Centre de documentation
scientifique

3D radiative transfer modeling over complex vegetation canopies and forest reconstruction from LIDAR measurements / Jianbo Qi (2019)  

Public Titre : 3D radiative transfer modeling over complex vegetation canopies and forest reconstruction from LIDAR measurements Type de document : Thèse/HDR Auteurs : Jianbo Qi, Auteur ; Jean-Philippe Gastellu-Etchegorry, Directeur de thèse ; Guangjian Yan, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2019 Importance : 154 p. Format : 21 x 30 cm Note générale : bibliographie Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Surfaces et interfaces continentales, hydrologie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] couvert végétal [Termes IGN] données lidar [Termes IGN] forêt [Termes IGN] indice foliaire [Termes IGN] interface graphique [Termes IGN] milieu anisotrope [Termes IGN] modèle de transfert radiatif [Termes IGN] modélisation 3D [Termes IGN] rendu réaliste [Termes IGN] scène 3D [Termes IGN] semis de points Index. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing is needed for better managing vegetation covers. Hence, three-dimensional (3D) radiative transfer (RT) modeling is essential for understanding remote sensing signals of complex 3D vegetation covers. Due to the complexity of 3D models, one-dimensional (1D) RT models are commonly used to retrieve vegetation parameters, e.g., leaf area index (LAI), from remote sensing data. However, 1D models are not adapted to actual vegetation covers because they abstract them as schematic 1D layers, which is not realistic. Much effort is devoted to the conception of 3D RT models that can consider the 3D architecture of vegetation covers. However, developing an efficient 3D RT model that works on large and realistic scenes is still a challenging task. Major difficulties are the intensive computational costs of 3D RT simulation and the acquisition of detailed 3D canopy structures. Therefore, 3D RT models usually only work on abstracted scenes or small realistic scenes. Scene abstraction may cause uncertainties, and the small-scale approach is not compatible with most satellite observations (e.g., MODIS). The computer graphics community provides the most accurate and efficient models (i.e., renderers). However, the initial renderer models were not designed for accurate RT modeling, which explains the difficulty to use them for remote sensing applications. Recently emerged advanced techniques in computer graphics and light detection and ranging area (LiDAR) make it more possible to solve the above problems. 3D RT can be greatly accelerated due to the increasing computer power and improvement of rendering algorithms (e.g., ray-tracing acceleration and computational optimization). Also, 3D high-resolution information from LiDARs and photogrammetry become more accessible to reconstruct realistic 3D scenes. This approach requires new processing methods to combine 3D information and 3D RT models, which is of great importance for better remote sensing survey of vegetation. This thesis is focused on 1) Development of a 3D RT model based on recent ray-tracing techniques and 2) Retrieval of 3D leaf volume density (LVD) for constructing 3D forest scenes. This first chapter presents the development of an efficient 3D RT model, named LESS (LargE-Scale remote sensing data and image Simulation framework). LESS makes full use of ray-tracing algorithms. Specifically, it simulates multispectral BRF and scene radiative budget with a weighted forward photon tracing method, and sensor images (e.g., fisheye images) or large-scale (e.g. 1 km2) spectral images are simulated with a backward path tracing method. In the forward mode, a "virtual photon" algorithm is used to simulate accurate BRF with few photons. The backward mode is used to simulate thermal infrared images and also atmosphere RT. LESS efficiency and accuracy were demonstrated with a model intercomparison and field measurements. In addition, LESS has an easy-to-use graphic user interface (GUI) to input parameters, construct and visualize 3D scenes. 3D forest reconstruction is done with a simulated LiDAR dataset to assess approaches that retrieve LVD from airborne LiDAR data. The dataset is simulated with the discrete anisotropic radiative transfer model (DART). Note de contenu : 1- Introduction 2- LESS: Ray-tracing based 3D radiative transfer model 3- Accuracy evaluation of LESS 4- Hybrid scene structuring for accelerating 3D radiative transfer 5- Physical interpretation of leaf area index from LiDAR data 6- Voxel-based reconstruction and simulation of 3D forest scene 7- Conclusions and perspectives Numéro de notice : 25915 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Surfaces et interfaces continentales, hydrologie : Toulouse 3 : 2019 Organisme de stage : CESBIO nature-HAL : Thèse DOI : sans En ligne : https://hal.science/tel-02498603 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96024

Scale effect in indirect measurement of leaf area index / Guangjian Yan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3475 - 3484 Titre : Scale effect in indirect measurement of leaf area index Type de document : Article/Communication Auteurs : Guangjian Yan, Auteur ; Ronghai Hu, Auteur ; Yiting Wang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3475 - 3484 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] indice foliaire [Termes IGN] longueur [Termes IGN] mesure optique [Termes IGN] méthode de mesure [Termes IGN] modèle non linéaire [Termes IGN] surface hétérogène Résumé : (Auteur) Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one. Numéro de notice : A2016-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2519098 En ligne : https://doi.org/10.1109/TGRS.2016.2519098 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82995 [article]