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Affiner la recherche Interroger des sources externesConvex hull: another perspective about model predictions and map derivatives from remote sensing data / Jean-Pierre Renaud (2021)
Titre : Convex hull: another perspective about model predictions and map derivatives from remote sensing data Type de document : Article/Communication Auteurs : Jean-Pierre Renaud Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : ARBRE / AgroParisTech (2007 -) Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings Projets : DEEPSURF / Pironon, Jacques Importance : pp 71 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attribut non spatial
[Termes IGN] convexité
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] erreur systématique
[Termes IGN] modèle de simulation
[Termes IGN] modèle linéaireMots-clés libres : enveloppe convexe Résumé : (auteur) [introduction] In forest inventories as well as in the process of building models, obtaining an efficient sample is a central goal to reach precise estimates of forest attributes (Hawbaker et al. 2009, Frazer et al. 2011, Grafström et al. 2014, Saarela et al. 2015, Bouvier et al. 2019). In a model-based approach, a plots sample must cover adequately the variability of the considered forest attributes in order to minimise prediction error. Different strategies have been proposed to efficiently distribute the field sampling units in the auxiliary space of the remote sensing data (e.g. Hawbaker et al. 2009, Grafström et al. 2014). Some authors have proposed to stratify Airborne Laser Scanning data (ALS) to optimize sampling (Hawbaker et al. 2009, Frazer et al. 2011), and Maltamo et al. (2011) compared different field plot selection strategies in order to optimise models precision. Interestingly, White et al. (2013) applied convex hull approach to show uncovered forest structures by the field calibration sampling units, since large prediction errors could be associated with model extrapolations, resulting in potentially biased map derivatives. In this research, we use convex hull to identify the proportion of extrapolated pixels, computed their distance to the calibration domain and estimated bias associated to the linear model predictions on an ALS case study. Numéro de notice : C2021-030 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1919 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98997 
Titre : High resolution mapping of forest resources and prediction reliability using multisource inventory approach Type de document : Article/Communication Auteurs : Ankit Sagar Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : ARBRE / AgroParisTech (2007 -) Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings, INCA 2021, 41th Indian National Cartographic Association international conference, Cartography for self-reliant India 27/10/2021 29/10/2021 Chandigarh Inde open access proceedings Projets : DEEPSURF / Pironon, Jacques Importance : pp 219 - 221 Langues : Anglais (eng) Descripteur : [Termes IGN] capital sur pied
[Termes IGN] données multisources
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] ressources forestières
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) [introduction] National forest inventory (NFI) provides precise forest resource estimates at national up to regional scale but could not support local estimates with high precision because of inadequate number of field plots. The forest managers and stakeholders prefer local estimates at fine spatial resolution (Chirici et al. 2020). Multi source-national forest inventory (MS-NFI) opens the possibility for wall-to-wall mapping of forest attributes with good precision at high spatial resolution. MS-NFI rely on the combination of NFI data with auxiliary data (remote sensing data, thematic map, etc.), and in many cases, this combination is modelled through a non-parametric k-nearest neighbour (k-NN) approach. k-NN is capable in predicting several attributes in a single model with a low prediction bias. The major drawbacks of k-NN are its inability to predict beyond the range of training data (Magnussen et al. 2010), the lack of well-established variance estimator (McRoberts et al. 2011) and its decreasing performance with increasing dimensionality. The estimation maps for the forest resources are important (Tomppo et al. 2008; Chirici et al., 2020), but their prediction uncertainties have also to be taken into consideration. Methods have been proposed recently to map the prediction uncertainty (Esteban et al, 2019) and these maps have been included into an inferential framework (Saarela et al, 2020). In this study we propose a method building upon bootstrap model-based estimator (McRoberts et al. 2011) to estimate forest attributes of interest at pixel level and address the problem of extrapolation and precision of estimation by providing maps for both at high spatial resolution. For sake of concision, results were presented for growing stock volume (GSV) only. Numéro de notice : C2021-031 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1986 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1986 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98995 Documents numériques
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Titre : Improving GEDI footprint geolocation using a high resolution digital terrain model Type de document : Article/Communication Auteurs : Anouk Schleich, Auteur ; Maxime Soma, Auteur ; Sylvie Durrieu, Auteur ; Cédric Vega Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : TOSCA SLIM / Pironon, Jacques Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings Importance : pp 179 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fauchée
[Termes IGN] géoréférencement
