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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]