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Mapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 14 - 26 Titre : Mapping dead forest cover using a deep convolutional neural network and digital aerial photography Type de document : Article/Communication Auteurs : Jean-Daniel Sylvain, Auteur ; Guillaume Drolet, Auteur ; Nicolas Brown, Auteur Année de publication : 2019 Article en page(s) : pp 14 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] arbre mort [Termes IGN] base de données forestières [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couvert forestier [Termes IGN] feuillu [Termes IGN] forêt boréale [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] peuplement mélangé [Termes IGN] Pinophyta [Termes IGN] Québec (Canada) [Termes IGN] santé des forêts Résumé : (Auteur) Tree mortality is an important forest ecosystem variable having uses in many applications such as forest health assessment, modelling stand dynamics and productivity, or planning wood harvesting operations. Because tree mortality is a spatially and temporally erratic process, rates and spatial patterns of tree mortality are difficult to estimate with traditional inventory methods. Remote sensing imagery has the potential to detect tree mortality at spatial scales required for accurately characterizing this process (e.g., landscape, region). Many efforts have been made in this sense, mostly using pixel- or object-based methods. In this study, we explored the potential of deep Convolutional Neural Networks (CNNs) to detect and map tree health status and functional type over entire regions. To do this, we built a database of around 290,000 photo-interpreted trees that served to extract and label image windows from 20 cm-resolution digital aerial images, for use in CNN training and evaluation. In this process, we also evaluated the effect of window size and spectral channel selection on classification accuracy, and we assessed if multiple realizations of a CNN, generated using different weight initializations, can be aggregated to provide more robust predictions. Finally, we extended our model with 5 additional classes to account for the diversity of landcovers found in our study area. When predicting tree health status only (live or dead), we obtained test accuracies of up to 94%, and up to 86% when predicting functional type only (broadleaf or needleleaf). Channel selection had a limited impact on overall classification accuracy, while window size increased the ability of the CNNs to predict plant functional type. The aggregation of multiple realizations of a CNN allowed us to avoid the selection of suboptimal models and help to remove much of the speckle effect when predicting on new aerial images. Test accuracies of plant functional type and health status were not affected in the extended model and were all above 95% for the 5 extra classes. Our results demonstrate the robustness of the CNN for between-scene variations in aerial photography and also suggest that this approach can be applied at operational level to map tree mortality across extensive territories. Numéro de notice : A2019-316 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.07.010 Date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93353 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 63 - 76 Titre : Automatic canola mapping using time series of Sentinel 2 images Type de document : Article/Communication Auteurs : Davoud Ashourloo, Auteur ; Hamid Salehi Shahrabi, Auteur ; Mohsen Azadbakht, Auteur ; Hossein Aghighi, Auteur ; Hamed Nematollahi, Auteur ; Abbas Alimohammadi, Auteur ; Ali Akbar Matkan, Auteur Année de publication : 2019 Article en page(s) : pp 63 - 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture de précision [Termes IGN] Brassica napus subsp. napus [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] image Sentinel-MSI [Termes IGN] Iran [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Oklahoma (Etats-Unis) [Termes IGN] rendement agricole [Termes IGN] série temporelle Résumé : (Auteur) Different techniques utilized for mapping various crops are mainly based on using training dataset. But, due to difficulties of access to a well-represented training data, development of automatic methods for detection of crops is an important need which has not been considered as it deserves. Therefore, main objective of present study was to propose a new automatic method for canola (Brassica napus L.) mapping based on Sentinel 2 satellite time series data. Time series data of three study sites in Iran (Moghan, Gorgan, Qazvin) and one site in USA: (Oklahoma), were used. Then, spectral reflectance values of canola in various spectral bands were compared with those of the other crops during the growing season. NDVI, Red and Green spectral bands were successfully applied for automatic identification of canola flowering date using the threshold values. Examination of the fisher function indicated that multiplication of the near-infrared (NIR) band by the sum of red and green bands during the flowering date is an efficient index to differentiate canola from the other crops. The Kappa and overall accuracy (OA) for the four study sites were more than 0.75 and 88%, respectively. Results of this research demonstrated the potential of the proposed approach for canola mapping using time series of remotely sensed data. Numéro de notice : A2019-317 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.08.007 Date de publication en ligne : 09/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93355 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 94 - 107 Titre : Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis Type de document : Article/Communication Auteurs : Wenxia Dai, Auteur ; Bisheng Yang, Auteur ; Xinlian Liang, Auteur ; Zhen Dong, Auteur ; Ronggang Huang, Auteur ; Yunsheng Wang, Auteur ; Wuyan Li, Auteur Année de publication : 2019 Article en page(s) : pp 94 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme ICP [Termes IGN] canopée [Termes IGN] données TLS (télémétrie) [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] fusion de données multisource [Termes IGN] image ADAR [Termes IGN] semis de points [Termes IGN] surveillance forestière Résumé : (Auteur) Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) systems