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European Forest Types: toward an automated classification / Francesca Giannetti in Annals of Forest Science [en ligne], vol 75 n° 1 (March 2018)
Titre : European Forest Types: toward an automated classification Type de document : Article/Communication Auteurs : Francesca Giannetti, Auteur ; Anna Barbati, Auteur ; Leone Davide Mancini, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de tri
[Termes IGN] base de règles
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification pixellaire
[Termes IGN] Europe (géographie physique)
[Termes IGN] Fagus (genre)
[Termes IGN] milieu naturel
[Termes IGN] système d'information forestier
[Termes IGN] système d'information géographique
[Termes IGN] système expert
[Vedettes matières IGN] Inventaire forestier
Résumé : (Auteur) Key message: The 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.
Context: Forest 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.
Aims: This 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.
Methods: A 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.
Results: The 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%.
Conclusion: The 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.
Numéro de notice : A2018-318 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-017-0674-6 Date de publication en ligne : 03/01/2018 En ligne : https://doi.org/10.1007/s13595-017-0674-6 Format de la ressource électronique : URL article Permalink :
in Annals of Forest Science [en ligne] > vol 75 n° 1 (March 2018)[article]On discovering co-location patterns in datasets : a case study of pollutants and child cancers / Jundong Li in Geoinformatica [en ligne], vol 20 n° 4 (October - December 2016)
Titre : On discovering co-location patterns in datasets : a case study of pollutants and child cancers Type de document : Article/Communication Auteurs : Jundong Li, Auteur ; Aibek Adilmagambetov, Auteur ; Mohomed Shazan Mohomed Jabbar, Auteur Année de publication : 2016 Article en page(s) : pp 651 - 692 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] algorithme de tri
[Termes IGN] analyse spatiale
[Termes IGN] co-positionnement
[Termes IGN] enfant
[Termes IGN] exploration de données géographiques
[Termes IGN] polluant
[Termes IGN] santé
[Termes IGN] test statistique
Résumé : (Auteur) We intend to identify relationships between cancer cases and pollutant emissions by proposing a novel co-location mining algorithm. In this context, we specifically attempt to understand whether there is a relationship between the location of a child diagnosed with cancer with any chemical combinations emitted from various facilities in that particular location. Co-location pattern mining intends to detect sets of spatial features frequently located in close proximity to each other. Most of the previous works in this domain are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds, and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. Our proposed approach is focused on a grid based transactionization? of the geographic space, and is designed to mine datasets with extended spatial objects. It is also capable of incorporating uncertainty of the existence of features to model real world scenarios more accurately. We eliminate the necessity of using a global threshold by introducing a statistical test to validate the significance of candidate co-location patterns and rules. Experiments on both synthetic and real datasets reveal that our algorithm can detect a considerable amount of statistically significant co-location patterns. In addition, we explain the data modelling framework which is used on real datasets of pollutants (PRTR/NPRI) and childhood cancer cases. Numéro de notice : A2016-813 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0254-1 En ligne : http://dx.doi.org/10.1007/s10707-016-0254-1 Format de la ressource électronique : URL article Permalink :
in Geoinformatica [en ligne] > vol 20 n° 4 (October - December 2016) . - pp 651 - 692[article]Comparing raster map for spatial modelling and analysis / M. Kuhnert in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 8 (August 2005)
Titre : Comparing raster map for spatial modelling and analysis Type de document : Article/Communication Auteurs : M. Kuhnert, Auteur ; A. Voinov, Auteur ; R. Seppelt, Auteur Année de publication : 2005 Article en page(s) : pp 975 - 984 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme de tri
[Termes IGN] analyse comparative
[Termes IGN] analyse spatiale
[Termes IGN] cartographie numérique
[Termes IGN] données maillées
Résumé : (Auteur) The comparison of spatial patterns is recognized as an important task in landscape ecology especially when spatially explicit simulation modeling or remote sensing is applied. Yet, there is no agreed procedure for doing that, probably because different problems require different algorithms. We explored a variety of existing algorithms and modified some of them to compare grid-based maps with categorical attributes. A new algorithm based on the "expanding window" approach was developed and compared to other known algorithms. The goal was to offer simple and flexible procedures for comparing spatial patterns in grid based maps that do not take into consideration object shapes and sizes of the maps. The difference between maps was characterized by three values: quantity, location, and distance between corresponding categories in the maps. Combinations of these indices work as good criteria to quantify differences between maps. A web-based survey was set up, in which participants were asked to grade the similarity of ten pairs of maps. These results were then used to compare how well the various algorithms can perform relative to the visual comparisons obtained; they were also used to calibrate existing algorithms. Numéro de notice : A2005-337 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article Permalink :
in Photogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 8 (August 2005) . - pp 975 - 984[article]
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