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Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 313 Titre : Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image Type de document : Article/Communication Auteurs : Salma Benmokhtar, Auteur ; Marc Robin, Auteur ; Mohamed Maanan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 313 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] cartographie hydrographique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] fond marin [Termes descripteurs IGN] herbier marin [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] Maroc [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] plante aquatique d'eau salée [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] typologie [Termes descripteurs IGN] Zostera noltii Résumé : (auteur) The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers. Numéro de notice : A2021-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10050313 date de publication en ligne : 07/05/2021 En ligne : https://doi.org/10.3390/ijgi10050313 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97679 [article]

Geomorphic analysis of Xiadian buried fault zone in Eastern Beijing plain based on SPOT image and unmanned aerial vehicle (UAV) data / Yanping Wang in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 12 n° 1 (2021) . - pp 261 - 278 Titre : Geomorphic analysis of Xiadian buried fault zone in Eastern Beijing plain based on SPOT image and unmanned aerial vehicle (UAV) data Type de document : Article/Communication Auteurs : Yanping Wang, Auteur ; Pinliang Dong, Auteur ; Yueqin Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 261 - 278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] auscultation topographique [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] faille géologique [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image SPOT 5 [Termes descripteurs IGN] MNS SRTM [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] réseau de drainage [Termes descripteurs IGN] zone à risque Résumé : (auteur) This study presents geomorphic analysis of Xiadian buried fault in eastern Beijing plain (China), based on the analysis of a Satellite Pour l’Observation de la Terre (SPOT-5) image, a high-resolution digital elevation model (DEM) derived from an unmanned aerial vehicle (UAV) system, SRTM DEM and field investigation. Interpretations of the SPOT-5 image show that the pits always distribute between fault scarp segments or shallow grooves. The geomorphic features near the fault show echelon arrangements caused by dextral strike-slip activities of the fault. Based on this, the characteristics of stress field in this area have been clearly inferred. At centimeter-level accuracy, UAV-derived DEM profiles can clearly show micro tectonic landforms such as fault scarps, shallow grooves, steep slopes, and pits. Combined with previous research and field measurements, the evolution rates in length and height of the fault scarps are analysed. Furthermore, the deflection analysis of the drainage system also shows the characteristics of the continuous strike slip activity of the Xiadian fault. The study can provide valuable insight into geomorphic analysis of buried and semi-buried active faults in plain areas with increasingly frequent human activities. Numéro de notice : A2021-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1870168 date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1080/19475705.2020.1870168 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96905 [article]

Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 557 - 564 Titre : Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion Type de document : Article/Communication Auteurs : Olivier Stocker, Auteur ; Arnaud Le Bris , Auteur Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Projets : TOSCA Parcelle / Le Bris, Arnaud Article en page(s) : pp 557 - 564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels. Numéro de notice : A2020-504 Affiliation des auteurs : UGE-LaSTIG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-557-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95644 [article]

A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 15 - 23 Titre : A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations Type de document : Article/Communication Auteurs : Laure Chandelier , Auteur ; Laurent Coeurdevey, Auteur ; Sébastien Bosch, Auteur ; Pascal Favé, Auteur ; Roland Gachet, Auteur ; Alain Orsoni, Auteur ; Thomas Tilak , Auteur ; Alexis Barot, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 15 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] compensation par bloc [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] formatage [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] image SPOT-HRS [Termes descripteurs IGN] informatique en nuage [Termes descripteurs IGN] Institut national de l'information géographique et forestière (France) [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] spatiotriangulation Résumé : (auteur) High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010. Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy. This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented. Numéro de notice : A2020-474 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-15-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-15-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95613 [article]

Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)  

Public [article]  inPlos one > vol 15 n° 5 (May 2020) . - n° 0233110 Titre : Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system Type de document : Article/Communication Auteurs : Minh Hai Pham, Auteur ; Thi Hoai Do, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0233110 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme de recherche [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] Viet Nam [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Background : Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). Methods : This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Results : The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation. Conclusion : From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover. Numéro de notice : A2020-833 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : https://doi.org/10.1371/journal.pone.0233110 date de publication en ligne : 21/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0233110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97667 [article]

Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

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Multi-Spatial Resolution Satellite and sUAS Imagery for Precision Agriculture on Smallholder Farms in Malawi / Brad G. Peter in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)  

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Global iterative geometric calibration of a linear optical satellite based on sparse GCPs / Yingdong Pi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Unsupervised Satellite Image Time Series Analysis using Deep Learning Techniques / Ekaterina Kalinicheva (2020)  

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Vers une occupation du sol France entière par imagerie satellite à très haute résolution / Tristan Postadjian (2020)  

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Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])  

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A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

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More surface detail with one-two-pixel matching / Ewelina Rupnik (2019)    

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Multimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)  

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Urban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)  

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