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Model-dependent forest stand-level inference with and without estimates of stand-effects / Magnussen, Steen in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)  

Public [article]  inForestry, an international journal of forest research > vol 90 n° 5 (December 2017) . - pp 675 - 685 Titre : Model-dependent forest stand-level inference with and without estimates of stand-effects Type de document : Article/Communication Auteurs : Magnussen, Steen, Auteur ; Johannes Breidenbach, Auteur Année de publication : 2017 Article en page(s) : pp 675 - 685 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Forest stands are important units of management. A stand-by-stand estimation of the mean and variance of an attribute of interest (Y) remains a priority in forest enterprise inventories. The advent of powerful and cost effective remotely sensed auxiliary variables (X) correlated with Y means that a census of X in the forest enterprise is increasingly available. In combination with a probability sample of Y, the census affords a model-dependent stand-level inference. It is important, however, that the sampling design affords an estimation of possible stand-effects in the model linking X to Y. We demonstrate, with simulated data, that failing to quantify non-zero stand-effects in the intercept of a linear population-level model can lead to a serious underestimation of the uncertainty in a model-dependent estimate of a stand mean, and by extension a confidence interval with poor coverage. We also provide an approximation to the variance of stand-effects in an intercept for the case when a sampling design does not afford estimation. Furthermore, we propose a method to correct a potential negative bias in an estimate of the variance of stand-effects when a sampling design prescribes few stands with small within-stand sample sizes. Numéro de notice : A2017-903 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx023 En ligne : https://doi.org/10.1093/forestry/cpx023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93197 [article]

Stand-level wind damage can be assessed using diachronic photogrammetric canopy height models / Jean-Pierre Renaud in Annals of Forest Science [en ligne], vol 74 n° 4 (December 2017)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 74 n° 4 (December 2017) Titre : Stand-level wind damage can be assessed using diachronic photogrammetric canopy height models Type de document : Article/Communication Auteurs : Jean-Pierre Renaud, Auteur ; Cédric Vega , Auteur ; Sylvie Durrieu, Auteur ; Jonathan Lisein, Auteur ; Magnussen, Steen, Auteur ; Philippe Lejeune, Auteur ; Meriem Fournier, Auteur Année de publication : 2017 Projets : FOR-WIND / Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] dommage matériel [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] photogrammétrie [Termes descripteurs IGN] placette d'échantillonnage [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] tempête [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message : Diachronic photogrammetric canopy height models can be used to quantify at a fine scale changes in dominant height and wood volume following storms. The regular renewal of aerial surveys makes this approach appealing for monitoring forest changes. Context : The increasing availability of aerial photographs and the development of dense matching algorithms open up new possibilities to assess the effects of storm events on forest canopies. Aims : The objective of this research is to assess the potential of diachronic canopy height models derived from photogrammetric point clouds (PCHM) to quantify changes in dominant height and wood volume of a broadleaved forest following a major storm. Methods : PCHMs derived from aerial photographs acquired before and after a storm event were calibrated using 25 field plots to estimate dominant height and volume using various modeling approaches. The calibrated models were combined with a reference damage maps to estimate both the within-stand damage variability, and the amount of volume impacted. Results : Dominant height was predicted with a root mean squared error (RMSE) of 4%, and volume with RMSEs ranging from 24 to 32% according to the type of model. The volume impacted by storm was in the range of 42–76%. Overall, the maps of dominant height changes provided more details about within-stand damage variability than conventional photointerpretation do. Conclusion : The study suggests a promising potential for exploiting PCHM in pursuit of a rapid localization and quantification of wind-throw damages, given an adapted sampling design to calibrate models. Numéro de notice : A2017-733 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-017-0669-3 En ligne : https://doi.org/10.1007/s13595-017-0669-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88546 [article]

A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds / Magnussen, Steen in Remote sensing of environment, vol 184 (October 2016)  

