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Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)
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Titre : Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine Type de document : Article/Communication Auteurs : Thuan Sarzynski, Auteur ; Xingli Giam, Auteur ; Luis Carrasco, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Elaeis guineensis
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] image radar moirée
[Termes IGN] occupation du sol
[Termes IGN] Sumatra
[Termes IGN] surveillance agricole
[Termes IGN] utilisation du solRésumé : (auteur) Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia. Numéro de notice : A2020-455 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12071220 Date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.3390/rs12071220 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95554
in Remote sensing > vol 12 n° 7 (April 2020)[article]Deformation detection through the realization of reference frames / Nestoras Papadopoulos in Journal of applied geodesy, vol 14 n° 2 (April 2020)
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Titre : Deformation detection through the realization of reference frames Type de document : Article/Communication Auteurs : Nestoras Papadopoulos, Auteur ; Melissinos Paraskevas, Auteur ; Ioannis Katsafados, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 133 – 148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] déformation de la croute terrestre
[Termes IGN] détection de changement
[Termes IGN] géodynamique
[Termes IGN] Grèce
[Termes IGN] Hellenic Military Geographical Service
[Termes IGN] modèle de déformation tectonique
[Termes IGN] positionnement cinématique
[Termes IGN] repère de référence
[Termes IGN] réseau de triangulation
[Termes IGN] système de référence géodésiqueRésumé : (auteur) Hellenic Military Geographical Service (HMGS) has established and measured various networks in Greece which constitute the geodetic infrastructure of the country. One of them is the triangulation network consisting of about 26.000 pillars all over Greece. Classical geodetic measurements that held by the Hellenic Military Geographic Service (HMGS) through the years have been used after adjustment for the state reference frame which materializes the current Hellenic Geodetic Reference System of 1987 (HGRS87). The aforementioned Reference System (RS) is a static one and is in use since 1990. Through the years especially in the era of satellite navigation systems many Global Navigation Satellite System (GNSS) networks have been established. The latest such network materialized by HMGS is ongoing and covers until now more than the 2/3 of the country. It is referenced by International GNSS Service (IGS) permanent stations and consists a local densification IGS08 Reference Frame. Firstly, this gives the opportunity to calculate transformation parameters between the two systems and a statistical analysis of the residuals leads to intermediate conclusions. After that and in conjunction with existing past transformations, tectonic deformations and their directions are concluded. Moreover past GPS observations on the same pillars in compare to the newer ones give also a sense of tectonic displacements. Greece is one of the most tectonically active countries in Europe and the adoption of a modern kinematic or semi-kinematic geodetic datum is a necessity as it should incorporate a deformation model like 3d velocities on the reference frame realization. The detection of geodynamic changes is a continuous need and should be taken into consideration at each epoch. Numéro de notice : A2020-215 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0056 Date de publication en ligne : 18/01/2020 En ligne : https://doi.org/10.1515/jag-2019-0056 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94908
in Journal of applied geodesy > vol 14 n° 2 (April 2020) . - pp 133 – 148[article]Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
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Titre : Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification Type de document : Article/Communication Auteurs : Congcong Wen, Auteur ; Lina Yang, Auteur ; Xiang Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 50 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] fusion de données
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] précision de la classification
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] traitement de semis de pointsRésumé : (auteur) Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. Traditional point cloud classification methods mostly focus on the development of handcrafted point geometry features and employ machine learning-based classification models to conduct point classification. In recent years, the advances of deep learning models have caused researchers to shift their focus towards machine learning-based models, specifically deep neural networks, to classify airborne LiDAR point clouds. These learning-based methods start by transforming the unstructured 3D point sets to regular 2D representations, such as collections of feature images, and then employ a 2D CNN for point classification. Moreover, these methods usually need to calculate additional local geometry features, such as planarity, sphericity and roughness, to make use of the local structural information in the original 3D space. Nonetheless, the 3D to 2D conversion results in information loss. In this paper, we propose a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we design a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method demonstrates superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model achieves a new state-of-the-art performance on powerline, car, and facade categories. Moreover, to demonstrate the generalization abilities of the proposed method, we conduct further experiments on the 2019 Data Fusion Contest Dataset. Our proposed method achieves superior performance than the comparing methods and accomplishes an overall accuracy of 95.6% and an average F1 score of 0.810. Numéro de notice : A2020-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.004 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94743
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 50 - 62[article]A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions / Shahryar K. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
