Centre de documentation
scientifique

High resolution mapping of forest resources and prediction reliability using multisource inventory approach / Ankit Sagar (2021)    

Public Titre : High resolution mapping of forest resources and prediction reliability using multisource inventory approach Type de document : Article/Communication Auteurs : Ankit Sagar , Auteur ; Cédric Vega , Auteur ; Christian Piedallu, Auteur ; Olivier Bouriaud , Auteur ; Jean-Pierre Renaud , Auteur Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : ARBRE / AgroParisTech (2007 -) Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings, INCA 2021, 41th Indian National Cartographic Association international conference, Cartography for self-reliant India 27/10/2021 29/10/2021 Chandigarh Inde open access proceedings Projets : DEEPSURF / Pironon, Jacques Importance : pp 219 - 221 Langues : Anglais (eng) Descripteur : [Termes IGN] capital sur pied [Termes IGN] données multisources [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] ressources forestières [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) [introduction] National forest inventory (NFI) provides precise forest resource estimates at national up to regional scale but could not support local estimates with high precision because of inadequate number of field plots. The forest managers and stakeholders prefer local estimates at fine spatial resolution (Chirici et al. 2020). Multi source-national forest inventory (MS-NFI) opens the possibility for wall-to-wall mapping of forest attributes with good precision at high spatial resolution. MS-NFI rely on the combination of NFI data with auxiliary data (remote sensing data, thematic map, etc.), and in many cases, this combination is modelled through a non-parametric k-nearest neighbour (k-NN) approach. k-NN is capable in predicting several attributes in a single model with a low prediction bias. The major drawbacks of k-NN are its inability to predict beyond the range of training data (Magnussen et al. 2010), the lack of well-established variance estimator (McRoberts et al. 2011) and its decreasing performance with increasing dimensionality. The estimation maps for the forest resources are important (Tomppo et al. 2008; Chirici et al., 2020), but their prediction uncertainties have also to be taken into consideration. Methods have been proposed recently to map the prediction uncertainty (Esteban et al, 2019) and these maps have been included into an inferential framework (Saarela et al, 2020). In this study we propose a method building upon bootstrap model-based estimator (McRoberts et al. 2011) to estimate forest attributes of interest at pixel level and address the problem of extrapolation and precision of estimation by providing maps for both at high spatial resolution. For sake of concision, results were presented for growing stock volume (GSV) only. Numéro de notice : C2021-031 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1986 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1986 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98995

Documents numériques

peut être téléchargé High resolution mapping ... - diaporama - pdf auteur Adobe Acrobat PDF