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Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (October 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101866 Titre : Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data Type de document : Article/Communication Auteurs : Michael J. Campbell, Auteur ; Philip E. Dennison, Auteur ; Matthew Thompson, Auteur Année de publication : 2022 Article en page(s) : n° 101866 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] Californie (Etats-Unis) [Termes IGN] chemin le moins coûteux, algorithme du [Termes IGN] données issues des réseaux sociaux [Termes IGN] durée de trajet [Termes IGN] mobilité urbaine [Termes IGN] navigation pédestre [Termes IGN] pente [Termes IGN] planification urbaine [Termes IGN] trace GPS [Termes IGN] Utah (Etas-Unis) Résumé : (auteur) Accurately predicting pedestrian travel times is critically valuable in emergency response, wildland firefighting, disaster management, law enforcement, and urban planning. However, the relationship between pedestrian movement and landscape conditions is highly variable between individuals, making it difficult to estimate how long it will take broad populations to get from one location to another on foot. Although functions exist for predicting travel rates, they typically oversimplify the inherent variability of pedestrian travel by assuming the effects of landscapes on movement are universal. In this study, we present an approach for predicting the variability in pedestrian travel rates and times using a large, crowdsourced database of GPS tracks. Acquired from the outdoor recreation website AllTrails, these tracks represent nearly 2000 hikes on a diverse range of trails in Utah and California, USA. We model travel rates as a function of the slope of the terrain by generating a series of non-linear percentile models from the 2.5 th to the 97.5 th by 2.5 percentiles. The 50 th percentile model, representing the hiking speed of the typical individual, demonstrates marked improvement over existing slope-travel rate functions when compared to an independent test dataset. Our results demonstrate novel capacity to estimate travel time variability, with modeled percentiles being able to predict actual percentiles with less than 10% error. Travel rate functions can also be applied to least cost path analysis to provide variability in travel times. Numéro de notice : A2022-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2022.101866 Date de publication en ligne : 20/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101866 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101452 [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)  

Public [article]  inAGU Advances > vol 3 n° 4 (August 2022) . - n° e2021AV000654 Titre : Losses of tree cover in California driven by increasing fire disturbance and climate stress Type de document : Article/Communication Auteurs : Jonathan A. Wang, Auteur ; James T. Randerson, Auteur ; Michael L. Goulden, Auteur ; Clarke A. Knight, Auteur ; John J. Battles, Auteur Année de publication : 2022 Article en page(s) : n° e2021AV000654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte de la végétation [Termes IGN] changement climatique [Termes IGN] stress hydrique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forests provide natural climate solutions for sequestering carbon and mitigating climate change, yet are increasingly threatened by increasing temperature and disturbance. Understanding these threats requires accurate information on vegetation dynamics and their drivers, which is currently lacking in many regions experiencing rapid climate change such as California. To address this, we combined remote sensing observations with geospatial databases to develop annual maps of vegetation cover (tree, shrub, and herbaceous) and disturbance type (fire, harvest, and forest die-off) in California at 30 m resolution from 1985 to 2021. Considering both changes in cover fraction and areal extent, California lost 4,566 km2 of its tree cover area (6.7% relative to initial cover) since 1985. Substantial gains in tree cover area during the 1990s were more than offset by fire-driven declines since 2000, resulting in greater shrub and herbaceous cover area. Tree cover loss occurred in all ecoregions but was most severe in the southern mountains, where losses from wildfire were not compensated by regrowth in undisturbed areas. Fires and tree cover area loss generally occurred where summer temperatures were greater than 17.5°C, whereas net tree cover gain often occurred in cooler areas, suggesting that ongoing climate warming is threatening forests in many areas. California's vegetation is undergoing rapid transformation, with disturbance rates and climate change posing substantial potential risks to the integrity of California's terrestrial carbon sink. Numéro de notice : A2022-143 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1029/2021AV000654 Date de publication en ligne : 06/07/2022 En ligne : https://doi.org/10.1029/2021AV000654 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102447 [article]

Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California / Huineng Yan in Journal of geodesy, vol 96 n° 7 (July 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 7 (July 2022) . - n° 47 Titre : Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California Type de document : Article/Communication Auteurs : Huineng Yan, Auteur ; Wujiao Dai, Auteur ; Lei Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Californie (Etats-Unis) [Termes IGN] déformation de la croute terrestre [Termes IGN] données GNSS [Termes IGN] faille géologique [Termes IGN] filtrage spatiotemporel [Termes IGN] fusion de données [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] modélisation spatiale [Termes IGN] rééchantillonnage [Termes IGN] série temporelle Résumé : (auteur) The spatio-temporal random effects (STRE) model is a classic dynamic filtering model, which can be used to fuse GNSS and InSAR deformation data. The STRE model uses a certain time span of high spatial resolution Interferometric Synthetic Aperture Radar (InSAR) time series data to establish a spatial model and then obtain a deformation result with high spatio-temporal resolution through the state transition equation recursively in time domain. Combined with the Kalman filter, the STRE model is continuously updated and modified in time domain to obtain higher accuracy result. However, it relies heavily on the prior information and personal experience to establish an accurate spatial model. To the authors' knowledge, there are no publications which use the STRE model with multiple sets of different deformation monitoring data to verify its applicability and reliability. Here, we propose an improved STRE model to automatically establish accurate spatial model to improve the STRE model, then apply it to the fusion of GNSS and InSAR deformation data in the San Francisco Bay Area covering approximately 6000 km2 and in Southern California covering approximately 100,000 km2. Our experimental results show that the improved STRE model can achieve good fusion effects in both study areas. For internal inspection, the average error RMS values in the two regions are 0.13 mm and 0.06 mm for InSAR and 2.4 and 2.8 mm for GNSS, respectively; for Jackknife cross-validation, the average error RMS values are 6.0 and 1.3 mm for InSAR and 4.3 and 4.8 mm for GNSS in the two regions, respectively. We find that the deformation rate calculated from the fusion results is highly consistent with the existing studies, the significant difference in the deformation rate on both sides of the major faults in the two regions can be clearly seen, and the area with abnormal deformation rate corresponds well to the actual situation. These results indicate that the improved STRE model can reduce the reliance on prior information and personal experience, realize the effective fusion of GNSS and InSAR deformation data in different regions, and also has the advantages of high accuracy and strong applicability. Numéro de notice : A2022-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/s00190-022-01636-7 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.1007/s00190-022-01636-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101165 [article]

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)  

Public [article]  inRemote sensing of environment > vol 269 (February 2022) . - n° 112822 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 forestière Résumé : (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 [article]

Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 22 (November-2 2021) . - n° 4625 Titre : Spatial variability of suspended sediments in San Francisco Bay, California Type de document : Article/Communication Auteurs : Niky C. Taylor, Auteur ; Raphael M. Kudela, Auteur Année de publication : 2021 Article en page(s) : n° 4625 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] baie [Termes IGN] échantillonnage [Termes IGN] estuaire [Termes IGN] image Sentinel-MSI [Termes IGN] pas d'échantillonnage au sol [Termes IGN] qualité des eaux [Termes IGN] réflectance [Termes IGN] San Francisco [Termes IGN] sédiment [Termes IGN] spectroradiométrie [Termes IGN] surface de l'eau [Termes IGN] surveillance du littoral [Termes IGN] turbidité des eaux [Termes IGN] variabilité Résumé : (auteur) Understanding spatial variability of water quality in estuary systems is important for making monitoring decisions and designing sampling strategies. In San Francisco Bay, the largest estuary system on the west coast of North America, tracking the concentration of suspended materials in water is largely limited to point measurements with the assumption that each point is representative of its surrounding area. Strategies using remote sensing can expand monitoring efforts and provide a more complete view of spatial patterns and variability. In this study, we (1) quantify spatial variability in suspended particulate matter (SPM) concentrations at different spatial scales to contextualize current in-water point sampling and (2) demonstrate the potential of satellite and shipboard remote sensing to supplement current monitoring methods in San Francisco Bay. We collected radiometric data from the bow of a research vessel on three dates in 2019 corresponding to satellite overpasses by Sentinel-2, and used established algorithms to retrieve SPM concentrations. These more spatially comprehensive data identified features that are not picked up by current point sampling. This prompted us to examine how much variability exists at spatial scales between 20 m and 10 km in San Francisco Bay using 10 m resolution Sentinel-2 imagery. We found 23–80% variability in SPM at the 5 km scale (the scale at which point sampling occurs), demonstrating the risk in assuming limited point sampling is representative of a 5 km area. In addition, current monitoring takes place along a transect within the Bay’s main shipping channel, which we show underestimates the spatial variance of the full bay. Our results suggest that spatial structure and spatial variability in the Bay change seasonally based on freshwater inflow to the Bay, tidal state, and wind speed. We recommend monitoring programs take this into account when designing sampling strategies, and that end-users account for the inherent spatial uncertainty associated with the resolution at which data are collected. This analysis also highlights the applicability of remotely sensed data to augment traditional sampling strategies. In sum, this study presents ways to supplement water quality monitoring using remote sensing, and uses satellite imagery to make recommendations for future sampling strategies. Numéro de notice : A2021-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13224625 Date de publication en ligne : 17/11/2021 En ligne : https://doi.org/10.3390/rs13224625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99022 [article]

Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)  

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Apport de la photogrammétrie satellite pour la modélisation du manteau neigeux / César Deschamps-Berger (2021)  

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Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)  

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Quantification probabiliste des taux de déformation crustale par inversion bayésienne de données GPS / Colin Pagani (2021)  

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Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)  

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Automated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)  

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Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Improving operational radar rainfall estimates using profiler observations over complex terrain in Northern California / Haonan Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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