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Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site / Yoni Gavish in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 1 - 12 Titre : Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site Type de document : Article/Communication Auteurs : Yoni Gavish, Auteur ; Jerome O’Connell, Auteur ; Charles J. Marsh, Auteur ; Cristina Tarantino, Auteur ; Palma Blonda, Auteur ; Valeria Tomaselli, Auteur ; William E. Kunin, Auteur Année de publication : 2018 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] habitat (nature) [Termes IGN] occupation du sol [Termes IGN] performance [Termes IGN] site Natura 2000 Résumé : (Auteur) The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre‐defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2–3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into “black-box” based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps. Numéro de notice : A2018-071 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.002 Date de publication en ligne : 05/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89430 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018023	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing / Jerome O’Connell in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 109 (November 2015) . - pp 165 - 177 Titre : Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing Type de document : Article/Communication Auteurs : Jerome O’Connell, Auteur ; Ute Bradter, Auteur ; Tim G. Benton, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 165 - 177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse d'image orientée objet [Termes IGN] Angleterre [Termes IGN] Aves [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] habitat d'espèce [Termes IGN] image aérienne [Termes IGN] image infrarouge couleur Résumé : (auteur) Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability. Numéro de notice : A2015-862 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.09.007 Date de publication en ligne : 09/10/2015 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.09.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79243 [article]