Mexico's National Forest and Soils Inventory 2004-2009
Mexico, 2004 - 2007
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Reference ID
MEX_2004-2009_INFyS_v01_EN_M_v01_A_ESS
Producer(s)
Comisión Nacional Forestal, Gerencia de Sistema Nacional de Monitoreo Forestal
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Sep 06, 2024
Last modified
Sep 25, 2024
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Identification
Survey ID Number
MEX_2004-2009_INFyS_v01_EN_M_v01_A_ESS
Title
Mexico's National Forest and Soils Inventory 2004-2009
Country
| Name | Country code |
|---|---|
| Mexico | MEX |
Study type
Forest resource survey
Series Information
National Forest Inventory 2000
Abstract
The National Forest and Soil Inventory 2004-2009 includes field information on the conditions of the terrain and the species of trees, shrubs and herbs by type of vegetation of all plant communities in the national territory.
The design of the INFyS is based on the classification system of the Land Use and Vegetation Charter (series I and III) at a scale of 1:250,000 prepared every five years by the National Institute of Statistical and Geographic Information (INEGI) and the design and technology was approved with Canada and the United States so Mexico is able to report the status of forest resources both nationally and internationally.
The design of the INFyS is based on the classification system of the Land Use and Vegetation Charter (series I and III) at a scale of 1:250,000 prepared every five years by the National Institute of Statistical and Geographic Information (INEGI) and the design and technology was approved with Canada and the United States so Mexico is able to report the status of forest resources both nationally and internationally.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Fields/plots
Scope
Notes
The design of the INFyS is due to the generation of data at the national level, as well as support to federal entities in the generation of state forest inventories. In addition, in accordance with the General Law of Sustainable Forest Development, the INFyS included the measurement of the dynamics of change, so the field sampling design allowed the annual remeasurement of one fifth of the total conglomerates established at the national level, in this way, coverage of the national territory is achieved every five years.
The methodology contemplates obtaining periodic data that contributes to obtaining parameters of interest for the evaluation of the country's forest resources as:
a) Volumetric stocks
b) Increase in volume of coniferous roundwood
c) Damage to standing trees
d) Soil degradation indicators
e) Estimation of aerial biomass
f) Estimation of organic carbon in soil
g) Surface forest fuel load (Mg/ha)
h) Structure and composition of forest fuel beds
The methodology contemplates obtaining periodic data that contributes to obtaining parameters of interest for the evaluation of the country's forest resources as:
a) Volumetric stocks
b) Increase in volume of coniferous roundwood
c) Damage to standing trees
d) Soil degradation indicators
e) Estimation of aerial biomass
f) Estimation of organic carbon in soil
g) Surface forest fuel load (Mg/ha)
h) Structure and composition of forest fuel beds
Topics
| Topic |
|---|
| Forest inventory |
| Forest assessment |
| Forest survey |
| Timber production |
Keywords
| Keyword |
|---|
| forest resources |
| forest ecosystem |
| forest land cover |
| forest area |
| forest vegetation |
| tree-species diversity |
| forest type |
| mangroves |
| forest health |
| forest growth |
| forest soils |
| biomass |
| carbon stocks |
| volume |
| timber |
| deadwood |
| canopy cover |
| forest regenaration |
| land use |
| land-use change |
| wildfires |
Coverage
Geographic Coverage
National coverage
Universe
INFyS is a land-based survey that covers all types of forests and other forest lands in the country. The target population of INFyS includes all naturally occurring forest vegetation in the country, comprising temperate and tropical forests, and vegetation in arid and semi-arid zones, palm groves, mangroves, hydrophilic communities, and other forest areas.