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de terrainRésumé : (auteur) [introduction] In 2018, NASA launched the Global Ecosystem Dynamics Investigation (GEDI) mission, a high resolution lidar system installed onboard the International Space Station (ISS). It is producing high quality 3D observations of the Earth surface structure, which are highly relevant to study forest ecosystems at a global scale (Qi et al. 2019). GEDI data is composed of 25 m diameter circular footprints for which the waveform of the received energy intensity returned by the ground is recorded. Each GEDI footprint is georeferenced and its positioning accuracy (for version 1 releases) is estimated at 15-20 m in planimetry with a systematic component of 8-10 m and a noise of the order of 8 m (1). A final horizontal geolocation accuracy of 8 m is expected after further processing in the final version (Dubayah et al. 2020). Compared to most other spatial satellites the ISS is much closer to earth, causing more variations in its orientation and altitude. Therefore, geolocating data acquired by ISS sensors is more diffucult than geolocating data aquired by satellites (Dou et al. 2014). An improved geolocation of GEDI data is mandatory to evaluate their quality, by comparison with other earth observation data or field measurements, and to further facilitate their integration in ecosystem monitoring approaches. We propose a method to improve the georeferencing of GEDI footprints using a precise Digital Terrain Model (DTM). Numéro de notice : C2021-053 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1973 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99223
Titre : Unit-level small area estimation of forest inventory with GEDI auxiliary information Type de document : Article/Communication Auteurs : Shaohui Zhang, Auteur ; Cédric Vega Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : 1-Pas de projet / Pironon, Jacques Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings Importance : pp 136 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] aire naturelle (écologie)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] patrimoine naturelRésumé : (auteur) National Forest Inventories (NFIs) play an important role in understanding the state of forests at the national and regional levels. Forest inventory for small territorial areas, such as municipalities, is also important for decision-makers. However, information is relatively limited at this level. As a result, developing small area estimation (SAE) approaches has gained increasing popularity in the field of forest inventory. It enables prediction of forest attributes for sub-populations using regression models based on auxiliary data commonly derived from remote sensing techniques over an area of interest (AOI). It has been reported that SAE can improve the precision of forest inventory without increasing costs (Mandallaz, Breschan and Hill 2013) and may produce reliable predictions of forest attributes locally, even when field plots are not available (Rao 2014). Tomppo (2006) is a pioneer in the use of auxiliary data for multisource forest inventory. Previously, common sources of auxiliary data often came from satellite-based imagery (McRoberts et al. 2007), digital aerial photogrammetry (Breidenbach et al. 2018), and airborne laser scanning (Magnussen et al. 2014). NASA’s newly-launched Global Ecosystem Dynamics Investigation (GEDI) is a full waveform LiDAR instrument aboard the International Space Station (ISS). Its products consist of footprint measurements projected to cover 4% of the global land surface by the end of its mission (Dubayah et al. 2020). This will provide an unprecedented opportunity to systematically collect samples of forest information that can be used in SAE on a large scale. The objective of this study is to explore the possibility of using GEDI auxiliary data to improve the accuracy of forest inventory for a large natural area in central France (Sologne), as well as for smaller sub-areas defined by French administrative boundaries (departments). The results will then be compared against estimates obtained from simple random sampling (SRS), to assess the efficiency of the auxiliary data. Numéro de notice : C2021-062 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1941 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1941 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99383
Titre : Vegetation stratum occupancy prediction from airborne LiDAR 3D point clouds Type de document : Article/Communication Auteurs : Ekaterina Kalinicheva Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : 1-Pas de projet / Pironon, Jacques Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings Importance : pp 41 - 43 Note générale : Data sets in https://zenodo.org/badge/DOI/10.5281/zenodo.5555758.svg Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] capteur aérien
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de pointsRésumé : (auteur) [introduction] Estimating the structure of vegetation is a crucial first step for many environmental and ecological applications (Daubenmire 1956). In particular, pasture land management requires estimating the occupancy of the different vegetation strata within agricultural parcels. This is a time-consuming undertaking, often performed with in situ ocular approximate measurements. Nowadays, airborne platforms allow public and private actors to gather high accuracy geometric and radiometric data over large areas (Chen 2007). Bolstered by the compelling improvements (Guo et al., 2020) and increased accessibility of deep learning for 3D point clouds, we propose a 3D deep learning method to estimate the occupancy of different vegetation strata from airborne LiDAR and camera sensors. Our method predicts raster occupancy maps for three vegetation strata (lower, medium, and higher) from 3D point clouds. Our training scheme allows our network to only be supervised with aggregated occupancy values at the plot level, which are easier to produce than point or pixel-level annotations. We also propose to use priors on the stratum elevation and the occupancy maps to improve the legibility and interpretability of the resulting maps. Numéro de notice : C2021-032 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1909 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98996 Proccedings of the 18th European VLBI for geodesy and astrometry working meeting, Vienna, 12-13 April 2007 / Johannes Böhm (2007)
PermalinkTropospheric delay modelling at radio wavelengths for space geodetic techniques / Johannes Böhm (2007)
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