2. Data models and data input

Anon. 1998. FRA 2000. Guidelines for assessments in tropical and sub-tropical countries. Forest Resources Assessment Program. Working paper 2. FAO. (available at http://www.fao.org/forestry/fo/fra/docs/FRA_Wp2eng.PDF.)

Three basic and complementary information generation mechanisms, or modules, available to FRA 2000 are a) the assessment based on existing information and b) assessment based on newly generated information through a global remote sensing survey, and c) special studies. For every new assessment the data sources used in the previous assessments are consulted and new sources identified through contacts in the countries. When new sources are identified, they are compared with the older information to determine which provides the best and most reliable baseline. Comparability between two (or more) information sources is also evaluated, verifying their utility to serve as representative surveys in a continuous time-series. Information sources for the FAO assessments typically include: Tabular data derived from forest inventories and land cover surveys; Forest and land cover maps in digital or analogue format; Documents (inventory reports, etc.). Aggregation of original (national) classes into a single FRA 2000 category and disaggregation of original (national) classes into two or more FRA 2000 standard categories are usually required. The original country statistics and the derived statistics adjusted to the FRA standard classification should be reported. All data sources require an appraisal of their reliability and significance for use in FRA 2000. This appraisal helps to identify information quality, including geographic, methodological and thematic gaps, in addition to tracking the country�s progress in advancing the quality of their assessments. Standardized criteria for objective evaluations of information source reliability are not easily developed or carried out. As countries develop inventories and assessments for national purposes and conduct them under conditions unique to their situation, there are inevitable inconsistencies between various national surveys. Statistics also require an adjustment to common reference years, namely 1990 and 2000. Standardization to common reference years is conducted with the help of an adjustment function or forest-cover change model, which relates forest cover changes to ancillary variables, i.e. human population change and the ecological zone of the respective forest area. Where thresholds differ, experts with good local knowledge of the vegetation and familiarity with the inventory providing the baseline statistics are needed to make adjustments for classifications.


Anon. 1999. Forest Resources Information System (FORIS). Concepts and Status Report. Forest Resources Assessment Program. Working paper 7.. FAO. (available at http://www.fao.org/forestry/fo/fra/docs/FRA_Wp7eng.PDF.)

FORIS includes both existing source information and derived information. All distributed information should be verified, meaning that all data items should have a source reference, and the data processing should be transparent. Countries validate and approve information concerning their country before publication. Data ownership is distributed among users; Security (user priviliges, backup, documentation, dependencies on persons, defined system manager/ownership) should be tight; Integration with other system development efforts is essential. The baseline resources information in FORIS is structured by Geographic units in one dimension, and by thematic subject in another. A third dimension is along the information production process (see 4.1), i.e. the degree of processing that has been applied. Thematic subjects: Country description; Forest cover; Plantations; Volume / biomass; Protected areas / conservation; Forest change; Ownership; NWGS; Fires; Wood supply. The administration of geographic units is a known obstacle when producing FAO reports. Countries change name, split, merge or simply should not be shown. Furthermore, they can be grouped in different ways depending on the purpose of the report. Finally, countries are subdivided into administrative units, or into other arbitrary units based on e.g. climatic conditions. Information Production Process: Input (data collection; reviewing; source data entry); Analysis (data processing iterations; reporting); Output (distribution).


Anon. 2003. ViaVoice. IBM. (available at http://www-3.ibm.com/software/voice/viavoice/.)

Web page describing voice-based entry of information.


Kleinn, C. 2002. New technologies and methodologies for national forest inventories. In Unasylva 210, 53, 10-15. (available at http://www.fao.org/docrep/005/y4001e/y4001e03.htm.)

The main tree attributes measured in the field are diameters and heights. In addition, measurements of distances and angles are required for navigation, plot establishment and recording of tree positions when GPS is not used. Ultrasonic and laser devices facilitate distance and angle measurements. Electronic distance measurement greatly increases the speed of navigation to plot locations, although GPS may replace it. Electronic distance meters (ultrasonic or laser based) facilitate the measurement of tree positions by distance and azimuth from the plot centre. If the measurement device is connected to a mobile data logger, data can be stored directly without the need for paper forms. Procedures for checking data directly in the field allow field crews to correct errors and inconsistencies immediately, which improves data quality. A next step will be direct connection of mobile data loggers to a central database via mobile communication and Internet access. The central database would then be permanently updated and checking procedures could be adjusted immediately and uniformly for all field crews. However, such online data entry is expected to provide less of a gain in quality than immediate digital storage of data in the field; a periodic data transfer (once a week or so) appears to be sufficient.


Miles, P.D. 2001. Information Systems for the Forest Resource Analyst. Proc. IUFRO Conf. �Forest Biometry, Modelling and Information Science�, University of Greenwich, June 2001.. (available at http://cms1.gre.ac.uk/conferences/iufro/proceedings/miles3.pdf.)

Describes the Forest Inventory Mapmaker Program. The development of standard data formats led to the development of data mining tools. Data mining tools permit the analyst to focus on the big picture - monitoring the condition of the forest resource.


Thomson, A.J. 2000. Elicitation and representation of Traditional Ecological Knowledge, for use in forest management. In Computers and Electronics in Agriculture, 27, 155-165. (available at http://www.pfc.cfs.nrcan.gc.ca/programs/tek/index_e.html.)

Describes use of interview-based data for determining indicators in a manner that allows people to see how their information is interpreted and used.


Tokola, T., A. Turkia, J. Sarkeala, and J. Soimasuo. 1997. An entity-relationship model for forest inventory. In Can. J. For. Res., 27, 1586-1594.

�Changing data needs require flexible database management and integration of multi-source data.� A data model helps one to perceive, organize and describe data in a conceptual schema that includes both the data and the operations for manipulating the data set. The main elements of forest resource information are (1) map data based on stratification, and (2) field plot data based on sampling. Information systems may use prior knowledge and mathematical models to derive information. Systems analysts build the data model and information system: if the model is flexible, it can be easily adapted for new cases. It is important to record the history of transformations of the data. The system was designed to easily link to models. Predefined report layouts are used.


Varjo, J., Korotkov, A.V. and Najera, J. Systems 2000 16-20th May, Hyytiälä Finland. 2000. UN-ECE/FAO Temperate and Boreal Forest Resource Assessment-2000 - An International System for Collecting, Processing and Disseminating Information on Forest Resources. Proc. Forestry Information Systems 2000 16-20th May, Hyytiälä Finland. (available at http://www.metsa.fi/eng/tat/jointweek/pdf/varjo_fao.pdf.)

Describes a data collection system where data entry is based on use of Excel and Access databases. Specification of methods of adjustment of national information to the TBFRA was required, including description of errors. Notes and comments were integral part of the information system. TBRFA data should not be interpreted without prior familiarization with these notes and comments. The data validation and analysis system requires much manual work to maintain and update data. The data dissemination system presents TBFRA information in 3 forms: paper, digital via www, and original database. Access tables transferred to Excel for graphics. Organization and maintenance of the database is the most critical factor in the TBFRA data system. The current structure is carried over from earlier approach, precluding automation of some features. Updating TBFRA information by countries can be problematic; requires many manual steps. The possibility of dealing with the problems depends on resources.