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Titre : A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions Type de document : Article/Communication Auteurs : Shahryar K. Ahmad, Auteur ; Faisal Hossain, Auteur ; Hisham Eldardiry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2471 - 2480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bangladesh
[Termes IGN] climat tropical
[Termes IGN] eau de surface
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-8
[Termes IGN] image PlanetScope
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] plan d'eau
[Termes IGN] radar à antenne synthétique
[Termes IGN] reconnaissance de surface
[Termes IGN] surveillance hydrologique
[Termes IGN] télédétection spatiale
[Termes IGN] zone humideRésumé : (auteur) Consistent estimation of water surface area from remote sensing remains challenging in regions such as South Asia with vegetation, mountainous topography, and persistent monsoonal cloud cover. High-resolution optical imagery, which is often used for global inundation mapping, is highly impacted by clouds, while synthetic aperture radar (SAR) imagery is not impacted by clouds and is affected by both topographic layover and vegetation. Here, we compare and contrast inundation extent measurements from visible (Landsat-8 and Sentinel-2) and SAR (Sentinel-1) imagery. Each data type (wavelength) has complementary strengths and weaknesses which were gauged separately over selected water bodies in Bangladesh. High-resolution cloud-free PlanetScope imagery at 3-m resolution was used as a reference to check the accuracy of each technique and data type. Next, the optical and radar images were fused for a rule-based water area classification algorithm to derive the optimal decision for the water mask. Results indicate that the fusion approach can improve the overall accuracy by up to 3.8%, 18.2%, and 8.3% during the wet season over using the individual products of Landsat8, Sentinel-1, and Sentinel-2, respectively, at three sites, while providing increased observational frequency. The fusion-derived products resulted in overall accuracy ranging from 85.8% to 98.7% and Kappa coefficient varying from 0.61 to 0.83. The proposed SAR-visible fusion technique has potential for improving satellite-based surface water monitoring and storage changes, especially for smaller water bodies in humid tropical climate of South Asia. Numéro de notice : A2020-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950705 Date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950705 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94868
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2471 - 2480[article]Genetic variation of introduced red oak (Quercus rubra) stands in Germany compared to North American populations / Tim Pettenkofer in European Journal of Forest Research, vol 139 n° 2 (April 2020)
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Titre : Genetic variation of introduced red oak (Quercus rubra) stands in Germany compared to North American populations Type de document : Article/Communication Auteurs : Tim Pettenkofer, Auteur ; Reiner Finkeldey, Auteur ; Markus Müller, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 321 – 331 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne
[Termes IGN] Amérique du nord
[Termes IGN] analyse comparative
[Termes IGN] génétique forestière
[Termes IGN] Quercus rubra
[Termes IGN] variationRésumé : (auteur) Although Northern red oak (Quercus rubra L.) is the most important introduced deciduous tree species in Germany, only little is known about its genetic variation. For the first time, we describe patterns of neutral and potentially adaptive nuclear genetic variation in Northern red oak stands across Germany. For this purpose, 792 trees were genotyped including 611 trees from 12 stands in Germany of unknown origin and 181 trees from four populations within the natural distribution area in North America. Our marker set included 12 potentially adaptive (expressed sequence tag-derived simple sequence repeat = EST SSR) and 8 putatively selectively neutral nuclear microsatellite (nSSR) markers. Our results showed that German stands retain comparatively high levels of genetic variation at both EST-SSRs and nSSRs, but are more similar to each other than to North American populations. These findings are in agreement with earlier chloroplast DNA analyses which suggested that German populations originated from a limited geographic area in North America. The comparison between potentially adaptive and neutral microsatellite markers did not reveal differences in the analyzed diversity and differentiation measures for most markers. However, locus FIR013 was identified as a potential outlier locus. Due to the absence of signatures of selection in German stands, we suggest that introduced populations were established with material from provenances that were adapted to environmental conditions similar to those in Germany. However, we analyzed only a limited number of loci which are unlikely to be representative of adaptive genetic differences among German stands. Our results suggest that the apparent introduction from a limited geographic range in North America may go along with a reduced adaptive potential. Numéro de notice : A2020-345 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-019-01256-5 Date de publication en ligne : 18/01/2020 En ligne : https://doi.org/10.1007/s10342-019-01256-5 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95225
in European Journal of Forest Research > vol 139 n° 2 (April 2020) . - pp 321 – 331[article]Improving geospatial query performance of an interoperable geographic situation‐awareness system for disaster response / Chuanrong Zhang in Transactions in GIS, Vol 24 n° 2 (April 2020)
PermalinkImproving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)
PermalinkMonitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])
PermalinkMultichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
PermalinkMultiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
PermalinkMultitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data / Tomás Fernández in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
PermalinkOnline flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)
PermalinkA Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
PermalinkSize-class structure of the forests of Finland during 1921–2013: a recovery from centuries of exploitation, guided by forest policies / Helena M. Henttonen in European Journal of Forest Research, vol 139 n° 2 (April 2020)
PermalinkSpatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)
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