)
was based on the FAO classification details of which
are in FAO (1998). Obviously, not all classes could
be equally well distinguished in the aerial photo
plots and in the field. The upper level classes
correspond to the FAO classification, the more
detailed classes are in part nationally adapted.
Gallery forests, for example, elongated narrow
forest strips along waters in otherwise non-forested
landscape was specifically introduced for this study,
as it was known that this type of forest is typical in
many regions in Costa Rica. Secondary forest is a
forest type which bears, depending on the develop-
ment stage, some challenges in what refers to
distinction to other forest types. Secondary forest
grows on land which had been under different land
use before. They are recognized above all by a
specific species composition which can be assessed
in the field; in the aerial photographs crown structure
and contextual information were used to distinguish
it from other forest types. A set of ground checked
examples (interpretation key) served as reference
for aerial photograph interpretation. However,
observation errors are present for all attributes
assessed in forest inventories, and interpretation
confusions of land use classes can never be
completely eliminated.
3.3. Sampling design
For the establishment of aerial photo plots
systematic sampling was used and a basic square
grid with a 15 km side was laid out over the country
with a grid orientation defined as north–south and the
starting point being x = 289500, y = 895500 in the
rectangular geodetic grid Lambert Sur. A total of 228
points fell onto the territory of Costa Rica (light gray
dots in Fig. 2) out of which na = 159 points are covered
by aerial photographs (square frames in Fig. 2).
For the field plots a subset of the aerial photo
sample points was selected. Due to budget con-
straints a maximum of 40 field samples could be
established, so that a 2 (east–west) � 3 (north–south)
subset of the basic 15 km � 15 km grid was chosen
resulting in a grid of 30 km � 45 km (bold dots in
Fig. 2).
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–23 13
Table 1
Land use classification used in this study
Class
Forest
Primary forest Closed
Medium
Open
Young secondary forest Closed
Medium
Open
Advanced secondary forest Closed
Medium
Open
Forest plantation Closed
Medium
Open
Gallery forest Closed
Medium
Open
Other wooded land
Other land without trees Shrubs
Fallows
TOF* land Woody grass land
(5–10% crown cover)
Other land
Other land without trees Barren land
Natural Grass land
Woody grass land
(<5% crown cover)
TOF land: land with
trees outside forests
Annual crops
Cultivated land Perennial crops
Range land
Built up areas Built up area with blocks
Built up area—no blocks
Inland water
Other non interpreted
(in aerial photography)
The density sub-classes ‘‘closed’’, ‘‘medium’’, ‘‘open’’ refer to
crown cover percentages of >70%, 40–70%, and 10–40%, respec-
tively, following FAO definitions.* TOF stands for trees outside the forest. The area on which these
trees are found is named TOF land.
3.4. Plot design
Fig. 3 shows the plot design for the aerial photo plot
and for the field plot. The aerial photo plots for
interpretation were established on the aerial photo
closest to the sample point. The plot center was close
to the center of the photograph, keeping the level of
geometric distortions relatively low. The technically
better alternative of image rectification was beyond
the projects resources and ortho-rectified or geo-
referenced photographs were not available at that
time. Because the size of all aerial photo plots is fixed
at a square of 9 cm � 9 cm on the contact print of the
aerial photograph, the corresponding plot size in the
field will actually vary as a function of topography and
image characteristics. Among the 159 aerial photo
plots the actual scale varied between about 1:32,000
and 1:53,000, thus the resulting size of the plots in the
field varied between about 2.7 and 4.5 km. None-
theless, the size of the field plots is fixed.