Public [article]  inRemote sensing of environment > vol 184 (October 2016) . - pp 496 - 505 Titre : A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds Type de document : Article/Communication Auteurs : Magnussen, Steen, Auteur ; Erik Naesset, Auteur ; Gerald Kändler, Auteur ; P. Adler, Auteur ; Jean-Pierre Renaud, Auteur ; Terje Gobakken, Auteur Année de publication : 2016 Article en page(s) : pp 496 - 505 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] inférence [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] Norvège [Termes descripteurs IGN] restitution [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Forest inventories, with a probability sampling of a target variable Y and a potentially very large number of auxiliary variables (X) obtained from an aerial laser scanner or photogrammetry, are faced with the issue of model and variable selection when a model for linking Y to X is formulated. To bypass this step we propose a generic functional regression model (FRM) for use in both a design- and a model-based framework of inference. We demonstrate applications of FRM with inventory data from France, Germany, and Norway. The generic FRM achieved results that were comparable to those obtained with more traditional approaches based on model and variable selections. The proposed FRM generates interpretable regression coefficients and enables testing of practically relevant hypotheses regarding estimated models. Numéro de notice : A2016-706 Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.rse.2016.07.035 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82077 [article]

Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data / Ronald E. McRoberts in Remote sensing of environment, vol 115 n° 12 (december 2011)  

Public [article]  inRemote sensing of environment > vol 115 n° 12 (december 2011) . - pp 3165 - 3174 Titre : Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; Magnussen, Steen, Auteur ; Erkki Tomppo, Auteur ; Gherardo Chirici, Auteur Année de publication : 2011 Article en page(s) : pp 3165 - 3174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] regroupement de données Résumé : (auteur) Nearest neighbors techniques have been shown to be useful for estimating forest attributes, particularly when used with forest inventory and satellite image data. Published reports of positive results have been truly international in scope. However, for these techniques to be more useful, they must be able to contribute to scientific inference which, for sample-based methods, requires estimates of uncertainty in the form of variances or standard errors. Several parametric approaches to estimating uncertainty for nearest neighbors techniques have been proposed, but they are complex and computationally intensive. For this study, two resampling estimators, the bootstrap and the jackknife, were investigated and compared to a parametric estimator for estimating uncertainty using the k-Nearest Neighbors (k-NN) technique with forest inventory and Landsat data from Finland, Italy, and the USA. The technical objectives of the study were threefold: (1) to evaluate the assumptions underlying a parametric approach to estimating k-NN variances; (2) to assess the utility of the bootstrap and jackknife methods with respect to the quality of variance estimates, ease of implementation, and computational intensity; and (3) to investigate adaptation of resampling methods to accommodate cluster sampling. The general conclusions were that support was provided for the assumptions underlying the parametric approach, the parametric and resampling estimators produced comparable variance estimates, care must be taken to ensure that bootstrap resampling mimics the original sampling, and the bootstrap procedure is a viable approach to variance estimation for nearest neighbor techniques that use very small numbers of neighbors to calculate predictions. Numéro de notice : A2011-610 Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2011.07.002 En ligne : https://doi.org/10.1016/j.rse.2011.07.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78400 [article]

Efficient multiresolution spatial predictions for large data arrays / Magnussen, Steen in Remote sensing of environment, vol 109 n° 4 (30 August 2007)

Public [article]  inRemote sensing of environment > vol 109 n° 4 (30 August 2007) . - pp 451 - 463 Titre : Efficient multiresolution spatial predictions for large data arrays Type de document : Article/Communication Auteurs : Magnussen, Steen, Auteur ; Erik Naesset, Auteur ; Michael A. Wulder, Auteur Année de publication : 2007 Article en page(s) : pp 451 - 463 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] interpolation inversement proportionnelle à la distance [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] résolution multiple [Termes descripteurs IGN] signal laser Résumé : (Auteur) Imputations of missing values and optimal smoothing with massive data arrays poses a computational challenge since ordinary kriging becomes infeasible. Imputation and smoothing with standard algorithms like inverse distance weighted nearest neighbour interpolation (IDW) and interpolation on triangulated irregular networks (TIN/IP) fail to incorporate the spatial structure and ignore information beyond the neighbourhood. Multiresolution spatial models (MRSM) or approximate kriging methods adapted to handling massive data sets can be expected to do better than IDW and TIN/IP in terms of mean square errors of prediction (MSEP). We illustrate a MRSM that is efficient, computationally fast, and easy to implement. In two forestry examples with imputation of LiDAR range values the MRSM achieved a lower MSEP than IDW, TIN/IP, and fixed ranked kriging. MRSM appear as especially attractive for the construction of a DTM from last return LiDAR pulses. A third example demonstrates MRSM for efficient smoothing. Numéro de notice : A2007-319 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28682 [article]

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