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Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Marco A. Contreras, Auteur ; Jun Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 385 - 392 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre caducifolié
[Termes IGN] canopée
[Termes IGN] croissance végétale
[Termes IGN] densité des points
[Termes IGN] distribution spatiale
[Termes IGN] houppier
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Kentucky (Etats-Unis)
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] sous-bois
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest – a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types. Numéro de notice : A2017-519 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86481
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 385 - 392[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Approche d’estimation du volume-tige de peuplements forestiers par combinaison de données Landsat et données terrain : Application à la pineraie de Tlemcen-Algérie / Kada Bencherif in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)
[article]
Titre : Approche d’estimation du volume-tige de peuplements forestiers par combinaison de données Landsat et données terrain : Application à la pineraie de Tlemcen-Algérie Type de document : Article/Communication Auteurs : Kada Bencherif, Auteur ; Houari Tadj, Auteur Année de publication : 2017 Article en page(s) : pp 3 - 11 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] Algérie
[Termes IGN] classification dirigée
[Termes IGN] classification pixellaire
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] peuplement forestier
[Termes IGN] pineraie
[Termes IGN] placette d'échantillonnage
[Termes IGN] strate végétale
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Une approche méthodologique s’appuyant sur la combinaison de données satellitaires et données de terrain est proposée pour l‘estimation du volume-tige de peuplements forestiers hétérogènes ou peu homogènes. L’objectif est d’évaluer la disponibilité forestière, en inventoriant moins de 1% de la surface étudiée et avec une erreur max. de 15%. L’approche consiste en la réalisation de trois étapes principales : i) Analyse de la variance sur le volume-tige, ii) classification des données satellitaires et iii) Désignation et inventaire des pixels-échantillons. L’analyse et le calcul de la variance permet d’orienter les calculs du volume en fonction de sa variabilité dans les différentes strates de la forêt alors que la classification des données satellitaires vise à obtenir une stratification de la forêt. La troisième étape consiste en la sélection de pixels-échantillons sur l’image classifiée puis la géolocalisation, l’installation et le cubage des placettes-terrain correspondantes (même dimension spatiale que le pixel de l’image utilisée). Appliquée sur une futaie peu homogène de pin d’Alep (forêt de Tlemcen, Nord-Ouest algérien), l’approche a permis d’estimer un volume global sur pied du peuplement égal à 30 595 m3 m3±15.6% et ce en inventoriant 0.4% seulement de la surface totale. L’analyse de variance sur 12 placettes-échantillons a mis en évidence le caractère peu homogène de la forêt et la faible variabilité du volume-tige. Cependant, Elle fait apparaître aussi que la stratification apporte une légère amélioration à la précision (15.6%) contre 17.6% sans stratification. La classification supervisée d’une image Landsat (Mai 2002) par la méthode du maximum de vraisemblance (précision moyenne de 96%) a permis de stratifier la zone étudiée en six classes (forêt très dense, forêt dense, forêt claire, matorral, herbacées, autres). Pour chaque strate de forêt, le cubage complet de 4 placettes-échantillons comparables en dimension au pixel (30m×30m), a fourni le volume-tige moyen par pixel alors que la généralisation de celui-ci à l’ensemble des pixels a permis de déterminer le volume total de chaque strate. Vu les confusions générées par la classification supervisée au profit des objets pistes, routes et matorral, le volume global a été revu à la baisse (taux de réduction de 10%) et la valeur du volume total corrigé était de 27 535 m3±15.6%, une erreur, bien que non conforme à celle exigée par l’aménagement forestier (max ±10%), s’approche de celle généralement admise (une moyenne de ±15%) pour certains inventaires simplifiés. Numéro de notice : A2017-524 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.52638/rfpt.2017.343 Date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.52638/rfpt.2017.343 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86545
in Revue Française de Photogrammétrie et de Télédétection > n° 215 (mai - août 2017) . - pp 3 - 11[article]
Titre : Surface reconstruction based on forest terrestrial LiDAR data Type de document : Thèse/HDR Auteurs : Jules Morel, Auteur ; Marc Daniel, Directeur de thèse ; Cédric Vega , Directeur de thèse ; Alexandra Bac, Directeur de thèse Editeur : Aix-en-Provence : Université d'Aix-Marseille Année de publication : 2017 Importance : 178 p. Format : 21 x 30 cm Note générale : bibliographie
A dissertation presented to the Department of Mathématique et Informatique in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Computer ScienceLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] distribution de Poisson
[Termes IGN] données lidar
[Termes IGN] données TLS (télémétrie)
[Termes IGN] fonction de base radiale
[Termes IGN] interpolation
[Termes IGN] modélisation de la forêt
[Termes IGN] placette d'échantillonnage
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] structure de la végétationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In recent years, the capacity of LiDAR technology to capture detailed information about forests structure has attracted increasing attention in the field of forest science. In particular, the terrestrial LiDAR arises as a promising tool to retrieve geometrical characteristics of trees at a millimeter level. This thesis studies the surface reconstruction problem from scattered and unorganized point
