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Offering the appetite for the monitoring of European forests a diversified diet / Jean-Daniel Bontemps in Annals of Forest Science [en ligne], vol 79 n° 1 (December 2022)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 79 n° 1 (December 2022) . - n° 19 Titre : Offering the appetite for the monitoring of European forests a diversified diet Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur ; Olivier Bouriaud , Auteur ; Cédric Vega , Auteur ; Laura Bouriaud , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 19 Note générale : bibliographie NB article d'opinion Langues : Anglais (eng) Descripteur : [Termes IGN] Europe (géographie politique) [Termes IGN] intégration [Termes IGN] inventaire forestier étranger (données) [Termes IGN] inventaire forestier national (données France) [Termes IGN] politique publique [Termes IGN] ressources forestières [Termes IGN] santé des forêts [Termes IGN] surveillance forestière [Termes IGN] Union Européenne [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forest monitoring in Europe is turning matter of renewed political concern, and a possible role for ICP Forests health monitoring has been suggested to meet this goal (Ann For Sci 78:94, 2021). Multipurpose national forest inventory (NFI) surveys yet offer a sampling effort by two orders of magnitude greater than ICP level 1, have accomplished substantial methodological and harmonization progresses in the recent years, and therefore form a decisive contributor to future European forest monitoring incentives. Possible paths for the future development of a pan-European, comprehensive and more accurate monitoring are designed that stress a crucial need to build on the assets of the existing forest monitoring programs and favor their cooperation, in order to limit the co-existence of distinct forest monitoring processes. Numéro de notice : A2022-320 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01139-7 Date de publication en ligne : 11/04/2022 En ligne : http://dx.doi.org/10.1186/s13595-022-01139-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100432 [article]

A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 97 (May 2021) . - n° 102303 Titre : A new small area estimation algorithm to balance between statistical precision and scale Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Jean-Pierre Renaud, Auteur ; Ankit Sagar , Auteur ; Olivier Bouriaud , Auteur Année de publication : 2021 Projets : LUE / Université de Lorraine, DIABOLO / Packalen, Tuula, ARBRE/CHM-era / Jolly, Anne Article en page(s) : n° 102303 Note générale : bibliographie This research was funded by The French Environmental Management Agency (ADEME), grant number 16-60-C0007. The methods and algorithms for processing photogrammetric data were supported by DIABOLO project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Ankit Sagar received the financial support of the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, through the project Impact DeepSurf. Langues : Anglais (eng) Descripteur : [Termes IGN] arbre BSP [Termes IGN] capital sur pied [Termes IGN] données auxiliaires [Termes IGN] données de terrain [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier national (données France) [Termes IGN] réduction d'échelle [Termes IGN] seuillage [Termes IGN] surface terrière [Vedettes matières IGN] Sylviculture Résumé : (auteur) Combining national forest inventory (NFI) data with auxiliary information allows downscaling and improving the precision of NFI estimates for small domains, where normally too few field plots are available to produce reliable estimates. In most situations, small domains represent administrative units that could greatly vary in size and forested area. In small and poorly sampled domains, the precision of estimates often drop below expected standards. To tackle this issue, we introduce a downscaling algorithm generating the smallest possible groups of domains satisfying prescribed sampling density and estimation error. The binary space partitioning algorithm recursively divides the population of domains in two groups while the prescribed precision conditions are fulfilled. The algorithm was tested on two major forest attributes (i.e. growing stock and basal area) in an area of 7,500 km2 dominated by hardwood forests in the centre of France. The estimation domains consisted in 157 municipalities. The field data included 819 NFI plots surveyed during a 5 years period. The auxiliary data consisted in 48 metrics derived from a forest map, photogrammetric models and Landsat images. A model-assisted framework was used for estimation. For each forest attribute, the best model was selected using a best-subset approach using a Bayesian Information Criteria. The retained models explained 58% and 41% of the observed variance for the growing stocks and basal areas respectively. The performance of the algorithm was evaluated using a minimum of 3 NFI points per domain and estimation errors varying from 10 to 50%. For a target estimation error set to 10%, the algorithm led to a limited number of estimation domains ( The algorithm provides a flexible estimation framework for small area estimation. The key advantages of the approach are relying on its capacity to produce estimations based on a preselected precision threshold and to produce results over the whole area of interest, avoiding areas without any estimates. The algorithm could also be used on any kind of polygon layers (not only administrative ones), provided that the field sampling design enable estimation. This makes the proposed algorithm a convenient tool notably for decision makers and forest managers. Numéro de notice : A2021-067 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2021.102303 Date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.jag.2021.102303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96992 [article]

