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A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data / Linhua Ma in Science of the total environment, vol 859 n° 1 (February 2023)  

Public [article]  inScience of the total environment > vol 859 n° 1 (February 2023) . - n° 159917 Titre : A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data Type de document : Article/Communication Auteurs : Linhua Ma, Auteur ; Yuanlai Cui, Auteur ; Bo Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 159917 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] Chine [Termes IGN] Corée [Termes IGN] données multisources [Termes IGN] Etats-Unis [Termes IGN] humidité du sol [Termes IGN] image à haute résolution [Termes IGN] image infrarouge [Termes IGN] Italie [Termes IGN] méthane [Termes IGN] modélisation [Termes IGN] réflectance du sol [Termes IGN] rizière [Termes IGN] système d'information géographique [Termes IGN] variation saisonnière Résumé : (auteur) Quantification of regional methane (CH4) gas emission in the paddy fields is critical under climate warming. Mechanism models generally require numerous parameters while empirical models are too coarse. Based on the mechanism and structure of the widely used model CH4MOD, a GIS-based Regional CH4 Emission Calculation (GRMC) method was put forward by introducing multiple sources of remote sensing images, including MOD09A1, MOD11A2, MOD15A2H as well as local water management standards. The stress of soil moisture condition (f(water)) on CH4 emissions was quantified by calculating the redox potential (Eh) from days after flooding or falling dry. The f(water)-t curve was calculated under different exogenous organic matter addition. Combining the f(water)-t curve with local water management standards, the seasonal variation of f(water) was obtained. It was proven that f(water) was effective in reflecting the regulation role of soil moisture condition. The GRMC was tested at four Eddy Covariance (EC) sites: Nanchang (NC) in China, Twitchell (TWT) in the USA, Castellaro (CAS) in Italy and Cheorwon (CRK) in Korea and has been proven to well track the seasonal dynamics of CH4 emissions with R2 ranges of 0.738–0.848, RMSE ranges of 31.94–149.22 mg C/m2d and MBE ranges of −66.42- -14.79 mg C/m2d. The parameters obtained in Nanchang (NC) site in China were then applied to the Ganfu Plain Irrigation System (GFPIS), a typical rice planting area of China, to analyse the spatial-temporal variations of CH4 emissions. The total CH4 emissions of late rice in the GFPIS from 2001 to 2013 was in the range of 14.47–20.48 (103 t CH4-C). Ts caused spatial variation of CH4 production capacity, resulting in the spatial variability of CH4 emissions. Overall, the GRMC is effective in obtaining CH4 emissions from rice fields on a regional scale. Numéro de notice : A2023-015 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.159917 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.159917 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102133 [article]

Temporal spectrum of spatial correlations between GNSS station position time series / Yujiao Niu in Journal of geodesy, vol 97 n° 2 (February 2023)  

Public [article]  inJournal of geodesy > vol 97 n° 2 (February 2023) . - n° 12 Titre : Temporal spectrum of spatial correlations between GNSS station position time series Type de document : Article/Communication Auteurs : Yujiao Niu, Auteur ; Paul Rebischung , Auteur ; Min Li, Auteur ; Na Wei, Auteur ; Chuang Shi, Auteur ; Zuheir Altamimi , Auteur Année de publication : 2023 Article en page(s) : n° 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle [Termes IGN] bruit blanc [Termes IGN] corrélation automatique de points homologues [Termes IGN] filtrage du bruit [Termes IGN] série temporelle [Termes IGN] station GNSS [Termes IGN] transformation de Fourier [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) The background noise in Global Navigation Satellite Systems (GNSS) station position time series is known to be both temporally and spatially correlated. Its temporal correlations are well modeled and routinely taken into account when deriving parameters of interest like station velocities. On the other hand, a general model of the spatial correlations in GNSS time series is lacking, and they are usually ignored, although their consideration could benefit several purposes such as offset detection, velocity estimation or spatial filtering. In order to improve the realism of current spatio-temporal correlation models, we investigate in this study how the spatial correlations of GNSS time series vary with the temporal frequency. A frequency-dependent measure of the spatial correlations is therefore introduced and applied to station position time series from the latest reprocessing campaign of the International GNSS Service (IGS), as well as to Precise Point Positioning time series provided by the Nevada Geodetic Laboratory (NGL). Different spatial correlation regimes are thus evidenced at different temporal frequencies. The different levels of spatial correlations between IGS and NGL datasets furthermore suggest that some part of the spatially correlated background noise in GNSS time series consists of GNSS errors rather than aperiodic Earth surface deformation signal. Numéro de notice : A2025-145 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-023-01703-7 Date de publication en ligne : 06/02/2023 En ligne : https://doi.org/10.1007/s00190-023-01703-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102746 [article]