Field plots were established on a 2 � 3 subset of
the aerial photo plot locations. The center of the
clusters of four sub-plots corresponded to the center of
the aerial photo plot. In order to make the per-plot
information content high – i.e. to keep intracluster
correlation low – relatively large sub-plots were
designed, and the distance between sub-plots within
the cluster plot was kept relatively large. Those two
features, of course, had to be defined within the limits
of what was practically feasible. For the square shaped
clusters a side length of 500 m was defined which
appeared to be the maximum possible under the forest
and topographic conditions in the country. Around the
perimeter of this square four rectangular sub-plots
with side length of 150 m were located. The strip-
shaped sub-plots had a width of 20 m, thus allowing
relatively good visibility of 10 m to the right and left
while walking on the central track. Each of the sub-
plots covered an area of 0.3 ha and the total cluster plot
an area of 1.2 ha. The sub-plot area was determined
according to prior information on estimated tree
density stemming from earlier forest inventories in
Costa Rica. In each of those major sub-plots smaller
sub-plots were nested for observation of smaller tree
diameter classes (Table 2).
Outside forest, nested plots were not installed
because of the expected low density of smaller
dimension trees. There, trees with dbh > 10 cm were
measured and registered on the entire area of the sub-
plot.
Photographs of the surroundings of the field plots
were taken to document current land use and are part
of the inventory documentation.
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2314
Fig. 2. Sampling design: a 15 km � 15 km base grid (small gray dots) was laid out over the country. The squares mark the position of the 159
aerial photographs that were selected to establish the aerial photo plots; the aerial photographs were used that were closest to the respective
sample points. In the north, except for the northern west coast, there were no photographs available. The bold dots mark the subset of 40 field
sample points (on a sub-grid of 30 km � 45 km).
Table 2
Sizes of the different levels of sub-plots
Level Plant size Shape and size of the plot
All sub-plot dbh > 30 cm Rectangle: 150 m � 20 m (3000–5000 m2)
Nested plot level 1 dbh < 10 < 30 cm Rectangle 20 � 10 m (400 m2)
Nested plot level 2 h > 1.3 m and dbh < 10cm Circle r = 3.99 m (50 m2)
Nested plot level 3 0.3 m < h < 1.3 m Circle r = 1.26 m (5 m2)
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–23 15
Fig. 3. Summary of plot design. Top left: aerial photo plots were centered in the selected aerial photographs; the size of the square plots was
defined as 9 cm on the contact print. Top right: field cluster plot design, each cluster plot consists of four rectangular sub-plots. Bottom: design of
one of the four sub-plots. Size and shape and tree-dimensions for the nested sub-plots is given in detail in Table 2. Each 150 m sub-plot was
subdivided into three identical 50 m sections with regeneration plots at the end of each.
The observation units for the interviews about the
use of the forest and tree resource were the land
owners or farm managers or the responsible officials in
the case of public forest or protected areas.
3.5. Analysis and estimation
For both approaches, field observations, and aerial
photo interpretation, the sample design was systematic
sampling. For the analysis, we employed random
sampling estimators. These yield unbiased estimations
of means and totals, but biased approximations for the
standard error, which is usually considerably upwards
biased, so that the actual precision in terms of the
actual, but unknown, confidence interval is usually
much better than what the random sampling estima-
tors yield (the nominal confidence interval). For the
area estimation from the aerial photo plots the ratio
estimator was considered using the area of the
individual sample plots as covariate. However, for
none of the area classes there was a sufficiently strong
estimated correlation to eventually justify the use of
the ratio estimator. For the estimations of the tree
attributes in the different classes, cluster sizes were
different; the ratio estimator was applied using the
total sampled area in the specific class as ancillary
variable.
For estimation of volume over bark the formula of
Lojan (1966) was applied, which is also commonly
used in management inventories in Costa Rica:
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2316
log Vol = 2.03986 log10 dbh + 0.779 log10 h� 4.07682.
4.07682. This model yields commercial stem volume,
i.e. for diameters greater than 30 cm. The formula
was applied here uniformly to all sample trees
(dbh > 30 cm) without making a distinction between
commercial and non-commercial species, so that the
growing stock given includes all wood with a
diameter greater than 30 cm; for a breakdown into
actually commercial and non-commercial wood
volume one would have to distinguish species-wise
between different utilization classes. The volume
function was developed for a tropical wet lowland
forest formation; however, it was applied here
uniformly to sample trees from all life zones.
