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Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model / Zensheng Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 339 - 359 Titre : Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model Type de document : Article/Communication Auteurs : Zensheng Wang, Auteur ; Feidong Lu, Auteur ; Zhaohui Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 339 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] approche hiérarchique [Termes IGN] classification bayesienne [Termes IGN] dynamique spatiale [Termes IGN] estimation bayesienne [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle de simulation [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] Shenzhen [Termes IGN] téléphonie mobile [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (auteur) Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics. Numéro de notice : A2023-057 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2117363 En ligne : https://doi.org/10.1080/13658816.2022.2117363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102393 [article]

Species-specific deadwood density, its controlling factors and its role in the estimation of deadwood C stock of a Virgin European Beech-Silver Fir Mixed Forest in the Southern Carpathians / Ion Catalin Petritan in SSRN [preprint electronic journal], vol 2023 ([01/02/2023])  

Public [article]  inSSRN [preprint electronic journal] > vol 2023 [01/02/2023] Titre : Species-specific deadwood density, its controlling factors and its role in the estimation of deadwood C stock of a Virgin European Beech-Silver Fir Mixed Forest in the Southern Carpathians Type de document : Article/Communication Auteurs : Ion Catalin Petritan, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba [Termes IGN] bois mort [Termes IGN] Carpates [Termes IGN] décomposition [Termes IGN] densité du bois [Termes IGN] estimation statistique [Termes IGN] Fagus sylvatica [Termes IGN] peuplement mélangé [Termes IGN] puits de carbone [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Deadwood is a fundamental structural and functional component of forests, with a crucial role in supporting the forest biodiversity and nutrient and carbon cycling. Precise deadwood density estimates are necessary to evaluate the biomass and carbon stocked in this component. For a better understanding of the deadwood dynamics in natural forests, given its higher abundance, it is important to achieve deeper knowledge about its decay rate and how it is influenced by environmental factors. In this study, we estimated dry deadwood density for two different tree species, silver fir (Abies alba) and European beech (Fagus sylvatica) and for three snags and five logs decomposition classes (class 1 representing snag/log deadwood at early stages of decomposition and class 3/5 representing snags or logs, respectively, at its most advanced state of decomposition) in a virgin mixed beech-fir forest in the Southern Carpathians. The goal of this study was to assess how deadwood density is influenced by different abiotic (moisture, elevation, slope, aspect) and wood-related factors (rottenness, position of the sampling along the deadwood piece, the contact with the soil).For snags, the mean dry density showed a reduced variability within decomposition classes (484-326 kg.m-3 for beech and 374-319 kg.m-3 for fir), compared to the logs (486-139 kg.m-3 for beech and 359-161 kg.m-3 for fir). While the mass moisture varied slowly in the first three decay classes (around 60-80%), it increased sharply in the last two decay classes of logs (> 140% in the fourth classes and > 350% in the last one). The rottenness increased with the decay degree in a similar way for both species. The contact of logs with the soil influenced positively the moisture of the log, but the position of the sampling along the piece did not play any significant role in the variability of density. The density estimates per decay classes were used to compare the amount of carbon (C) sequestered as deadwood for each species. The mean biomass of C as deadwood at Sinca virgin forest varied greatly among the 21 plots from 0.36 to 41.16 MgC ha-1, with a mean value of 15.96 ± 2.36 (±SE) MgC ha-1.Our study suggests that volume-based calculations might yield biased quantitative estimates of C stored as deadwood unless a local estimate of dead wood density corrected per species and decomposition class is applied. Moreover, using an averaged value of dry density instead of dry density value for each decay class may result in an overestimation of 22% on the estimation of C stock sequestered as deadwood. Thus, our study may also help planning future inventories of C stocks in other virgin forests and for other species, (e.g., make emphasis in estimating densities in all decay classes). Furthermore, it could serve as a methodological basis for more specific research designed to uncover the potential influence of different forest management practices on dry deadwood density. Numéro de notice : A2023-085 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.2139/ssrn.4350235 En ligne : https://dx.doi.org/10.2139/ssrn.4350235 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102857 [article]

Geographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model / Zhenyang Du in Journal of Marine Systems, vol 237 (January 2023)  