Producers and sponsors
Primary investigators
| Name |
|---|
| Comisión Nacional Forestal |
| Gerencia de Sistema Nacional de Monitoreo Forestal |
Producers
| Name |
|---|
| Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias |
| United States Forest Service |
| The Canadian forest Service |
| Finnish Forest Research Institute |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| Secretaría de Medio Ambiente y Recursos Naturales | SEMARNAT |
| Comisión Nacional Forestal | CONAFOR |
Sampling
Sampling Procedure
The INFyS uses a systematic-stratified sampling design in two stages, in which 26,220 primary sampling plots (clusters) are located. The country was divided into quadrangular regions (5×5 km panels), which allowed obtaining a consistent distribution of the total number of clusters over the national territory. Clusters or primary sampling units (UMP) are composed of four secondary sampling units (UMS) or sites. The equidistance between clusters varies according to the type of vegetation in question: 5×5 km for temperate, sub-humid and humid forests; 10×10 km for semi-arid forest vegetation types; and 20×20 km for arid forest vegetation types. The 2.5×2.5 km national sampling grid allows for the implementation of state and/or municipal forest inventories in greater detail.
For forests, each UMP is a circular plot of one hectare (56.42 m radius), with four secondary sampling units (UMS) geometrically arranged in an inverted "Y" shape with respect to the north. UMS 1 is also the center of the UMP and UMS 2, 3 and 4 are peripheral. The distance from the center of UMS 1 to each of the other UMS is 45.14 m. The azimuth for locating sites 2, 3, and 4 from the center of site 1 is 0°, 120, and 240° respectively. For jungles, each UMS is a rectangular plot of 40×10 m.
Within the cluster, measurements and observations are made on the different elements of the vegetation and soil. Secondary sampling units (UMS) have a nested design with sampling subsites of different dimensions, according to the object of study. In UMS, specific information such as the diameter of the tree at breast height (dbh) and the height, species, damage, and severity of the tree; and other dasometric characteristics are included for trees with dbh equal to or greater than 7.5 cm. In the case of vegetation in arid zones, data are recorded on larger individuals (with a height equal to or greater than 25 cm, except for globose life forms where the threshold is 10 cm in height). In the center of each UMS, a circular subplot of 12.56 square meters is established to collect data on trees with dbh less than 7.5 cm and height greater than or equal to 25 cm. Finally, in the center of each UMS, a plot of 1 square meter (1×1 m) is established to collect information on shrubs and other non-tree species, such as grasses, ferns and lichens; Data on soil condition, the presence of organic matter, dead wood and erosion are also recorded. A full description of the field methodology for data collection can be found at: https://old-snigf.cnf.gob.mx/wp-content/uploads/Resultados%20Hist%C3%B3ricos%20INFyS/2004-2009/Anexos/Anexo%208%20manual%20y%20procedimientos%20para%20el%20muestreo%20de%20campo.pdf
For forests, each UMP is a circular plot of one hectare (56.42 m radius), with four secondary sampling units (UMS) geometrically arranged in an inverted "Y" shape with respect to the north. UMS 1 is also the center of the UMP and UMS 2, 3 and 4 are peripheral. The distance from the center of UMS 1 to each of the other UMS is 45.14 m. The azimuth for locating sites 2, 3, and 4 from the center of site 1 is 0°, 120, and 240° respectively. For jungles, each UMS is a rectangular plot of 40×10 m.
Within the cluster, measurements and observations are made on the different elements of the vegetation and soil. Secondary sampling units (UMS) have a nested design with sampling subsites of different dimensions, according to the object of study. In UMS, specific information such as the diameter of the tree at breast height (dbh) and the height, species, damage, and severity of the tree; and other dasometric characteristics are included for trees with dbh equal to or greater than 7.5 cm. In the case of vegetation in arid zones, data are recorded on larger individuals (with a height equal to or greater than 25 cm, except for globose life forms where the threshold is 10 cm in height). In the center of each UMS, a circular subplot of 12.56 square meters is established to collect data on trees with dbh less than 7.5 cm and height greater than or equal to 25 cm. Finally, in the center of each UMS, a plot of 1 square meter (1×1 m) is established to collect information on shrubs and other non-tree species, such as grasses, ferns and lichens; Data on soil condition, the presence of organic matter, dead wood and erosion are also recorded. A full description of the field methodology for data collection can be found at: https://old-snigf.cnf.gob.mx/wp-content/uploads/Resultados%20Hist%C3%B3ricos%20INFyS/2004-2009/Anexos/Anexo%208%20manual%20y%20procedimientos%20para%20el%20muestreo%20de%20campo.pdf
Deviations from the Sample Design
The UMP were labeled as initial if the information was recorded in the field in its original location, however, when the location conditions of the site do not allow exact positioning and a displacement over latitude or longitude is required, it is labeled as replacement. If labeled as a replacement, it must be located within a distance of no more than 450 m, within the same plant community and never reported as without plant cover.
