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Weak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests / Thomas Asbeck in European Journal of Forest Research, vol 140 n° 3 (June 2021)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 3 (June 2021) . - pp 721 - 731 Titre : Weak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests Type de document : Article/Communication Auteurs : Thomas Asbeck, Auteur ; Julian Frey, Auteur Année de publication : 2021 Article en page(s) : pp 721 - 731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Abies alba [Termes IGN] composition d'un peuplement forestier [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Forêt-Noire, massif de la [Termes IGN] gestion forestière durable [Termes IGN] modélisation de la forêt [Termes IGN] Picea abies [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Understanding the relationship of stand structural complexity and forest management is relevant to create desired stand structures by adapting management strategies under changing disturbance scenarios and climatic conditions. To overcome difficulties in differentiating between strict categories of silvicultural practices and to describe the impact of forest management more appropriate, we used a continuous indicator of forest management intensity (ForMI). The ForMI consists of three components including volumes of natural deadwood, non-native tree species and harvested trees. There are a great number of approaches to quantify stand structure; here we used the recently established stand structural complexity index (SSCI) which represents a density-dependent as well as vertical measure of complexity based on the distribution of points in 3D space inventoried by terrestrial laser scanning. The data collection took place in 135 one-hectare plots managed under close-to-nature forest management (CTNFM) located in the Black Forest, Germany. We build generalized additive models to test the relationship of the SSCI with the ForMI. The model results did not prove a significant relationship between the SSCI and the ForMI, but components of the ForMI showed significant relationships to the SSCI. Our results indicate that the relationship between stand structural complexity and forest management intensity is, while plausible, not trivial to demonstrate. We conclude that forest managers have a relatively wide range of choices in CTNFM to adapt forests within a similar range of management intensity as presented here to future challenges, since management intensity does not change the forest structure drastically. Numéro de notice : A2021-431 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10342-021-01361-4 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1007/s10342-021-01361-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97793 [article]

Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks / Felix Schiefer in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 205-215 Titre : Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks Type de document : Article/Communication Auteurs : Felix Schiefer, Auteur ; Teja Kattenborn, Auteur ; Annett Frick, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 205-215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] arbre (flore) [Termes IGN] carte forestière [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] espèce végétale [Termes IGN] Forêt-Noire, massif de la [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier local [Termes IGN] segmentation sémantique [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution ( Numéro de notice : A2020-706 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.015 date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.015 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96236 [article]

Modeling soil erosion after mechanized logging operations on steep terrain in the Northern Black Forest, Germany / Julian Haas in European Journal of Forest Research, vol 139 n°4 (August 2020)  

Public [article]  inEuropean Journal of Forest Research > vol 139 n°4 (August 2020) . - pp 549 – 565 Titre : Modeling soil erosion after mechanized logging operations on steep terrain in the Northern Black Forest, Germany Type de document : Article/Communication Auteurs : Julian Haas, Auteur ; Helmer Schack-Kirchner, Auteur ; Friederike Lang, Auteur Année de publication : 2020 Article en page(s) : pp 549 – 565 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] érosion [Termes IGN] exploitation forestière [Termes IGN] foresterie [Termes IGN] Forêt-Noire, massif de la [Termes IGN] pente [Termes IGN] ruissellement [Termes IGN] sédiment [Vedettes matières IGN] Sylviculture Résumé : (auteur) Climate change makes it necessary to re-evaluate the erosion potential of forest infrastructure. We used the Forest Service WEPP interfaces (FS WEPP) to compare soil erosion potentials of two competing logging practices in steep terrain in the Northern Black Forest, Germany: (1) Felling with harvesters and logging with forwarders in slope line with optional traction supporting winches. (2) Felling by chainsaw, logging with a cable winch, and further transport of logs via forest dirt roads. After forest harvest we measured erosion, runoff, and DOC concentration in runoff from 50 m sections of two machine tracks, two cable tracks, and a dirt road for 2 years. The erosion measurements were used to validate FS WEPP management options and a regionally adjusted CLIGEN input file. With these parameterizations we compared the erosion potential of the two practices on subcatchment scale by modeling return periods and total sediment export with FS WEPP. Model results show that logging operations with heavy machinery in slope line are less prone to soil erosion than logging operations including winch logging and additional dirt roads. The former produces less sediment in its worst-case configuration than the latter in its most moderate configuration by a factor of two. Model results also show that erosion prevention benefits from long periods of 10 years between two harvests. Numéro de notice : A2020-426 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10342-020-01269-5 date de publication en ligne : 11/03/2020 En ligne : https://doi.org/10.1007/s10342-020-01269-5 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95491 [article]

CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 415 - 424 Titre : CNN-based dense image matching for aerial remote sensing images Type de document : Article/Communication Auteurs : Shunping Ji, Auteur ; Jin Liu, Auteur ; Meng Lu, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] couple stéréoscopique [Termes IGN] image aérienne [Termes IGN] Munich [Termes IGN] réseau neuronal convolutif [Termes IGN] Stuttgart [Termes IGN] ville [Termes IGN] zone urbaine Résumé : (Auteur) Dense stereo matching plays a key role in 3D reconstruction. The capability of using deep learning in the stereo matching of remote sensing data is currently uncertain. This article investigated the application of deep learning–based stereo methods in aerial image series and proposed a deep learning–based multi-view dense matching framework. First, we applied three typical convolutional neural network models, MC-CNN, GC-Net, and DispNet, to aerial stereo pairs and compared the results with those of the SGM and a commercial software, SURE. Second, on different data sets, the generalization ability of each network is evaluated by using direct transfer learning with models pretrained on other data sets and by fine-tuning with a small number of target training data. Third, we present a deep learning–based multi-view dense matching framework where the multi-view geometry is introduced to further refine matching results. Three sets of aerial images as the main data sets and two open-source sets of street images as auxiliary data sets are used for testing. Experiments show that, first, the performance of deep learning–based stereo methods is slightly better than traditional methods. Second, both the GC-Net and the MC-CNN have demonstrated good generalization ability and can obtain satisfactory results on aerial images using a pretrained model on several available stereo benchmarks. Third, multi-view geometry constraints can further improve the performance of deep learning–based methods, which is better than that of the multi-view–based SGM and SURE. Numéro de notice : A2019-246 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.415 date de publication en ligne : 01/06/2019 En ligne : https://doi.org/10.14358/PERS.85.6.415 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93002 [article]

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Land cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models / Diego Marcos in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) Titre : Land cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models Type de document : Article/Communication Auteurs : Diego Marcos, Auteur ; Michele Volpi, Auteur ; Benjamin Kellenberger, Auteur ; Devis Tuia, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Bade-Wurtemberg (Allemagne) [Termes IGN] carte d'occupation du sol [Termes IGN] enrichissement sémantique [Termes IGN] étiquette [Termes IGN] filtrage numérique d'image [Termes IGN] image à ultra haute résolution [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object’s orientation and on a sensor’s flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters. Numéro de notice : A2018-491 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.021 date de publication en ligne : 19/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91227 [article]

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Open land cover from OpenStreetMap and remote sensing / Michael Schultz in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)  

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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)  

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An exploration of future patterns of the contributions to OpenStreetMap and development of a contribution index / Jamal Jokar Arsanjani in Transactions in GIS, vol 19 n° 6 (December 2015)  

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Building cities using UAV / Martin Schwall in Geoinformatics, vol 18 n° 8 (December 2015)

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Le suivi de l'occupation du sol à travers le monde / Romain Pfältzer in Cahiers de l'Institut d'aménagement et d'urbanisme de la région Île-de-France, n° 168 (décembre 2013)

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LiDAR surveys in the upper Rhine Valley: New insights for archeological and landscape applications / F. Basoge in Revue Française de Photogrammétrie et de Télédétection, n° 193 (Janvier 2011)

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Des preuves venues de l'espace : Traduction de l'original paru en décembre 2009 dans la revue allemande "Der Eisenbahningenieur" / K. Herrmann in XYZ, n° 125 (décembre 2010 - février 2011)

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68e voyage du Comité des forêts / Didier Paillereau in Forêts de France, n° 519 (décembre 2008)

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Premières applications de la technique du lidar appliquée à l'étude des sites patrimoniaux, les exemples allemands et alsaciens / B. Sittler in Revue Française de Photogrammétrie et de Télédétection, n° 186 (Juin 2007)

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Filtering airborne Laser scanner data: a wavelet-based clustering method / T. Thuy in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 11 (November 2004)

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