4. Implementation of the inventory
The inventory was the first one carried out in the
framework of FAO–FRA’s newly shaped project of
assistance to national forest assessments. Develop-
ment of a feasible example of a low intensity sampling
forest inventory, its timely implementation, and
initiating the generation of technical capacity for
large area forest inventories were among the major
targets of the exercise.
Implementation in Costa Rica was done jointly by
staff of Sistema Nacional de Areas de Conservacion
(SINAC), the Costa Rican authority responsible for
forestry issues, and Centro Agronomico Tropical de
Investigacion y Ensenanza (CATIE), an international
agricultural research center.
The interpretation of the plots on the aerial
photographs was carried out at CATIE. For that
purpose an aerial photo interpretation key was
developed with specifications and examples for all
of the classes as given in Table 1.
To do the field plot measurements efficiently, local
knowledge was imperative. This refers to the knowl-
edge of accessibility, road infrastructure, forest types
and species encountered etc., but also to the familiarity
with forest and land owners in a region. Many forest
engineers in Costa Rica work as independent
consultants and are frequently contracted by forest
owners to prepare the forest management plans
required by law for any timber harvesting. Conse-
quently, those forest engineers have locally specific
experience and also experience in forest inventory
field work. Six of those foresters were contracted as
field crew leaders for field measurements in different
parts of the country. Each one of these consultants
worked on two to nine field sample locations. It was
part of the responsibility of the field crew leader to
contract technicians and assistants to build up a
complete field crew. Field work was organized by the
field crew leaders in coordination with the inventory’s
headquarters in CATIE. Transportation was up to the
field crew leaders who used their own cars. They also
used their own forest measurement devices, including
GPS, all of which was checked for completeness and
proper functioning by the coordination team. The
decision to contract a relatively high number of field
teams has some implications that are discussed at a
later point.
Preparation of all materials required for field work
was done by the inventory’s coordination team in
CATIE. From there, each field crew leader received a
set of topographic maps, copies of the corresponding
sections of the aerial photographs with the cluster plot
printed, field manual, and the form sheets. Preparation
of the field visits was supported by SINAC through
their regional offices in that they helped identifying
land owners and establishing contacts with them in
many cases. Explicit consent of the land owners was a
mandatory prerequisite, because most field plots were
on private lands and also extended outside forest onto
cultivated land. Those first contacts with the owners
gave the chance to explain the inventory’s scope and
objectives and to define appointments for the inter-
views. In many cases farm workers were hired as
guides and assistants for the inventory. This added to
creating confidence and the farm workers expertly
guided the field crews.
A field manual describing all necessary details of
the field work was written and guided the field crews.
Field crew leaders and technicians received a 3-day
training where scope and objectives of the inventory
were presented and discussed, and all technical steps
of the measurements were presented, studied and
practiced, including the use of the field form sheets
and GPS. Each field crew was accompanied by a
member of the coordination team to the first field
sample location.
Field work supervision was done following
different strategies: (1) check cruises after the regular
measurements and (2) field crews were repeatedly
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–23 17
accompanied during their regular work by a member
of the coordination team. In addition, the land use
classifications done in the field by the field crews was
cross-checked with those interpreted from the aerial
photographs.
Species identification is a major issue and problem
in forest inventories in tropical regions, where tree
species diversity is high. The commercial species are
usually known by the foresters and by the farm
workers so that they are identified with a relatively
high reliability. Not so, however, for the as-of-yet non-
commercial species: it may be assumed that the
majority of unidentified trees belong to the class of
non-commercial species. A certain percentage of
unidentified trees must be accepted in a large area
forest inventory. A higher level of accuracy of species
identification would require efforts that are usually
beyond the resources available for a forest inventory
(hiring a dendrologist to accompany the field crews
and/or collecting samples of unidentified trees in the
field and bringing them to a herbarium for identifica-
tion).