Eco-environment and coupling coordination and quantification of urbanization in Yangtze River delta considering spatial non-stationarity / Yaqiu Zhang in Geocarto international, vol 37 n° 27 ([20/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 27 [20/12/2022] . - pp 14843 - 14862 Titre : Eco-environment and coupling coordination and quantification of urbanization in Yangtze River delta considering spatial non-stationarity Type de document : Article/Communication Auteurs : Yaqiu Zhang, Auteur ; Quanhua Zhao, Auteur ; Peizhen Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 14843 - 14862 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] delta [Termes IGN] distribution spatiale [Termes IGN] Fleuve bleu (Chine) [Termes IGN] mégalopole [Termes IGN] protection de l'environnement [Termes IGN] surveillance de l'urbanisation [Termes IGN] urbanisation Résumé : (auteur) Since the 21st century, the rapid development of China’s mega-city clusters has posed a major threat to the healthy and coordinated development of cities. Therefore, it is necessary to be develop the comprehend the state of coupling coordination among mega-city cluster and EEQ under mesoscale. In this study, the largest Yangtze River Delta urban agglomeration is taken as the research object, NS-RSEI is constructed to evaluate the EEQ of the Yangtze River Delta, and the coupling coordination mechanism on the long-time series of the Yangtze River Delta in recent 20 years is explored by means of spatio-temporal analysis. The outcome verify that CCD of the Yangtze River Delta growth with a strong spatial dependence from 2001 to 2020, showing a spatial distribution pattern of " East West high-low". Above all, this study shows that urbanization is the main factor determining the development of CCD. In addition, compared with the traditional remote sensing eco-environment monitoring model, NS-RSEI proposed in this study shows better ability in mesoscale environmental monitoring, and provides great convenience for mesoscale EEQ evaluation. This study fills the research gap of the interactive coupling mechanism between urbanization and eco-environment quality of mesoscale mega-city group, and provides a new perspective on the sustainable development of megacity clusters. Numéro de notice : A2022-935 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2091161 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1080/10106049.2022.2091161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102673 [article]

Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 24 [20/10/2022] . - pp 7220 - 7246 Titre : Driving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression Type de document : Article/Communication Auteurs : Ines Grigorescu, Auteur ; Gheorghe Kucsicsa, Auteur ; Bianca Mitrică, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7220 - 7246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] analyse spatio-temporelle [Termes IGN] croissance urbaine [Termes IGN] étalement urbain [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] modélisation spatiale [Termes IGN] régression logistique [Termes IGN] Roumanie [Termes IGN] zone urbaine Résumé : (auteur) The paper investigates built-up areas expansion after the 1990 in one of the highly urbanized regions of Romania - Romanian Plain, in order to explore the urban sprawl phenomena and its temporal and regional disparities in relation to some of the main distance driving factors. The research uses Landsat 4/5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and Landsat 8 Operational Land Imager (OLI) imagery to derive built-up areas and quantify their expansion over time in relation to fourteen distance explanatory factors: i.e. previous built-up areas, main road infrastructure, Bucharest city’s boundary, location of the urban centres classified according to demographic size and main economic function, forest land and water bodies. To estimate the influence of the predictors, the binary logistic regression was applied. Furthermore, to estimate the effectiveness of the predictor set in the variation of built-up areas expansion, the pseudo R2 was calculated and discussed. Moreover, to understand the future potential trend of urban sprawl and its spatial pattern, the probability maps were generated by integrating the regression coefficients of the statistically significant predictors into the spatial modeling. For the results performance assessment, the statistic Receiver Operating Characteristic and the pixel-based comparison between the real and predicted data were used. To assess possible differences at spatial and temporal scale, the analysis was carried out at regional level, for two periods: 1990–2002 and 2002–2018. In general, our findings show inverse relationship between the distance driving factors and built-up areas expansion, but the estimated predictive power suggests important disparities within the study area over the analysed periods. Overall, the statistical analysis indicate that the distance to previous build-up areas, distance to road infrastructure, distance to Bucharest and other large urban centres, and distance to urban centres with dominant industrial and service functions were more influential to urban sprawl after 1990. Furthermore, the predicted spatial data shows the highest potential of urban sprawl in the future around Bucharest, in the proximity of existing built-up areas and road infrastructure. Because of its predictive character, the present study is to be a useful tool for land managers and policy makers. Numéro de notice : A2022-777 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1967465 Date de publication en ligne : 23/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1967465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101832 [article]

