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Comparing 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)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 810 Titre : Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion Type de document : Article/Communication Auteurs : Nitzan Malachy, Auteur ; Imri Zadak, Auteur ; Offer Rozenstein, Auteur Année de publication : 2022 Article en page(s) : n° 810 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse spectrale [Termes IGN] covariance [Termes IGN] cultures [Termes IGN] données lidar [Termes IGN] hauteur de la végétation [Termes IGN] hétérogénéité spatiale [Termes IGN] image captée par drone [Termes IGN] modèle de croissance végétale [Termes IGN] régression linéaire [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] zone d'intérêt Résumé : (auteur) Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (Kc). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The Kc time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between Kc and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and Kc. Height was best predicted using the Mean and the Sample methods for all three crops (R2 = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of Kc (R2 = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications. Numéro de notice : A2022-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14040810 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.3390/rs14040810 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99774 [article]

Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event / Victoria Graffigna in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 888 Titre : Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event Type de document : Article/Communication Auteurs : Victoria Graffigna, Auteur ; Manuel Hernández-Pajares, Auteur ; Francisco Azpilicueta, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données météorologiques [Termes IGN] gradient de troposphère [Termes IGN] phénomène climatique extrême [Termes IGN] positionnement ponctuel précis [Termes IGN] retard troposphérique zénithal [Termes IGN] station GNSS [Termes IGN] surveillance météorologique [Termes IGN] tempête [Termes IGN] Texas (Etats-Unis) [Termes IGN] vapeur d'eau Résumé : (auteur) GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events. Numéro de notice : A2022-166 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14040888 Date de publication en ligne : 12/02/2022 En ligne : https://doi.org/10.3390/rs14040888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99791 [article]

A method of vision aided GNSS positioning using semantic information in complex urban environment / Rui Zhai in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 869 Titre : A method of vision aided GNSS positioning using semantic information in complex urban environment Type de document : Article/Communication Auteurs : Rui Zhai, Auteur ; Yunbin Yuan, Auteur Année de publication : 2022 Article en page(s) : n° 869 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] apprentissage profond [Termes IGN] cartographie et localisation simultanées [Termes IGN] centrale inertielle [Termes IGN] filtre de Kalman [Termes IGN] GNSS assisté pour la navigation [Termes IGN] information sémantique [Termes IGN] milieu urbain [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement par GNSS [Termes IGN] segmentation sémantique [Termes IGN] système de numérisation mobile [Termes IGN] vision par ordinateur Résumé : (auteur) High-precision localization through multi-sensor fusion has become a popular research direction in unmanned driving. However, most previous studies have performed optimally only in open-sky conditions; therefore, high-precision localization in complex urban environments required an urgent solution. The complex urban environments employed in this study include dynamic environments, which result in limited visual localization performance, and highly occluded environments, which yield limited global navigation satellite system (GNSS) performance. In order to provide high-precision localization in these environments, we propose a vision-aided GNSS positioning method using semantic information by integrating stereo cameras and GNSS into a loosely coupled navigation system. To suppress the effect of dynamic objects on visual positioning accuracy, we propose a dynamic-simultaneous localization and mapping (Dynamic-SLAM) algorithm to extract semantic information from images using a deep learning framework. For the GPS-challenged environment, we propose a semantic-based dynamic adaptive Kalman filtering fusion (S-AKF) algorithm to develop vision aided GNSS and achieve stable and high-precision positioning. Experiments were carried out in GNSS-challenged environments using the open-source KITTI dataset to evaluate the performance of the proposed algorithm. The results indicate that the dynamic-SLAM algorithm improved the performance of the visual localization algorithm and effectively suppressed the error spread of the visual localization algorithm. Additionally, after vision was integrated, the loosely-coupled navigation system achieved continuous high-accuracy positioning in GNSS-challenged environments. Numéro de notice : A2022-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.3390/rs14040869 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.3390/rs14040869 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99792 [article]

Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 874 Titre : Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests Type de document : Article/Communication Auteurs : Chong Zhang, Auteur ; Jiawei Zhou, Auteur ; Huiwen Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 874 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] échantillonnage de données [Termes IGN] entropie [Termes IGN] estimation quantitative [Termes IGN] feuillu [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] peuplement mélangé [Termes IGN] Pinophyta [Termes IGN] segmentation d'image Résumé : (auteur) High-resolution UAV imagery paired with a convolutional neural network approach offers significant advantages in accurately measuring forestry ecosystems. Despite numerous studies existing for individual tree crown delineation, species classification, and quantity detection, the comprehensive situation in performing the above tasks simultaneously has rarely been explored, especially in mixed forests. In this study, we propose a new method for individual tree segmentation and identification based on the improved Mask R-CNN. For the optimized network, the fusion type in the feature pyramid network is modified from down-top to top-down to shorten the feature acquisition path among the different levels. Meanwhile, a boundary-weighted loss module is introduced to the cross-entropy loss function Lmask to refine the target loss. All geometric parameters (contour, the center of gravity and area) associated with canopies ultimately are extracted from the mask by a boundary segmentation algorithm. The results showed that F1-score and mAP for coniferous species were higher than 90%, and that of broadleaf species were located between 75%–85.44%. The producer’s accuracy of coniferous forests was distributed between 0.8–0.95 and that of broadleaf ranged in 0.87–0.93; user’s accuracy of coniferous was distributed between 0.81–0.84 and that of broadleaf ranged in 0.71–0.76. The total number of trees predicted was 50,041 for the entire study area, with an overall error of 5.11%. The method under study is compared with other networks including U-net and YOLOv3. Results in this study show that the improved Mask R-CNN has more advantages in broadleaf canopy segmentation and number detection. Numéro de notice : A2022-168 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14040874 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.3390/rs14040874 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99793 [article]

How to boost close-range remote sensing courses using a serious game: Uncover in a fun way the complexity and transversality of multi-domain field acquisitions / Loïca Avanthey in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 817 Titre : How to boost close-range remote sensing courses using a serious game: Uncover in a fun way the complexity and transversality of multi-domain field acquisitions Type de document : Article/Communication Auteurs : Loïca Avanthey, Auteur ; Laurent Beaudoin, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Formation [Termes IGN] enseignement supérieur [Termes IGN] jeu sérieux [Termes IGN] télédétection Résumé : (auteur) Close-range remote sensing, and more particularly, its acquisition part that is linked to field robotics, is at the crossroads of many scientific and engineering fields. Thus, it takes time for students to acquire the solid foundations needed before practicing on real systems. Therefore, we are interested in a means that allow students without prerequisites to quickly appropriate the fundamentals of this interdisciplinary field. For this, we adapted a haggle game to the close-range remote sensing theme. In this article, we explain the mechanics that serve our educational purposes. We have used it, so far, for four academic years with hundreds of students. The experience was assessed through quality surveys and quizzes to calculate success indicators. The results show that the serious game is well appreciated by the students. It allows them to better structure information and acquire a good global vision of multi-domain acquisition and data processing in close-range remote sensing. The students are also more involved in the rest of the lessons; all of this helps to facilitate their learning of the theoretical parts. Thus, we were able to shorten the time before moving on to real practice by replacing three lesson sessions with one serious game session, with an increase in mastering fundamental skills. The designed serious game can be useful for close-range remote sensing teachers looking for an effective starting lesson. In addition, teachers from other technical fields can draw inspiration from the creation mechanisms described in this article to create their own adapted version. Such a serious game is also a good asset for selecting promising students in a recruitment context. Numéro de notice : A2022-243 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14040817 Date de publication en ligne : 06/09/2022 En ligne : https://doi.org/10.3390/rs14040817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102878 [article]