A simple spreadsheet database was created for data
entry. The field crew leaders entered their own data,
made a first check and sent the file together with the
original form sheets to the headquarters, at the latest, 2
weeks after data collection. The coordination team did
plausibility checks, and possible remaining incon-
sistencies were immediately communicated to the
field team leaders for review and correction. Data
ownership is with SINAC.
Preparation and organization of the interviews cost
time but were not a major problem, as owners needed
to be contacted, anyway, to ask for permission to enter
their lands. However, it turned out to be difficult in
some cases to arrange for an appointment for the
interview. Interviews were then carried out by the field
team leaders who had received corresponding training
before. A sociologist accompanied each one of the
field team leaders to their first interviews.
5. Results
Results originate from the data gathered in the field,
and from the aerial photo interpretation (referring
to the approximately 70% of the territory of Costa
Rica covered by the aerial photographs available).
The results from the field data can be broken down into
three major categories (1) results for the tree resource
in forest, (2) results for the tree resource outside forest,
and (3) results from the interviews with forest users.
5.1. Results from field sample plots
Of the 40 field sample points, 32 had complete
cluster plots installed. For five sample points access
problems (either due to topographic reasons or denied
access) allowed only the establishment of a part of the
cluster plot; these plots were taken into account in the
analysis according to their weights. One cluster plot
came to lie completely in inland water (Lago Arenal)
and two plot in the middle of completely forested
national parks; these points were not searched in the
field. For area estimation, they were counted as water
and primary forest, respectively. For the estimation of
the forest characteristics these plots were left out,
reducing the sample size for the estimations; no
missing plot techniques were applied.
The data, though characterized by a relatively low
sampling intensity, allowed an analysis of key forestry
variables such as area per forest type, volume, basal
area, and tree density. The same results were
calculated for the tree resource on non-forest land.
Contrary to the estimations from the aerial photo plots,
field plots covered the entire country so that analysis
and results given refer to the whole of Costa Rica. A
breakdown to smaller political or geographical units
was not carried out.
The major results are summarized in Tables 3
and 4, where per hectare estimations are broken down
to forest types for basal area, commercial volume and
number of trees. Separately, basal area and number of
trees is also given for the diameter class 10–30 cm for
which commercial volume was obviously not calcu-
lated. The rightmost columns in Tables 3 and 4 give
area estimates.
Total forest area is estimated with 48.4% (Table 4);
this estimate comprises all forest types and includes,
for example, also an estimated 9.7% of young
secondary forest where no bigger trees are yet
established. Relative standard error of the forest area
estimate is 9.3%, and (assuming t = 2) the limits of the
95% nominal confidence interval would be 38.7 and
58.1%. Due to the use of the random sampling
estimators for systematic sampling the true but
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2318
Table 3
Estimations per forest category (dbh > 30 cm, all species; S.E.: standard error, given in parenthesis)
Forest type Estimations per ha for dbh > 30cm (S.E.% in
parenthesis)
Estimations per ha for
10 cm < dbh < 30 cm
Area proportion (relative
S.E. in parenthesis)
Basal area (m2) Volume (m3) Number of trees Basal area (m2) Number of trees
Gallery 15.68 (32.4) 63.46 (105.1) 65.35 (17.0) 6.82 312.5 0.0221 (32.7%)
Plantation 2.44 (–) 12.09 (–) 26.67 (–) 13.35 486.4 0.0238 (43.8%)
Primary 18.49 (9.4) 137.74 (14.0) 87.75 (10.2) 7.63 304.2 0.2560 (14.6%)
Secondary 5.00 (25.7) 29.83 (40.6) 30.12 (21.2) 4.20 195.3 0.1779 (20.1%)
Non-forest 1.64 (22.8) 7.77 (39.3) 8.73 (24.1) 0.61 24.3 0.5162 (16.1%)
unknown actual confidence interval is likely to be
much narrower. Sampling error is higher when
estimates are broken down to forest classes as of
Table 3. Most of the total forest is is primary forest
(25.6%), and secondary forest (17.8%) of which more
than half is young secondary forest. Forest plantations
cover an estimated 2.4%, and gallery forest 2.2%—
these two classes are relatively rare compared to the
others, and are therefore not covered by many
samples. That leads to a higher sampling error. Only
two sub-plots fell, for example, into forest plantations.
