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scientifique

Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

Public [article]  inRemote sensing of environment > vol 268 (January 2022) . - n* 112760 Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] biomasse aérienne [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation bayesienne [Termes IGN] forme d'onde [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Résumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495 [article]

Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization / Frédéric André in Annals of Forest Science [en ligne], vol 78 n° 4 (December 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 4 (December 2021) . - n° 92 Titre : Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization Type de document : Article/Communication Auteurs : Frédéric André, Auteur ; Louis de Wergifosse, Auteur ; François de Coligny, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Belgique [Termes IGN] couvert forestier [Termes IGN] croissance des arbres [Termes IGN] densité du feuillage [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] estimation bayesienne [Termes IGN] Fagus sylvatica [Termes IGN] houppier [Termes IGN] Leaf Mass per Area [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de transfert radiatif [Termes IGN] peuplement mélangé [Termes IGN] photosynthèse [Termes IGN] production primaire brute [Termes IGN] production primaire nette [Termes IGN] Quercus sessiliflora [Termes IGN] structure d'un peuplement forestier [Vedettes matières IGN] Foresterie Résumé : (auteur) Key message: The best options to parametrize a radiative transfer model change according to the response variable used for fitting. To predict transmitted radiation, the turbid medium approach performs much better than the porous envelop, especially when accounting for the intra-specific variations in leaf area density but crown shape has limited effects. When fitting with tree growth data, the porous envelop approach combined with the more complex crown shape provides better results. When using a joint optimization with both variables, the better options are the turbid medium and the more detailed approach for describing crown shape and leaf area density. Context: Solar radiation transfer is a key process of tree growth dynamics in forest. Aims: Determining the best options to parametrize a forest radiative transfer model in heterogeneous oak and beech stands from Belgium. Methods: Calibration and evaluation of a forest radiative transfer module coupled to a spatially explicit tree growth model were repeated for different configuration options (i.e., turbid medium vs porous envelope to calculate light interception by trees, crown shapes of contrasting complexity to account for their asymmetry) and response variables used for fitting (transmitted radiation and/or tree growth data). Results: The turbid medium outperformed the porous envelope approach. The more complex crown shapes enabling to account for crown asymmetry improved performances when including growth data in the calibration. Conclusion: Our results provide insights on the options to select when parametrizing a forest radiative 3D-crown transfer model depending on the research or application objectives. Numéro de notice : A2021-768 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01106-8 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1007/s13595-021-01106-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99010 [article]

Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (15 december 2021)  

Public [article]  inForest ecology and management > vol 502 (15 december 2021) . - n° 119714 Titre : Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models Type de document : Article/Communication Auteurs : Arne Nothdurft, Auteur ; Christoph Gollob, Auteur ; Ralf Krasnitzer, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119714 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Autriche [Termes IGN] bois sur pied [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] échantillonnage [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] lasergrammétrie [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle de régression [Termes IGN] modèle mathématique [Termes IGN] tempête [Termes IGN] volume en bois [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) A spatial regression model framework is presented to predict growing stock volume loss due to storm Adrian which caused heavy forest damage in the upper Gail valley in Carinthia, Austria, in October 2018. Model parameters were estimated using growing stock volume measured with a terrestrial laser scanner on 62 sample plots distributed across five sub-regions. Predictor variables were derived from high resolution vegetation height measurements collected during an airborne laser scanning campaign. Non-spatial and spatial candidate models were proposed and assessed based on fit to observed data and out-of-sample prediction. Spatial Gaussian processes associated model intercepts and regression coefficients were used to capture spatial dependence. Results show a spatially-varying coefficient model, which allowed the intercept and regression coefficients to vary spatially, yielded the best fit and prediction. Two approaches were considered for prediction over blowdown areas: 1) an areal approach that viewed each blowdown as a single prediction unit indexed by its centroid; and 2) a block approach where each blowdown was partitioned into smaller prediction units to better align with sample plots’ spatial support. Joint prediction was used to acknowledge spatial dependence among block units. Results demonstrated the block approach is preferable as it mitigated change-of-support issues encountered in the areal approach. Despite the small sample size, predictions for 55% of the total 564 blowdown areas, accounting for 93% of the total loss, had a coefficient of variation less than 25%. Key advantages of the proposed regression framework and chosen Bayesian inferential paradigm, were the ability to quantify uncertainty in spatial covariance parameters, propagate parameter uncertainty through to prediction, and provide statistically valid prediction point and interval estimates for individual blowdowns and collections of blowdowns at the sub-region and region scale via posterior predictive distribution summaries. Numéro de notice : A2021-770 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119714 Date de publication en ligne : 07/10/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98822 [article]

