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Forest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)  

Public [article]  inForests > vol 13 n° 9 (september 2022) . - n° 1416 Titre : Forest tree species classification based on Sentinel-2 images and auxiliary data Type de document : Article/Communication Auteurs : Haotian You, Auteur ; Yuanwei Huang, Auteur ; Zhigang Qin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] dioxyde d'azote [Termes IGN] distribution spatiale [Termes IGN] Extreme Gradient Machine [Termes IGN] image Sentinel-MSI [Termes IGN] phénologie [Termes IGN] précipitation [Termes IGN] réflectance spectrale [Termes IGN] température de l'air [Termes IGN] texture du sol [Termes IGN] topographie locale Résumé : (auteur) Most research on forest tree species classification based on optical image data uses information such as spectral reflectance, vegetation index, texture, and phenology data. However, owing to the limited spectral resolution of multispectral images and the high cost of hyperspectral data, there is room for improvement in the classification of tree species in large areas based on optical images. The combined application of multispectral images and other auxiliary data can provide a new method for improving tree species classification accuracy. Hence, Sentinel-2 images were used to extract spectral reflectance, spectral index, texture, and phenological information. Data for topography, precipitation, air temperature, ultraviolet aerosol index, NO2 concentration, and other variables were included as auxiliary data. Models for forest tree species classification were constructed through feature combination and feature optimization using the random forest (RF), gradient tree boost (GTB), support vector machine (SVM), and classification and regression tree (CART) algorithms. The classification results of 16 feature combinations with the 4 classification methods were compared, and the contributions of different features to the classification models of forest tree species were evaluated. Finally, the optimal classification model was selected to identify the spatial distribution of forest tree species in the study area. The model based on feature optimization gave the best results among the 16 feature combination models. The overall accuracy and kappa coefficient were increased by 18% and 0.21, respectively, compared with the spectral classification model, and by 17% and 0.20, respectively, compared with the spectral and spectral index classification model. By analyzing the feature optimization model, it was found that terrain, ultraviolet aerosol index, and phenological information ranked as the top three features in terms of importance. Although the importance of spectral reflectance and spectral index features was lower, the number of feature variables accounted for a large proportion of the total. The importance of commonly used texture features was limited, and these features were not present in the feature optimization model. The RF algorithm had the highest classification accuracy, with an overall accuracy of 82.69% and a kappa coefficient of 0.80, among the four classification algorithms. The results of GTB were close to those of RF, and the difference in overall classification accuracy was only 0.14%. However, the results of the SVM and CART algorithms were relatively weaker, with overall classification accuracies of about 70%. It can be concluded that the combined application of Sentinel-2 images and auxiliary data can improve forest tree species classification accuracy. The model based on feature optimization achieved the highest classification accuracy among the 16 feature combination models. The spectral reflectance and spectral index data extracted from optical images are useful for tree species classification, but the effect of texture features was very limited. Auxiliary data, such as topographic features, ultraviolet aerosol index, phenological features, NO2 concentration features, topographic diversity features, precipitation features, temperature features, and multi-scale topographic location index data, can effectively improve forest tree species classification accuracy. The RF algorithm had the highest accuracy, and it can be used for tree species classification space distribution identification. The combined application of Sentinel-2 images and auxiliary data can improve classification accuracy, but the highest accuracy of the model was only 82.69%, which leaves room for improvement. Thus, more effective auxiliary data and the vertical structural parameters extracted from satellite LiDAR can be combined with multispectral images to improve forest tree species classification accuracy in future research. Numéro de notice : A2022-754 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13091416 Date de publication en ligne : 02/09/2022 En ligne : https://doi.org/10.3390/f13091416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101757 [article]

Variations of urban NO2 pollution during the COVID-19 outbreak and post-epidemic era in China: A synthesis of remote sensing and In situ measurements / Chunhui Zhao in Remote sensing, vol 14 n° 2 (January-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 2 (January-2 2022) . - n° 419 Titre : Variations of urban NO2 pollution during the COVID-19 outbreak and post-epidemic era in China: A synthesis of remote sensing and In situ measurements Type de document : Article/Communication Auteurs : Chunhui Zhao, Auteur ; Chengzin Zhang, Auteur ; Jinan Lin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] dioxyde d'azote [Termes IGN] épidémie [Termes IGN] image Sentinel-5P-TROPOMI [Termes IGN] impact sur l'environnement [Termes IGN] pollution atmosphérique [Termes IGN] qualité de l'air [Termes IGN] variation temporelle Résumé : (auteur) Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production. Numéro de notice : A2022-102 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14020419 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.3390/rs14020419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99567 [article]

Centrality and city size effects on NO2 ground and tropospheric concentrations within European cities / Yufei Wei (2021) 

