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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)  

Public [article]  inRemote sensing > vol 11 n° 8 (August 2019) . - n° 991 Titre : Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators Type de document : Article/Communication Auteurs : Dinesh Babu Irulappa Pillai Vijayakumar , Auteur ; Jean-Pierre Renaud, Auteur ; François Morneau , Auteur ; Ronald E. McRoberts, Auteur ; Cédric Vega , Auteur Année de publication : 2019 Projets : DIABOLO / Packalen, Tuula Article en page(s) : n° 991 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] Orléans, forêt d' (Loiret) [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] Pinus pinaster [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] Quercus pedunculata [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] Sologne (France) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. Numéro de notice : A2019-382 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11080991 date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.3390/rs11080991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93456 [article]

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data / Barry T. Wilson in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 137 (March 2018) . - pp 29 - 46 Titre : Harmonic regression of Landsat time series for modeling attributes from national forest inventory data Type de document : Article/Communication Auteurs : Barry T. Wilson, Auteur ; Joseph F. Knight, Auteur ; Ronald E. McRoberts, Auteur Année de publication : 2018 Article en page(s) : pp 29 - 46 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] attribut [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Minnesota (Etats-Unis) [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] régression harmonique [Termes descripteurs IGN] série temporelle Résumé : (Auteur) Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009–2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10–20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher. Numéro de notice : A2018-077 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89439 [article]

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Preface for the SilviLaser 2015 special section / Sylvie Durrieu in Remote sensing of environment, vol 194 (June 2017)  

Public [article]  inRemote sensing of environment > vol 194 (June 2017) . - pp 412 - 413 Titre : Preface for the SilviLaser 2015 special section Type de document : Article/Communication Auteurs : Sylvie Durrieu, Auteur ; Cédric Vega , Auteur ; Richard A. Fournier, Auteur ; Ronald E. McRoberts, Auteur Congrès : Silvilaser 2015, 14th conference on Lidar Applications for Assessing and Managing Forest Ecosystems (28 - 30 septembre 2015; La Grande Motte, France) , Auteur Année de publication : 2017 Article en page(s) : pp 412 - 413 Langues : Anglais (eng) Numéro de notice : A2017-886 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtSansCL DOI : 10.1016/j.rse.2017.03.018 date de publication en ligne : 23/03/2017 En ligne : https://doi.org/10.1016/j.rse.2017.03.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91863 [article]

Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique / Matteo Mura in Remote sensing of environment, vol 186 (1 December 2016)  

Public [article]  inRemote sensing of environment > vol 186 (1 December 2016) . - pp 678 - 686 Titre : Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique Type de document : Article/Communication Auteurs : Matteo Mura, Auteur ; Ronald E. McRoberts, Auteur ; Gherardo Chirici, Auteur ; Marco Marchetti, Auteur Année de publication : 2016 Article en page(s) : pp 678 - 686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Italie [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] structure d'un peuplement forestier Résumé : (auteur) Forest structural diversity plays a major role for forest management, conservation and restoration and is recognized as a fundamental aspect of forest biodiversity. The assessment, maintenance and restoration of a diversified forest structure have become major foci in the effort to preserve forest ecosystems from loss of biological diversity. However, the assessment of forest biodiversity is difficult because it involves multiple components and is characterized using multiple variables. The objective of the study was to develop a methodological approach for predicting, mapping, and constructing a statistical inference for a multiple-variable index of forest structural diversity. The method included three key components: (i) use of the k-Nearest Neighbors (k-NN) technique, field plot data, and airborne laser scanning metrics to predict multiple forest structural diversity variables simultaneously, (ii) incorporation of multiple diversity variable predictions into a single index, and (iii) construction of a statistically rigorous inference for the population mean of the index. Three structural diversity variables were selected to illustrate the method: growing stock volume and the standard deviations of tree diameter at breast-height and tree height. Optimization of the k-NN technique produced mean relative deviations less in absolute value than 0.04 for predictions for each of the three structural diversity variables, R2 values between 0.50 and 0.66 which were in the range of values reported in the literature, and a confidence interval for the population mean of the index whose half-width was approximately 5% of the mean. Finally, the spatial pattern depicted in the resulting map of forest structural diversity for the study area contributed to validating the proposed method. Numéro de notice : A2016-769 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.rse.2016.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82419 [article]

The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass / Ronald E. McRoberts in Annals of Forest Science [en ligne], vol 73 n° 4 (December 2016)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 73 n° 4 (December 2016) . - pp 839 - 847 Titre : The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2016 Article en page(s) : pp 839 - 847 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] forêt [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Minnesota (Etats-Unis) [Termes descripteurs IGN] Norvège [Termes descripteurs IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message : When areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose. Context : Remote sensing-based maps are well-established as means of increasing the precision of estimates of forest inventory parameters. The general practice is to use maps whose dates correspond closely to the dates of ground data. However, as national forest inventories move to continuous inventories, deviations between map and ground data dates increase. Aims : The aim was to assess the degree to which remote sensing-based maps can be used to increase the precision of estimates despite differences between map and ground data dates. Methods : For study areas in the USA and Norway, maps were constructed for each of two dates, and model-assisted regression estimators were used to estimate inventory parameters using ground data whose dates differed by as much as 11 years from the map dates. Results : For the Minnesota study area that had little change, 7-year differences in dates had little effect on the precision of estimates of proportion forest area. For the Norwegian study area that experienced considerable change, 11-year differences in dates had a detrimental effect on the precision of estimates of mean biomass per unit area. Conclusions : The effects of differences in map and ground data dates were less important than temporal change in the study area. Numéro de notice : A2016--168 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0485-6 date de publication en ligne : 12/05/2015 En ligne : https://doi.org/10.1007/s13595-015-0485-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87210 [article]

Propagating uncertainty through individual tree volume model predictions to large-area volume estimates / Ronald E. McRoberts in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)  

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A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data / Gherardo Chirici in Remote sensing of environment, vol 176 (April 2016)  

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Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass / Timothy G. Gregoire in Remote sensing of environment, vol 173 (February 2016)  

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Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images / Andrew L. Fleming in European Journal of Forest Research, vol 134 n° 1 (January 2015)  

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Estimating forest attribute parameters for small areas using nearest neighbors techniques / Ronald E. McRoberts in Forest ecology and management, vol 272 (mai 2012)  

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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)  

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vol 30 n° 19 - October 2009 - ForestSat 2007, [actes], operational tools in forestry using remote sensing techniques, Montpellier, 5 - 7 November 2007 (Bulletin de International Journal of Remote Sensing IJRS) / Ronald E. McRoberts  

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Proceedings of the 8th Annual Forest Inventory and Analysis Symposium, 2006 October 16–19, Monterey, CA. Washington (DC) / Ronald E. McRoberts (2009)  

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vol 110 n° 4 - 30/10/2007 - Forestsat 2007 (Bulletin de Remote sensing of environment) / Ronald E. McRoberts

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