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Regularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model / Zemin Wu in Journal of geodesy, vol 96 n° 4 (April 2022)
[article]
Titre : Regularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model Type de document : Article/Communication Auteurs : Zemin Wu, Auteur ; Shaofeng Bian, Auteur Année de publication : 2022 Article en page(s) : n° 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] affaiblissement géométrique de la précision
[Termes IGN] méthode des moindres carrés
[Termes IGN] phase GNSS
[Termes IGN] positionnement par GNSS
[Termes IGN] régularisation de Tychonoff
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) The strength of the GNSS precise positioning model degrades in cases of a lack of visible satellites, poor satellite geometry or uneliminated atmospheric delays. The least-squares solution to a weak GNSS model may be unreliable due to a large mean squared error (MSE). Recent studies have reported that Tikhonov’s regularization can decrease the solution’s MSE and improve the success rate of integer ambiguity resolution (IAR), as long as the regularization matrix (or parameter) is properly selected. However, there are two aspects that remain unclear: (i) the optimal regularization matrix to minimize the MSE and (ii) the IAR performance of the regularization method. This contribution focuses on these two issues. First, the “optimal” Tikhonov’s regularization matrix is derived conditioned on an assumption of prior information of the ambiguity. Second, the regularized integer least-squares (regularized ILS) method is compared with the integer least-squares (ILS) method in view of lattice theory. Theoretical analysis shows that regularized ILS can increase the upper and lower bounds of the success rate and reduce the upper bound of the LLL reduction complexity and the upper bound of the search complexity. Experimental assessment based on real observed GPS data further demonstrates that regularized ILS (i) alleviates the LLL reduction complexity, (ii) reduces the computational complexity of determinate-region ambiguity search, and (iii) improves the ambiguity fixing success rate. Numéro de notice : A2022-262 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01585-7 Date de publication en ligne : 28/03/2022 En ligne : https://doi.org/10.1007/s00190-021-01585-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100251
in Journal of geodesy > vol 96 n° 4 (April 2022) . - n° 22[article]Evaluation of the mixed-effects model and quantile regression approaches for predicting tree height in larch (Larix olgensis) plantations in northeastern China / Longfei Xie in Canadian Journal of Forest Research, Vol 52 n° 3 (March 2022)
[article]
Titre : Evaluation of the mixed-effects model and quantile regression approaches for predicting tree height in larch (Larix olgensis) plantations in northeastern China Type de document : Article/Communication Auteurs : Longfei Xie, Auteur ; Faris Rafi Almay Widagdo, Auteur ; Zheng Miao, Auteur ; Lihu Dong, Auteur ; Fengri Li, Auteur Année de publication : 2022 Article en page(s) : pp 309 - 319 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] biométrie
[Termes IGN] Chine
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] hauteur des arbres
[Termes IGN] Larix olgensis
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] régression non linéaire
[Termes IGN] régression par quantileRésumé : (auteur) Tree height (H) is one of the most important tree variables and is widely used in growth and yield models, and its measurement is often time-consuming and costly. Hence, height–diameter (H–D) models have become a great alternative, providing easy-to-use and accurate tools for H prediction. In this study, H–D models were developed for Larix olgensis A. Henry in northeastern China. The Chapman–Richards function with three predictors (diameter at breast height, dominant tree height, and relative size of individual trees) performed best. Nonlinear mixed-effects (NLME) models and nonlinear quantile regressions (NQR9, nine quantiles; NQR5, five quantiles; and NQR3, three quantiles) were further used and improved the generalized H–D model, successfully providing accurate H predictions. In addition, the H predictions were calibrated using several measurements from subsamples, which were obtained from different sampling designs and sizes. The results indicated that the predictive accuracy was higher when calibrated by using any number of height measurements for the NLME model and more than three height measurements for the NQR3, NQR5, and NQR9 models. The best sampling strategy for the NLME and NQR models involved sampling medium-sized trees. Overall, the newly developed H–D models can provide highly accurate height predictions for L. olgensis. Numéro de notice : A2022-313 Affiliation des auteurs : non IGN Autre URL associée : Draft Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0184 En ligne : https://doi.org/10.1139/cjfr-2021-0184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100412
in Canadian Journal of Forest Research > Vol 52 n° 3 (March 2022) . - pp 309 - 319[article]Influence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain / Imran Ahmad in Applied geomatics, vol 14 n° 1 (March 2022)
[article]
Titre : Influence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain Type de document : Article/Communication Auteurs : Imran Ahmad, Auteur ; Jahier Abbas Shaaikh, Auteur ; Assefa Fenta, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 57 - 63 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] autocorrélation spatiale
[Termes IGN] érosion
[Termes IGN] Ethiopie
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle de régression
[Termes IGN] modèle RUSLE
[Termes IGN] régression
[Termes IGN] système d'information géographiqueRésumé : (auteur) An attempt has been made in this study to analyze the hotspots of soil erosion in Denbie Woreda, Ethiopia. A total of five determinant factors were multiplied (using raster calculator in GIS domain) and integrated to obtain the soil erosion hotspots of the area. These five determinant factors were used as exploratory variables to understand the spatial behavior of soil loss rates. The ordinary least squared (OLS) regression model diagnostics showed that the R-squared and adjusted R-squared values of the explanatory variables are 0.71 and 0.75 respectively. Variance inflation factor (VIF) values of the OLS range between 1.03 and 1.47 indicating the absence of multicollinearity among explanatory variables. Koenker (BP) statistic is statistically insignificant (p > 0.005) indicating that relationships modeled are consistent. The OLS regression model was subject to different tests to confirm its reliability. Spatial autocorrelation tool (Global Moran’s I) result showed that the residuals exhibit a Gaussian spatial pattern. This study proved that all the determinant factors of Revised Universal Soil Loss Equation played a significant role towards demarcation of soil loss rates. Numéro de notice : A2022-217 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00409-9 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.1007/s12518-021-00409-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100087
