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Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass / Timothy G. Gregoire in Remote sensing of environment, vol 173 (February 2016)
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Titre : Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass Type de document : Article/Communication Auteurs : Timothy G. Gregoire, Auteur ; Erik Naesset, Auteur ; Ronald E. McRoberts, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 98 - 108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse aérienne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] incertitude géométrique
[Termes IGN] inférence statistique
[Termes IGN] varianceRésumé : (auteur) For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded. Numéro de notice : A2016--160 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2015.11. En ligne : https://doi.org/10.1016/j.rse.2015.11.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87012
in Remote sensing of environment > vol 173 (February 2016) . - pp 98 - 108[article]Utilisation d’une source laser pulsée à haute energie comme source acoustique large bande en milieu liquide / Jean-Pierre Sessarego in Traitement du signal, vol 33 n° 1 (2016-1)
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Titre : Utilisation d’une source laser pulsée à haute energie comme source acoustique large bande en milieu liquide Type de document : Article/Communication Auteurs : Jean-Pierre Sessarego, Auteur ; Régine Guillermin, Auteur ; Amélie Jarnac, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 95 - 111 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] laser
[Termes IGN] milieu marin
[Termes IGN] onde acoustique
[Termes IGN] signal acoustique
[Termes IGN] télédétection acoustiqueRésumé : (auteur) Ce travail est relatif à l’étude expérimentale d’un problème d’opto-acoustique non linéaire, consistant à générer un signal acoustique dans l’eau à partir d’un laser pulsé térawatt (TW). La source acoustique obtenue a pu être reliée au phénomène de filamentation qui produit une contraction du faisceau initial, accompagnée de la formation d’un plasma. Des travaux relativement récents ont montré que les lasers de ce type pouvaient être utilisés pour produire des sources acoustiques déportées, avec des applications intéressantes pour l’acoustique sous-marine. Le spectre de la source acoustique obtenue a été étudié à l’aide de plusieurs hydrophones couvrant une très large bande de fréquence, et le diagramme de directivité a été mesuré dans deux plans (plan du filament et plan perpendiculaire au filament). Le niveau acoustique de la source en fonction de l’énergie, de la durée, et de la longueur d’onde de l’impulsion laser, a également été étudié. Numéro de notice : A2016-277 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80834
in Traitement du signal > vol 33 n° 1 (2016-1) . - pp 95 - 111[article]A wavelet-based echo detector for waveform LiDAR data / Cheng-Kai Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
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Titre : A wavelet-based echo detector for waveform LiDAR data Type de document : Article/Communication Auteurs : Cheng-Kai Wang, Auteur ; Yi-Hsing Tseng, Auteur ; Chi-Kuei Wang, Auteur Année de publication : 2016 Article en page(s) : pp 757 - 769 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] forme d'onde
[Termes IGN] modèle numérique de surface
[Termes IGN] onde lidar
[Termes IGN] ondelette
[Termes IGN] semis de points
[Termes IGN] signal laserRésumé : (Auteur) This paper presents a wavelet-based (WB) echo detector that can recover the echoes missed by a light detection and ranging (LiDAR) system via on-the-fly detection. An on-the-fly detection method normally utilizes a simple threshold (TH) to register a target point. Points that belong to weak and/or overlapping echoes are much complicated and are easily missed by TH approaches. The proposed detector based on wavelet transformation is robust to noise and is capable of resolving overlapping echoes. It is thus expected to be good at handling missing echoes. A simulated waveform data set and a real waveform data set of a forest area were both used in this paper. The simulated waveform data were utilized to compare the proposed detector with zero crossing (ZC) and Gaussian decomposition (GD) detectors in terms of their ability to deal with weak or overlapping echoes. The real waveform data set acquired from Leica ALS60 was used to demonstrate a WB algorithm for exploring the missing echoes. Experiments using the simulated data showed that the WB and GD detectors are superior to the ZC detector in finding overlapping echoes. The WB algorithm performs well when dealing with overlapping echoes with a low signal-to-noise ratio. Experiments using the real waveform data show that 31.5% additional weak or overlapping echoes can be detected by the WB detector compared with the point cloud provided by the system. With such additional points, the mean and root-mean-square errors of the digital elevation model differences can be improved from 0.72 and 0.79 m to 0.16 and 0.59 m, respectively. Numéro de notice : A2016-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465148 En ligne : https://doi.org/10.1109/TGRS.2015.2465148 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79999
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 757 - 769[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Assessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)
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Titre : Assessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area Type de document : Thèse/HDR Auteurs : Phil Wilkes, Auteur Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2016 Collection : ITC Dissertation num. 280 Importance : 180 p. ISBN/ISSN/EAN : 978-90-365-4038-4 Note générale : bibliographie
Dissertation to obtain the Double-Badged Degree of Doctor at the University of Twente, Enschede, The Netherlands; and RMIT University, Melbourne, AustraliaLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] allométrie
[Termes IGN] canopée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] ombre
[Termes IGN] placette d'échantillonnage
[Termes IGN] régression
[Termes IGN] semis de points
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] Victoria (Australie)Index. décimale : 33.80 Lasergrammétrie Résumé : (auteur) The attribution of forest structure forms an integral part of international monitoring and reporting obligations with regard to sustainable forest management. Furthermore, detailed information about forest structure allows land managers and forest scientists to determine a forests ability to provide ecosystems services. Currently, forest attribution is achieved using a network of forest inventory plots that are revisited periodically. This approach comprises a sparse sample, both temporally and spatially, that may not capture variance in forest structure. This is particularly true in dynamic native forests where variability in forest structure can be high. In recent years the capability of remote sensing techniques has been realised for sustainable forest management applications. Advantages of a remote sensing approach include synoptic and high temporal coverage as well as reduced costs to the end - user. Furthermore, recent advancement in active sensors, such as Light Detection and Ranging Instruments (LiDAR) have allowed for detailed three - dimensional forest measurement of structure across large areas.
