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An open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102663 Titre : An open science and open data approach for the statistically robust estimation of forest disturbance areas Type de document : Article/Communication Auteurs : Saverio Francini, Auteur ; Ronald E. McRoberts, Auteur ; Giovanni d' Amico, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] coupe rase (sylviculture) [Termes IGN] détection de changement [Termes IGN] estimation statistique [Termes IGN] Fagus sylvatica [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] Italie [Termes IGN] méthode robuste [Termes IGN] perturbation écologique [Termes IGN] Quercus cerris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus pubescens [Termes IGN] Quercus sessiliflora [Termes IGN] surveillance forestière Résumé : (auteur) Forest disturbance monitoring is critical for understanding forest-related greenhouse gas emissions and for determining the role of forest management in mitigating climate change. Multiple algorithms for the automated mapping of forest disturbance using remotely sensed imagery have been developed and applied; however, variability in natural and anthropogenic disturbance phenomena, as well as image acquisition conditions, can result in maps that may be incomplete or that contain inaccuracies that prevent their use for directly estimating areas of disturbance. To reduce errors in reporting disturbance areas, stratified estimators can be applied to obtain statistically robust area estimates, while simultaneously circumventing the need to conduct a complete census or in situations where such a census may not be possible. We present a semi-automated procedure for implementation in Google Earth Engine, 3I3D-GEE, for regional to global mapping of forest disturbance (including clear-cut harvesting, fire, and wind damage) and sample-based estimation of related areas using data from the processing capacity of Google Earth Engine. Documentation for the application is also provided in Appendix A. Using Sentinel-2 (S2) imagery, our procedure was applied and tested for 2018 in Italy for which the approximately 11 million ha of forests (mostly Q. pubescens, Q. robur, Q. cerris, Q. petraea, and Fagus sylvatica) serve as an appropriate case study because national statistics on forest disturbance areas are not available. To decrease the overall standard errors of the area estimates, the sampling intensities in areas where greater variability in the form of greater commission and omission errors are expected can be increased. To this end, we augmented the predicted forest disturbance map with a buffer class consisting of a two-pixel buffer (20 m) on each side of the disturbance class boundary. We selected a reference sample of 19,300 points: a simple random sample of 9,300 points from the buffer and simple random samples of 5000 from each of the undisturbed and disturbed classes. The reference sample was photointerpreted using fine resolution orthophotos (30 cm) and S2 imagery. While the estimate of the disturbed area obtained by adding the areas of pixels classified as disturbed was 41,732 ha, the estimate obtained using the unbiased stratified estimator was 27% greater at 57,717716 ha. Regarding map accuracy, we found several omission errors in the buffer (53.4%) but none (0%) in the undisturbed map class. Similarly, among the 1035 commission errors, the majority (7 4 4) were in the buffer class. The methods presented herein provide a useful tool that can be used to estimate areas of forest disturbance, which many nations must report as part of their commitment to international conventions and treaties. In addition, the information generated can support forest management, enabling the forest sector to monitor stand-replacing forest harvesting over space and time. Numéro de notice : A2022-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102663 En ligne : https://doi.org/10.1016/j.jag.2021.102663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99437 [article]

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 IGN] arbre caducifolié [Termes IGN] classification barycentrique [Termes IGN] feuillu [Termes IGN] image Landsat-8 [Termes IGN] inférence statistique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] Orléans, forêt domaniale d' (Loiret) [Termes IGN] photogrammétrie numérique [Termes IGN] Pinus pinaster [Termes IGN] Pinus sylvestris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes 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) Autre URL associée : vers HAL 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 IGN] attribut [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] plus proche voisin, algorithme du [Termes IGN] régression harmonique [Termes 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 Année de publication : 2017 Conférence : SilviLaser 2015, 14th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2015 30/09/2015 La Grande Motte France open access proceedings 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 IGN] classification barycentrique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] indice de diversité [Termes IGN] inférence statistique [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Italie [Termes IGN] optimisation (mathématiques) [Termes 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 DOI : 10.1016/j.rse.2016.09.010 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)  

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

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

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