National Forest Inventory of Costa Rica, 2014-2015
Costa Rica, 2013 - 2014
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CRI_2014_2015_NFI_v01_M_v01_A_ESS
National Forest Inventory of Costa Rica,
2014-2015
| Name | Country code |
|---|---|
| Costa Rica | CRI |
Forest resource survey
Pilot study conducted in Costa Rica during 2000–2001 that used a low-intensity sampling approach to produce national-level forest inventory information (Inventario forestal piloto 2001, Kleinn et al. 2001).
Costa Rica’s first National Forest Inventory (INF-CR) plays a strategic role for the State Forestry Administration (AFE). Its main purpose is to fulfil two key responsibilities: firstly, to comply with the legal requirement to inventory and assess the country’s forest resources, as well as their utilization and industrialization; and secondly, to provide the basis for the Monitoring, Reporting and Verification (MRV) system required by the national REDD+ strategy.
The INF-CR aims to generate essential information for the sustainable management of forest resources, ecosystem services, and trees outside forests. Furthermore, it is designed to be repeated periodically, enabling the estimation and reporting of changes in forest cover and carbon stocks, which are indispensable for meeting national and international commitments.
Under the Forestry Law Regulations, sustainable forest management is defined as the management of forest resources to meet current needs without compromising the ability of forests to continue providing goods and services to future generations.
The overall objective of the INF-CR is to determine the stock, characteristics, and condition of the country’s forest resources in order to guide planning and decision-making regarding their management and administration. Its specific objectives include measuring forest cover by forest type, assessing growing stock, biomass and carbon, estimating deforestation, forest degradation and recovery, strengthening the REDD+ MRV system, and supporting national forest planning.
Sample survey data [ssd]
Fields/Plots
Scope
The following variables were measured and recorded, and are grouped into four categories:
- Dasometric Variables of Individuals
Within the UMP, all trees and lianas with a diameter at breast height (DBH) greater than 10 cm are identified and measured:
- Diameter at breast height (DBH).
- Height: Total height and commercial height are measured on at least two trees per 10 cm diameter class.
- Spatial position: The relative location (X, Y) of each tree within the plot boundaries is recorded.
- Count: The total number of trees meeting the minimum diameter criterion is determined.
- Species Composition
- Botanical identification: Each individual is identified by its common and scientific name, with standardized alphanumeric codes assigned for biodiversity analysis.
- Site and Environment Characterization Variables
Although these variables refer to the plot area, they document the environmental context and conservation status:
- Forest integrity: Evidence of natural disturbances (landslides, fires, wind damage, floods) or anthropogenic disturbances (logging, stumps, trails, presence of barbed wire fences).
- Geophysical variables: The slope of the terrain (in percentage) is measured and the topographical position (ridge, valley, plain, etc.) is identified.
- Protected areas: Presence of water bodies such as rivers,
streams, brooks or springs. - Succession stage: Classification of the site as mature or secondary forest.
- Carbon and Soil Variables (Associated with the Primary Sampling Unit (PSU))
Specific points are located within or on the edges of the PSU to collect data on other carbon pools:
- Deadwood: Quantified along a 20-metre transect located equidistant from the centre of the PSU.
- Soils: Samples are collected to determine bulk density and total organic carbon content.
- Litter: Its depth (in cm) is measured and samples are collected for weighing at the corners of the PSU.
| Topic |
|---|
| Environmental Study |
| Forest Inventory |
| Forest Assessment |
Coverage
National coverage.
Six land-cover types were defined as the target population for sampling, with each forest type considered a separate stratum for the design of the National Forest Inventory (NFI).
The distribution of the target population among the six strata was as follows:
- Mature forest: 40.05 percent
- Secondary forest: 24.33 percent
- Mangrove forest: 0.94 percent
- Palm forest: 1.22 percent
- Tree-covered grassland: 31.54 percent
- Forest plantations: 1.93 percent
The total area of the target population was 38 668.96 km², representing approximately 75.7 percent of the country's total land area (51 100 km²).
Producers and sponsors
| Name |
|---|
| National System of Conservation Areas (SINAC, Sistema Nacional de Áreas de Conservación) |
| German Agency for Technical Cooperation (GIZ, Agencia Alemana de Cooperación Técnica) |
| Ministry of Environment and Energy (MINAE, Ministerio de Ambiente y Energía) |
| Name |
|---|
| Central American Commission for Environment and Development (CCAD, Comisión Centroamericana de Ambiente y Desarrollo) |
| Name | Abbreviation |
|---|---|
| German Agency for Technical Cooperation (GIZ, Agencia Alemana de Cooperación Técnica) | GIZ |
Sampling
The inventory applies a stratified systematic sampling design based on a regular grid of points stratified by land use and land cover type. The plots to be surveyed were selected systematically with a random start (Cochran 1977) across the national territory from a 10,166-point grid with a spacing of 2.5 km between points (Ortiz et al. 2013).