From Tables 3 and 4 we may compare some
attributes of the tree resource inside and outside the
forest. Growing stock per hectare for dbh > 30 cm (all
species) is highest in primary forest, followed by
gallery forest, which has in many cases characteristics
of primary forests. For plantation forests, where only
few sample plots were growing stock is low; the
estimations for the diameter class 10–30 cm show that
most of the trees are in smaller dimensions. Mean
growing stock is also low for secondary forest where a
basal area of 5 m2/ha is found for trees >30 cm and a
similar quantity (4.2 m2/ha) in the class 10–30 cm. A
considerable proportion of the secondary forest is
young secondary forest where there is very little basal
area so that it is actually not surprising that the overall
mean is low.
Table 4
Estimations according to forest/non-forest (dbh > 30 cm, all species; S.E.: s
forest)
Class Estimations
Basal area/ha (m2) Volume/ha (m3)
1 (forest) 12.5 (12.4) 87.8 (17.4)
0 (TOF* land) 1.8 (23.0) 8.7 (38.5)
2 (others) – –
* TOF stands for trees outside the forest. The area on which these tree
Gallery forests were estimated to cover 2.2% of the
country. Given the narrowness of these strip-like
gallery forests, this is a surprisingly high figure and
underlines the overall relevance of this forest type,
which is important for biodiversity conservation, for
water and soil protection and also for cattle farmers.
The authors do not know of other studies providing a
large area quantitative estimation of the area of this
forest type. Assuming an average width of gallery
forests of 50 m (which, of course, varies according to
factors such as life zone and surrounding land uses)
and an area of 51,000 km2 for the territory of Costa
Rica, we obtain from this 2.2% area estimate an
estimated total length of gallery forests of 22,440 km
and an average density of gallery forests in Costa Rica
of 440 m/km2. Yet, gallery forests are only found in
otherwise not forested lands so that its density should
not be related to the total area of the country but to the
estimated 53.8% of non-forest area (non-forest + -
gallery forest area = 51.6% + 2.2% = 53.8%). Then
gallery forest density is obviously much higher and is
estimated to be 817 m/km2!
Taking from Table 4 the estimates of mean volume
per hectare for forest and for other lands with trees
(land with trees outside forest, ‘‘TOF land’’), the
average volume per hectare of the latter is about 10%
of the average forest volume (for trees >30 cm). Using
tandard error, given in parenthesis; TOF-land: land with trees outside
Area proportion (relative standard
error in parenthesis)No of trees/ha
61.8 (11.3) 0.4838 (9.3%)
9.7 (23.2) 0.4337 (10.0%)
– 0.0825 (32.0%)
s are found is named TOF land.
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–23 19
the total area of 51,000 km2 of Costa Rica and the area
estimations from Table 4 the estimated total forest
wood volume (dbh > 30 cm, all species) is 216.64
million m3 and for non-forest is 19.24 million m3: an
estimated 8.2% of the countries total wood volume
(dbh > 30 cm) is found outside forest.
Applying suitable models those estimations may be
converted into approximations for biomass and for
carbon stored in the tree resource of the country.
5.2. Area estimations resulting from aerial photo
interpretation
The interpretation of the aerial photo plots
allowed generation of area estimations for the
70% of the territory of Costa Rica which is covered
by the aerial photographs available. The numerical
results are of inferior interest as they refer only to a
part of the country, which is being defined by the
availability of aerial photographs. However, the
results are interesting for a comparison of the low
intensity field sample with the higher intensity aerial
photo sample.
