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There’s no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles / Richard J. Hewitt in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 12 (December 2022) Titre : There’s no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles Type de document : Article/Communication Auteurs : Richard J. Hewitt, Auteur ; Majid Shadman Roodposhti, Auteur ; Brett A. Bryan, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] automate cellulaire [Termes IGN] étalonnage de modèle [Termes IGN] étalonnage des données [Termes IGN] incertitude des données [Termes IGN] utilisation du sol Résumé : (auteur) Cellular automata models are popular tools for exploring future land change pathways. But simulation modelling approaches often focus too narrowly on calibration against historic reference maps, limiting the diversity of possible outcomes. We argue that, contrary to what is commonly believed, there is no ‘best model’, and that model specification and calibration accuracy depend on the objective of the research. We propose a multi-model ensemble approach, in which a wide range of models and calibration rules sets are systematically tested against multiple metrics. We apply our approach to a case study in Spain. No single model performed well for all statistics, illustrating the danger of cherry-picking statistics for best performance. In our case study, accounting for historic land changes in model design was useful for simulating compact urban development, but limited the variability of simulation outcomes. The accessibility model driver improved urban pattern replication, while suitability without accessibility was useful for simulating low-density development encroaching on natural areas. Rather than abandoning calibrations that show low agreement with reference maps based on a small number of metrics we should seek to understand what each metric is telling us and use this information to enrich the diversity of simulated outcomes. Numéro de notice : A2022-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2098299 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2098299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101372 [article]

Characterizing the calibration domain of remote sensing models using convex hulls / Jean-Pierre Renaud in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102939 Titre : Characterizing the calibration domain of remote sensing models using convex hulls Type de document : Article/Communication Auteurs : Jean-Pierre Renaud , Auteur ; Ankit Sagar , Auteur ; Pierre Barbillon, Auteur ; Olivier Bouriaud , Auteur ; Christine Deleuze, Auteur ; Cédric Vega , Auteur Année de publication : 2022 Projets : DEEPSURF / Pironon, Jacques, ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 102939 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] données allométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] erreur systématique [Termes IGN] étalonnage de modèle [Termes IGN] étalonnage des données [Termes IGN] extrapolation [Termes IGN] placette d'échantillonnage Résumé : (auteur) The ever-increasing availability of remote sensing data allows production of forest attributes maps, which are usually made using model-based approaches. These map products are sensitive to various bias sources, including model extrapolation. To identify, over a case study forest, the proportion of extrapolated predictions, we used a convex hull method applied to the auxiliary data space of an airborne laser scanning (ALS) flight. The impact of different sampling efforts was also evaluated. This was done by iteratively thinning a set of 487 systematic plots using nested sub-grids allowing to divide the sample by two at each level. The analysis were conducted for all alternative samples and evaluated against 56 independent validation plots. Residuals of the extrapolated validation plots were computed and examined as a function of their distance to the model calibration domain. Extrapolation was also characterized for the pixels of the area of interest (AOI) to upscale at population level. Results showed that the proportion of extrapolated pixels greatly reduced with an increasing sampling effort. It reached a plateau (ca. 20% extrapolation) with a sampling intensity of ca. 250-calibration plots. This contrasts with results on model’s root mean squared error (RMSE), which reached a plateau at a much lower sampling intensity. This result emphasizes the fact that with a low sampling effort, extrapolation risk remains high, even at a relatively low RMSE. For all attributes examined (i.e., stand density, basal area, and quadratic mean diameter) estimations were generally found to be biased for validation plots that were extrapolated. The method allows an easy identification of map pixels that are out of the calibration domain, making it an interesting tool to evaluate model transferability over an area of interest (AOI). It could also serve to compare “competing” models at a variable selection phase. From a model calibration perspective, it could serve a posteriori, to evaluate areas (in the auxiliary space) that merit further sampling efforts to improve model reliability. Numéro de notice : A2022-581 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102939 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102939 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101341 [article]

An integrated framework of global sensitivity analysis and calibration for spatially explicit agent-based models / Jeon-Young Kang in Transactions in GIS, vol 26 n° 1 (February 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 1 (February 2022) . - pp 100 - 128 Titre : An integrated framework of global sensitivity analysis and calibration for spatially explicit agent-based models Type de document : Article/Communication Auteurs : Jeon-Young Kang, Auteur ; Alexander Michels, Auteur ; Andrew Crooks, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 100 - 128 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de sensibilité [Termes IGN] analyse de variance [Termes IGN] épidémie [Termes IGN] étalonnage de modèle [Termes IGN] maladie virale [Termes IGN] méthode de Monte-Carlo [Termes IGN] Miami [Termes IGN] modèle de simulation [Termes IGN] modèle orienté agent [Termes IGN] WebSIG Résumé : (auteur) Calibration of agent-based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well-calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA-CAL. Specifically, variance-based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high-performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak. Numéro de notice : A2022-176 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12837 Date de publication en ligne : 03/09/2021 En ligne : https://doi.org/10.1111/tgis.12837 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99832 [article]

Conventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 4 Titre : Conventional and neural network-based water vapor density model for GNSS troposphere tomography Type de document : Article/Communication Auteurs : Chen Liu, Auteur ; Yibin Yao, Auteur ; Chaoqian Xu, Auteur Année de publication : 2022 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] classification par réseau neuronal [Termes IGN] erreur absolue [Termes IGN] étalonnage de modèle [Termes IGN] modèle météorologique [Termes IGN] propagation troposphérique [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) Global navigation satellite system (GNSS) water vapor (WV) tomography is a promising technique to reconstruct the three-dimensional (3D) WV field. However, this technique usually suffers from the ill-posed problem caused by the poor geometry of GNSS rays, resulting in underdetermined tomographic equations. Such equations often rely on iterative methods for solving, but conventional iterative approaches require accurate initial WV density. To address this demand, we proposed two models for WV density estimation. One is the conventional model (CO model) that consists of an exponential model and a linear least-squares model, which are used to describe the spatial and temporal variability of the WV density, respectively. The other is a neural network model (NN model) that uses a backpropagation neural network (BPNN) to fit the nonlinear variation of WV density in both spatial and temporal domains. WV density derived from a Hong Kong (HK) radiosonde station (RS) during 2020 was used to validate the proposed models. Validation results show that both models well describe the spatial and temporal distribution of the WV density. The NN model exhibits better prediction performance than the CO model in terms of root mean square error (RMSE) and bias. We also applied the proposed models to GNSS WV tomography to test their performance in extreme weather conditions. Test results show that the proposed model-based GNSS tomography can correct the content of WV density but cannot accurately sense its irregular distribution. Numéro de notice : A2022-005 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01188-x Date de publication en ligne : 23/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01188-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98920 [article]

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)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 4 (August 2021) . - pp 847 - 868 Titre : Calibration of the process-based model 3-PG for major central European tree species Type de document : Article/Communication Auteurs : David I. Forrester, Auteur ; Martina Lena Hobi, Auteur ; Amanda S. Mathys, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 847 - 868 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière [Termes IGN] changement climatique [Termes IGN] estimation bayesienne [Termes IGN] étalonnage de modèle [Termes IGN] Europe centrale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de croissance végétale [Termes IGN] modélisation de la forêt [Termes IGN] peuplement mélangé [Termes IGN] régression [Termes IGN] Suisse [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Process-based forest models are important tools for predicting forest growth and their vulnerability to factors such as climate change or responses to management. One of the most widely used stand-level process-based models is the 3-PG model (Physiological Processes Predicting Growth), which is used for applications including estimating wood production, carbon budgets, water balance and susceptibility to climate change. Few 3-PG parameter sets are available for central European species and even fewer are appropriate for mixed-species forests. Here we estimated 3-PG parameters for twelve major central European tree species using 1418 long-term permanent forest monitoring plots from managed forests, 297 from un-managed forest reserves and 784 Swiss National Forest Inventory plots. A literature review of tree physiological characteristics, as well as regression analyses and Bayesian inference, were used to calculate the 3-PG parameters. The Swiss-wide calibration, based on monospecific plots, showed a robust performance in predicting forest stocks such as stem, foliage and root biomass. The plots used to inform the Bayesian calibration resulted in posterior ranges of the calibrated parameters that were, on average, 69% of the prior range. The bias of stem, foliage and root biomass predictions was generally less than 20%, and less than 10% for several species. The parameter sets also provided reliable predictions of biomass and mean tree sizes in mixed-species forests. Given that the information sources used to develop the parameters included a wide range of climatic, edaphic and management conditions and long time spans (from 1930 to present), these species parameters for 3-PG are likely to be appropriate for most central European forests and conditions. Numéro de notice : A2021-717 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01370-3 Date de publication en ligne : 18/03/2021 En ligne : https://doi.org/10.1007/s10342-021-01370-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98630 [article]

Geographical and temporal huff model calibration using taxi trajectory data / Shuhui Gong in Geoinformatica, vol 25 n° 3 (July 2021)  

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Parallel computing for fast spatiotemporal weighted regression / Xiang Que in Computers & geosciences, vol 150 (May 2021)  

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Use of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September-2 2020)  

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Analysis of chlorophyll concentration in potato crop by coupling continuous wavelet transform and spectral variable optimization / Ning Liu in Remote sensing, vol 12 n° 17 (September-1 2020)  

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A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)  

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Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory / Francesco Minunno in Forest ecology and management, vol 440 (15 May 2019)  

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Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning / Yimin Chen in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)  

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Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship / Z. Adamec in Journal of forest science, vol 61 n° 10 (October 2015)  

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