This thesis presents new metrics, techniques and acquisition specifications for the attribution of forest canopy over large areas (e.g. comprising two or more forest types where forest structure maybe unknown a priori) using active and passive remote sensing. In particular, the focus is on attributes that quantify the vertical structure of forests; canopy height and canopy vertical structure. Canopy height is a commonly measured multipurpose attribute that is utilised, for example, to estimate biomass. Attribution of the canopy height profile, although less common, is important for mapping habitat suitability, biomass and fire susceptibility. Current techniques to attribute forests tend to be tailored to a particular forest type or location and therefore application of these models across large areas is unreliable. Here the aim is to develop metrics and techniques that are transferable between different forest types and applicable to forests where there is no prior knowledge of forest structure.
Here a multi - scale remote sensing approach was taken, where plot scale measurements were upscaled to attribute large areas. Initially, existing LiDAR derived metrics applicable at the plot scale were tested at three 5 km x 5 km study areas in Victoria, Australia where forests cover a broad range of structural types. Results indicate existing metrics of canopy height were applicable across the range of forest types, for example the 95 th percentile of LiDAR derived height estimated inventory measured canopy height with a RMSE of 12% (~5 m). An existing mixture modelling technique to attribute the canopy height profile was found unsuitable when applied across heterogeneously forested landscape. This was due to the inability to parameterise the model correctly without a priori knowledge of forest structure e.g. presence or absence of shade tolerant layers. For this reason a new technique was developed utilising a nonparametric regression of LiDAR derived gap probability that generalised the canopy profile. Taking the second derivative of the regression curve identified locations within the canopy that correspond with canopy strata, this therefore allowed a dynamic attribution of canopy vertical structure. Model output was validated with a crown volume modelling approach at 24 plots, where crown models were parameterised with inventory data and allometry. Results indicate this technique can estimate the number of canopy strata with a RMSE of 0. 41 strata. Furthermore, the new technique met the transferability criteria , as a universal regression coefficient was transferable between forest types with different structural attributes.
As LiDAR acquisition that cover large areas will inevitably encounter a range of forest types, parameters for attributing canopy structure that were transferable between forest types were investigated; in particular sampling frequency. To effectively assess a range of pulse densities would require repeat capture over a study area at a range of flying heights , which would be prohibitively expensive. For this reason a new technique was developed that systematically thinned point clouds. This technique differs from previous approaches by allowing simulation of multi - return instruments as well as repeat capture of the same plot. Six sites from around Australia were utilised which covered a broad range of forest types, from open savanna to tropical rainforest. For a suite of metrics, the ability of progressively less dense point clouds ( 4 – 0. 05 pl m - 2 ) to estimate canopy structure was estimated by comparison with higher density data (10 pl m - 2 ). Results indicate that canopy structure can be adequately attributed with data captured at 0.5 pl m - 2 . When pulse densities are Techniques derived at the plot scale were then applied to estimate canopy height across 2.9 million hectares of heterogeneous forest. Canopy height in the study area ranged from 0 – 70 m and comprised forest types from open woodland to tall closed canopy rainforest. LiDAR derived canopy height was used to t rain ensemble regression tree s (random forest) , where predictor datasets included synoptic passive optical imagery and other ancillary spatial datasets , such as Landsat TM and MODIS. Results suggest canopy height can be estimated with a RMSE of 30% (5.5 m) when validated with an independent inventory dataset. This is a similar error to that reported in previous studies for less complex forests and is within the European Space Agency target for canopy height estimation. However, model output did show a systematic error, where the height of short and tall forests were over and underestimated respectively. This was corrected by subtracting a model led estimate of error from the random forest output. Production of a canopy height map over a large area allowed for a consistent product that covered a broad range of forest types, derivation at a 30 m resolution allowed the identification of landscape features such as logging coupes. The presented technique utilised an open source computing framework as well as freely available predictor datasets to facilitate uptake of by land management agencies and forest scientists.Note de contenu : Chapter 1 : Introduction
