Centre dedocumentation
scientifique

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 descripteurs IGN] agrume [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs 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 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]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020021	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020023	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2020022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use / Alexis Comber in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 5 (October 2019) . - pp 879 - 891 Titre : Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use Type de document : Article/Communication Auteurs : Alexis Comber, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 879 - 891 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] métadonnées [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] télédétection [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community. Numéro de notice : A2019-549 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12559 date de publication en ligne : 08/07/2019 En ligne : https://doi.org/10.1111/tgis.12559 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94199 [article]

Scalable individual tree delineation in 3D point clouds / Jinhu Wang in Photogrammetric record, vol 33 n° 163 (September 2018)  

Public [article]  inPhotogrammetric record > vol 33 n° 163 (September 2018) . - pp 315 - 340 Titre : Scalable individual tree delineation in 3D point clouds Type de document : Article/Communication Auteurs : Jinhu Wang, Auteur ; Roderik Lindenbergh, Auteur ; Massimo Menenti, Auteur Année de publication : 2018 Article en page(s) : pp 315 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] délimitation [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] lasergrammétrie [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Manually monitoring and documenting trees is labour intensive. Lidar provides a possible solution for automatic tree‐inventory generation. Existing approaches for segmenting trees from original point cloud data lack scalable and efficient methods that separate individual trees sampled by different laser‐scanning systems with sufficient quality under all circumstances. In this study a new algorithm for efficient individual tree delineation from lidar point clouds is presented and validated. The proposed algorithm first resamples the points using cuboid (modified voxel) cells. Consecutively connected cells are accumulated by vertically traversing cell layers. Trees in close proximity are identified, based on a novel cell‐adjacency analysis. The scalable performance of this algorithm is validated on airborne, mobile and terrestrial laser‐scanning point clouds. Validation against ground truth demonstrates an improvement from 89% to 94% relative to a state‐of‐the‐art method while computation time is similar. Numéro de notice : A2018-619 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12247 date de publication en ligne : 16/07/2018 En ligne : https://doi.org/10.1111/phor.12247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92863 [article]

Inference on forest attributes and ecological diversity of trees outside forest by a two-phase inventory / Marco Marchetti in Annals of Forest Science [en ligne], vol 75 n° 2 (June 2018)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 75 n° 2 (June 2018) Titre : Inference on forest attributes and ecological diversity of trees outside forest by a two-phase inventory Type de document : Article/Communication Auteurs : Marco Marchetti, Auteur ; Vittorio Garfì, Auteur ; Caterina Pisani, Auteur ; Sara Franceschi, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre hors forêt [Termes descripteurs IGN] biodiversité végétale [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] données dendrométriques [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] Molise (Italie) [Termes descripteurs IGN] puits de carbone [Vedettes matières IGN] Végétation et changement climatique Résumé : (Auteur) Key message: Trees outside forests (TOF) have crucial ecological and social-economic roles in rural and urban contexts around the world. We demonstrate that a large-scale estimation strategy, based on a two-phase inventory approach, effectively supports the assessment of TOF’s diversity and related climate change mitigation potential. Context: Although trees outside forest (TOF) affect the ecological quality and contribute to increase the social and economic developments at various scales, lack of data and difficulties to harmonize the known information currently limit their integration into national and global forest inventories. Aims: This study aims to develop and test a large-scale estimation framework to assess ecological diversity and above-ground carbon stock of TOF. Methods: This study adopts a two-phase inventory approach. Results: In the surveyed territory (Molise region, Central Italy), all the attributes considered (tree abundance, basal area, wood volume, above-ground carbon stock) are concentrated in a few dominant species. Furthermore, carbon stock in TOF above-ground biomass is non-negligible (on average: 28.6 t ha−1). Compared with the low field sampling effort (0.08% out of 52,796 TOF elements), resulting uncertainty of the estimators are more than satisfactory, especially those regarding the diversity index estimators (relative standard errors Conclusion: The proposed approach can be suitably applied on vast territories to support landscape planning and maximize ecosystem services balance from TOF. Numéro de notice : A2018-326 Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0718-6 date de publication en ligne : 16/03/2018 En ligne : https://doi.org/10.1007/s13595-018-0718-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90469 [article]

From Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 13 - 30 Titre : From Google Maps to a fine-grained catalog of street trees Type de document : Article/Communication Auteurs : Steve Branson, Auteur ; Jan Dirk Wegner, Auteur ; David Hall, Auteur ; Nico Lang, Auteur ; Konrad Schindler, Auteur ; Pietro Perona, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 30 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] architecture pipeline [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Google Maps [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] Pasadena [Termes descripteurs IGN] photo-interprétation assistée par ordinateur [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] ville Résumé : (Auteur) Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect >70% of the street trees, assign correct species to >80% for 40 different species, and correctly detect and classify changes in >90% of the cases. Numéro de notice : A2018-068 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89426 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018011	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2018012	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2018013	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Estimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data / Joachim Maack in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)  

Permalink

A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing / Wenwu Tang in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Permalink

Developing an integrated cloud-based spatial-temporal system for monitoring phenology / M. Cope in Ecological Informatics, vol 39 (May 2017)  

Permalink

Optimizing the bioindication of forest soil acidity, nitrogen and mineral nutrition using plant species / Paulina E. Pinto in Ecological indicators, vol 71 (December 2016)  

Permalink

Axe A - Connaissance de la flore et des végétations. Bilan d'exécution 2012. Arrêté Région 12 006968 01-CBN001 du 21 mai 2012 / Alexis Mikolajczak (2013) 

Permalink

De l'herbier à la carte : Représentation cartographique des collections et des données botaniques / J.L. Guillaumet in Le monde des cartes, n° 206 (décembre 2010)

Permalink

Innovation en matière d'assurance qualité : la base de saisie en ligne "RenecoFlore" / Sylvaine Camaret in Rendez-vous techniques, Hors-série n° 4 (2008)

Permalink

A new bioclimatic model calibrated with vegetation for Mediterranean forest areas / Michel Vennetier in Annals of Forest Science, Vol 65 n° 7 (October - November 2008)

Permalink

Détection des haies et segmentation automatique / A. Dommanget in Géomatique expert, n° 57 (01/07/2007)

Permalink

Applications de la télédétection à l'observation de l'état sanitaire des forêts / G. Hildebrandt (1992)

Permalink