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Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 1 - 18 Titre : Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation Type de document : Article/Communication Auteurs : Kathrin Maier, Auteur ; Andrea Nascetti, Auteur ; Ward van Pelt, Auteur ; Gunhild Rosqvist, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] bande infrarouge [Termes IGN] épaisseur [Termes IGN] erreur moyenne quadratique [Termes IGN] géoréférencement direct [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] manteau neigeux [Termes IGN] modèle numérique de surface [Termes IGN] photogrammétrie aérienne [Termes IGN] positionnement cinématique en temps réel [Termes IGN] qualité du modèle [Termes IGN] reconstruction 3D [Termes IGN] structure-from-motion [Termes IGN] Suède Résumé : (Auteur) More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high-resolution 3D snow-covered surface models (7 cm/pixel) of alpine areas up to eight hectares in a fast, reliable and affordable way. The test sites’ average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a significant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point measurements and regional-scale remote sensing and complements large-scale remote sensing data and snow model output with an adequate validation source. Numéro de notice : A2022-066 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.020 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99783 [article]

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Détection des micro et macroplastiques à partir de mesures spectrales / Martin Cubaud (2022) 

Public Titre : Détection des micro et macroplastiques à partir de mesures spectrales Type de document : Mémoire Auteurs : Martin Cubaud, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 82 p. Format : 21 x 30 cm Note générale : Bibliographie Mémoire de fin d'études, cycle des ingénieurs ENSG 3ème année Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse de données [Termes IGN] apprentissage automatique [Termes IGN] bande infrarouge [Termes IGN] déchet [Termes IGN] dégradation de l'environnement [Termes IGN] détection d'anomalie [Termes IGN] détection de cible [Termes IGN] image captée par drone [Termes IGN] image hyperspectrale [Termes IGN] matière plastique [Termes IGN] plateau continental [Termes IGN] pollution [Termes IGN] spectrométrie Index. décimale : MPT Mémoires de fin d'études du Master Méthodes physiques en télédétection Résumé : (Auteur) La pollution plastique pose d’importants problèmes pour les organismes vivants, et nécessite donc d’être surveillée de manière fiable et efficace. Le présent rapport de stage compare différentes méthodes pour détecter et identifier la nature de déchets plastiques à partir d’images hyperspectrales dans l’infrarouge court (SWIR, entre 1 et 2,5 µm) prises par drone au-dessus de surfaces continentales : détection d’anomalies, indices spectraux, détection de cibles et apprentissage automatique. Il s’intéresse également à la quantification de l’abondance sub-pixellique des plastiques, et notamment des microplastiques d’une taille inférieure à 5 mm. Note de contenu : Introduction 1. Analyse des données 1.1 Présentation des données 1.2 Analyse et comparaison de spectres 2. Méthodologie 19 2.1 Réduction de dimension 2.2 Détection des plastiques 2.3 Démélange spectral 2.4 Métriques d’évaluation 3. Résultats 3.1 Détection des plastiques 3.2 Quantification de l’abondance sub-pixellique de plastique 4. Discussion 4.1 Détection et identification 4.2 Identification des polymères 4.3 Quantification de l’abondance sub-pixellique de plastique Conclusion Numéro de notice : 26936 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de fin d'études IT Organisme de stage : Office National d’Etudes et de Recherches Aérospatiales ONERA Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102060

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Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)  

Public [article]  inForests > vol 12 n° 12 (December 2021) . - n° 1768 Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] bande infrarouge [Termes IGN] coefficient de corrélation [Termes IGN] couvert forestier [Termes IGN] détection de contours [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle statistique [Termes IGN] Mongolie intérieure (Chine) [Termes IGN] régression [Termes IGN] surveillance de la végétation Résumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318 [article]

Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])  

