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Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak / A.P. Rudke in Remote sensing of environment, vol 289 (May 2003)  

Public [article]  inRemote sensing of environment > vol 289 (May 2003) . - n° 113514 Titre : Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak Type de document : Article/Communication Auteurs : A.P. Rudke, Auteur ; J.A. Martins, Auteur ; R. Hallak, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] correction atmosphérique [Termes IGN] dioxyde d'azote [Termes IGN] épidémie [Termes IGN] image Sentinel-5P-TROPOMI [Termes IGN] image Terra-MODIS [Termes IGN] pollution atmosphérique [Termes IGN] qualité de l'air [Termes IGN] Sao Paulo Résumé : (auteur) Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO2 column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO2 and particulate material (PM; coarse: PM10 and fine: PM2.5)]. For this purpose, tropospheric NO2 obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM10, most stations showed correlations lower than 0.2, which were not significant. The results for PM2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO2 proved to be a good predictor for NO2 concentrations at ground level. Considering all stations with NO2 measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO2 throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO2) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO2). Our results demonstrate that Tropospheric NO2 column densities can serve as good predictors of NO2 concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data. Numéro de notice : A2023-170 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2023.113514 Date de publication en ligne : 21/02/2023 En ligne : https://doi.org/10.1016/j.rse.2023.113514 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102930 [article]

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]

Adding tree rings to North America's national forest inventories: An essential tool to guide drawdown of atmospheric CO2 / Margaret E.K. Evans in BioScience, vol 72 n° 3 (March 2022)  

Public [article]  inBioScience > vol 72 n° 3 (March 2022) . - pp 233 - 246 Titre : Adding tree rings to North America's national forest inventories: An essential tool to guide drawdown of atmospheric CO2 Type de document : Article/Communication Auteurs : Margaret E.K. Evans, Auteur ; R. Justin DeRose, Auteur ; Stefan Klesse, Auteur ; Martin P. Girardin, Auteur ; Kelly A. Heilman, Auteur ; M. Ross Alexander, Auteur ; André Arsenault, Auteur ; Flurin Babst, Auteur ; Mathieu Bouchard, Auteur ; Sean M. P. Cahoon, Auteur ; Elisabeth M. Campbell, Auteur ; Michael Dietze, Auteur ; Louis Duchesne, Auteur ; David Frank, Auteur ; Courtney L. Giebink, Auteur ; Armando Gómez-Guerrero, Auteur ; Genaro Gutiérrez García, Auteur ; Edward H. Hogg, Auteur ; Juha Metsaranta, Auteur ; Clémentine Ols , Auteur ; et al., Auteur Année de publication : 2022 Projets : ARBRE / AgroParisTech (2007 -), LUE / Université de Lorraine Article en page(s) : pp 233 - 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Amérique du nord [Termes IGN] cerne [Termes IGN] dendrochronologie [Termes IGN] dioxyde de carbone [Termes IGN] gaz à effet de serre [Termes IGN] inventaire forestier étranger (données) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Tree-ring time series provide long-term, annually resolved information on the growth of trees. When sampled in a systematic context, tree-ring data can be scaled to estimate the forest carbon capture and storage of landscapes, biomes, and—ultimately—the globe. A systematic effort to sample tree rings in national forest inventories would yield unprecedented temporal and spatial resolution of forest carbon dynamics and help resolve key scientific uncertainties, which we highlight in terms of evidence for forest greening (enhanced growth) versus browning (reduced growth, increased mortality). We describe jump-starting a tree-ring collection across the continent of North America, given the commitments of Canada, the United States, and Mexico to visit forest inventory plots, along with existing legacy collections. Failing to do so would be a missed opportunity to help chart an evidence-based path toward meeting national commitments to reduce net greenhouse gas emissions, urgently needed for climate stabilization and repair. Numéro de notice : A2022-031 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/biosci/biab119 Date de publication en ligne : 08/12/2021 En ligne : https://doi.org/10.1093/biosci/biab119 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99282 [article]

Observational constraint on the climate sensitivity to atmospheric CO2 concentrations changes derived from the 1971-2017 global energy budget / Jonathan Chenal in Journal of climate, vol 2022 ([01/03/2022])  

Public [article]  inJournal of climate > vol 2022 [01/03/2022] . - 49 p. Titre : Observational constraint on the climate sensitivity to atmospheric CO2 concentrations changes derived from the 1971-2017 global energy budget Type de document : Article/Communication Auteurs : Jonathan Chenal , Auteur ; Benoit Meyssignac, Auteur ; Aurélien Ribes, Auteur ; Robin Guillaume-Castel, Auteur Année de publication : 2022 Article en page(s) : 49 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement [Termes IGN] analyse diachronique [Termes IGN] atmosphère terrestre [Termes IGN] changement climatique [Termes IGN] dioxyde de carbone [Termes IGN] énergie [Termes IGN] gaz à effet de serre [Termes IGN] incertitude des données [Termes IGN] régression Résumé : (auteur) The estimate of the historical effective climate sensitivity (histeffCS) is revisited with updated historical observations of the global energy budget in order to derive an observational constraint on the effective sensitivity of climate to CO2 (CO2effCS). A regression method based on observations of the energy budget over 1971-2017 is used to estimate the histeffCS (4.34 [2.17;22.83] K, median and 5-95% range). Then, climate model simulations are used to evaluate the distance between the histeffCS and the CO2effCS. The observational estimate of the histeffCS and the distance between the histeffCS and the CO2effCS are combined to derive an observational constraint on CO2effCS of 5.46 [2.40;35.61] K. The main sources of uncertainty in the CO2effCS estimate comes from the uncertainty in aerosol forcing and in the top of the atmosphere energy imbalance. Further uncertainty arises from the pattern effect correction estimated from climate models. There is confidence in the lower end of the 5-95% range derived from our method as it relies only on reliable recent data and it makes full use of the observational record since 1971. This important result suggests that observations of the global energy budget since 1971 are poorly consistent with climate sensitivity to CO2 below 2.4 K. Unfortunately, the upper end of the 5-95% range derived from the regression method is above 30 K. It means that the observational constraint derived from observations of the global energy budget since 1971 is too weak (i.e. the uncertainty is too large) to provide any relevant information on the credibility of high CO2effCS. Numéro de notice : A2022-322 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1175/JCLI-D-21-0565.1 Date de publication en ligne : 14/03/2022 En ligne : https://doi.org/10.1175/JCLI-D-21-0565.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100149 [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]

Forest products in the global bioeconomy / P.J. Verkerk (2022)  

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

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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)  

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CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia / Louis de Wergifosse in Annals of Forest Science, vol 77 n° 3 (September 2020)  

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Global Climate [in “State of the Climate in 2019"] / A. Ades in Bulletin of the American Meteorological Society, vol 101 n° 8 (August 2020)  

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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)  

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The impact of drought on total ozone flux in a mountain Norway spruce forest / Thomas Agyei in Journal of forest science, vol 66 n° 7 (juillet 2020)  

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What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 / Christopher M. Wade in Forests, vol 11 n° 5 (May 2020)  

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Filière forêt-bois et atténuation du changement climatique / Alice Roux (2020)  

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The forestry & wood sector and climatic change mitigation / Alice Roux (2020)  

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