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Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)
[article]
Titre : Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery Type de document : Article/Communication Auteurs : Donato Morresi, Auteur ; Raffaella Marzano, Auteur ; Emanuele Lingua, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112800 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] cartographie des risques
[Termes IGN] détection de changement
[Termes IGN] forêt alpestre
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] phénologie
[Termes IGN] Piémont (Italie)
[Termes IGN] réflectance spectrale
[Termes IGN] risque naturel
[Termes IGN] variation saisonnière
[Termes IGN] zone sinistréeRésumé : (auteur) Deriving burn severity from multispectral satellite data is a widely adopted approach to infer the degree of environmental change caused by fire. Burn severity maps obtained by thresholding bi-temporal indices based on pre- and post-fire Normalized Burn Ratio (NBR) can vary substantially depending on temporal constraints such as matched acquisition and optimal seasonal timing. Satisfying temporal requirements is crucial to effectively disentangle fire and non-fire induced spectral changes and can be particularly challenging when only a few cloud-free images are available. Our study focuses on 10 wildfires that occurred in mountainous areas of the Piedmont Region (Italy) during autumn 2017 following a severe and prolonged drought period. Our objectives were to: (i) generate reflectance composites using Sentinel-2 imagery that were optimised for seasonal timing by embedding spatial patterns of long-term land surface phenology (LSP); (ii) produce and validate burn severity maps based on the modelled relationship between bi-temporal indices and field data; (iii) compare burn severity maps obtained using either a pair of cloud-free Sentinel-2 images, i.e. paired images, or reflectance composites. We proposed a pixel-based compositing algorithm coupling the weighted geometric median and thematic spatial information, e.g. long-term LSP metrics derived from the MODIS Collection 6 Land Cover Dynamics Product, to rank all the clear observations available in the growing season. Composite Burn Index data and bi-temporal indices exhibited a strong nonlinear relationship (R2 > 0.85) using paired images or reflectance composites. Burn severity maps attained overall classification accuracy ranging from 76.9% to 83.7% (Kappa between 0.61 and 0.72) and the Relative differenced NBR (RdNBR) achieved the best results compared to other bi-temporal indices (differenced NBR and Relativized Burn Ratio). Improvements in overall classification accuracy offered by the calibration of bi-temporal indices with the dNBR offset were limited to burn severity maps derived from paired images. Reflectance composites provided the highest overall classification accuracy and differences with paired images were significant using uncalibrated bi-temporal indices (4.4% to 5.2%) while they decreased (2.8% to 3.2%) when we calibrated bi-temporal indices derived from paired images. The extent of the high severity category increased by ~19% in burn severity maps derived from reflectance composites (uncalibrated RdNBR) compared to those from paired images (calibrated RdNBR). The reduced contrast between healthy and burnt conditions associated with suboptimal seasonal timing caused an underestimation of burnt areas. By embedding spatial patterns of long-term LSP metrics, our approach provided consistent reflectance composites targeted at a specific phenological stage and minimising non-fire induced inter-annual changes. Being independent from the multispectral dataset employed, the proposed pixel-based compositing approach offers new opportunities for operational change detection applications in geographic areas characterised by persistent cloud cover. Numéro de notice : A2022-095 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112800 Date de publication en ligne : 22/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99534
in Remote sensing of environment > vol 269 (February 2022) . - n° 112800[article]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)
[article]
Titre : Mapping global flying aircraft activities using Landsat 8 and cloud computing Type de document : Article/Communication Auteurs : Fen Zhao, Auteur ; Lang Xia, Auteur ; Arve Kylling, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aéronef
[Termes IGN] analyse spatio-temporelle
[Termes IGN] aviation civile
[Termes IGN] carte thématique
[Termes IGN] climat
[Termes IGN] détection d'objet
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] informatique en nuage
[Termes IGN] navigation aérienne
[Termes IGN] trafic aérienRésumé : (auteur) Satellite-based remote sensing might provide a potential way for monitoring the global flight activities and their environment impacts, while the remote sensing community pays less attention on it. In this study, we presented a flying aircraft detection algorithm which effectively handles the noise on Landsat 8 OLI cirrus band caused by energetic particles in the South Atlantic Anomaly region, and a new framework based on cloud infrastructure was proposed to map global flying aircraft activities from 2013 to 2020 using Landsat 8 Operational Land Imager (OLI) data. Validation was performed for 254 scenes recorded for various cloudy and surface conditions and vapor contents. The overall percentages of false alarms and omissions for these validation images were 5.37% and 7.80%, respectively. Limited to the resolution of Landsat data, cloud, the size and flight altitude of the aircraft, 42.99% flying aircraft were undetected compared with the