Public [article]  inJournal of Marine Systems > vol 237 (January 2023) Titre : Geographic-dependent variational parameter estimation: A case study with a 2D ocean temperature model Type de document : Article/Communication Auteurs : Zhenyang Du, Auteur ; Xuefeng Zhang, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] assimilation des données [Termes IGN] estimation statistique [Termes IGN] modèle océanographique [Termes IGN] océanographie spatiale [Termes IGN] température de surface de la mer [Termes IGN] teneur en chaleur de l'océan Résumé : (auteur) Using observational information to tune uncertain physical parameters in an ocean model via a robust data assimilation method has great potential to reduce model bias and improve the quality of sea temperature analysis and prediction. However, how observational information should be used to optimize geographic-dependent parameters through four-dimensional variational (4DVAR) data assimilation, which is one of the most prevailing assimilation methods, has not been fully studied. In this study, a two-step 4DVAR method is proposed to enhance parameter correction when the assimilation model contains biased geographic-dependent parameters within a biased model framework. Here, the biased parameters are set to an oceanic eddy diffusion coefficient, Kv, that plays an important role in modulating synoptic, seasonal and long-term changes in ocean heat content. Within a twin assimilation experiment framework, the temperature “observations” generated from sampling a “truth” model are assimilated into a biased model to investigate to what extent Kv can be estimated using the 4DVAR method when Kv remains geographic-dependent. The results show that the geographic-dependent Kv distribution can be optimally estimated to further improve the sea temperature analysis performance compared with the state estimation only method. In addition, the model prediction performance is also discussed with optimally estimated parameters under various conditions of noisy and/or sparse ocean observations. These results provide some insights for the prediction of ocean temperature mixing and stratification in a 3D primitive ocean numerical model using 4DVAR data assimilation. Numéro de notice : A2023-080 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jmarsys.2022.103824 En ligne : https://doi.org/10.1016/j.jmarsys.2022.103824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102716 [article]

Bayesian hyperspectral image super-resolution in the presence of spectral variability / Fei Ye in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 12 (December 2022) . - n° 5545613 Titre : Bayesian hyperspectral image super-resolution in the presence of spectral variability Type de document : Article/Communication Auteurs : Fei Ye, Auteur ; Zebin Wu, Auteur ; Yang Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5545613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] processus gaussien [Termes IGN] réflectance [Termes IGN] signature spectrale [Termes IGN] théorème de Bayes Résumé : (auteur) Synthesizing a high-resolution (HR) hyperspectral image (HSI) by merging a low-resolution (LR) HSI with a corresponding HR multispectral image (MSI) has become a promising HSI super-resolution scheme. Most existing HSI-MSI fusion methods are effective to some extent, while several challenges remain. First, the spectral response of a given material exhibits considerable variability due to different acquisition times and conditions, however, variations in spectral signatures are often neglected. Second, a majority of off-the-shelf methods require predefined degradation operators, which can be unavailable in practice. To tackle the above issues, we introduce a novel fusion approach with a Bayesian framework. Specifically, we regard the up-sampled LR-HSI as the low-frequency component of the underlying HR-HSI. We characterize the texture features of high- and low-frequency components, respectively, which can enlarge modeling capacity and bypass the absence of degradation operators. Furthermore, we depict the relative smoothness of reflectance spectra with the Gaussian process. Extensive experiments on synthesized and real datasets illustrate the superiority of the proposed strategy in terms of fusion performance and robustness to spectral variability. Numéro de notice : A2022-908 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3228313 Date de publication en ligne : 12/12/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3228313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102339 [article]

A data-driven framework to manage uncertainty due to limited transferability in urban growth models / Jingyan Yu in Computers, Environment and Urban Systems, vol 98 (December 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 98 (December 2022) . - n° 101892 Titre : A data-driven framework to manage uncertainty due to limited transferability in urban growth models Type de document : Article/Communication Auteurs : Jingyan Yu, Auteur ; Alex Hagen-Zanker, Auteur ; Naratip Santitissadeekorn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] estimation bayesienne [Termes IGN] étalement urbain [Termes IGN] Europe (géographie politique) [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle stochastique [Termes IGN] simulation dynamique Résumé : (auteur) The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scenario development which, consists of three components - (i) multi-area, multi-period calibration, (ii) growth mode clustering, and (iii) cross-application. The framework finds clusters of parameters, referred to as growth modes: within the clusters, parameters represent similar spatial development trajectories; between the clusters, parameters represent substantially different spatial development trajectories. The framework is tested with a stochastic dynamic urban growth model across European functional urban areas over multiple time periods, estimated using a Bayesian method on an open global urban settlement dataset covering the period 1975–2014. The results confirm a lack of transferability, with reduced confidence in the model over the validation period, compared to the calibration period. Over the calibration period the probability that parameters estimated specifically for an area outperforms those for other areas is 96%. However, over an independent validation period, this probability drops to 72%. Four growth modes are identified along a gradient from compact to dispersed spatial developments. For most training areas, spatial development in the later period is better characterized by one of the four modes than their own historical parameters. The results provide strong support for using identified parameter clusters as a tool for data-driven and quantitative scenario development, to reflect part of the uncertainty of future spatial development trajectories. Numéro de notice : A2022-799 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101892 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101966 [article]

Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])  

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A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)  

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An estimation method to reduce complete and partial nonresponse bias in forest inventory / James A. Westfall in European Journal of Forest Research, vol 141 n° 5 (October 2022)  

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Assessing logging residues availability for energy production by using forest management plans data and geographic information system (GIS) / Luca Nonini in European Journal of Forest Research, vol 141 n° 5 (October 2022)  

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A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers / Qasim Khan in Geocarto international, vol 37 n° 20 ([20/09/2022])  

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Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)  

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Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)  

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A general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)  

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Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series / Kevin Gobron in Journal of geodesy, vol 96 n° 7 (July 2022)  

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Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference / Xiao Huang in Transactions in GIS, vol 26 n° 4 (June 2022)  

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