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Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data / Elisa Schäfer in Ecological indicators, vol 64 (May 2016)  

Public [article]  inEcological indicators > vol 64 (May 2016) . - pp 49 - 58 Titre : Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data Type de document : Article/Communication Auteurs : Elisa Schäfer, Auteur ; Janne Heiskanen, Auteur ; Vuokko Heikinheimo, Auteur ; Petri Pellikka, Auteur Année de publication : 2016 Article en page(s) : pp 49 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] arbre (flore) [Termes IGN] biodiversité [Termes IGN] canopée [Termes IGN] forêt tropicale [Termes IGN] image aérienne [Termes IGN] Kenya [Termes IGN] placette d'échantillonnage [Termes IGN] segmentation d'image Résumé : (auteur) With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data. Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed. According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE = 3 species; r2 = 0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest. In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be applied also for estimating beta diversity. Numéro de notice : A2016-346 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecolind.2015.12.026 En ligne : http://dx.doi.org/10.1016/j.ecolind.2015.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81035 [article]