FlightRadar24. Instead of using the Google Earth Engine, we employed more flexible cloud computing techniques, Google Cloud Storage and Google Calculation Engine, to construct our framework for the larger volume data. A total of 1.94 million Landsat images were analyzed to obtain the activities maps, and the results showed that globally flying aircraft increased by 25.85% from 2014 to 2019 (the year 2013 was excluded for the low coverage of Landsat scenes), with an annual rate of 4.31%. In 2020, flying aircraft were reduced by 40% compared with 2019 due to the influence of COVID-19 and traveling restrictions, and Europe was the most severely affected by COVID-19, with an 84.59% decline of flying aircraft in April 2020. This study provides a unique long-term supplement to monitor aviation activities and their climate impact. Numéro de notice : A2022-090 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.12.003 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.12.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99506
in ISPRS Journal of photogrammetry and remote sensing > vol 184 (February 2022) . - pp 19 - 30[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022021 SL Revue Centre de documentation Revues en salle Disponible 081-2022023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)
[article]
Titre : Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning Type de document : Article/Communication Auteurs : Feng Zhao, Auteur ; Rui Sun, Auteur ; Liheng Zhong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112822 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte thématique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déboisement
[Termes IGN] image Sentinel-SAR
[Termes IGN] récolte de bois
[Termes IGN] Rondonia (Brésil)
[Termes IGN] série temporelle
[Termes IGN] surveillance forestièreRésumé : (auteur) Compared with disturbance maps produced at annual or multi-year time steps, monthly mapping of forest harvesting can provide more temporal details needed for studying the socio-economic drivers (e.g., differentiating salvage logging and slash-and-burn from other timber harvesting) of harvesting and characterizing the associated intra-annual carbon and hydrological dynamics. Frequent cloud cover limits the application of optical remote sensing in timely mapping of forest changes. The freely available Sentinel-1 synthetic aperture radar (SAR) sensor provides an unprecedented opportunity to achieve more frequent mapping of forest harvesting than ever before (i.e., at monthly interval). The unique landscape pattern of forest harvesting from Sentienl-1 data (i.e., how a harvested patch contrasts to surrounding intact forests) holds critical information for harvesting mapping but have not been fully explored. In this study, we propose a deep learning-based (i.e., U-Net) approach using the landscape pattern from Sentinel-1 data to produce monthly maps of forest harvesting in two deforestation hotspots - California, USA and Rondônia, Brazil – for as long as three years. Our results show that (1) our proposed approach is reliable (mean F1 scores (the geometric mean of user's and producer's accuracies) 0.74–0.78; mean IoU (the area of intersection over union between the prediction part and target part) 0.59–0.65) for monthly forest harvesting mapping with Sentinel-1 data, outperforming the traditional object-based approach (0.38–0.43 in IoU). The varying harvesting pattern from Sentinel-1 data can be recognized by the U-Net bottleneck block as whole entities, which is the key advantage of our proposed approach; (2) multi-temporal SAR filtering is helpful for improving the accuracies of our proposed approach (increased F1 and IoU for 0.04 and 0.06, respectively); (3) our proposed model can be trained using samples collected during a particular time period over one location and be fine-tuned using sparse local samples from a new area to achieve optimal performance, and hence can greatly reduce training data collection effort when applied to new study sites; (4) forest harvesting maps produced using our approach revealed substantial variations in monthly harvesting activities: in Rondônia, most of the forest harvest occurred in July/August (the dry season) and about 14% of the dry season harvesting were followed by fires (i.e., slash-and-burn); in California, the rates of forest harvesting were relatively stable, but abnormally high values could occur due to salvage logging after big fires. Our novel approach for mapping forest harvesting at monthly interval represents an important step towards timely monitoring of forest harvesting and assisting stakeholders in developing sustainable strategy of forest management, especially for regions with frequent cloud cover. Numéro de notice : A2022-078 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112822 Date de publication en ligne : 08/12/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112822 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99745
in Remote sensing of environment > vol 269 (February 2022) . - n° 112822[article]Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)
[article]
Titre : Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers Type de document : Article/Communication Auteurs : Jacques Mourey, Auteur ; Pascal Lacroix, Auteur ; Pierre-Allain Duvillard, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 445 - 460 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] capteur actif
[Termes IGN] capteur non-imageur
[Termes IGN] carte thématique
[Termes IGN] détection de changement
[Termes IGN] éboulement
[Termes IGN] modèle numérique de terrain
[Termes IGN] Mont-Blanc, massif du
[Termes IGN] onde sismique
[Termes IGN] pergélisol