Forest area is estimated as 54.6% with a relatively
low (relative) sampling error of 3.8%, where forest is
comprised of all forest classes. The large national
parks are within the 70% of the territory of Costa Rica
Table 5
Comparison of area estimations from interpretation of aerial photo plots (
subset of field observations (column ‘‘field’’) where n = 26
First level land
cover class
Estimated area
(%)
Relative standard
error (%)
Second
AP Field AP Field
Forest 54.6% 54.6% 3.8% 12.8% Primary
Young s
Advance
Gallery
Forest p
Land with trees
outside forest
42.1% 39.5% 5.2% 18.9% Pasture
Other no
(shrubs,
and peri
annual a
Land without trees 2.9% 5.8% 20.7% 60.2% Fallow l
Non inter-pretable 0.4% n.a. 25.0% n.a. Under c
Both refer to the same area of about 70% of the territory of Costa Rica for w
for the purpose of comparison and obviously do not yield estimations fo
covered by the aerial photographs. Primary and
secondary forests share about the same percentage
of forest area of about 24%. Forest plantations are
estimated to cover 2.3% of the land area, however,
with a relatively high standard error. In Table 5 the
aerial photo plot based estimates (n = 159) are
compared to the estimates obtained from the matching
2 � 3 subset (n = 26) of field sample points. The
estimates for total forest area are the same to the first
decimal – which is incidentally – while the estimates
for the forest classes differ, particularly for secondary
forest. Advanced secondary forest estimates are
considerably higher in the aerial photographs than
in the field. The contrary is true for young secondary
forest. Assuming that the classification done in the
field plots is closer to truth, it is likely that the
differences between the estimates come from confu-
sions in aerial photo interpretation of young and
advanced secondary forest, although the relatively
large standard errors must also be taken into account
when comparing these figures.
5.3. Some results from interviews with forest users
The interviews with the forest owners had a pilot
character: the main goals were to gather experience
and to further develop the integration of the
column ‘‘AP’’) with n = 159, and estimates from the corresponding
level land cover class Estimated area
(%)
Relative standard
error (%)
AP Field AP Field
forest 24.9% 27.7% 10.7% 27.1%
econdary forest 8.3% 13.0% 8.4% 35.6%
d secondary forest 16.3% 10.6% 7.2% 33.4%
forest 2.9% 3.3% 13.0% 51.0%
lantations 2.3% 0.0% 33.0% –
land 27.1% 26.0% 5.7% 20.9%
n-forest land with trees
natural grass land, urban
-urban areas, settlements,
nd permanent crops)
15.1% 13.6% 11.4% 37.0%
and and water – –
louds, shadows – –
hich cover with aerial photographs was given. The results are given
r the whole of the country.
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2320
assessment of socio-economic information into large
area forest inventories.
Interviews were planned for all owners whose
property came to lie inside the cluster plots. However,
not all owners could be identified or were available for
interviews so that a total of 82 interviews were carried
out in 28 sample points. The non-response rate with
respect to the envisaged sample of forest owners to be
interviewed was about 27%. Of the 82 interviewees,
about two-third were forest owners, the rest state
officials, including those responsible for protected
areas. Interviews took between 0.5 and 1 h.
We present a few examples of results here.
The forest owners were asked about their expectations
and about their wishes with respect to tree density in
forest and outside forest: whether they expect (and
would like to have) a bigger, smaller or equal number
of trees. Surprisingly, the response rate was only
around 50% for both questions as half of them
responded not to have corresponding expectations or
wishes (‘‘not applicable’’). Of those who responded,
most expected for the future about the same tree
density in forest and outside forest. When asked about
the wishes with respect to tree density, around two
thirds hoped to have more trees on their land, where
they refered more to trees outside forest than to trees
in forest.
When asked about the actual use and major
products extracted, mainly timber, fuel wood and
meat were cited—both for forest and non-forest. For
forest land more products were given like fruits,
forage, medicinal and ornamental plants.
6. Discussion and conclusions
The national level forest inventory described in this
article has some particularities. It is an inventory that
builds upon a low sampling intensity, and for the first
time in the country, the tree resource on all lands was
included in a systematic manner.