Public contenu dans European Colloquium on Theoretical and Quantitative Geography 2021, Manchester, 3-5 November 2021 / Nuno Pinto (2021)   Titre : Centrality and city size effects on NO2 ground and tropospheric concentrations within European cities Type de document : Article/Communication Auteurs : Yufei Wei, Auteur ; Geoffrey Caruso, Auteur ; Rémi Lemoy, Auteur Editeur : Manchester [Royaume-Uni] : Manchester University Press Année de publication : 2021 Conférence : ECTQG 2021, 22nd European Colloquium on Theoretical and Quantitative Geography 03/11/2021 05/11/2021 Manchester Royaume-Uni Open Access Abstracts Importance : pp 396 - 400 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] centre urbain [Termes IGN] dioxyde d'azote [Termes IGN] Europe (géographie politique) [Termes IGN] image Sentinel-5P-TROPOMI [Termes IGN] pollution atmosphérique [Termes IGN] ville Numéro de notice : C2021-078 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100065

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The strong and the stronger: The effects of increasing ozone and nitrogen dioxide concentrations in pollen of different forest species / Sónia Pereira in Forests, vol 12 n° 1 (January 2021)  

Public [article]  inForests > vol 12 n° 1 (January 2021) . - n° 88 Titre : The strong and the stronger: The effects of increasing ozone and nitrogen dioxide concentrations in pollen of different forest species Type de document : Article/Communication Auteurs : Sónia Pereira, Auteur ; Maria Fernández-González, Auteur ; Alexandra Guedes, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 88 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer negundo [Termes IGN] analyse comparative [Termes IGN] Betula pendula [Termes IGN] Corylus avellana [Termes IGN] dioxyde d'azote [Termes IGN] Europe (géographie politique) [Termes IGN] indice de stress [Termes IGN] ozone [Termes IGN] pollen [Termes IGN] pollution atmosphérique [Termes IGN] protection de l'environnement [Termes IGN] Quercus pedunculata [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) The knowledge of pollen sensitivity and tolerance to stress factors such as air pollution is important for forest sustainability, ensuring the most efficient production with the highest benefits and lowest resource losses. This study intended to evaluate the influence of common air pollutants in four forest trees species, Betula pendula Roth, Corylus avellana L., Acer negundo L. and Quercus robur L., through a comparative analysis at the same experimental conditions. We aimed to investigate the effect that may occur in pollen fertility, protein content, oxidative stress and wall composition after exposure in vitro to ozone and nitrogen dioxide at concentration levels for vegetation protection in Europe. Our results suggest changes in pollen viability, protein content and differential sensitivity related to ROS synthesis, NADPH oxidase activity, as well as in wall composition. The results indicate that NO2 exposure affected more the pollen species studied mostly at the highest concentration exposure. As for ozone, there were less significant differences between samples; however, a different behavior occurs in O3 expositions, where the most influence happens at the legal limit for vegetation protection in Europe. Our study showed that significant pollen functions could be compromised even at common air pollutant’s concentrations. Numéro de notice : A2021-143 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12010088 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/f12010088 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97046 [article]

Cyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS / Philippe Apparicio in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)  

Public [article]  inRevue internationale de géomatique > vol 30 n° 3-4 (juillet - décembre 2020) . - pp 155 - 179 Titre : Cyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS Type de document : Article/Communication Auteurs : Philippe Apparicio, Auteur ; Jérémy Gelb, Auteur ; Paula Negron-Poblete, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 155 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] bicyclette [Termes IGN] dioxyde d'azote [Termes IGN] estimation bayesienne [Termes IGN] Mexico (Mexique) [Termes IGN] pollution acoustique [Termes IGN] pollution atmosphérique [Termes IGN] système d'information géographique [Termes IGN] temps réel [Termes IGN] trafic routier Résumé : (Auteur) Air pollution and road traffic noise are two important environmental nuisances that could be harmful to the health and well-being of urban populations. In Mexico City, as in many North American cities, there has been an upusurge in bicycle ridership. However, Mexico City is also well known for having high levels of noise and air pollution. The purpose of this study is threefold: 1) evaluate cyclists' exposure to air pollution (nitrogen dioxide) and road traffic noise; 2) identify local factors that increase or reduce cyclists' exposure, in paying particular attention to the type of road and bicycle path or lane used; and 3) evaluate the influence of real-time traffic density on cyclists' exposure. A total of 19 bicycle trips made in central Mexico City neighbourhoods were analyzed, representing nearly 11 hours and 137 km. The results of the Bayesian models show that type of road and bicyle infrastructure taken by the cyclist, and proximity to a main artery all have significant impacts on exposure levels. Finally, the variables introduced to control for the traffic encountered by cyclists had a significant positive effect on noise exposure, and a positive but not significant effect on nitrogen dioxide exposure. Numéro de notice : A2020-879 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.3166/rig.2021.00110 En ligne : https://doi.org/10.3166/rig.2021.00110 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100219 [article]

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