An initial pre-sampling phase was used to refine the estimate of basal area variability in order to calculate the sample size more accurately, validate travel and measurement times, and assess the effectiveness of the field forms and instruments. For this initial phase, a 5x5 km grid (2048 points) was used to select sites, establishing specific distances according to the stratum (e.g. 25 km for mature forest and 10 km for wooded grassland); 107 sites were successfully established and measured. Results are available in the Downloads section.
During the second phase, a primary plot and subplots of varying sizes were established within the plot boundaries to measure dasometric and edaphic variables, as follows:
- Primary sampling unit (PSU) measuring 20 × 50 m (1,000 m²). Trees with a diameter at breast height (DBH) greater than 10 cm were identified and measured. Total height and commercial height were also measured for at least two trees per 10 cm diameter class.
- Secondary sampling unit (UMS) of 10×20 m (200 m²). Trees with a DBH between 2 and 9.9 cm were identified and measured. Total height was also measured on at least two trees per 2 cm diameter class.
- Tertiary sampling unit (UMT). This plot consists of three circular subplots, each with a radius of 1 m,
located at the ends and centre of the PSU's baseline. Regeneration (trees, shrubs, palms and tree ferns) with a total height of 1.5 m or more and a DBH of less than 2 cm is recorded here. - Quaternary sampling unit (UMC). This is a 1 m² plot located randomly in the south-western half of the PSU. The abundance (count) of herbaceous plants is measured by taxonomic groups.
- Litter sampling unit (UMH). This plot consists of four square subplots, each 50 cm
on a side (0.25 m²) and located at each of the corners of the PSU. In each subplot, the depth (in cm) of the litter is measured at the centre and at two opposite corners. The litter from each subplot is collected and weighed, then mixed, and a composite sample of 1000 grams is taken. - Deadwood sampling unit (UMMm). This component was measured along a 20 m transect located at equal distances from the central point of the PSU.
- Soil sampling. A composite soil sample was taken to determine organic and total carbon, and a soil cylinder was taken to determine density.
Secondary, tertiary and quaternary plots, fallen deadwood and litter plots are established in forest strata (mature and secondary forests) and in palm forests and mangrove stands only where site conditions permit. In wooded grassland strata and forest plantations, only the PSU is established.
The response rate was 97.9 percent. 280 plots from a sample of 286 plots were measured.
The weighting criterion was the size of the stratum.
Data collection
| Start | End |
|---|---|
| 2013 | 2014 |
2014 - 2015
- Field measurement [field]
All the technicians took part in a training course before starting the fieldwork. Routine checks were carried out to assess the consistency, integrity and completeness of the dasometric, dendrological and positioning data.
The quality assessment of the forest inventory data was carried out by an independent team, which visited and corrected 10 percent of the plots established for each stratum.
Data processing
Data processing was undertaken by an international external company with expertise in forest inventory data management. The workflow was implemented in Microsoft Excel using specialized macros designed to automate data validation, consistency checks, error detection, and the generation of derived variables.
Data appraisal
The uncertainty assessment included only sampling errors derived from the probabilistic sampling design. Non-sampling errors were not quantified and were therefore excluded from the uncertainty analysis (Programa REDD/CCAD-GIZ - SINAC. 2015.).
Data Access
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes |
Personal data is private, whilst land parcel data may be shared subject to the technical justification of the study for which it is required. The legislation currently in force in Costa Rica is the Law on the Protection of Individuals with regard to the Processing of their Personal Data (Law No. 8968). This legislation regulates the use, collection and storage of personal information by public or private entities. |
Micro datasets disseminated by FAO shall only be allowed for research and statistical purposes. Any user which requests access working for a commercial company will not be granted access to any micro dataset regardless of their specified purpose. Users requesting access to any datasets must agree to the following minimal conditions:
- The micro dataset will only be used for statistical and/or research purposes;
- Any results derived from the micro dataset will be used solely for reporting aggregated information, and not for any specific individual entities or data subjects;
- The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO;
- The micro dataset cannot be re-disseminated by users or shared with anyone other than the individuals that are granted access to the micro dataset by FAO.
Programa REDD/CCAD-GIZ - SINAC. 2015. Inventario Nacional Forestal de Costa Rica 2014-2015. Resultados y Caracterización de los Recursos Forestales. Preparado por: Emanuelli, P., Milla, F., Duarte, E., Emanuelli, J., Jiménez, A. y Chavarría, M.I. Programa Reducción de Emisiones por Deforestación y Degradación Forestal en Centroamérica y la República Dominicana (REDD/CCAD/GIZ) y Sistema Nacional de Áreas de Conservación (SINAC) Costa Rica. San José, Costa Rica. 380 p.
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | |
|---|---|---|
| Henry Ramírez Molina | SINAC | [email protected] |
| Adriana Aguilar Porras | SINAC | [email protected] |
| Información Inventario Forestal Nacional | SINAC | [email protected] |
Metadata production
DDI_CRI_2014_2015_NFI_v01_M_v01_A_ESS_FAO
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Statistics Division | ESS | Food and Agriculture Organization of the United Nations | Metadata adapted for FAM |