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Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)
[article]
Titre : Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review Type de document : Article/Communication Auteurs : André Duarte, Auteur ; Nuno Borralho, Auteur ; Pedro Cabral, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 911 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image captée par drone
[Termes IGN] insecte nuisible
[Termes IGN] maladie parasitaire
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] santé des forêts
[Termes IGN] structure-from-motion
[Termes IGN] surveillance forestièreRésumé : (auteur) Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented. Numéro de notice : A2022-483 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13060911 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.3390/f13060911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100897
in Forests > vol 13 n° 6 (June 2022) . - n° 911[article]True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)
[article]
Titre : True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points Type de document : Article/Communication Auteurs : Mojdeh Ebrahimikia, Auteur ; Ali Hosseininaveh, Auteur Année de publication : 2022 Article en page(s) : pp 161 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] détection de contours
[Termes IGN] détection du bâti
[Termes IGN] distorsion d'image
[Termes IGN] graphe
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotographie
[Termes IGN] orthophotoplan numérique
[Termes IGN] photogrammétrie aérienne
[Termes IGN] pixel de contour
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] zone urbaineRésumé : (auteur) After considering state-of-the-art algorithms, this paper presents a novel method for generating true orthophotos from unmanned aerial vehicle (UAV) images of urban areas. The procedure consists of four steps: 2D edge detection in building regions, 3D edge graph generation, digital surface model (DSM) modification and, finally, true orthophoto and orthomosaic generation. The main contribution of this paper is concerned with the first two steps, in which deep-learning approaches are used to identify the structural edges of the buildings and the estimated 3D edge points are added to the point cloud for DSM modification. Running the proposed method as well as four state-of-the-art methods on two different datasets demonstrates that the proposed method outperforms the existing orthophoto improvement methods by up to 50% in the first dataset and by 70% in the second dataset by reducing true orthophoto distortion in the structured edges of the buildings. Numéro de notice : A2022-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12409 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1111/phor.12409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101065
in Photogrammetric record > vol 37 n° 178 (June 2022) . - pp 161 - 184[article]Efficient dike monitoring using terrestrial SFM photogrammetry / Laurent Froideval in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : Efficient dike monitoring using terrestrial SFM photogrammetry Type de document : Article/Communication Auteurs : Laurent Froideval, Auteur ; Christophe Conessa, Auteur ; Xavier Pellerin Le Bas, Auteur ; Laurent Benoit, Auteur ; Dominique Mouazé, Auteur Année de publication : 2022 Article en page(s) : pp 359 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] digue
[Termes IGN] sable
[Termes IGN] semis de points
[Termes IGN] série temporelle
[Termes IGN] structure-from-motion
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) Nature based solutions are growing rapidly in order to mitigate in the near future the effects of climate change and rise of sea level on most anthropogenic coasts. In that frame, the CHERbourg bLOC (CHERLOC) project aims to study new coastal engineering solutions (overtopping, sediment transport) thanks to two new artificial units in two test sites (Normandy, France) considering biodiversity preservation but also societal acceptability. This study details an efficient method to monitor such coastal infrastructure using terrestrial Structure from Motion (SfM). In 2021, surveys were conducted to acquire pictures in April, May, June and November. A time series of 3D photogrammetric models was generated using open source SfM software. The first model was georeferenced using Ground Control Points (GCP) measured by Differential Global Navigation Satellite System (DGNSS) so that it could be used as a reference for the following point clouds using surrounding ripraps assumed to be non-mobile through the period of the study. The georeferencing Root Mean Square Error (RMSE) was found to be 1.8 cm for the April model whereas RMSEs of relative registrations of the following dates were found to be sub-centimetric. These results can be used to observe and measure blocks displacements as well as sand volumes evolution throughout the time series. The biggest displacement was found to be 23 cm between April and June. Sand topographic variation shows a continuous accumulation on selected cross-sections between April and November with an overall height accumulation of about 30 cm. Sand volumes measurements show consistent results with an added volume of 3.67 m3 on the previous areas. Numéro de notice : A2022-429 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-359-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-359-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100734
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 359 - 366[article]Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
[article]
Titre : Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation Type de document : Article/Communication Auteurs : Toshihiro Sakamoto, Auteur ; Daisuke Ogawa, Auteur ; Satoko Hiura, Auteur ; Nobusuke Iwasaki, Auteur Année de publication : 2022 Article en page(s) : pp 323 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande spectrale
[Termes IGN] blé (céréale)
[Termes IGN] chlorophylle
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] orthophotoplan numérique
[Termes IGN] point d'appui
[Termes IGN] précision du positionnement
[Termes IGN] rizière
[Termes IGN] structure-from-motionRésumé : (Auteur) Vegetation indices (VIs), such as the green chlorophyll index and normalized difference vegetation index, are calculated from visible and near-infrared band images for plant diagnosis in crop breeding and field management. The DJI P4 Multispectral drone combined with the Agisoft Metashape Structure from Motion/Multi View Stereo software is some of the most cost-effective equipment for creating high-resolution orthomosaic VI images. However, the manufacturer's procedure results in remarkable location estimation inaccuracy (average error: 3.27–3.45 cm) and alignment errors between spectral bands (average error: 2.80–2.84 cm). We developed alternative processing procedures to overcome these issues, and we achieved a higher positioning accuracy (average error: 1.32–1.38 cm) and better alignment accuracy between spectral bands (average error: 0.26–0.32 cm). The proposed procedure enables precise VI analysis, especially when using the green chlorophyll index for corn, and may help accelerate the application of remote sensing techniques to agriculture. Numéro de notice : A2022-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00064R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00064R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101379
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 323 - 332[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022052 SL Revue Centre de documentation Revues en salle Disponible 105-2022051 SL Revue Centre de documentation Revues en salle Disponible A context feature enhancement network for building extraction from high-resolution remote sensing imagery / Jinzhi Chen in Remote sensing, vol 14 n° 9 (May-1 2022)
[article]
Titre : A context feature enhancement network for building extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Jinzhi Chen, Auteur ; Dejun Zhang, Auteur ; Yiqi Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] structure-from-motionRésumé : (auteur) The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy. Numéro de notice : A2022-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092276 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092276 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100663
in Remote sensing > vol 14 n° 9 (May-1 2022) . - n° 2276[article]Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations / Francesco Solano in Ecological indicators, vol 138 (May 2022)PermalinkAutomated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkAssessment of RTK quadcopter and structure-from-motion photogrammetry for fine-scale monitoring of coastal topographic complexity / Stéphane Bertin in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkDirect photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkComparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment / Longfei Zhou in Urban Forestry & Urban Greening, vol 69 (March 2022)PermalinkEstimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds / Jiayuan Lin in Urban Forestry & Urban Greening, vol 69 (March 2022)PermalinkEvaluating the 3D integrity of underwater structure from motion workflows / Ian M. Lochhead in Photogrammetric record, vol 37 n° 177 (March 2022)PermalinkMonitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)PermalinkUltrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)Permalink