Total forest area was estimated by the field plots to
be 48.4%, though with a relatively wide confidence
interval. It is one of the great advantages of inventories
on a statistical basis, that those precision estimates can
be directly calculated. The forest cover of 48.4% is
higher than earlier estimates though not off the trend
line in Fig. 1. In the Forest Resources Assessment
2000 of FAO, total forest area for Costa Rica was
given as 38.5% (FAO, 2001a, p. 244), a figure that lies
just outside the nominal confidence interval of the
forest area estimate in this study. It is hypothesized
that the difference is mainly due to the forest class
‘‘young secondary forest’’ which possibly has not
been fully accounted for in earlier assessments, and
the area of which has increased recently due to
abandonment of pasture land. Our estimated total
forest area of 48.4% is relatively close to the 46.3%
that was found for the year 2000 in the most recent
satellite imagery based mapping study (EOSL, CCT
and FONAFIFO, 2002).
Despite of the low field sampling intensity a
precision is achieved for a number of attributes in the
range of a standard error of 20%. This has also to do
with the plot design where a relatively large cluster
plot was used. Compared to national forest inventories
with thousands of sample plots, where standard errors
in the order of magnitude of 1% and better are
achieved, the standard errors found here appear high;
however, errors of around 20% are frequently accepted
by foresters for smaller area forest management
inventories.
A major point of interpretation in forest inventories
is the error budget. The standard error is not the only
source of error, but measurement errors and model
errors are also present and of commonly unknown
magnitude. In this study, several measures were taken
to keep these errors low: control measurements by
independent supervision teams were were done, and
the land cover classification from the aerial photo-
graphs was checked at the field sampling locations and
used to correct and calibrate the aerial photograph
interpretation.
Contrary to other national forest inventories where
much higher sample sizes are used, the low intensity
sampling employed here does not permit subdivision
into regional strata (province-wise, for example),
because the sample in each stratum would be too
small. When regionalized results were desired, a local
densification of the grid would be necessary, or other
information sources would need to be integrated:
when combined with a more intensive remote sensing
survey, the precision of area estimations can con-
siderably be improved.
As a direct effect of low sampling intensity, results
were produced in a relatively short time period of 9
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–23 21
months so that up-to-date results were made available
to forest politicians quickly.
Results were generated for both forest and non-
forest lands so that the relative relevance of the tree
resource outside forest can be assessed. An estimated
8.2% of the country’s total wood volume
(dbh > 30 cm) is found outside forest. For a relatively
highly forested country like Costa Rica, where much
of the forest is not accessible, or in National Parks or
protected areas, and thus not available for timber
extraction or other forms of wood utilization, this is a
considerable amount in absolute terms. It becomes
obvious that the 8.2% of wood resource outside forest
not only serves a series of environmental services
(such as biodiversity conservation, water and soil
protection) but also has a high economic value as they
constitute a resource that is relatively easily acces-
sible. The Forest Development Plan for Costa Rica
(MINAE et al., 2001) states that an estimated 30% of
the (registered) raw material for the wood industry
comes from non-forest land. It is therefore not only
desirable, but virtually mandatory for the forestry
sector to recognize TOF as a serious ‘‘forest’’
resource, and it makes sense to integrate TOF into
forest inventories and into forest resource planning.
The present study demonstrates how to integrate
the inventory of TOF into a forest inventory in a
straightforward manner: the sample plots were simply
extended to non-forest land. Although the sampling
and plot design chosen had not been optimized with
respect to the non-forest tree resource, the results give
the information expected with sufficient precision.
A important point in inventory implementation was
that a relatively high number of field teams were
involved. This was primarily done to utilize the local
knowledge of the team leaders which usually extends
only to parts of the country, and to reduce travel cost.
On the other hand, it considerably increased the
coordination efforts in terms of training and super-
vision. Overall experiences of field team performance
and data quality were good, only in one case a contract
had been cancelled.
A positive side effect of contracting several field
crews was that they were thoroughly trained and that
concept and techniques of large area forest inventories
were made better known in the forest-expert com-
munity in the country (which is a major point in FAO
FRAs project to support national forest assessments).
