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Deep learning for toponym resolution: Geocoding based on pairs of toponyms / Jacques Fize in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 818 Titre : Deep learning for toponym resolution: Geocoding based on pairs of toponyms Type de document : Article/Communication Auteurs : Jacques Fize, Auteur ; Ludovic Moncla , Auteur ; Bruno Martins, Auteur Année de publication : 2021 Article en page(s) : n° 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes IGN] analyse de groupement [Termes IGN] apprentissage profond [Termes IGN] échantillonnage [Termes IGN] géocodage [Termes IGN] matrice de co-occurrence [Termes IGN] site wiki [Termes IGN] toponyme [Termes IGN] zone d'intérêt Résumé : (auteur) Geocoding aims to assign unambiguous locations (i.e., geographic coordinates) to place names (i.e., toponyms) referenced within documents (e.g., within spreadsheet tables or textual paragraphs). This task comes with multiple challenges, such as dealing with referent ambiguity (multiple places with a same name) or reference database completeness. In this work, we propose a geocoding approach based on modeling pairs of toponyms, which returns latitude-longitude coordinates. One of the input toponyms will be geocoded, and the second one is used as context to reduce ambiguities. The proposed approach is based on a deep neural network that uses Long Short-Term Memory (LSTM) units to produce representations from sequences of character n-grams. To train our model, we use toponym co-occurrences collected from different contexts, namely textual (i.e., co-occurrences of toponyms in Wikipedia articles) and geographical (i.e., inclusion and proximity of places based on Geonames data). Experiments based on multiple geographical areas of interest—France, United States, Great-Britain, Nigeria, Argentina and Japan—were conducted. Results show that models trained with co-occurrence data obtained a higher geocoding accuracy, and that proximity relations in combination with co-occurrences can help to obtain a slightly higher accuracy in geographical areas with fewer places in the data sources. Numéro de notice : A2021-927 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi10120818 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.3390/ijgi10120818 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99293 [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 : https://doi.org/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]

Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech / Fanny Petibon in Remote sensing of environment, vol 264 (October 2021)  

Public [article]  inRemote sensing of environment > vol 264 (October 2021) . - n° 112601 Titre : Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech Type de document : Article/Communication Auteurs : Fanny Petibon, Auteur ; Ewa A. Czyż, Auteur ; Giulia Ghielmetti, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] anisotropie [Termes IGN] diagnostic foliaire [Termes IGN] échantillonnage [Termes IGN] Fagus sylvatica [Termes IGN] feuille (végétation) [Termes IGN] France (administrative) [Termes IGN] incertitude spectrale [Termes IGN] indicateur biologique [Termes IGN] phénologie [Termes IGN] réflectance spectrale [Termes IGN] réflectance végétale [Termes IGN] saison [Termes IGN] spectroradiomètre [Termes IGN] Suisse Résumé : (auteur) The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over one growing season (2.7 ± 1.7%), or between individuals sampled over one week (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual at the time of the measurement. However, measurements of variation within individuals at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches toward a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Numéro de notice : A2021-808 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112601 Date de publication en ligne : 29/07/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98868 [article]

Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (15 december 2021)  

