Compilation of a European forest map from Portugal to the Ural mountains based on earth observation data and forest statistics

Schuck, Andreas; Päivinen, Risto; Häme, Tuomas; Brusselen, Jo Van; Kennedy, Pamela J.; Folving, Sten

doi:10.1016/s1389-9341(03)00024-8

Cited by 34 publications

(21 citation statements)

References 5 publications

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“…Mayaux et al, 1998). In Europe, Schuck et al (2003) found good agreement between AVHRR and (groundbased) forest-inventory data. As an adjunct to FRA2000, a 'global forest-cover map' was produced in collaboration with USGS.…”

Section: Fao and The Use Of 'Country' Datamentioning

confidence: 78%

Assessing the world's forests

Mather

2005

Global Environmental Change

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Section: Fao and The Use Of 'Country' Datamentioning

confidence: 78%

Assessing the world's forests

Mather

2005

Global Environmental Change

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“…Forests cover over 35% of Europe (MCPFE Liaison Unit Warsaw, UNECE, FAO 2007;Schuck et al 2003). These forests provide important goods and services to the human society.…”

Section: Introductionmentioning

confidence: 99%

Statistical mapping of tree species over Europe

et al. 2011

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In order to map the spatial distribution of twenty tree species groups over Europe at 1 km 9 1 km resolution, the ICP-Forest Level-I plot data were extended with the National Forest Inventory (NFI) plot data of eighteen countries. The NFI grids have a much smaller spacing than the ICP grid. In areas with NFI plot data, the proportions of the land area covered by the tree species were mapped by compositional kriging. Outside these areas, these proportions were mapped with a multinomial multiple logistic regression model. A soil map, a biogeographical map and bioindicators derived from temperature and precipitation data were used as predictors. Both methods ensure that the predicted proportions are in the interval [0,1] and sum to 1. The regression predictions were iteratively scaled to the National Forest Inventory statistics and the Forest map of Europe. The predicted proportions for the twenty tree species were validated by the Bhattacharryya distance between predicted and observed proportions at 230 plot data separated from the calibration data. Besides, the map with the predicted dominant species was validated by computing the error matrix. The median Bhattacharryya distance in the subarea with NFI plot data was 1.712, whereas in the subarea with ICP-Level-I data, this was 2.131. The scaling did not significantly decrease the Bhattacharryya distance. The estimated overall accuracy of this map was 43%. In areas with NFI plot data, overall accuracy was 57%, outside these areas 33%. This gain was mainly attributable to the much denser plot data, less to the prediction method.

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“…There are several existing maps that show forest classes, have a pan-European coverage, and are based on RS data (Bartholomé and Belward, 2005;Defries et al, 2000;Friedl et al, 2002;Häme et al, 2001;Hansen et al, 2000;Schuck et al, 2003). All these maps were derived from images having spatial resolutions coarser than 100 m. This limited spatial resolution does not necessarily cause problems in deriving local forest area estimates.…”

Section: Introductionmentioning

confidence: 99%