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Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS) / Murat Durmaz in Journal of geodesy, vol 89 n° 4 (April 2015)
[article]
Titre : Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS) Type de document : Article/Communication Auteurs : Murat Durmaz, Auteur ; Mahmut Onur Karslioglu, Auteur Année de publication : 2015 Article en page(s) : pp 347 - 360 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] B-Spline
[Termes IGN] erreur systématique
[Termes IGN] harmonique sphérique
[Termes IGN] modèle ionosphérique
[Termes IGN] régression multiple
[Termes IGN] teneur totale en électrons
[Termes IGN] teneur verticale totale en électronsRésumé : (auteur) There are various global and regional methods that have been proposed for the modeling of ionospheric vertical total electron content (VTEC). Global distribution of VTEC is usually modeled by spherical harmonic expansions, while tensor products of compactly supported univariate B-splines can be used for regional modeling. In these empirical parametric models, the coefficients of the basis functions as well as differential code biases (DCBs) of satellites and receivers can be treated as unknown parameters which can be estimated from geometry-free linear combinations of global positioning system observables. In this work we propose a new semi-parametric multivariate adaptive regression B-splines (SP-BMARS) method for the regional modeling of VTEC together with satellite and receiver DCBs, where the parametric part of the model is related to the DCBs as fixed parameters and the non-parametric part adaptively models the spatio-temporal distribution of VTEC. The latter is based on multivariate adaptive regression B-splines which is a non-parametric modeling technique making use of compactly supported B-spline basis functions that are generated from the observations automatically. This algorithm takes advantage of an adaptive scale-by-scale model building strategy that searches for best-fitting B-splines to the data at each scale. The VTEC maps generated from the proposed method are compared numerically and visually with the global ionosphere maps (GIMs) which are provided by the Center for Orbit Determination in Europe (CODE). The VTEC values from SP-BMARS and CODE GIMs are also compared with VTEC values obtained through calibration using local ionospheric model. The estimated satellite and receiver DCBs from the SP-BMARS model are compared with the CODE distributed DCBs. The results show that the SP-BMARS algorithm can be used to estimate satellite and receiver DCBs while adaptively and flexibly modeling the daily regional VTEC. Numéro de notice : A2015-342 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-014-0779-8 Date de publication en ligne : 23/11/2014 En ligne : https://doi.org/10.1007/s00190-014-0779-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76712
in Journal of geodesy > vol 89 n° 4 (April 2015) . - pp 347 - 360[article]Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm / Oumer S. Ahmed in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)
[article]
Titre : Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm Type de document : Article/Communication Auteurs : Oumer S. Ahmed, Auteur ; Steven E. Franklin, Auteur ; Michael A. Wulder, Auteur ; Joanne C. White, Auteur Année de publication : 2015 Article en page(s) : pp 89 - 101 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] composition d'un peuplement forestier
[Termes IGN] couvert forestier
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat
[Termes IGN] régression multiple
[Termes IGN] série temporelle
[Termes IGN] Vancouver (Colombie britannique)Résumé : (auteur) Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = −0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = −0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm. Numéro de notice : A2015-470 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.11.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.11.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77172
in ISPRS Journal of photogrammetry and remote sensing > vol 101 (March 2015) . - pp 89 - 101[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Effects of LiDAR point density and landscape context on estimates of urban forest biomass / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)
[article]
Titre : Effects of LiDAR point density and landscape context on estimates of urban forest biomass Type de document : Article/Communication Auteurs : Kunwar K. Singh, Auteur ; Gang Chen, Auteur ; James B. McCarter, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2015 Article en page(s) : pp 310 - 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] composition d'un peuplement forestier
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuillu
[Termes IGN] forêt urbaine
[Termes IGN] régression multipleRésumé : (auteur) Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density. Numéro de notice : A2015-471 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.12.021 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.12.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77173
in ISPRS Journal of photogrammetry and remote sensing > vol 101 (March 2015) . - pp 310 - 322[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2015031 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
