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Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)  

Public Titre : Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations Type de document : Article/Communication Auteurs : Adnane Chakir , Auteur ; Pierre-Louis Frison , Auteur ; Saïd Khabba, Auteur ; Jamal Ezzahar, Auteur ; Ludovic Villard, Auteur ; Pascal Fanise, Auteur ; Nadia Ouaadi, Auteur ; V. Ledantec, Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 3801 - 3804 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande C [Termes IGN] coefficient de rétrodiffusion [Termes IGN] cohérence temporelle [Termes IGN] image Sentinel-SAR [Termes IGN] Maroc [Termes IGN] Olea europaea [Termes IGN] verger Résumé : (auteur) C-band radar remote sensing is a suitable tool for monitoring agricultural areas on a large scale, providing access to information on vegetation such as plant biomass, or on the surface water content of the soil. Recent studies suggest that the water state and the physiological functioning of trees influence radar response leading to marked daily profiles of both radar backscattering coefficient and temporal coherence. The objective of this paper is to make a preliminary comparison between the temporal evolution of Sentinel-1 radar data and in situ radar measurements over a Mediterranean olive orchard located in Morocco. In situ radar data consist in quad polarizations measurements realized from a 20m high tower, every 15 minutes, for the period extending from May 2019 to October 2020. Numéro de notice : C2021-051 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9553129 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9553129 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99415

Time series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data / Hemant Sahu in Geocarto international, vol 35 n° 14 ([15/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 14 [15/10/2020] . - pp 1627 - 1639 Titre : Time series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data Type de document : Article/Communication Auteurs : Hemant Sahu, Auteur ; Dipanwita Haldar, Auteur ; Abhishek Danodia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1627 - 1639 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] coefficient de rétrodiffusion [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] modèle de rétrodiffusion [Termes IGN] polarisation [Termes IGN] série temporelle [Termes IGN] variable biophysique (végétation) [Termes IGN] verger Résumé : (auteur) Potential of Sentinel-1A SAR data was assessed for the time-series analysis of orchard biophysical parameters and crop system. The study revealed characteristics variations in the backscatter coefficient with respect to time and polarization for age in VH polarization than in VV and ratio of VV/VH polarization showing discrimination of young orchard particularly in VV polarization. The parameter of the orchard (age, DBH, canopy radius and visual height) shows a promising relationship with backscatter coefficient. Out of several regression models, VV channel responds with a fair regression coefficient of 0.54, 0.52, 0.48 and 0.44 for height with rmse of 0.5, 1.3, 0.7 and 0.6 for age, DBH, canopy radius and visual height, respectively. Multiple regression coefficient of 0.61 was observed for January 2018 in VV polarization as best date for study. These empirical relationships have potential for the inverse backscatter modelling. Numéro de notice : A2020-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583776 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96003 [article]

A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 97 - 106 Titre : A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; José Marcato Junior, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 97 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Brésil [Termes IGN] carte de confiance [Termes IGN] Citrus (genre) [Termes IGN] détection d'arbres [Termes IGN] géolocalisation [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] inventaire de la végétation [Termes IGN] réseau neuronal convolutif [Termes IGN] verger Résumé : (Auteur) Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees. Numéro de notice : A2020-045 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.010 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94525 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

CHP toolkit : case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations / Karolina D. Fieber in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5071 - 5080 Titre : CHP toolkit : case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations Type de document : Article/Communication Auteurs : Karolina D. Fieber, Auteur ; Ian J. Davenport, Auteur ; James M. Ferryman, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 5071 - 5080 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] forme d'onde pleine [Termes IGN] hauteur des arbres [Termes IGN] Leaf Area Index [Termes IGN] logiciel libre [Termes IGN] verger Résumé : (Auteur) This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations' canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumping can be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models. Numéro de notice : A2016-894 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2550623 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2550623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83074 [article]

Analysis of full-waveform LiDAR data for classification of an orange orchard scene / Karolina D. Fieber in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 63 - 82 Titre : Analysis of full-waveform LiDAR data for classification of an orange orchard scene Type de document : Article/Communication Auteurs : Karolina D. Fieber, Auteur ; Ian J. Davenport, Auteur ; James M. Ferryman, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 63 - 82 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Citrus sinensis [Termes IGN] classification [Termes IGN] données lidar [Termes IGN] lidar à retour d'onde complète [Termes IGN] verger Résumé : (Auteur) Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient Y was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and Y. For single-peak waveforms the scatterplot of Y versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return Y values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the Y versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient Y of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties. Numéro de notice : A2013-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.05.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.05.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32550 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Détection et identification de zones de végétation arborée et viticole : utilisation d’images satellite RapidEye et de données BDOrtho / Arnaud Le Bris (2013)

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Modélisations géométriques plurielles d'un espace agricole en évolution : application à la palmeraie de l'oasis de Figuig (Maroc) / G. Janty in Revue internationale de géomatique, vol 22 n° 3 (septembre - novembre 2012)  

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Automatic detection and segmentation of orchards using very high resolution imagery / Selim Aksoy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

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Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)  

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Terraced landscapes characterization / D. Rizzo in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)  

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Plants manquants dans des cultures : Un algorithme de localisation à partir d'image à très haute résolution spatiale / Jean-Christophe Foltête in Revue internationale de géomatique, vol 17 n° 1 (mars – mai 2007)  

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Influence de l'organisation spatiale du parcellaire, des pratiques agricoles et des éléments du paysage sur les densités du carpocapse, un bio-agresseur du pommier / Bernadette Ricci (2006)

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A texture orientation estimator for discriminating between forests, orchards, vineyards, and tilled fields / Roger Trias-Sanz (2005)  

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The EuroSTARRS airborne campaign in support of the SMOS mission: first results over land surfaces / K. Saleh in International Journal of Remote Sensing IJRS, vol 25 n° 1 (January 2004)  

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Exploitation d'une image très haute résolution pour la cartographie des plantations et de la végétation naturelle dans la région de Marrakech / Y. Gauthier (2004)

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