Response Rate
Between 2004 and 2009, 79% of the clusters in the national sampling grid were sampled. The clusters that were not sampled were mainly because they were inaccessible; the owners did not grant permission for security reasons, among others (e.g. possession of the property, land or social conflicts).
Weighting
The ratio estimator statistical method is utilized to estimate key forest indicators (such as volume, biomass, carbon stocks, basal area, and tree density, among others), as outlined in Velasco et al. (2003), available at http://cienciasforestales.inifap.gob.mx/index.php/forestales/article/view/882. This method offers data on a per-hectare basis for relevant indicators, using the ratio of two sampled values to estimate a population parameter. In the context of forest inventories, this technique commonly involves using the proportion of sampling units containing a specific attribute of interest (e.g., trees, volume, or basal area) to estimate the total proportion of the population possessing that attribute; information on confidence intervals and the relative sampling error is also provided by this statistical method.
Forest indicators are calculated at the national level by weighin, taking into account the area of each stratum. This ensures that the indicators accurately represent the composition and variability of the entire forest resource across the country.
Forest indicators are calculated at the national level by weighin, taking into account the area of each stratum. This ensures that the indicators accurately represent the composition and variability of the entire forest resource across the country.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2004-08-10 | 2007-12-19 |
Data Collection Mode
Field measurement [field]
Data Processing
Data Editing
Cluster sampling was carried out by companies external to the institution. CONAFOR requires companies to have permanent internal supervision of their personnel in terms of the physical collection of field data and in the capture phase, in such a way that conceptual errors are minimal or non-existent.
CONAFOR also implemented external supervision, which consisted of hiring a company that built 10% of the sampled sites. CONAFOR carried out a statistical analysis of the data from the supervised conglomerates, comparing the parameters obtained with the data from the supervisory company with the data collected previously.
Upon receipt of the information, CONAFOR reviewed and compared the information collected on paper and the same information digitized through a “capture client” prior to being incorporated into the INFyS database. Finally, debugging processes have been applied that allowed the estimation error to be reduced.
CONAFOR also implemented external supervision, which consisted of hiring a company that built 10% of the sampled sites. CONAFOR carried out a statistical analysis of the data from the supervised conglomerates, comparing the parameters obtained with the data from the supervisory company with the data collected previously.
Upon receipt of the information, CONAFOR reviewed and compared the information collected on paper and the same information digitized through a “capture client” prior to being incorporated into the INFyS database. Finally, debugging processes have been applied that allowed the estimation error to be reduced.