Titre : Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images Type de document : Article/Communication Auteurs : Andrew L. Fleming, Auteur ; Guangxing Wang, Auteur ; Ronald E. McRoberts, Auteur Année de publication : 2015 Article en page(s) : pp 125 - 137 Langues : Anglais (eng) Descripteur : [Termes IGN] carte thématique
[Termes IGN] Etats-Unis
[Termes IGN] Illinois (Etats-Unis)
[Termes IGN] image Landsat-TM
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] prédiction
[Termes IGN] puits de carbone
[Termes IGN] régression linéaire
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Accurate spatial estimation of forest carbon stocks and their spatial uncertainties at local, regional, national, and global scales is a critical step in global carbon cycle modeling and management. This study aimed at enhancing the methods that are currently used in this area by combining plot data from the forest inventory and analysis program of the U.S. Forest Service and free landsat thematic mapper image data. Three mapping methods including linear regression, sequential Gaussian co-simulation, and block co-simulation algorithm were compared with respect to the accuracy of forest carbon stock estimates obtained for a study area in Southern Illinois, USA. The results indicated that although the linear regression resulted in smaller prediction errors than the sequential Gaussian co-simulation and the block co-simulation approaches, it also produced both negative and unreasonably large estimates, which is a serious drawback. Moreover, the sequential Gaussian co-simulation and the block co-simulation produced not only accurate carbon predictions, but also uncertainties for the local estimates. In addition, the block co-simulation approach scaled up both forest carbon stocks and the input uncertainties from finer to coarser spatial resolutions as is required for mapping forest carbon at national and global scales. Thus, the co-simulation and block co-simulation algorithms resolved an important current methodological challenge. Numéro de notice : A2015-190 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-014-0838-y Date de publication en ligne : 05/08/2014 En ligne : https://doi.org/10.1007/s10342-014-0838-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75966
in European Journal of Forest Research > vol 134 n° 1 (January 2015) . - pp 125 - 137[article]
Titre : Regression Modeling Strategies : With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis Type de document : Monographie Auteurs : Frank E. Harrell Jr., Auteur Editeur : Springer International Publishing Année de publication : 2015 Importance : 582 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-319-19425-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] Bootstrap (statistique)
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] R (langage)
[Termes IGN] régression linéaire
[Termes IGN] régression logistiqueRésumé : (éditeur) This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. Note de contenu : Introduction
1- General Aspects of Fitting Regression Models
2- Missing Data
3- Multivariable Modeling Strategies
4- Describing, Resampling, Validating, and Simplifying the Model
5- R Software
6- Modeling Longitudinal Responses using Generalized Least Squares
7- Case Study in Data Reduction
8- Overview of Maximum Likelihood Estimation
9- Binary Logistic Regression
10- Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death
11- Logistic Model Case Study 2: Survival of Titanic Passengers
12- Ordinal Logistic Regression
13- Case Study in Ordinal Regression, Data Reduction, and Penalization
14- Regression Models for Continuous Y and Case Study in Ordinal Regression
15- Transform-Both-Sides Regression
16- Introduction to Survival Analysis
17- Parametric Survival Models
18- Case Study in Parametric Survival Modeling and Model Approximation
19- Cox Proportional Hazards Regression Model
20- Case Study in Cox RegressionNuméro de notice : 25813 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-19425-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95076 Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: a case study in the city of Saskatoon (SK, Canada) / Li Shen in Open geosciences, vol 7 n° 1 (January 2015)PermalinkPermalinkQuantification et cartographie de la structure forestière à partir de la texture des images Pléiades / Benoit Beguet in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)PermalinkAn inventory of the above ground biomass in the Mau Forest Ecosystem, Kenya / Mwangi James Kinyanjui in Open journal of forestry, vol 4 n° 10 (July 2014)PermalinkPermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)PermalinkIntegrating disparate lidar data at the national scale to assess the relationships between height above ground, land cover and ecoregions / Jason M. Stocker in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)PermalinkA photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery / Jonathan Lisein in Forests, vol 4 n° 4 (december 2013)PermalinkImproved topographic mapping through high-resolution SAR interferometry with atmospheric effect removal / Mingsheng Liao in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)PermalinkSensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area / Magdalini Pleniou in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)Permalink