A national forest resources assessment for Costa Rica based on low intensity sampling

www.elsevier.com/locate/foreco

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: [email protected] (C. Kleinn), [email protected] (C. Ramırez), [email protected] (P. Holmgren),

[email protected] (S.L. Valverde), [email protected] (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.


(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).


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.


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)


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:


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


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


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.


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.


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


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


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.

References

Cunia, T., 1978. On the objectives and methodology of national

forest inventory systems. In: Proceedings of the IUFRO Inter-

national Conference on National Forest Inventory, Bukarest

June, 18–14, pp. XI–XXVIII.

EOSL, CCT and FONAFIFO, 2002. Estudio de cobertura forestal de

Costa Rica con imagines Landsat TM 7 para el ano 2000.

Unpublished Report, 12 pp.

FAO 1998. FRA 2000 Terms and Definitions, FRA Working Paper

No.1, 19 pp. (http://www.fao.org/forestry/fo/fra/publications.js-

p?lang_id=1).

FAO 2000a. Bibliografıa comentada—Cambios en la cobertura

forestal: Costa Rica. FAO-FRA Working Paper No. 36. 55 pp.

(http://www.fao.org/forestry/fo/fra/docs/Wp36_span.pdf).

FAO, 2000b. Trees outside the forest. Unasylva Issue 200.

FAO, 2001a. Global Forest Resources Assessment 2000. Main

Report. FAO Forestry Paper 140, 479 pp.

FAO, 2001b. Estudio Piloto para el Inventario Forestal Nacional en

Costa Rica—Iniciativa de FAO FRA. Forest Resources Assess-

ment Working Paper No. 66, FAO, Rome.

Hartshorn, G., Hartshorn, L., Atmetlla, A., Gomez, L.D., Mata, A.,

Mata, L., Morales, R., Ocampo, R., Pool, D., Quesada, C.,

Solera, C., Solorzano, R., Stiles, G., Tosi Jr., J., Umana, A.,

Villalobos, C., Wells, R., 1982. Costa Rica – Country Environ-

mental Profile. A Field Study. Tropical Science Center, San Jose,

Costa Rica, 123 pp.

Keogh, R.M., 1984. Changes in the forest cover of Costa Rica

through history. Turrialba 34 (3), 325–331.

Kleinn, C., 2000. On large area inventory and assessment of trees

outside forests. Unasylva 51 (200), 3–10.

Kleinn, C., Corrales, L., Morales, D., 2002. Large area forest cover

estimates in the tropics—the case of Costa Rica. Environ. Monit.

Assess. 73 (1), 17–40.

Leonard, H.J., 1986. Recursos Naturales y Desarrollo Economico en

America Central. Un perfit ambiental regional, Seria tecnica,

informe tecnico/CATIE, no. 127, 267 pp.

MINAE, PNUD, ONF 2001. Plan Nacional de Desarrollo Forestal

2001–2010, 80 pp.

Lojan, L., 1966. Una formula para estimar volumenes en un bosque

tropical humedo. Turrialba 16 (1), 67–72.

Persson, R., Janz, K., 1997. Assessment and Monitoring of Forest

and Tree Resources. Topic 1. World Forestry Congress, Antalya,

Turkey, pp. 17–29.

Sader, S., Joyce, A.T., 1988. Deforestation rates and trends in Costa

Rica, 1940–1983. Biotropica 20 (1), 11–19.

http://www.fao.org/forestry/fo/fra/publications.jsp?lang_id=1

http://www.fao.org/forestry/fo/fra/publications.jsp?lang_id=1

http://www.fao.org/forestry/fo/fra/docs/Wp36_span.pdf


Sadio, S., Kleinn, C., Michaelsen, T. (Eds.), 2002.Proceedings—

Expert Consultation on Enhancing the Contribution of Trees

Outside Forests to Sustainable Livelihoods. Rome 26–28

November 2001FAO Forest Conservation, Research and Educa-

tion Service, Forest Resources Division, 171 pp..

Saket, M., 2002. FAO’s approach to support national forest inven-

tories and assessments. Background paper to the UN-ECE/FAO

Expert Consultation Kotka IV, Kotka, Finland, 1–5 July 2002.

Schreuder, H., 2001. Sampling using thematic mapping, very large

scale photgraphy, and ground sampling for measurement and

strategic surveys. In: Proceedings of a IUFRO 4.11 Conference.

Forest Biometry, Modelling and Information Science. London,

UK, June 2001. Internet document http://cms1.gre.ac.uk/con-

ferences/iufro/proceedings/Schreuder4.pdf.

Sylvander, B., 1981. Bosques del paıs y su distribucion por pro-

vincias. Universidad Estatal a Distancia, San Jose, Costa Rica,

120 pp. (= Documento de trabajo No. 15/1978 DGF/PNUD/

FAO/72/013).

Thuresson, T., 2002. Feasibility of low intensity sampling approach

for national inventories, including cases. Background paper to

the UN-ECE/FAO Expert Consultation Kotka IV, Kotka, Fin-

land, 1–5 July 2002.

http://cms1.gre.ac.uk/conferences/iufro/proceedings/Schreuder4.pdf

http://cms1.gre.ac.uk/conferences/iufro/proceedings/Schreuder4.pdf

A national forest resources assessment for Costa Rica based on low intensity sampling

Documents

Transcript of A national forest resources assessment for Costa Rica based on low intensity sampling