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Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 1 - 18 Titre : Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions Type de document : Article/Communication Auteurs : Benjamin U. Meinen, Auteur ; Derek T. Robinson, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] carte topographique [Termes IGN] cours d'eau [Termes IGN] détection de changement [Termes IGN] érosion [Termes IGN] hétérogénéité spatiale [Termes IGN] image à très haute résolution [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] photogrammétrie aérienne [Termes IGN] reconstruction 3D [Termes IGN] rive [Termes IGN] structure-from-motion [Termes IGN] télémètre laser terrestre Résumé : (auteur) Highly accurate digital surface models are an essential part of change-over-time analyses for monitoring erosion processes. Streambank topography presents a unique challenge for surface mapping due to dense riparian vegetation, canopy cover, and rapidly changing elevation values. The spatial heterogeneity of stream corridors has made the calculation of streambank erosion across larger spatial extents difficult. Contemporary technologies such as terrestrial laser scanners (TLS) and unmanned aerial vehicles (UAVs) offer new approaches for streambank topography mapping at very high spatial resolutions across varying spatial extents. To evaluate the accuracy of different technologies for streambank topography mapping, we compared streambank surface models derived via a UAV using structure-from-motion and from traditional aerial photogrammetry (i.e. Southwestern Ontario Orthoimagery Project; SWOOP) to that of a TLS benchmark across seven streambank segments. Additional comparisons were made for 22 manually measured stream transects to that of a TLS benchmark. Compared to our benchmark, the UAV-derived streambank surface model was the most accurate with an average root-mean-square-error of 0.104 m. Errors in the UAV surface model were correlated with georeferencing error. The UAV had an average 52% success rate for reconstructing the streambank topography across all field campaigns and was able to map up to 2037 m of streambank in one hour. The streambank surface model derived from traditional aerial photogrammetry and manual transect measurements had average root-mean-square-errors of 0.238 m and 0.274 m respectively. Both aerially-derived surface models tended to over measure elevation values compared to the TLS, whereas manual transect measurements consistently under measured elevation. Numéro de notice : A2020-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1597294 Date de publication en ligne : 26/03/2019 En ligne : https://doi.org/10.1080/01431161.2019.1597294 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94890 [article]

Cattle detection and counting in UAV images based on convolutional neural networks / Wen Shao in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 31 - 52 Titre : Cattle detection and counting in UAV images based on convolutional neural networks Type de document : Article/Communication Auteurs : Wen Shao, Auteur ; Rei Kawakami, Auteur ; Ryota Yoshihashi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 31 - 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bovin [Termes IGN] chevauchement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] comptage [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] modélisation 3D Résumé : (auteur) For assistance with grazing cattle management, we propose a cattle detection and counting system based on Convolutional Neural Networks (CNNs) using aerial images taken by an Unmanned Aerial Vehicle (UAV). To improve detection performance, we take advantage of the fact that, with UAV images, the approximate size of the objects can be predicted when the UAV’s height from the ground can be assumed to be roughly constant. We resize an image to be fed into the CNN to an optimum resolution determined by the object size and the down-sampling rate of the network, both in training and testing. To avoid repetition of counting in images that have large overlaps to adjacent ones and to obtain the accurate number of cattle in an entire area, we utilize a three-dimensional model reconstructed by the UAV images for merging the detection results of the same target. Experiments show that detection performance is greatly improved when using the optimum input resolution with an F-measure of 0.952, and counting results are close to the ground truths when the movement of cattle is approximately stationary compared to that of the UAV’s. Numéro de notice : A2020-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1624858 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/01431161.2019.1624858 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94891 [article]

Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests / Sadeepa Jayathunga in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 53 - 73 Titre : Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests Type de document : Article/Communication Auteurs : Sadeepa Jayathunga, Auteur ; Toshiaki Owari, Auteur ; Satoshi Tsuyuki, Auteur ; Yasumasa Hirata, Auteur Année de publication : 2020 Article en page(s) : pp 53 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de groupement [Termes IGN] couvert forestier [Termes IGN] forêt de feuillus [Termes IGN] gestion forestière [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] photogrammétrie aérienne [Termes IGN] photographie aérienne latérale [Termes IGN] Pinophyta [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Forest canopy structure is an important parameter in multipurpose forest management. An understanding of forest structure plays a particularly important role in the management of uneven-aged forests. The identification of vertical and horizontal variations in forest canopy structure using a ground-based survey is resource intensive, hence often demands for alternative data sources. In this study, one of the advanced remote sensing (RS) techniques, i.e. digital aerial photogrammetry was used to characterize forest canopy structure in a mixed conifer–broadleaf forest. We used aerial imagery acquired with a fixed-wing unmanned aerial vehicle (UAV) platform to produce RS metrics that could be used to classify and map forest structure types at landscape scale. Our results demonstrated that few structural and spectral metrics derived from UAV photogrammetric data, e.g. mean height, standard deviation of height, canopy cover, and percentage broadleaf vegetation cover, could characterize the forest structure across landscapes, particularly at the forest management compartment level, in a limited amount of time. We used cluster analysis for classification of forest structure types and identified five forest structure classes with varying levels of forest canopy structural complexity: (1) short, open-canopy, conifer-dominated structure; (2) short, dense-canopy, broadleaf-dominated structure; (3) tall, closed-canopy, broadleaf-dominated structure; (4) very tall, closed-canopy, conifer-dominated structure with a relatively high degree of variation in canopy height; and (5) very tall, closed-canopy, conifer-dominated structure with a relatively low degree of variation in canopy height. These classes showed relationships with forest management activities (e.g. selection harvesting) and natural disturbances (e.g. typhoon damage). Spatial distribution of forest canopy structural complexity that was revealed in this study is capable of providing important information for forest management planning and habitat modelling. Further, the simple, and flexible data-driven method used in this study to characterize forest structure has the potential to be applied with necessary changes over larger landscapes and different forest types for characterizing and mapping forest structural complexity. Numéro de notice : A2020-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1648900 Date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648900 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94892 [article]

Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products / Mengmeng Li in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 152 - 170 Titre : Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products Type de document : Article/Communication Auteurs : Mengmeng Li, Auteur ; Chang-qing Ke, Auteur ; Hongjie Xie, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 152 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] Arctique, océan [Termes IGN] changement climatique [Termes IGN] épaisseur de la glace [Termes IGN] forme d'onde [Termes IGN] glace de mer [Termes IGN] image Cryosat [Termes IGN] variation saisonnière Résumé : (auteur) As a fundamental climate state variable, sea ice thickness (SIT) has exhibited a declining trend over the past five decades. Here, we present a quantitative comparison of three CryoSat-2 (CS-2) SIT products from the Alfred-Wegener-Institute (AWI), the National Snow and Ice Data Centre (NSIDC), and the European Space Agency (ESA) during the growth season (October to April) from 2010 to 2018 with Operation IceBridge (OIB) data. The results show that the NSIDC SIT product is the closest to the OIB SIT, with ESA SIT exhibiting the highest bias. During each growth season, the SIT differences between AWI and NSIDC gradually decrease, while such differences between ESA and NSIDC increase for first-year ice (FYI) and decrease then increase for multiyear ice (MYI). The difference between ESA and NSIDC is larger than that between AWI and NSIDC. Moreover, the rather large differences between ESA and NSIDC are mainly located in thin ice areas. Consistent to SIT comparative results, sea ice freeboard for ESA is higher than that for OIB, AWI and NSIDC, especially FYI freeboard. Sea ice freeboard for NSIDC is the closest to that for OIB. The comparative results indicate that the sources of the differences in SIT between the products mainly originate from the sea ice density and freeboard retrieval methods. The choices of different waveform retrackers and threshold assignments significantly influence the MYI freeboard retrievals due to the relatively thick snow depth and high surface roughness over MYI. Numéro de notice : A2020-211 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1637961 Date de publication en ligne : 02/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1637961 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94893 [article]