1.1. General introduction
1.2. Problem statement
1.3. Research questions
1.4. Thesis structure
Chapter 2 : Metrics of canopy vertical structure suitable for large area forest attribution
2.1. Introduction
2.1.1. Canopy height
2.1.2. Canopy vertical structure
2.1.3. Aims and objectives
2.2. Materials and methods
2.2.1. Study area
2.2.2. Forest inventory data
2.2.3. Airborne laser scanning data
2.3. Data processing
2.3.1. Canopy height
2.3.2. Canopy vertical structure
2.4. Results
2.4.1. Canopy height
2.4.2. Canopy height profiles
2.5. Discussion
2.6. Conclusion
Chapter 3 : Using discrete-return ALS to quantify number of canopy strata across diverse forest types
3.1. Introduction
3.2. Attributing canopy vertical structure
3.3. Application across a diverse forested landscape
3.3.1. ALS acquisition and preprocessing
3.3.2. Pgap from ALS
3.3.3. Derivation of smoothing coefficient (α)
3.3.4. Bootstrapping simulated point clouds
3.3.5. Validation with field inventory
3.4. Results and Discussion
3.4.1. Methodology evaluation
3.4.2. Validation results
3.4.3. Canopy vertical structure as an independent metric
3.5. Conclusion
Chapter 4 : Understanding the effects of ALS pulse density for metric retrieval across diverse forest types
4.1. Introduction
4.2. Method
4.2.1. Study area and data capture
4.2.2. Data processing
4.2.3. Metrics
4.3. Results
4.3.1. Canopy height
4.3.2. Canopy cover
4.3.3. Canopy vertical structure
4.3.4. Characteristics of thinned point clouds
4.4. Discussion
4.5. Conclusion
Chapter 5 : Mapping forest canopy height across large areas by upscaling ALS estimates with freely available satellite data
5.1. Introduction
5.2. Materials and methods
5.3. Results
5.3.1. Canopy height estimation
5.3.2. Validation with inventory data
5.3.3. Training and validation of random forest using smaller geographic areas
5.3.4. Simulating disparate ALS capture for training a random forest
5.4. Discussion
5.5. Conclusions
Chapter 6 : Summary and synthesis
6.1. Summary of results
6.2. Identifying trends in large area forest structure
6.3. Remote sensing in sustainable forest management: a future perspectiveNuméro de notice : 17249 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Remote sensing : Twente : 2016 Organisme de stage : RMIT DOI : sans En ligne : http://www.itc.nl/library/papers_2016/phd/wilkes.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81928
Titre : Automatisation du nettoyage de nuages de points Type de document : Mémoire Auteurs : Yohan Pensier, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2016 Importance : 71 p. Format : 21 x 30 cm Note générale : Bibliographie
mastère Photogrammétrie, positionnement et mesures de déformationsLangues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] 3DReshaper
[Termes IGN] C++
[Termes IGN] contrôle qualité
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effet de bord
[Termes IGN] filtrage du bruit
[Termes IGN] lancer de rayons
[Termes IGN] modélisation 3D
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] précision centimétrique
[Termes IGN] réseau ferroviaire
[Termes IGN] semis de points
[Termes IGN] valeur aberranteIndex. décimale : MPPMD Mémoires du mastère spécialisé Photogrammétrie, Positionnement et Mesures de Déformation Résumé : (Auteur) La division Assistance Travaux et Topographie (ATT) de la direction d’ingénierie et projet de SNCF Réseau réalise et pilote des opérations de levé topographiques 3D (voies, gares, …). À ce titre, elle a également pour mission d’effectuer le contrôle qualité des données livrées par les différents prestataires. Afin de pouvoir répondre rapidement à ces missions de grande échelle, un nombre important de techniques émergentes est déployé en complément de la topographie traditionnelle, notamment, le scanner laser dynamique ferroporté. La donnée 3D obtenue par ce type d’appareil est exhaustive, sauf en cas de masques, mais peut contenir des artefacts de mesures (bruits, points fantômes, …). Le stage réalisé devait donc permettre de : - Intégrer les usages de nettoyages de nuages de points de SNCF Réseau ; - Proposer et implémenter des filtres mathématiques pour automatiser le nettoyage des nuages de points (détection de points aberrants, comparaison de nuages, segmentation et classification du nuage de points, …) et les mettre en oeuvre en les intégrant dans les chaînes de traitement actuelles ; - Proposer une méthodologie applicable sur des zones très étendues (environ 100 km linéaires). Note de contenu : 1. APPROCHE INITIALE
1.1 Cadre du stage
1.2. Principes du levé LIDAR en milieu ferroviaire
1.3. Évaluation de l’existant
2. NETTOYAGE SEMI-AUTOMATIQUE DES NUAGES DE POINTS STATIQUES
2.1. Le logiciel 3DReshaper
2.2. Présentation du programme « filtrage_auto »
2.3. Résultats obtenus
3. AUTOMATISATION DU NETTOYAGE DE SCANS DYNAMIQUES
3.1. Méthode mathématique retenue
3.2. Le programme « Top_Gun »
3.3. Exemple de traitement
ConclusionNuméro de notice : 22662 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Mémoire PPMD Organisme de stage : SNCF Réseau Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84007 Documents numériques
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