Two types of available databases may be used to classify land. In many countries, national datasets of the type discussed below will be available. Otherwise, inventory compilers may use international datasets. Both types of databases are described below.
These will usually be based on existing data, updated annually or periodically. Typical sources of data include forest inventories, agricultural census and other surveys, censuses for urban and natural land, land registry data and maps.
Several projects have been undertaken to develop international land-use and land cover datasets at regional to global scales (Annex 3A.1 lists some of these datasets). Almost all of these datasets are stored as raster data generated using different kinds of satellite remote sensing imagery, complemented by ground reference data obtained by field survey or comparison with existing statistics/maps. These datasets can be used for:
• Estimating spatial distribution of land-use categories. Conventional inventories usually provide only the total sum of land-use area by classes. Spatial distribution can be reconstructed using international land-use and land cover data as auxiliary data where national data are not available.
• Reliability assessment of the existing land-use datasets. Comparison between independent national and international datasets can indicate apparent discrepancies, and understanding these may increase confidence in national data and/or improve the usability of the international data, if required for purposes such as extrapolation.
• When using an international dataset, inventory compilers should consider the following:
(i) The classification scheme (e.g., definition of land-use classes and their relations) may differ from that in the national system. The equivalence between the classification systems used by the country and the systems described in Section 3.2 (Land-use categories) therefore needs to be established by contacting the international agency and comparing their definitions with those used nationally.
(ii) Spatial resolution (typically 1km nominally but sometimes an order of magnitude more in practice) may be coarse, so national data may need aggregating to improve comparability.
(iii) Classification accuracy and errors in geo-referencing may exist, though several accuracy tests are usually conducted at sample sites. The agencies responsible should have details on classification issues and tests undertaken.
(iv) As with national data, interpolation or extrapolation will probably be needed to develop estimates for the time periods to match the dates required for reporting.
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