in Applied geomatics > vol 14 n° 1 (March 2022) . - pp 57 - 63[article]A novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)
[article]
Titre : A novel regression method for harmonic analysis of time series Type de document : Article/Communication Auteurs : Qiang Zhou, Auteur ; Zhe Zhu, Auteur ; George Xian, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 48 - 61 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse harmonique
[Termes IGN] détection de changement
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-SWIR
[Termes IGN] modèle de régression
[Termes IGN] réflectance
[Termes IGN] régression harmonique
[Termes IGN] série temporelle
[Termes IGN] variation saisonnièreRésumé : (auteur) Harmonic analysis of time series is an important technique to reveal seasonal land surface dynamics using remote sensing information. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) with different scenarios using Landsat time series data across the United States. First, we evaluated methods using Landsat surface reflectance time series within a single year. HAPO showed small and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD values of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series and the result suggested that HAPO was a better predictor of relatively short time series (less than4 years) with steady small RMSD values. When a longer time series (≥4 years) was used, all four methods disclosed similar RMSD values, but HAPO outperformed other three methods when there were temporal gaps. Last, we preliminarily tested how regression methods affected change detection and classification accuracy. HAPO showed the highest change detection accuracy of all tests in terms of F1 score when using the change threshold of 0.9999. In classification, HAPO produced the highest accuracy for short time series segments (one- or two-year time series). In contrast, all methods reached similar accuracy for 5-year time series. These results suggest that for areas that have large seasonal observation gaps or for time series that have less than 4 years records, HAPO can provide more consistent and accurate analytical results than other regression methods for harmonic analysis of time series. Numéro de notice : A2022-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.006 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99729
in ISPRS Journal of photogrammetry and remote sensing > vol 185 (March 2022) . - pp 48 - 61[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022031 SL Revue Centre de documentation Revues en salle Disponible Observational constraint on the climate sensitivity to atmospheric CO2 concentrations changes derived from the 1971-2017 global energy budget / Jonathan Chenal in Journal of climate, vol 2022 ([01/03/2022])
[article]
Titre : Observational constraint on the climate sensitivity to atmospheric CO2 concentrations changes derived from the 1971-2017 global energy budget Type de document : Article/Communication Auteurs : Jonathan Chenal , Auteur ; Benoit Meyssignac, Auteur ; Aurélien Ribes, Auteur ; Robin Guillaume-Castel, Auteur Année de publication : 2022 Article en page(s) : 49 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement
[Termes IGN] analyse diachronique
[Termes IGN] atmosphère terrestre
[Termes IGN] changement climatique
[Termes IGN] dioxyde de carbone
[Termes IGN] énergie
[Termes IGN] gaz à effet de serre
[Termes IGN] incertitude des données
[Termes IGN] régressionRésumé : (auteur) The estimate of the historical effective climate sensitivity (histeffCS) is revisited with updated historical observations of the global energy budget in order to derive an observational constraint on the effective sensitivity of climate to CO2 (CO2effCS). A regression method based on observations of the energy budget over 1971-2017 is used to estimate the histeffCS (4.34 [2.17;22.83] K, median and 5-95% range). Then, climate model simulations are used to evaluate the distance between the histeffCS and the CO2effCS. The observational estimate of the histeffCS and the distance between the histeffCS and the CO2effCS are combined to derive an observational constraint on CO2effCS of 5.46 [2.40;35.61] K. The main sources of uncertainty in the CO2effCS estimate comes from the uncertainty in aerosol forcing and in the top of the atmosphere energy imbalance. Further uncertainty arises from the pattern effect correction estimated from climate models. There is confidence in the lower end of the 5-95% range derived from our method as it relies only on reliable recent data and it makes full use of the observational record since 1971. This important result suggests that observations of the global energy budget since 1971 are poorly consistent with climate sensitivity to CO2 below 2.4 K. Unfortunately, the upper end of the 5-95% range derived from the regression method is above 30 K. It means that the observational constraint derived from observations of the global energy budget since 1971 is too weak (i.e. the uncertainty is too large) to provide any relevant information on the credibility of high CO2effCS. Numéro de notice : A2022-322 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1175/JCLI-D-21-0565.1 Date de publication en ligne : 14/03/2022 En ligne : https://doi.org/10.1175/JCLI-D-21-0565.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100149
in Journal of climate > vol 2022 [01/03/2022] . - 49 p.[article]Partitions of normalised multiple regression equations for datum transformations / Andrew Carey Ruffhead in Boletim de Ciências Geodésicas, vol 28 n° 1 ([01/03/2022])PermalinkReBankment : un algorithme pour déplacer les talus sur les cartes par moindres carrés / Guillaume Touya in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkSuspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkAnalysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure / Munir Ahmad in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)PermalinkA combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)PermalinkDeriving a tree growth model from any existing stand growth model / Quang V. Cao in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)PermalinkEfficient variance component estimation for large-scale least-squares problems in satellite geodesy / Yufeng Nie in Journal of geodesy, vol 96 n° 2 (February 2022)PermalinkEuropean-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.) / Jan-Peter George in Scientific reports, vol 12 (2022)PermalinkExploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkMapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)PermalinkSymbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography / Somnath Paramanik in Applied Geography, vol 139 (February 2022)PermalinkUse of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkCombining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)PermalinkContraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)PermalinkPermalinkDétection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)Permalink