Convex hull: another perspective about model predictions and map derivatives from remote sensing data / Jean-Pierre Renaud (2021)  

Public Titre : Convex hull: another perspective about model predictions and map derivatives from remote sensing data Type de document : Article/Communication Auteurs : Jean-Pierre Renaud, Auteur ; Ankit Sagar , Auteur ; Pierre Barbillon, Auteur ; Olivier Bouriaud , Auteur ; Christine Deleuze, Auteur ; Cédric Vega , Auteur Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : ARBRE / AgroParisTech 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 Projets : DEEPSURF / Pironon, Jacques Importance : pp 71 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] attribut non spatial [Termes IGN] convexité [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] erreur systématique [Termes IGN] modèle de simulation [Termes IGN] modèle linéaire Mots-clés libres : enveloppe convexe Résumé : (auteur) [introduction] In forest inventories as well as in the process of building models, obtaining an efficient sample is a central goal to reach precise estimates of forest attributes (Hawbaker et al. 2009, Frazer et al. 2011, Grafström et al. 2014, Saarela et al. 2015, Bouvier et al. 2019). In a model-based approach, a plots sample must cover adequately the variability of the considered forest attributes in order to minimise prediction error. Different strategies have been proposed to efficiently distribute the field sampling units in the auxiliary space of the remote sensing data (e.g. Hawbaker et al. 2009, Grafström et al. 2014). Some authors have proposed to stratify Airborne Laser Scanning data (ALS) to optimize sampling (Hawbaker et al. 2009, Frazer et al. 2011), and Maltamo et al. (2011) compared different field plot selection strategies in order to optimise models precision. Interestingly, White et al. (2013) applied convex hull approach to show uncovered forest structures by the field calibration sampling units, since large prediction errors could be associated with model extrapolations, resulting in potentially biased map derivatives. In this research, we use convex hull to identify the proportion of extrapolated pixels, computed their distance to the calibration domain and estimated bias associated to the linear model predictions on an ALS case study. Numéro de notice : C2021-030 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1919 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98997

Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource / Cédric Vega (2021) 

Public Titre : Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Milena Planells, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2021 Conférence : Atelier Theia 2021, Les utilisations de la télédétection pour la forêt 11/10/2021 30/09/2021 Montpellier France slides & videos Langues : Français (fre) Descripteur : [Termes IGN] données auxiliaires [Termes IGN] données multisources [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] modèle numérique [Termes IGN] prédiction [Termes IGN] segmentation en régions [Termes IGN] série temporelle [Termes IGN] surveillance forestière [Vedettes matières IGN] Inventaire forestier Numéro de notice : C2021-041 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99239

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peut être téléchargé Estimation et cartographie d’attributs forestiers ... - diaporama Adobe Acrobat PDF

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 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

Improving GEDI footprint geolocation using a high resolution digital terrain model / Anouk Schleich (2021)  

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Unit-level small area estimation of forest inventory with GEDI auxiliary information / Shaohui Zhang (2021)  

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Unit-level small area estimation of forest inventory with GEDI auxiliary information in France / Shaohui Zhang (2021) 

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Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa Pillai Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)  

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Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)  

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RegisTree: a registration algorithm to enhance forest inventory plot georeferencing / Maryem Fadili in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

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Bridging the gap: toward a French MS-NFI for territories / Jean-Pierre Renaud (2019)

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DIABOLO policy brief. Responding to European, national and regional challenges with harmonised forest information / Tuula Packalen (2019)  

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Toward the development of total volume and biomass functions using terrestrial lidar and NFI data / Cédric Vega (2019) 

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Towards an improved forest inventory using terrestrial laser scanning / Lina Jarboui (2019) 

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