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Auteur Matteo Mura |
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Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique / Matteo Mura in Remote sensing of environment, vol 186 (1 December 2016)
[article]
Titre : Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique Type de document : Article/Communication Auteurs : Matteo Mura, Auteur ; Ronald E. McRoberts, Auteur ; Gherardo Chirici, Auteur ; Marco Marchetti, Auteur Année de publication : 2016 Article en page(s) : pp 678 - 686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification barycentrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] indice de diversité
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Italie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Forest structural diversity plays a major role for forest management, conservation and restoration and is recognized as a fundamental aspect of forest biodiversity. The assessment, maintenance and restoration of a diversified forest structure have become major foci in the effort to preserve forest ecosystems from loss of biological diversity. However, the assessment of forest biodiversity is difficult because it involves multiple components and is characterized using multiple variables. The objective of the study was to develop a methodological approach for predicting, mapping, and constructing a statistical inference for a multiple-variable index of forest structural diversity. The method included three key components: (i) use of the k-Nearest Neighbors (k-NN) technique, field plot data, and airborne laser scanning metrics to predict multiple forest structural diversity variables simultaneously, (ii) incorporation of multiple diversity variable predictions into a single index, and (iii) construction of a statistically rigorous inference for the population mean of the index. Three structural diversity variables were selected to illustrate the method: growing stock volume and the standard deviations of tree diameter at breast-height and tree height. Optimization of the k-NN technique produced mean relative deviations less in absolute value than 0.04 for predictions for each of the three structural diversity variables, R2 values between 0.50 and 0.66 which were in the range of values reported in the literature, and a confidence interval for the population mean of the index whose half-width was approximately 5% of the mean. Finally, the spatial pattern depicted in the resulting map of forest structural diversity for the study area contributed to validating the proposed method. Numéro de notice : A2016-769 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.09.010 En ligne : http://dx.doi.org/10.1016/j.rse.2016.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82419
in Remote sensing of environment > vol 186 (1 December 2016) . - pp 678 - 686[article]A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data / Gherardo Chirici in Remote sensing of environment, vol 176 (April 2016)
[article]
Titre : A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data Type de document : Article/Communication Auteurs : Gherardo Chirici, Auteur ; Matteo Mura, Auteur ; Daniel McInerney, Auteur ; Nicolas Py , Auteur ; Erkki Tomppo, Auteur ; Lars T. Waser, Auteur ; Davide Travaglini, Auteur ; Ronald E. McRoberts, Auteur Année de publication : 2016 Article en page(s) : pp 282 - 294 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] forêt
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] plus proche voisin, algorithme duRésumé : (auteur) The k-Nearest Neighbors (k-NN) technique is a popular method for producing spatially contiguous predictions of forest attributes by combining field and remotely sensed data. In the framework of Working Group 2 of COST Action FP1001, we reviewed the scientific literature for forestry applications of k-NN. Information available in scientific publications on this topic was used to populate a database that was then used as the basis for a meta-analysis. We extracted qualitative and quantitative information from 260 experimental tests described in 148 scientific papers. The papers represented a geographic range of 26 countries and a temporal range from 1981 to 2013. Firstly, we describe the literature search and the information extracted and analyzed. Secondly, we report the results of the meta-analysis, especially with respect to estimation accuracies reported for k-NN applications for different configurations, different forest environments, and different input information. We also provide a summary of results that may reasonably be expected for those planning a k-NN application using remotely sensed data from different sensors and for different forest attributes. Finally, we identify some methodological publications that have advanced the state of the science with respect to k-NN. Numéro de notice : A2016--196 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.02.001 Date de publication en ligne : 13/02/2016 En ligne : https://doi.org/10.1016/j.rse.2016.02.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91859
in Remote sensing of environment > vol 176 (April 2016) . - pp 282 - 294[article]