clouds, captured in forested environment by a terrestrial LiDAR. We propose a sequence of algorithms dedicated to the reconstruction of forests plot attributes model: the ground and the woody structure of trees (i.e. the trunk and the main branches). In practice, our approaches model the surface with implicit function build with radial basis functions to manage the homogeneity and handle the noise of the sample data points. Our first focus is on the reconstruction of the ground surface whose level of detail is based on local complexity, through alternation between scale refinement, filtering and reconstruction. The result arises from the polygonization of the implicit function expressed as the merging of local approximations by compactly supported radial basis function used as partition of unity. Once the ground is modeled, the topology effects can be ignored in the following computation steps that focus on the modeling of trees. Traditionally, the processing of the woody part is achieved by a discrete reconstruction in the form of a stack of independent building blocks. From such a model, our approach developed for the ground is adapted to approximate the woody part of the tree by a more flexible continuous surface. Expressed as an implicit function, the tree model can be refined by an additional computational step in order to describe precisely the geometry. With this in mind, we propose a method dedicated to the fine reconstruction of occluded objects: from 3D samples presenting occlusions,
we use the previously described continuous model to guide a Poisson surface reconstruction. Thus, we guarantee the production of a watertight surface that approximates sharply the point cloud in the visible areas and extrapolates consistently the tree shape in the occlusions.Note de contenu : 1- Introduction
2- Terrestrial LiDAR scanning in forests
3- Survey on surface reconstruction
4- Reconstruction of open surface
5- Geometric model of trees
6- Reconstruction of partially occluded objects
7- Conclusion and perspectivesNuméro de notice : 25855 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : IMAGERIE Nature : Thèse française Note de thèse : PhD Thesis: Computer Science : Marseille : 2017 Organisme de stage : Institut Français de Pondichéri (Inde) nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017AIXM0039 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95472 Effective number of layers: A new measure for quantifying three-dimensional stand structure based on sampling with terrestrial LiDAR / Martin Ehbrecht in Forest ecology and management, vol 380 (15 november 2016)
[article]
Titre : Effective number of layers: A new measure for quantifying three-dimensional stand structure based on sampling with terrestrial LiDAR Type de document : Article/Communication Auteurs : Martin Ehbrecht, Auteur ; Peter Schall, Auteur ; Julia Juchheim, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 212 - 223 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuillu
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] voxelRésumé : (auteur) The relevance of stand structural heterogeneity for biodiversity conservation is increasingly recognized and efficient tools for its measurement are demanded. Here, we quantified forest structure by calculating the effective number of layers (ENL) for different Hill Numbers (0D, 1D, 2D) as a measure of vertical structure of a subplot. We than use sampling techniques to cover the horizontal structural variability within study plots. ENL describes the vertical structure based on the occupation of 1 m wide vertical layers by tree components relative to the total space occupation of a stand. Space occupation was quantified by a voxel-model obtained from terrestrial laser scanning (TLS) on 150 forest plots in Germany. We used a single scan approach, which requires less field work and post-processing compared to multiple-scans. Single-scan derived mean ENL and its coefficient of variation successfully differentiated forest structures over a wide range of even-aged, uneven-aged and unmanaged broadleaved and coniferous stands. ENL was correlated to the stand summary measures basal area, quadratic mean diameter and stem density as well as stand age. ENL can be used to describe structural heterogeneity and proved to be efficiently assessable by TLS. Numéro de notice : A2016-701 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2016.09.003 En ligne : http://dx.doi.org/10.1016/j.foreco.2016.09.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82064
in Forest ecology and management > vol 380 (15 november 2016) . - pp 212 - 223[article]Assessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)
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 Large-scale dynamics of a heterogeneous forest resource are driven jointly by geographically varying growth conditions, tree species composition and stand structure / Holger Wernsdörfer in Annals of Forest Science, Vol 69 n° 7 (October 2012)Permalink3-D mapping of a multi-layered Mediterranean forest using ALS data / António Ferraz in Remote sensing of environment, vol 121 (June 2012)Permalink3D segmentation of forest structure using an adaptive mean shift based procedure / António Ferraz (2010)PermalinkPermalinkNatural stand structures, disturbance regimes and successional dynamics in the Eurasian boreal forests: a review with special reference to Russian studies / Ekaterina Shorohova in Annals of Forest Science, Vol 66 n° 2 (march 2009)PermalinkAnalyse par télédétection et à différentes échelles de formations forestières hétérogènes : rôle de la structure de la végétation. Application aux boisements lâches méditerranéens / Jean Guy Boureau in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 140 (Octobre 1995)Permalink