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Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information / Agnese Marcelli in Silva fennica, vol 54 n° 2 (March 2020)  

Public [article]  inSilva fennica > vol 54 n° 2 (March 2020) Titre : Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information Type de document : Article/Communication Auteurs : Agnese Marcelli, Auteur ; Walter Mattioli, Auteur ; Nicola Puletti, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] acteurs de la filière bois-forêt [Termes IGN] bois sur pied [Termes IGN] échantillonnage [Termes IGN] image à haute résolution [Termes IGN] image Sentinel-MSI [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Italie [Termes IGN] Populus (genre) [Termes IGN] récolte de bois [Termes IGN] régression linéaire [Termes IGN] tessellation [Termes IGN] volume en bois Résumé : (auteur) Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed. Numéro de notice : A2020-323 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10247 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.14214/sf.10247 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95197 [article]

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 157 - 169 Titre : Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests Type de document : Article/Communication Auteurs : Nicola Puletti, Auteur ; Nicola Camarretta, Auteur ; Piermaria Corona, Auteur Année de publication : 2016 Article en page(s) : pp 157 - 169 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] feuillu [Termes IGN] forêt [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] matrice de confusion [Termes IGN] Pinophyta [Termes IGN] régression multivariée par spline adaptative Résumé : (auteur) The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic. Numéro de notice : A2016-832 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164909 En ligne : http://dx.doi.org/10.5721/EuJRS20164909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82716 [article]