Statistical Mapping of Tree Species over Europe

Statistical Mapping of Tree Species over Europe. Brus, D. J., Hengeveld, G. M., Walvoort, D. J. J., Goedhart, P. W., Heidema, A. H., Nabuurs, G. J., & Gunia, K. 131(1):145–157.

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In order to map the spatial distribution of twenty tree species groups over Europe at 1~km × 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.

@article{brusStatisticalMappingTree2012,
  title = {Statistical Mapping of Tree Species over {{Europe}}},
  author = {Brus, D. J. and Hengeveld, G. M. and Walvoort, D. J. J. and Goedhart, P. W. and Heidema, A. H. and Nabuurs, G. J. and Gunia, K.},
  date = {2012-04},
  journaltitle = {European Journal of Forest Research},
  volume = {131},
  pages = {145--157},
  issn = {1612-4669},
  doi = {10.1007/s10342-011-0513-5},
  url = {https://doi.org/10.1007/s10342-011-0513-5},
  abstract = {In order to map the spatial distribution of twenty tree species groups over Europe at 1~km × 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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-9264123,~to-add-doi-URL,europe,forest-resources,mapping,spatial-disaggregation,species-distribution},
  number = {1}
}

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