Public [article]  inForest ecology and management > vol 502 (15 december 2021) . - n° 119714 Titre : Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models Type de document : Article/Communication Auteurs : Arne Nothdurft, Auteur ; Christoph Gollob, Auteur ; Ralf Krasnitzer, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119714 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Autriche [Termes IGN] bois sur pied [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] échantillonnage [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] lasergrammétrie [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle de régression [Termes IGN] modèle mathématique [Termes IGN] tempête [Termes IGN] volume en bois [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) A spatial regression model framework is presented to predict growing stock volume loss due to storm Adrian which caused heavy forest damage in the upper Gail valley in Carinthia, Austria, in October 2018. Model parameters were estimated using growing stock volume measured with a terrestrial laser scanner on 62 sample plots distributed across five sub-regions. Predictor variables were derived from high resolution vegetation height measurements collected during an airborne laser scanning campaign. Non-spatial and spatial candidate models were proposed and assessed based on fit to observed data and out-of-sample prediction. Spatial Gaussian processes associated model intercepts and regression coefficients were used to capture spatial dependence. Results show a spatially-varying coefficient model, which allowed the intercept and regression coefficients to vary spatially, yielded the best fit and prediction. Two approaches were considered for prediction over blowdown areas: 1) an areal approach that viewed each blowdown as a single prediction unit indexed by its centroid; and 2) a block approach where each blowdown was partitioned into smaller prediction units to better align with sample plots’ spatial support. Joint prediction was used to acknowledge spatial dependence among block units. Results demonstrated the block approach is preferable as it mitigated change-of-support issues encountered in the areal approach. Despite the small sample size, predictions for 55% of the total 564 blowdown areas, accounting for 93% of the total loss, had a coefficient of variation less than 25%. Key advantages of the proposed regression framework and chosen Bayesian inferential paradigm, were the ability to quantify uncertainty in spatial covariance parameters, propagate parameter uncertainty through to prediction, and provide statistically valid prediction point and interval estimates for individual blowdowns and collections of blowdowns at the sub-region and region scale via posterior predictive distribution summaries. Numéro de notice : A2021-770 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119714 Date de publication en ligne : 07/10/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98822 [article]

The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba / Stefan Kaufmann in Forest ecology and management, vol 502 (15 december 2021)  

Public [article]  inForest ecology and management > vol 502 (15 december 2021) . - n° 119698 Titre : The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba Type de document : Article/Communication Auteurs : Stefan Kaufmann, Auteur ; Sarah-Katharina Funck, Auteur ; Franziska Paintner, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119698 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba [Termes IGN] Allemagne [Termes IGN] Bryophyte [Termes IGN] coupe rase (sylviculture) [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] échantillonnage [Termes IGN] écosystème forestier [Termes IGN] Fagus sylvatica [Termes IGN] habitat (nature) [Termes IGN] lichen [Termes IGN] Picea abies [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Lacking structural diversity in production forests has been evidenced to decrease epiphytic bryophytes and lichens. One approach to create structurally more diverse forests is retention forestry. Only a small number of studies focused on the effectiveness of retention measures in continuous-cover forestry. Most studies have been conducted in even-aged, clear-cut based management systems and applied different approaches, but they all have in common that the retained trees have been examined for epiphytes only after harvest. Thus, it remains unclear whether these trees or even a certain tree species could take the life-boat function for epiphytes on logged sites. Thus, prior to logging, we assessed epiphytic bryophytes and lichens on potential large living retention trees, here referred to as habitat trees (HT), of Abies alba and compared the diversity pattern to nearby average trees (AT; A. alba, Fagus sylvatica or Picea abies) of smaller sizes in selectively harvested continuous-cover forests. Selection of AT was based on the average stem diameter of all trees within the stand. We found that species richness and Simpson diversity of lichens were significantly higher on HT. For bryophytes, F. sylvatica AT showed significantly higher Simpson diversity. Mixed models revealed positive effects of F. sylvatica on bryophytes, whereas large stem diameters and elevation were the driving forces for lichens. Additionally, ordinations revealed clear patterns in species composition separating between conifers and broadleaved trees, and along increasing altitude and stem diameter. Concerning HT selection, we suggest to focus rather on the tree species diversity than on stem diameter, when aiming to protect epiphytic bryophytes and lichens. Numéro de notice : A2021-769 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119698 Date de publication en ligne : 30/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119698 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98821 [article]

Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)  

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Mixture effect on radial stem and shoot growth differs and varies with temperature / Maude Toïgo in Forest ecology and management, vol 488 (15 May 2021)  

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Performance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (May 2021)  

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Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment / Maxime Soma in Remote sensing of environment, vol 257 (May 2021)  

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A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)  

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Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([15/04/2021])  

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Forest fragmentation assessment using field-based sampling data from forest inventories / Habib Ramezani in Scandinavian journal of forest research, vol 36 n° 4 ([01/04/2021])  

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Robust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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Variations in temperate forest biomass ratio along three environmental gradients are dominated by interspecific differences in wood density / Baptiste Kerfriden in Plant ecology, vol 222 n° 3 (March 2021)  

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Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)  

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