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Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions / Kamil Maciuk in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 1 (January 2021) . - pp 61 - 73 Titre : Characteristics of seasonal variations and noises of the daily double-difference and PPP solutions Type de document : Article/Communication Auteurs : Kamil Maciuk, Auteur ; Inese Vārna, Auteur ; Chang Xu, Auteur Année de publication : 2021 Article en page(s) : pp 61 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] bruit rose [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] Lettonie [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station de référence [Termes descripteurs IGN] variance de Hadamard [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Long term GNSS observations provided by networks of the continuously operating reference stations (CORS) allow for determination of the global and local tectonic plate movements and seasonal variations. In recent years, PPP (Precise Point Positioning) technique has become increasingly popular and most likely in the future will replace relative positioning with CORS stations. In this paper, we discuss the difference of the velocity and seasonal component estimates of 25 Latvian CORS stations on the basis of daily PPP solutions from the Nevada Geodetic Laboratory and double-difference solutions from the Institute of Geodesy and Geoinformatics of the University of Latvia. Time series of each coordinate component for 9-year time period were determined by the usage of the Tsview software and seasonal existence of linear, annual, semi-annual factors and their uncertainties were determined. Breaks (e. g., antenna and receiver changes) were also taken into account. We then assessed the noise characteristics of these time series with the use of overlapping Hadamard variance (OHVAR). The result shows that OHVAR is computationally cheap, and the dominating power-law noise, including flicker and random walk. However Hadamard deviation of the PPP and double-difference solutions scatters differently for a whole year averaging time due to the different GNSS data strategies. Numéro de notice : A2021-045 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0042 date de publication en ligne : 08/12/2020 En ligne : https://doi.org/10.1515/jag-2020-0042 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96772 [article]

From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100 Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] base de données forestières [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] populus (genre) [Termes descripteurs IGN] série temporelle Résumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417 [article]

Pure and even-aged forestry of fast growing conifers under climate change: on the need of a silvicultural paradigm shift / Clémentine Ols in Environmental Research Letters, vol 16 n° inconnu ([01/01/2021])    

Public [article]  inEnvironmental Research Letters > vol 16 n° inconnu [01/01/2021] Titre : Pure and even-aged forestry of fast growing conifers under climate change: on the need of a silvicultural paradigm shift Type de document : Article/Communication Auteurs : Clémentine Ols , Auteur ; Jean-Daniel Bontemps , Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] plantation forestière [Termes descripteurs IGN] politique forestière [Termes descripteurs IGN] Préalpes (Europe) [Termes descripteurs IGN] production primaire brute [Termes descripteurs IGN] pseudotsuga menziesii [Termes descripteurs IGN] résilience [Termes descripteurs IGN] sylviculture [Termes descripteurs IGN] Union Européenne [Vedettes matières IGN] Economie forestière Résumé : (auteur) Pure and even-aged (PEA) forests of fast growing conifer species have for long been key providers of industrial raw material. Despite recent concern regarding their greater sensitivity to major natural disturbances, their impacts onto biodiversity and their funding efficiency, PEA conifer forests could remain a major economic target given the ongoing European strategy for bioeconomy. These forests are encountered across contrasted climates and in both native and introduced contexts across Europe giving rise to high uncertainties regarding their growth responses to current climate change. Using the French national forest inventory data, we inquired the radial growth of eight major conifer species in European forestry across 16 regional native and introduced PEA forest systems (n > 10,000 trees). Growth trends over the 2006-2016 period exhibited a negative association with absolute growth level in 2006, with strongest negative trends found for emblematic PEA forestry species (e.g. Norway spruce and Douglas-fir), and strongest positive trends for pine species (e.g. Scots pine). While the greater growth rate advantage of some species may shorten rotation and lower risk exposures for future decades, recent lowered productivity may affect the forest sector in the long run. The prevalence of PEA forests across European forest landscapes and their increasingly reported lower resilience to climate change compared to more complex forest systems call for the establishment of a long-term European forest policy strategy. Maintaining the environmental, social and economic benefits of forests should remain a priority in the European agenda, regardless of the financial costs at stake. Numéro de notice : A2021-060 Affiliation des auteurs : LIF (2020- ) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1088/1748-9326/abd6a7 date de publication en ligne : 24/12/2020 En ligne : https://doi.org/10.1088/1748-9326/abd6a7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96732 [article]

Documents numériques

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The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January 2021)  

Public [article]  inRemote sensing > vol 13 n° 1 (January 2021) . - n° 155 Titre : The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution Type de document : Article/Communication Auteurs : Dimitri I. Rukhovitch, Auteur ; Polina V. Koroleva, Auteur ; Danila D. Rukhovitch, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] dégradation des sols [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] érosion [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Russie [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Numéro de notice : A2021-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010155 date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010155 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96810 [article]

Unmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 133 - 154 Titre : Unmixing-based Sentinel-2 downscaling for urban land cover mapping Type de document : Article/Communication Auteurs : Fei Xu, Auteur ; Ben Somers, Auteur Année de publication : 2021 Article en page(s) : pp 133 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] Berlin [Termes descripteurs IGN] Bruxelles [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] Cologne [Termes descripteurs IGN] corrélation [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] matrice de co-occurrence [Termes descripteurs IGN] occupation du sol Résumé : (auteur) With the launch of Sentinel-2 new opportunities for large scale urban mapping arise. However, the spectral information embedded in the Sentinel-2 20 m spatial resolution bands cannot yet be fully explored in heterogeneous urban landscapes. The 20 m image pixels are often composed of different land covers, resulting in a difficult to interpret mixed pixel spectrum. Here, we propose an unmixing-based image fusion algorithm (UnFuSen2) that self-adapts to the spectral variability of varying land covers and improves the image fusion accuracy by constraining the unmixing equations on the basis of spectral mixing models and the correlation between spectral bands of coarse and fine spatial resolution, respectively. When compared to alternative state-of-the-art downscaling methods UnFuSen2 consistently showed the highest accuracy when applied across test sites in three different European cities (RMSEUnFuSen2 = 203 vs RMSEalternatives = [252, 337]). In a next step, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) on the downscaled Sentinel-2 image cube (i.e. ten 10 m bands) to generate subpixel urban land cover fractions. We compared our MESMA results against the traditional MESMA output as applied on the original Sentinel-2 image cube (i.e. four 10 m bands and six 20 m bands) and tested its robustness against reference data obtained over all three study sites. Results revealed an average decrease in RMSE of respectively 18% and 8% for impervious surface and vegetation fractions when our approach was compared to the traditional MESMA outcomes. Numéro de notice : A2021-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.009 date de publication en ligne : 26/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96419 [article]

Climate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model / Arne Nothdurft in Forest ecology and management, vol 478 ([15/12/2020])  

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Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)  

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Does recent fire activity impact fire-related traits of Pinus halepensis Mill. and Pinus sylvestris L. in the French Mediterranean area? / Bastien Romero in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

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Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)

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Exploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Flore et végétation d’une portion de côte en accrétion : sud du port de Taverna (côte orientale de la Corse) / Guilhan Paradis in Bulletin de la Société botanique du Centre-Ouest, n° 51 (2020)

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Large-scale stochastic flood hazard analysis applied to the Po River / A. Curran in Natural Hazards, vol 104 n° 3 (December 2020)  

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Legal aspects of registration the time of cadastral data creation or modification / Joanna Reczyńska in Reports on geodesy and geoinformatics, vol 110 n°1 (December 2020)  

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Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks / Felix Schiefer in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

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Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

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