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Auteur Pierre Barbillon |
Documents disponibles écrits par cet auteur (3)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesModelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information / Shaohui Zhang in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)
Titre : Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information Type de document : Article/Communication Auteurs : Shaohui Zhang, Auteur ; Cédric Vega Année de publication : 2022 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 103072 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] empreinte
[Termes IGN] gestion forestière
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation de la forêt
[Termes IGN] placette d'échantillonnage
[Termes IGN] Sologne (France)
[Termes IGN] variogramme
[Termes IGN] volume en boisRésumé : (auteur) The French National Forest Inventory provides detailed forest information up to large national and regional scales. Forest inventory for small areas of interest within a large population is equally important for decision making, such as for local forest planning and management purposes. However, sampling these small areas with sufficient ground plots is often not cost efficient. In response, small area estimation has gained increasing popularity in forest inventory. It consists of a set of techniques that enables predictions of forest attributes of subpopulation with the help of auxiliary information that compensates for the small field samples. Common sources of auxiliary information usually come from remote sensing technology, such as airborne laser scanning and satellite imagery. The newly launched NASA’s Global Ecosystem Dynamics Investigation (GEDI), a full waveform Lidar instrument, provides an unprecedented opportunity of collecting large-scale and dense forest sample plots given its sampling frequency and spatial coverage. However, the geolocation uncertainty associated with GEDI footprints create important challenges for their use for small area estimations. In this study, we designed a process that provides NFI measurements at plot level with GEDI auxiliary information from nearby footprints. We demonstrated that GEDI RH98 is equivalent to NFI dominant height at plot level. We stressed the importance of pairing NFI plots with nearby GEDI footprints, based on not only the distance in between but also their similarities, i.e., forest heights and forest types. Subsequently, these NFI-GEDI pairs were used for small area estimations following a two-phase sampling scheme. We showcased that, with an adequate sample size, small area estimation with GEDI auxiliary data can improve the accuracy of forest volume estimates. Numéro de notice : A2022-786 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.103072 Date de publication en ligne : 22/10/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101890
in International journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103072[article]
Titre : Characterizing the calibration domain of remote sensing models using convex hulls Type de document : Article/Communication Auteurs : Jean-Pierre Renaud Année de publication : 2022 Projets : DEEPSURF / Pironon, Jacques, ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 102939 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] données allométriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] erreur systématique
[Termes IGN] étalonnage de modèle
[Termes IGN] étalonnage des données
[Termes IGN] extrapolation
[Termes IGN] placette d'échantillonnageRésumé : (auteur) The ever-increasing availability of remote sensing data allows production of forest attributes maps, which are usually made using model-based approaches. These map products are sensitive to various bias sources, including model extrapolation. To identify, over a case study forest, the proportion of extrapolated predictions, we used a convex hull method applied to the auxiliary data space of an airborne laser scanning (ALS) flight. The impact of different sampling efforts was also evaluated. This was done by iteratively thinning a set of 487 systematic plots using nested sub-grids allowing to divide the sample by two at each level. The analysis were conducted for all alternative samples and evaluated against 56 independent validation plots. Residuals of the extrapolated validation plots were computed and examined as a function of their distance to the model calibration domain. Extrapolation was also characterized for the pixels of the area of interest (AOI) to upscale at population level. Results showed that the proportion of extrapolated pixels greatly reduced with an increasing sampling effort. It reached a plateau (ca. 20% extrapolation) with a sampling intensity of ca. 250-calibration plots. This contrasts with results on model’s root mean squared error (RMSE), which reached a plateau at a much lower sampling intensity. This result emphasizes the fact that with a low sampling effort, extrapolation risk remains high, even at a relatively low RMSE. For all attributes examined (i.e., stand density, basal area, and quadratic mean diameter) estimations were generally found to be biased for validation plots that were extrapolated. The method allows an easy identification of map pixels that are out of the calibration domain, making it an interesting tool to evaluate model transferability over an area of interest (AOI). It could also serve to compare “competing” models at a variable selection phase. From a model calibration perspective, it could serve a posteriori, to evaluate areas (in the auxiliary space) that merit further sampling efforts to improve model reliability. Numéro de notice : A2022-581 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102939 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102939 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101341
in International journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102939[article]
Titre : Convex hull: another perspective about model predictions and map derivatives from remote sensing data Type de document : Article/Communication Auteurs : Jean-Pierre Renaud 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 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 linéaireMots-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
