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Assessment of the Baspa basin glaciers mass budget using different remote sensing methods and modeling techniques / Vinay Kumar Gaddam in Geocarto international, vol 35 n° 3 ([01/03/2020])  

Public [article]  inGeocarto international > vol 35 n° 3 [01/03/2020] . - pp 296 - 316 Titre : Assessment of the Baspa basin glaciers mass budget using different remote sensing methods and modeling techniques Type de document : Article/Communication Auteurs : Vinay Kumar Gaddam, Auteur ; Anil V. Kulkarni, Auteur ; Anil Kumar Gupta, Auteur Année de publication : 2020 Article en page(s) : pp 296 - 316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande C [Termes descripteurs IGN] bilan de masse [Termes descripteurs IGN] cheminement géodésique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] fonte des glaces [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] MNS ASTER [Termes descripteurs IGN] MNS SRTM [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] température Résumé : (auteur) Glacial melt water is the key source for various socio-industrial and domestic activities in the Himalayas. Several recent studies suggest that glaciers are experiencing rapid melt. The glaciers health can be best assessed by mass balance. However, the mass balance investigations using in-situ methods for a large sample of glaciers are highly difficult in the Himalaya. Hence, remote sensing methods and modelling techniques are preferred. However, there is a lack of information on uncertainties associated with these methods in assessing the regional scale mass balance. Hence, these methods are applied to evaluate the regional scale mass budget of Baspa basin, Western Himalaya between 2000 and 2011. The total mass loss estimated using geodetic method amounts to −0.49 ± 0.1 gigatons, temperature index method to −0.43 ± 0.012 gigatons and AAR method to −0.36 ± 0.1 gigatons. Furthermore, this study highlights the limitations of these methods in mass loss evaluation in data scarce Himalayan regions. Numéro de notice : A2020-055 Nature : Article DOI : 10.1080/10106049.2018.1516247 date de publication en ligne : 06/01/2020 En ligne : https://doi.org/10.1080/10106049.2018.1516247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94568 [article]

Can mixed pine forests conserve understory richness by improving the establishment of understory species typical of native oak forests? / Daphne Lopez-Marcos in Annals of Forest Science [en ligne], Vol 77 n° 1 (mars 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 1 (mars 2020) . - 13 p. Titre : Can mixed pine forests conserve understory richness by improving the establishment of understory species typical of native oak forests? Type de document : Article/Communication Auteurs : Daphne Lopez-Marcos, Auteur ; Maria-Belen Turrion, Auteur ; Felipe Bravo, Auteur ; Carolina Martinez-Ruiz, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] Espagne [Termes descripteurs IGN] étage de végétation [Termes descripteurs IGN] forêt de zone humide [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] parcelle forestière [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] Pinus pinaster [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] quercus pyrenaica [Termes descripteurs IGN] régénération (sylviculture) [Termes descripteurs IGN] sous-étage [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: A positive effect of mixed pine forests ( Pinus sylvestris L. and Pinus pinaster Ait.) on the understory richness and tree regeneration was observed with respect to monospecific stands. Understory species typical of the native Pyrenean oak forests in the Iberian Peninsula contribute to maintaining high understory richness in such mixed pine forests. Context: The influence of stands characteristics on the understory in mixtures that combine coniferous tree species of the same genus deserves more study since they are frequent in Spain. Aims: To assess the effect of mixed versus monospecific stands of Pinus sylvestris L. and Pinus pinaster Ait. on the main tree species regeneration and understory species composition. Methods: Tree regeneration and understory species composition were inventoried in eighteen forest plots (6 triplets) in North-Central Spain. Each triplet consisted of two plots dominated either by Scots pine or Maritime pine and one mixed plot that contained both species. Results: The basal area (%) of both Pinus species was the only characteristic of the stands that significantly influenced the understory composition and tree regeneration. Characteristic species of humid and temperate zones, including P. sylvestris regeneration, dominated in Scots pine stands, and typical species of well-drained Mediterranean areas, including P. pinaster regeneration, dominated in Maritime pine stands. In mixed stands, the highest regeneration of the native Pyrenean oak with respect to monospecific stands was accompanied by understory species typical of native oak forests that share the same regeneration niche. Conclusion: Mixed pine forests allow the development of understory species better than monospecific forests. Numéro de notice : A2020-067 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-0919-7 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1007/s13595-020-0919-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94579 [article]

Transferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García 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 1 - 17 Titre : Transferring deep learning models for cloud detection between Landsat-8 and Proba-V Type de document : Article/Communication Auteurs : Gonzalo Mateo-García, Auteur ; Valero Laparra, Auteur ; Dan López-Puigdollers, Auteur ; Luis Gómez-Chova, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage par transformation [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] conversion de données [Termes descripteurs IGN] détection des nuages [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image PROBA [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] masque [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] seuillage de points Résumé : (Auteur) Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical properties of the acquired signals and propose a simple transfer learning approach using Landsat-8 and Proba-V sensors, whose images have different but similar spatial and spectral characteristics. Three types of experiments are conducted to demonstrate that transfer learning can work in both directions: (a) from Landsat-8 to Proba-V, where we show that models trained only with Landsat-8 data produce cloud masks 5 points more accurate than the current operational Proba-V cloud masking method, (b) from Proba-V to Landsat-8, where models that use only Proba-V data for training have an accuracy similar to the operational FMask in the publicly available Biome dataset (87.79–89.77% vs 88.48%), and (c) jointly from Proba-V and Landsat-8 to Proba-V, where we demonstrate that using jointly both data sources the accuracy increases 1–10 points when few Proba-V labeled images are available. These results highlight that, taking advantage of existing publicly available cloud masking labeled datasets, we can create accurate deep learning based cloud detection models for new satellites, but without the burden of collecting and labeling a large dataset of images. Numéro de notice : A2020-043 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.024 date de publication en ligne : 10/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94522 [article]

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Optimizing arbovirus surveillance using risk mapping and coverage modelling / Joni A. Downs in Annals of GIS, Vol 26 n°1 (January 2020)  

Public [article]  inAnnals of GIS > Vol 26 n°1 (January 2020) . - pp 13 - 23 Titre : Optimizing arbovirus surveillance using risk mapping and coverage modelling Type de document : Article/Communication Auteurs : Joni A. Downs, Auteur ; Mehrdad Vaziri, Auteur ; George Deskins, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 13 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] épizootie [Termes descripteurs IGN] Floride (Etats-Unis) [Termes descripteurs IGN] maladie infectieuse [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] optimisation spatiale [Termes descripteurs IGN] surveillance sanitaire [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Diseases carried by mosquitoes and other arthropods endanger human health globally. Though costly, surveillance efforts are vital for disease control and prevention This paper describes an approach for strategically configuring targeted disease surveillance sites across a study area. The methodology combines risk index mapping and spatial optimization modelling. The risk index is used to identify demand for surveillance, and the maximum covering location problem is used to select a specified number of candidate surveillance sites that covers the maximum amount of risk. The approach is demonstrated using a case study where optimal locations for sentinel surveillance sites are selected for the purposes of detecting eastern equine encephalitis virus in a county in the state of Florida. Optimal sentinel sites were selected under a number of scenarios that modelled different target populations (horses or humans), coverage distances (0.5, 1.0, and 1.5 km), and numbers of sites to select (1–12). Sentinel site selections for the horse and human models displayed different spatial patterns, with horse sites located largely in the west-central region and human ones in the north-central. Minor amounts of spatial overlap between the horse and human sites were observed, especially as coverage distances and numbers of sites were increased. Additionally, a near linear increase in risk coverage was observed as sites were incrementally added to the scenarios. This finding suggests that the number of sentinel sites within the ranges explored should be based on the maximum that can be funded, since they provide similar levels of benefit. Numéro de notice : A2020-117 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2019.1688391 date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1080/19475683.2019.1688391 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94736 [article]

Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9524 - 9533 Titre : Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning Type de document : Article/Communication Auteurs : Benjamin Kellenberger, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2019 Article en page(s) : pp 9524 - 9533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] faune locale [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] Namibie [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] recensement Résumé : (auteur) We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural Network (CNN)-based object detector on a new data set. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled ground truth, our goal is to train an animal detector that can be reused for repeated acquisitions, e.g., in follow-up years. Domain shifts between data sets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). TS uses Optimal Transport (OT) to find corresponding regions between the source and the target data sets in the space of CNN activations. The CNN scores in the source data set are used to rank the samples according to their likelihood of being animals, and this ranking is transferred to the target data set. Unlike conventional AL criteria that exploit model uncertainty, TS focuses on very confident samples, thus allowing quick retrieval of true positives in the target data set, where positives are typically extremely rare and difficult to find by visual inspection. We extend TS with a new window cropping strategy that further accelerates sample retrieval. Our experiments show that with both strategies combined, less than half a percent of oracle-provided labels are enough to find almost 80% of the animals in challenging sets of UAV images, beating all baselines by a margin. Numéro de notice : A2019-598 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927393 date de publication en ligne : 20/08/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2927393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94592 [article]

A time‐geographic approach to quantifying wildlife–road interactions / Rebecca W. Loraamm in Transactions in GIS, vol 23 n° 1 (February 2019)  

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Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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A review of accuracy assesment for object-based image analysis: from per pixel to per-polygon approaches [review article] / Su Ye in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)  

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Manuel d'analyse spatiale / Vincent Loonis (2018)    

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Geometric features and their relevance for 3D point cloud classification / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1 ([30/05/2017])  

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Utilisation d’un modèle numérique de hauteur en stratification des données de l’Inventaire Forestier National / Sophie Georges (2017) 

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Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping / L. Drăguț in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

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An impressionistic cartographic solution for base map land cover with coarse pixel data / Paulo Raposo in Cartographic perspectives, n° 83 (2016)  

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Use of remotely sensed auxiliary data for improving sample-based forest inventories / Svetlana Saarela (2015)  

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