Public [article]  inGeocarto international > vol 36 n° 20 [01/12/2021] . - pp 2264 - 2278 Titre : Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images Type de document : Article/Communication Auteurs : Mohammad Hossein Gamshadzaei, Auteur ; Majid Rahimzadegan, Auteur Année de publication : 2021 Article en page(s) : pp 2264 - 2278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multibande [Termes IGN] analyse spectrale [Termes IGN] Arménie [Termes IGN] bande infrarouge [Termes IGN] cartographie thématique [Termes IGN] détection d'objet [Termes IGN] eau de surface [Termes IGN] extraction de traits caractéristiques [Termes IGN] Google Earth [Termes IGN] image à haute résolution [Termes IGN] image satellite [Termes IGN] indice d'humidité [Termes IGN] Iran [Termes IGN] occupation du sol [Termes IGN] optimisation par essaim de particules [Termes IGN] polygone Résumé : (auteur) Various spectral indices have been introduced to detect water extent from satellite images with different performances in various regions. The aim of this study is to provide an efficient index using particle swarm optimization (PSO) algorithm to detect water spread areas from satellite images with similar performance in different regions. This index is introduced for images containing water absorption bands from visible to middle infrared wavelengths. Eleven images were prepared from different satellites and water bodies with various environmental conditions. In addition, 40 pixels from water and 40 pixels from non-water regions were selected as training data for PSO algorithm. Results were evaluated using digitized polygons of water bodies on high-resolution images of Google Earth. The best results in PSO-based water index (PSOWI) were obtained by the combination of two bands (red and middle infrared). PSOWI represented proper performance in the selected various land covers and satellite images. Numéro de notice : A2021-831 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1700554 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/10106049.2019.1700554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99004 [article]

Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS) / Langning Huo in Remote sensing of environment, Vol 255 (March 2021)  

Public [article]  inRemote sensing of environment > Vol 255 (March 2021) . - n° 112240 Titre : Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS) Type de document : Article/Communication Auteurs : Langning Huo, Auteur ; Henrik J. Persson, Auteur ; Eva Lindberg, Auteur Année de publication : 2021 Article en page(s) : n° 112240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande infrarouge [Termes IGN] écho radar [Termes IGN] houppier [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de stress [Termes IGN] indice de végétation [Termes IGN] insecte nuisible [Termes IGN] maladie parasitaire [Termes IGN] Picea mariana [Termes IGN] Scolytinae [Termes IGN] signature spectrale [Termes IGN] Suède [Termes IGN] vulnérabilité Résumé : (auteur) The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects of European spruce forests. A crucial measure in pest control is the removal of infested trees before the beetles leave the bark, which generally happens before the end of June. However, stressed tree crowns do not show any significant color changes in the visible spectrum at this early-stage of infestation, making early detection difficult. In order to detect the related forest stress at an early stage, we investigated the differences in radar and spectral signals of healthy and stressed trees. How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season, which covered the period before attacks (April), early-stage infestation (‘green-attacks’, May to July), and middle to late-stage infestation (August to October). The results show that spectral differences already existed at the beginning of the vegetation season, before the attacks. The spectral separability between the healthy and infested samples did not change significantly during the ‘green-attack’ stage. The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones. These stress-induced spectral changes could be more efficient indicators of early infestations than the ‘green-attack’ symptoms. In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019. The red and SWIR bands from Sentinel-2 showed the highest separability of healthy and stressed samples. The backscatter from Sentinel-1 and additional bands from Sentinel-2 contributed only slightly in the Random Forest classification models. We therefore propose the Normalized Distance Red & SWIR (NDRS) index as a new index based on our observations and the linear relationship between the red and SWIR bands. This index identified stressed forest with accuracies from 0.80 to 0.88 before the attacks, from 0.80 to 0.82 in the early-stage infestation, and from 0.81 to 0.91 in middle- and late-stage infestations. These accuracies are higher than those attained by established vegetation indices aimed at ‘green-attack’ detection, such as the Normalized Difference Water Index, Ratio Drought Index, and Disease Stress Water Index. By using the proposed method, we highlight the potential of using NDRS with Sentinel-2 images to estimate forest vulnerability to European spruce bark beetle attacks early in the vegetation season. Numéro de notice : A2021-190 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2020.112240 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1016/j.rse.2020.112240 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97111 [article]

Super-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

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Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)  

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Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data / Hua Su in Remote sensing, Vol 11 n° 20 (October-2 2019)  

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Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])  

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Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols / Sen Cao in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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Synergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])  

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Connecting infrared spectra with plant traits to identify species / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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Remote sensing of species diversity using Landsat 8 spectral variables / Sabelo Madonsela in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

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Evaluation of multisource data for glacier terrain mapping : a neural net approach / Aparna Shukla in Geocarto international, vol 32 n° 5 (May 2017)  

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Refining geometry from depth sensors using IR shading images / Gyeongmin Choe in International journal of computer vision, vol 122 n° 1 (March 2017)  

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