are effective ways to capture the 3D information of forests from complementary perspectives. Registration of the two sources of point clouds is necessary for various forestry applications. Since the forest point clouds show irregular and natural point distributions, standard registration methods working on geometric keypoints (e.g., points, lines, and planes) are likely to fail. Hence, we propose a novel method to register the ALS and TLS forest point clouds through density analysis of the crowns. The proposed method extracts mode-based keypoints by the mean shift method and aligns them by maximum likelihood estimation. Firstly, the differences in the point densities of the ALS and TLS crowns are minimized to produce analogous modes, which represent the local maxima of the underlying probability density function (PDF). The mode-based keypoints are then aligned through the coherent point drift (CPD) algorithm, which is independent of the descriptor similarities and considers the alignment as a maximum likelihood estimation problem. The sets of keypoints derived from the two data sources need not be equal. Finally, the recovered transformation is applied to the original point clouds and refined through the standard iterative closest point (ICP) algorithm. In contrast to some of the existing methods, the proposed method avoids the geometric description of the forest point clouds. Furthermore, additional information such as tree diameter or height is not required to evaluate the similarities. The experiments in this study were conducted in a Scandinavian boreal forest, located in Evo, Finland. The proposed method was tested on four datasets (ALS data: a circle with a diameter of 60 m, multi-scan TLS data: 32 × 32 m) with heterogeneous tree species and structures. The results showed that the proposed probabilistic-based method obtains a good performance with a 3D distance residual of 0.069 m, and improved the accuracy of the registration when compared with the existing methods. Numéro de notice : A2019-318 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93356 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Multi-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 135 - 146 Titre : Multi-sensor prediction of Eucalyptus stand volume: A support vector approach Type de document : Article/Communication Auteurs : Guilherme Silverio Aquino de Souza, Auteur ; Vicente Paulo Soares, Auteur ; Helio Garcia Leite, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 135 - 146 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] bande L [Termes IGN] Brésil [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal [Termes IGN] Eucalyptus (genre) [Termes IGN] image ALOS-AVNIR2 [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] régression multiple [Termes IGN] taux d'échantillonnage [Termes IGN] volume en bois Résumé : (Auteur) Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods. Numéro de notice : A2019-319 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.002 Date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93357 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Multiple-view geospatial comparison using web-based virtual globes / Liangfeng Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 235 - 246 Titre : Multiple-view geospatial comparison using web-based virtual globes Type de document : Article/Communication Auteurs : Liangfeng Zhu, Auteur ; Xinlan Chen, Auteur ; Zhiwen Li, Auteur Année de publication : 2019 Article en page(s) : pp 235 - 246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] analyse spatiale [Termes IGN] application web [Termes IGN] données localisées [Termes IGN] globe virtuel [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] outil d'aide à la comparaison [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Existing virtual globes, including both unique platforms and associated visualization applications, often present geospatial information with a single-view mode that restricts the user to a single dataset. Due to the absence of the functionality and user interface for coordinating multiple virtual-globe views, it is either hard or impossible to explore multiple different geospatial datasets simultaneously only using the existing virtual globes, especially when the datasets come in multiple sources, multiple spatial resolutions or multiple temporal scales. Here we present a general visualization framework that supports the exploration and comparison of various datasets with multiple coordinated views in the web-based virtual globe environment. This framework not only comprehensively considers the dynamic master/slave relationship between multiple virtual globes, but also effectively handles the coordination mechanism for diverse views to respond to users’ manipulations. We also implement a prototype application (termed MultiGlobe) and demonstrate its effectiveness over three typical application scenarios. The first case addresses the comparison of diverse imagery layers derived from different providers. A second case is examining multiple digital maps for a specific region or theme, such as time-varying LUCC datasets. As a final example, we compare and evaluate the accuracy of multiple DEMs generated from diverse data sources with different resolutions. Our informal evaluation with experts in exploratory visualization and spatial analysis confirms that the multiple-view-enhanced virtual globe can bring many benefits including focusing on spatial awareness, reducing cognitive efforts, coordinating interaction strategies, increasing browsing speed and enhancing comparison capabilities. Therefore, it can be incorporated into a variety of geospatial visualizations to replace or supplement the fixed single-view interfaces of the traditional virtual globe applications, empowering users with the ability to explore and compare multiple different datasets across the same geospatial area synchronously. Numéro de notice : A2019-329 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.isprsjprs.2019.08.016 Date de publication en ligne : 27/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.016 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93358 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt