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European Forest Types: toward an automated classification
Francesca Giannetti, Anna Barbati, Leone Davide Mancini, Davide Travaglini, Annemarie Bastrup-Birk, Roberto Canullo, Susanna Nocentini, Gherardo Chirici

European Forest Types: toward an automated classification

Science & Mathematics Published 2018 Verification Pending

Overview & Synopsis

AbstractKey messageThe outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping.ContextForest Types classification systems are aimed at stratifying forest habitats. Since 2006, a common scheme for classifying European forests into 14 categories and 78 types (European Forest Types, EFT) exists.AimsThis work presents an innovative method and automated classification system that, in an objective and replicable way, can accurately classify a given forest habitat according to the EFT system of nomenclature.MethodsA rule-based expert system (RBES) was adopted as a transparent approach after comparison with the well-known Random Forest (RF) classification system. The experiment was carried out based on the information acquired in the field in 2010 ICP level I plots in 17 European countries. The accuracy of the automated classification is evaluated by comparison with an independent classification of the ICP plots into EFT carried out during the BioSoil project field survey. Finally, the RBES automated classifier was tested also for a pixel-based classification of a pan-European distribution map of beech-dominated forests.ResultsThe RBES successfully classified 94% of the plots, against a 92% obtained with RF. When applied to the mapped domain, the accuracy obtained with the RBES for the beech forest map classification was equal to 95%.ConclusionThe RBES algorithm successfully automatically classified field plots and map pixels on the basis of the EFT system of nomenclature. The EFT system of nomenclature can be now easily and objectively implemented in operative transnational European forest monitoring programs.

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Document Information

Document Title European Forest Types: toward an automated classification
Author / Organization Francesca Giannetti, Anna Barbati, Leone Davide Mancini, Davide Travaglini, Annemarie Bastrup-Birk, Roberto Canullo, Susanna Nocentini, Gherardo Chirici
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2018
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-249442
Original Source Link Visit Source Page on hal.science »