Forest inventories are in Costa Rica mainly known for
smaller areas in the context of forest management
planning. Most of the large area forest studies in the
past years were satellite imagery based forest mapping
exercises, sometimes erroneously referred to as forest
inventories: a forest inventory comprises an assess-
ment of many more than area attributes, and many of
the forestry-relevant variables need to be observed and
measured in the field.
A critical methodological element is obviously that
rare events are not necessarily captured when a low
intensity field sampling is applied. This situation
occurred for forest plantations. They have some
relevance in Costa Rica, but the actual plantation
forest area is small relative to the total forest area. If
the assessment of such rare elements is an issue and
higher precision required for specific classes, addi-
tional efforts must be undertaken to increase the
information basis either by increasing the sample size
or by integration of other information sources like
remote sensing imagery. In the present study,
plantations were not captured sufficiently in the field
survey, but this area could be estimated from the aerial
photo plots with a modest precision.
For organizational reasons, interviews were limited
to forest owners, and focused on the forest and tree
resource of the land they own. This obviously gives
only a limited view into the forest use in a specific
area. Other forest user groups should be included in
such a survey, although the integration poses some
organizational and methodological challenges, as the
forest and tree resource is utilized in many manners,
obvious and non-obvious ones, legal and illicit ones,
and by many different actors who are not necessarily
easily to identify. An approach discussed, but not
implemented yet in this study, is to interview –
formally or informally – the local field assistants who
were contracted for the field work. Those who have the
necessary qualifications to assist to the field crews
(knowledge of the area, species identification cap-
abilities) are frequently also knowledgeable with
respect to the actual uses of forest and trees.
Another question in that context is whether it
makes sense to combine an inventory of biophysical
attributes (the classical forest inventory) with a socio-
economically oriented survey; one could also carry out
an independent survey which could be exclusively
focused on the interviews. The problems of combining
C. Kleinn et al. / Forest Ecology and Management 210 (2005) 9–2322
the two are obvious and have been addressed in the
preceding sections. But there are also a number of
advantages: the field team leaders who carry out the
interviews spend some time on the respective lands
and have some in-depth information about the land. As
the topics of the interviews are mostly related to the
tree and forest resource, it really made sense to have
persons with a strong forestry background carrying
out the interviews. Also, in some cases, it was during
those interviews that the forest owners learned for the
first time about some national programs of forestry
incentives.
Overall, this national level forest inventory
demonstrated that a low intensity sampling approach
allows generating of valuable, comprehensive and
statistically sound baseline forest information in a
relatively short time period; information that will help
the forest planners and politicians in decision making
and will support the government in international
reporting. After the promising experience and out-
come of this study, this approach is currently being
implemented and further developed in other countries
such as Guatemala, The Philippines and Cameroon.
The plan is to develop it into a baseline design for the
assessment of national forest information in the
framework of FAO FRA’s initiative to support national
forest assessments.
Acknowledgements
Our thanks are due to the officials at SINAC, the
entity in Costa Rica responsible for forest affairs, who
supported and accompanied this study in the most
cooperative way, above all to the director Luis Rojas,
and to Carlos Calvo and Francisco Rodriguez. The
study was carried out when the senior author was
heading the Statistics Section of CATIE (Tropical
Agricultural Research and Higher Education Center)
in Costa Rica. Our thanks go to the colleagues in
CATIE who contributed putting this study into place,
above all to Marco Chaves, David Morales, Gustavo
Lopez, Alejandro Cedeno, and Marianela Araya.
Thanks are also due to the field crews who diligently
carried out difficult field work, and to the many land
owners who granted access to their properties for us to
do the field measurements. Thanks go also to Franklin
Solano who contributed to developing and carrying
out the interviews with forest users and to Mohamed
Saket, FAO–FRA, who accompanied the development
of the inventory design. The suggestions of two
anonymous reviewers are highly appreciated. Last but
not least, thanks go to Mrs. Jordan Philipps and
Mr. Tzeng Yih Lam for the language review of the
manuscript.
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