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] saison
[Termes IGN] sismologie
[Termes IGN] surveillance géologique
[Termes IGN] température de l'airRésumé : (auteur) There are on average 35 fatal mountaineering accidents per summer in France. On average, since 1990, 3.7 of them have occurred every summer in the Grand Couloir du Goûter, on the classic route up Mont Blanc (4809 m a.s.l.). Rockfall is one of the main factors that explain this high accident rate and contribute to making it one of the most accident-prone areas in the Alps for mountaineers. In this particular context, the objective of this study is to document the rockfall activity and its triggering factors in the Grand Couloir du Goûter in order to disseminate the results to mountaineers and favour their adaptation to the local rockfall hazard. Using a multi-method monitoring system (five seismic sensors, an automatic digital camera, three rock subsurface temperature sensors, a traffic sensor, a high-resolution topographical survey, two weather stations and a rain gauge), we acquired a continuous database on rockfalls during a period of 68 d in 2019 and some of their potential triggering factors (precipitation, ground and air temperatures, snow cover, frequentation by climbers). At the seasonal scale, our results confirm previous studies showing that rockfalls are most frequent during the snowmelt period in permafrost-affected rockwalls. Furthermore, the unprecedented time precision and completeness of our rockfall database at high elevation thanks to seismic sensors allowed us to investigate the factors triggering rockfalls. We found a clear correlation between rockfall frequency and air temperature, with a 2 h delay between peak air temperature and peak rockfall activity. A small number of rockfalls seem to be triggered by mountaineers. Our data set shows that climbers are not aware of the variations in rockfall frequency and/or cannot/will not adapt their behaviour to this hazard. These results should help to define an adaptation strategy for climbers. Therefore, we disseminated our results within the mountaineering community thanks to the full integration of our results into the management of the route by local actors. Knowledge built during this experiment has already been used for the definition and implementation of management measures for the attendance in summer 2020. Numéro de notice : A2022-181 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.5194/nhess-22-445-2022 En ligne : https://doi.org/10.5194/nhess-22-445-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99859
in Natural Hazards and Earth System Sciences > vol 22 n° 2 (February 2022) . - pp 445 - 460[article]Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)
[article]
Titre : Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria Type de document : Article/Communication Auteurs : Maninder Singh Dhillon, Auteur ; Thorsten Dahms, Auteur ; Carina Kübert-Flock, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 677 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] carte d'occupation du sol
[Termes IGN] fusion de données
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] réflectance
[Termes IGN] surveillance de la végétation
[Termes IGN] utilisation du solRésumé : (auteur) The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. Numéro de notice : A2022-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030677 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.3390/rs14030677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99687
in Remote sensing > vol 14 n° 3 (February-1 2022) . - n° 677[article]Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkIncreasing territorial planning activities through viewshed analysis / Gheorghe-Gavrilă Hognogi in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)PermalinkAirborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkAn assessment of forest loss and its drivers in protected areas on the Copperbelt province of Zambia: 1972–2016 / Darius Phiri in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkAssessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India / Rajib Mitra in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkAtlas de l'invisible / James Cheshire (2022)PermalinkCharacteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)PermalinkA comparison of linear-mode and single-photon airborne LiDAR in species-specific forest inventories / Janne Raty in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkConstruction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique / Houssayn Meriche (2022)PermalinkCréation d’un indicateur de qualité de la desserte des transports pour des parcelles à une échelle locale / Nick Lin (2022)PermalinkPermalinkEditing maps of landscape elements according to their potential influence on animals by combining multi-source data: a case study about red foxes in urban environment / Laurence Jolivet (2022)PermalinkPermalinkExamining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)PermalinkGénération d’un jeu de données d’entraînement et mise en oeuvre d’une architecture de détection par deep learning des numéros de parcelles sur les plans du cadastre Napoléonien / Tiecoumba Ibrahim Tamela (2022)PermalinkGuidelines for standardizing the design of tactile maps: A review of research and best practice / Jakub Wabiński in Cartographic journal (the), vol 59 n° 3 (August 2022)PermalinkHarmonisation de la production cartographique dans le cadre des Programmes d’Actions de Prévention des Inondations / Nils Deslandes (2022)PermalinkHistorical Vltava River valley–various historical sources within web mapping environment / Jiří Krejčí in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)PermalinkPermalink