A method for precisely predicting satellite clock bias based on robust fitting of ARMA models / Guochao Zhang in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 3 Titre : A method for precisely predicting satellite clock bias based on robust fitting of ARMA models Type de document : Article/Communication Auteurs : Guochao Zhang, Auteur ; Songhui Han, Auteur ; Jun Ye, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] décalage d'horloge [Termes IGN] erreur systématique interfréquence d'horloge [Termes IGN] estimation bayesienne [Termes IGN] international GPS service for geodynamics [Termes IGN] série temporelle [Termes IGN] statistique mathématique [Termes IGN] valeur aberrante Résumé : (auteur) The precise satellite clock bias prediction is critical in improving the positioning, navigation and timing (PNT) service capabilities of the global navigation satellite system (GNSS). Due to the influence of satellite signal path and the observation environment, the satellite clock bias data usually contain outliers that heavily affect the accuracy of satellite clock bias prediction. Based on the time series ARMA model and Bayes statistical theory, we propose a method to precisely predict satellite clock bias and detect outliers in the historical sequence of satellite clock bias. At first, considering the effects of an additive outlier (AO) and innovative outlier (IO), a labeling model for robustly fitting the time series ARMA model and detecting AOs and IOs simultaneously is constructed based on the labeling method of classification variables. Second, the Bayes method for robustly fitting time series ARMA model is proposed based on the Bayes statistical theory. Furthermore, it develops an algorithm to precisely predict satellite clock bias using the Bayes method for robustly fitting the time series ARMA model mentioned above. Finally, in order to illustrate the performance of the method for precisely predicting satellite clock bias that we presented, three examples are designed based on the real GPS data come from the IGS official website, and the prediction results of the method are compared with that of original ARMA model (oARMA), quadratic polynomial model (QP) and gray model (GM). It is found that the method can precisely predict the satellite clock bias as well as accurately detect the outliers in the historical sequence. Numéro de notice : A2022-002 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01182-3 Date de publication en ligne : 20/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01182-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98827 [article]

Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation / Jingyan Yu in Computers, Environment and Urban Systems, vol 90 (November 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 90 (November 2021) . - n° 101689 Titre : Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation Type de document : Article/Communication Auteurs : Jingyan Yu, Auteur ; Alex Hagen-Zanker, Auteur ; Naratip Santitissadeekorn, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101689 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse diachronique [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] Corine Land Cover [Termes IGN] croissance urbaine [Termes IGN] estimation bayesienne [Termes IGN] Grande-Bretagne [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle dynamique Résumé : (auteur) Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration. This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures. The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions. Numéro de notice : A2021-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101689 Date de publication en ligne : 12/08/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101689 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98367 [article]

Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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Calibration of the process-based model 3-PG for major central European tree species / David I. Forrester in European Journal of Forest Research, vol 140 n° 4 (August 2021)  

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Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)  

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A Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([01/05/2021])  

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A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

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Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

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Optimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale / Linling Tang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Geospatial analysis of the spreading of COVID-19 In the United States / Otto Heimonen (2021)  

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Probabilistic positioning in mobile phone network and its consequences for the privacy of mobility data / Aleksey Ogulenko in Computers, Environment and Urban Systems, vol 85 (January 2021)  

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Climate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model / Arne Nothdurft in Forest ecology and management, vol 478 ([15/12/2020])  

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