Data Appraisal
Estimates of Sampling Error
The quantitative data analysis provides important dasometric indicators such as tree density, crown cover, above-ground biomass, carbon stocks, and timber volume. Statistical methods are used to generate reports for these indicators. The estimation of these relevant indicators is based on the ratio estimator (ER) approach, a statistical methodological approach developed to improve the accuracy of average values of relevant forest indicators based on the sampled area (Velasco, et. al., 2003; available at http://cienciasforestales.inifap.gob.mx/index.php/forestales/article/view/882
Data Appraisal
Some issues were encountered during data collection. The main problems included the inaccessibility of primary sampling units, leading to biased estimations of indicators. Errors in field data collection included mainly misidentification of tree species by scientific names, inconsistencies between tree height and diameter, and mislabeling of vegetation types.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| José Armando Alanís de la Rosa, Gerente de Sistema Nacional de Monitoreo Forestal | CONAFOR | [email protected] |
| Leonardo Ruiz Delgado, Subgerente de Instrumentos de Colecta de Datos | CONAFOR | [email protected] |
| Carlos Isaías Godínez Valdivia, Subgerencia de Administración de Bases de Datos del Sistena Nacional de Monitoreo Forestal | CONAFOR | [email protected] |
| Sergio Armando Villela Gaytán, Departamento de Inventario Forestal y de Suelos | CONAFOR | [email protected] |
| Rodrigo Ramos Madrigal, Departamento de Estadísticas Forestales. | CONAFOR | [email protected] |
Confidentiality
Personal data provided by contacts in the field are confidential and therefore classified as reserved and these are not provided to any person who requests information about the INFyS.
The Policy for the Management of Confidentiality in Statistical and Geographic Information (PGCIEG) establishes the general measures that must be implemented to manage the statistical confidentiality of the data provided by the System Informants (SI) under the National Statistical and Geographic Information System (SNIEG). The PGCIEG is mandatory for all State Units responsible for the generation and management of Information of National Interest (IIN), under the SNIEG criteria. PGCIEG is available at https://dof.gob.mx/nota_detalle.php?codigo=5634105&fecha=29/10/2021#gsc.tab=0.
CONAFOR is mandated to implement the PCMSIG, as it is the institution responsible for INFyS implementation which was determined as IIN, by an agreement published on May 28, 2014, in the Official Gazette of the Federation, available at https://www.dof.gob.mx/nota_detalle.php?codigo=5346488&fecha=28/05/2014#gsc.tab=0.
The PCIEG sets out measures to ensure the confidentiality of the data provided by the system's informants. The policy aims to protect the privacy of informants and ensure that the individual information they provide is kept confidential and used solely for statistical and geographic purposes.
Access conditions
Data available from an external repository
Citation requirements
"CONAFOR, (year of consultation). Database of the National Forest and Soils Inventory 2004-2009, Mexico"
Disclaimer and copyrights
Disclaimer
As an authorized end user of the INFYS database, you agree to the following terms:
CONAFOR is not responsible for how you interpret or apply the information from the database. Any decisions based on your interpretation release CONAFOR from responsibility. CONAFOR is also not responsible for discrepancies due to precision, rounding, numerical truncation, or technical changes that may affect the results.
Although CONAFOR strives to provide high-quality information and has implemented security measures to protect the data, it does not accept responsibility for any alteration or manipulation of the data once it is published on the website.
The website may contain links to other national and international organizations. However, CONAFOR does not take responsibility for the content or use of these sites.
The laws, regulations, and provisions on the website do not create new rights or obligations beyond those published in the Official Gazette of the Federation.
CONAFOR is not responsible for how you interpret or apply the information from the database. Any decisions based on your interpretation release CONAFOR from responsibility. CONAFOR is also not responsible for discrepancies due to precision, rounding, numerical truncation, or technical changes that may affect the results.
Although CONAFOR strives to provide high-quality information and has implemented security measures to protect the data, it does not accept responsibility for any alteration or manipulation of the data once it is published on the website.
The website may contain links to other national and international organizations. However, CONAFOR does not take responsibility for the content or use of these sites.
The laws, regulations, and provisions on the website do not create new rights or obligations beyond those published in the Official Gazette of the Federation.
Metadata production
DDI Document ID
DDI_MEX_2004-2009_INFyS_v01_EN_M_v01_A_ESS_FAO
Producers
| Name | Affiliation | Role |
|---|---|---|
| Dissemination and Outreach Team, Statistics Division | Food and Agriculture Organization | Metadata adapted for FAM |
DDI Document version
MEX_2004-2009_INFyS_v01_EN_M_v01_A_ESS_v01