Machine learning for spatial analyses in urban areas: a scoping review / Ylenia Casali in Sustainable Cities and Society, vol 85 (October 2022)  

Public [article]  inSustainable Cities and Society > vol 85 (October 2022) . - n° 104050 Titre : Machine learning for spatial analyses in urban areas: a scoping review Type de document : Article/Communication Auteurs : Ylenia Casali, Auteur ; Nazli Yonca Aydin, Auteur ; Tina Comes, Auteur Année de publication : 2022 Article en page(s) : n° 104050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse spatio-temporelle [Termes IGN] apprentissage automatique [Termes IGN] distribution spatiale [Termes IGN] espace urbain [Termes IGN] littérature [Termes IGN] source de données [Termes IGN] urbanisme [Termes IGN] ville durable [Termes IGN] zone urbaine Résumé : (auteur) The challenges for sustainable cities to protect the environment, ensure economic growth, and maintain social justice have been widely recognized. Along with the digitization, availability of large datasets, Machine Learning (ML) and Artificial Intelligence (AI) are promising to revolutionize the way we analyze and plan urban areas, opening new opportunities for the sustainable city agenda. Especially urban spatial planning problems can benefit from ML approaches, leading to an increasing number of ML publications across different domains. What is missing is an overview of the most prominent domains in spatial urban ML along with a mapping of specific applied approaches. This paper aims to address this gap and guide researchers in the field of urban science and spatial data analysis to the most used methods and unexplored research gaps. We present a scoping review of ML studies that used geospatial data to analyze urban areas. Our review focuses on revealing the most prominent topics, data sources, ML methods and approaches to parameter selection. Furthermore, we determine the most prominent patterns and challenges in the use of ML. Through our analysis, we identify knowledge gaps in ML methods for spatial data science and data specifications to guide future research. Numéro de notice : A2022-765 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104050 Date de publication en ligne : 12/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104050 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101786 [article]

Losses of tree cover in California driven by increasing fire disturbance and climate stress / Jonathan A. Wang in AGU Advances, vol 3 n° 4 (August 2022)  

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Spatial assessment of ecosystem services provisioning changes in a forest-dominated protected area in NE Turkey / Can Vatandaslar in Environmental Monitoring and Assessment, vol 194 n° 8 (August 2022)  

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Temporal transitions of demographic dot maps / Jeff Allen in International journal of cartography, vol 8 n° 2 (July 2022)  

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Characteristics of disease maps of zoonoses: A scoping review and a recommendation for a reporting guideline for disease maps / Inthuja Selvaratnam in Cartographica, vol 57 n° 2 (Summer 2022)  

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Analyzing spatio-temporal pattern of the forest fire burnt area in Uttarakhand using Sentinel-2 data / Shailja Mamgain in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

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Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data / Susmita Dasgupta in Science of the total environment, vol 839 (May 2022)  

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Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework / Evgeny Noi in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

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Analysis of spatio-temporal changes in forest biomass in China / Weiyi Xu in Journal of Forestry Research, vol 33 n° 1 (February 2022)  

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Mapping global flying aircraft activities using Landsat 8 and cloud computing / Fen Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)  

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Modalités et rythmes d'évolution des falaises des Vaches Noires (Normandie, France) : caractérisation et quantification des dynamiques hydrogravitaires par approches multi-scalaires / Thomas Roulland (2022)  

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