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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)
[article]
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 IGN] Allemagne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] inférence
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle de régression
[Termes IGN] Norvège
[Termes IGN] restitution
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierRésumé : (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 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.07.035 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
in Remote sensing of environment > vol 184 (October 2016) . - pp 496 - 505[article]Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
[article]
Titre : Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models Type de document : Article/Communication Auteurs : Subit Chakrabarti, Auteur ; Jasmeet Judge, Auteur ; Tara Bongiovanni, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 4629 - 4641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] cultures
[Termes IGN] désagrégation
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] humidité du sol
[Termes IGN] modèle de régressionRésumé : (Auteur) In this paper, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRMs) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at a fine scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM was disaggregated from 10 to 1 km using land cover (LC), precipitation, land surface temperature, leaf area index, and in situ observations of SM. This algorithm was evaluated using multiscale synthetic observations in NC Florida for heterogeneous agricultural LCs. It was found that the rmse for 96% of the pixels was less than 0.02 m 3/m3. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback-Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy. Numéro de notice : A2016-888 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2547389 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2547389 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83068
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4629 - 4641[article]A bootstrap test for constant coefficients in geographically weighted regression models / Chang-Lin Mei in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)
[article]
Titre : A bootstrap test for constant coefficients in geographically weighted regression models Type de document : Article/Communication Auteurs : Chang-Lin Mei, Auteur ; Min Xu, Auteur ; Ning Wang, Auteur Année de publication : 2016 Article en page(s) : pp 1622 - 1643 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] base de données déductive
[Termes IGN] Bootstrap (EDI)
[Termes IGN] inférence statistique
[Termes IGN] modèle de régression
[Termes IGN] processeur
[Termes IGN] régression géographiquement pondérée
[Termes IGN] test de performanceRésumé : (Auteur) Statistical tests for whether some coefficients really vary over space play an important role in using the geographically weighted regression (GWR) to explore spatial non-stationarity of the regression relationship. In view of some shortcomings of the existing inferential methods, we propose a residual-based bootstrap test to detect the constant coefficients in a GWR model. The proposed test is free of the assumption that the model error term is normally distributed and admits some useful extensions for identifying more complicated spatial patterns of the coefficients. Some simulation with comparison to the existing test methods is conducted to assess the test performance, including the accuracy of the bootstrap approximation to the null distribution of the test statistic, the power in identifying spatially varying coefficients and the robustness to collinearity among the explanatory variables. The simulation results demonstrate that the bootstrap test works quite well. Furthermore, a real-world data set is analyzed to illustrate the application of the proposed test. Numéro de notice : A2016-320 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1149181 En ligne : http://dx.doi.org/10.1080/13658816.2016.1149181 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80940
in International journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1622 - 1643[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016042 RAB Revue Centre de documentation En réserve L003 Disponible 079-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Approximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
[article]
Titre : Approximating prediction uncertainty for random forest regression models Type de document : Article/Communication Auteurs : John W. Coulston, Auteur ; Christine E. Blinn, Auteur ; Valerie A. Thomas, Auteur ; Randolph H. Wynne, Auteur Année de publication : 2016 Article en page(s) : pp 189 - 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] incertitude des données
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de régression
[Termes IGN] prédiction
[Termes IGN] variableRésumé : (auteur) Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models. Numéro de notice : A2016-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.3.189 En ligne : https://doi.org/10.14358/PERS.82.3.189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80506
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 189 - 197[article]Comparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover / Bonnie Ruefenacht in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
[article]
Titre : Comparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover Type de document : Article/Communication Auteurs : Bonnie Ruefenacht, Auteur Année de publication : 2016 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] canopée
[Termes IGN] image Landsat-TM
[Termes IGN] modèle de régression
[Termes IGN] mosaïquage d'images
[Termes IGN] Normalized Difference Vegetation IndexRésumé : (auteur) Landsat imagery mosaics developed using model II regression have been shown to successfully model percent tree-canopy cover (PTCC). Creating model II regression mosaics, however, is a time-consuming, manual process. The objective of this study is to evaluate the effectiveness of using more easily automated image composites techniques, such as median Landsat-5 image composites or maximum NDVI Landsat-5 image composites, as alternatives to model II regression mosaics for the modeling of PTCC. This study found all composite types were effective in modeling PTCC, but the maximum NDVI composites included anomalies, clouds, shadows, and tended to be pixelated, whereas the median composites and the model II regression mosaics had none of these issues. The median composite procedure is automated and was found to be an effective approach to statistically reduce a much larger ensemble of images on a pixel basis to create images suitable for vegetation modeling. Numéro de notice : A2016-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.3.189 En ligne : https://doi.org/10.14358/PERS.82.3.189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80515
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 199 - 211[article]A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis / W. Dai in Survey review, vol 47 n° 345 (November 2015)PermalinkEstimating the count of completeness errors in geographic data sets by means of a generalized Waring regression model / Francisco Javier Ariza-López in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)PermalinkPermalinkPermalinkUsing characteristic spectral bands of OMIS1 imaging spectrometer to retrieve urban land surface temperature / S.Y. Zhu in International Journal of Remote Sensing IJRS, vol 27 n°7-8 (April 2006)PermalinkA method for detecting large-scale forest covers change using coarse spatial resolution imagery / R.H. Fraser in Remote sensing of environment, vol 95 n° 4 (30/04/2005)PermalinkApplication of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data / S. Lee in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)PermalinkA robust method for filtering non-ground measurements from airborne Lidar data / F. Crosilla in GIS Geo-Informations-Systeme, vol 2004 n° 12 (Dezember 2004)PermalinkSeeing the trees in the forest: Using Lidar and multispectral data fusion with local filtering and variable window size for estimating tree height / S.C. Pospecu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 5 (May 2004)PermalinkGeographical weighting as a further refinement to regression modelling: an example focused on the NDVI-rainfall relationship / Giles M. Foody in Remote sensing of environment, vol 88 n° 3 (15/12/2003)Permalink