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Estimating forest and woodland aboveground biomass using active and passive remote sensing / Zhuoting Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 4 (April 2016)
[article]
Titre : Estimating forest and woodland aboveground biomass using active and passive remote sensing Type de document : Article/Communication Auteurs : Zhuoting Wu, Auteur ; Dennis Dye, Auteur ; John Vogel, Auteur ; Barry Middleton, Auteur Année de publication : 2016 Article en page(s) : pp 271 - 281 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Arizona (Etats-Unis)
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] capteur actif
[Termes IGN] capteur passif
[Termes IGN] données lidar
[Termes IGN] écosystème forestier
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat-8
[Termes IGN] surface forestièreRésumé : (auteur) Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14 Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States. Numéro de notice : A2016-179 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.82.4.271 En ligne : http://dx.doi.org/10.14358/PERS.82.4.271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80521
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 4 (April 2016) . - pp 271 - 281[article]Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana / Ibrahim Fayad in Remote sensing, vol 8 n° 3 (March 2016)
[article]
Titre : Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Nicolas Baghdadi, Auteur ; Jean-Stéphane Bailly, Auteur ; Nicolas Barbier, Auteur ; Valéry Gond, Auteur ; Bruno Hérault, Auteur ; Mahmoud El-Hajj, Auteur ; Frédéric Fabre, Auteur ; José Perrin, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] Guyane (département français)
[Termes IGN] hauteur des arbres
[Termes IGN] krigeage
[Termes IGN] régressionRésumé : (auteur) LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. Numéro de notice : A2016--121 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs8030240 En ligne : http://doi.org/10.3390/rs8030240 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84818
in Remote sensing > vol 8 n° 3 (March 2016)[article]Application des techniques de photogrammétrie par drone à la caractérisation des ressources forestières / Jonathan Lisein (2016)
Titre : Application des techniques de photogrammétrie par drone à la caractérisation des ressources forestières Type de document : Thèse/HDR Auteurs : Jonathan Lisein , Auteur ; Philippe Lejeune ; Marc Pierrot-Deseilligny , Directeur de thèse ; Philippe Lejeune, Directeur de thèse Editeur : Gembloux [Belgique] : Université de Liège - Gembloux Agro-Bio Tech Année de publication : 2016 Autre Editeur : Champs/Marne : Université Paris-Est Importance : 96 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat en vue de l'obtention du grade de docteur en sciences agronomiques et ingénierie biologique, en co-tutelle Université de Liège - Gembloux Agro-Bio Tech et Université Paris-EstLangues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Acer pseudoplatanus
[Termes IGN] Betula (genre)
[Termes IGN] caractérisation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] drone
[Termes IGN] forêt tempérée
[Termes IGN] Fraxinus excelsior
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] orthophotoplan numérique
[Termes IGN] peuplement forestier
[Termes IGN] Populus (genre)
[Termes IGN] Quercus pedunculataIndex. décimale : THESE Thèses et HDR Résumé : (auteur) […] Nous explorons les possibilités d'utilisation de mini-drones pour la caractérisation quantitative et qualitative de la ressource forestière. Nous nous intéressons en particulier à l'estimation de la hauteur des arbres et à la caractérisation de la composition spécifique au sein de peuplements forestiers. La hauteur de la canopée est une variable dendrométrique de première importance : elle est un bon indicateur du stade de développement des peuplements et intervient notamment dans les estimations de biomasse ou de niveau de productivité. La composition spécifique est une information essentielle en regard des principales fonctions que remplit la forêt (conservation, production, récréation, etc.). Nous avons comparé l'estimation de la hauteur des peuplements à partir de mesures LiDAR et celle obtenue par photogrammétrie. Bien que permettant une mesure de hauteur individuelle avec une incertitude de l'ordre de 1.04 m (RMSE) en feuillus, la photogrammétrie par drone sur des zones forestières est systématiquement moins précise que les mesures par LiDAR (RMSE de 0.83 m). Ces résultats sont cependant prometteurs, étant donné que la mesure sur terrain de la hauteur totale des arbres est également sujette à une importante imprécision. De plus, la grande flexibilité que confèrent les petits drones permet d'acquérir, au moment propice du stade de végétation, et l'information de relief de la canopée, et l'information spectrale. La période de fin de feuillaison, au début du mois de juin, s'est avérée le moment le plus propice à une discrimination automatique de cinq groupes d'essences feuillues (le chêne pédonculé, les bouleaux, l'érable sycomore, le frêne commun et les peupliers). Une erreur globale de classification des houppiers de 16% est obtenue avec des acquisitions monotemporelles, alors que l'utilisation d'images acquises à différentes dates permet encore d'améliorer cette classification (erreur globale de classification de 9% pour la meilleure combinaison de 3 dates). Les contraintes de la législation régissant l'utilisation des aéronefs sans pilote à bord restreignent le champ d'action des drones civils. Ainsi, afin d'assurer une sécurité pour tous les usagers de l'espace aérien, les opérations avec un drone sont limitées sous un seuil d'altitude et à une distance maximale du télépilote, ce qui ne permet pas une utilisation optimale de cette technologie pour la couverture de grands domaines forestiers (plusieurs milliers d'hectares). De plus, d'autres outils de télédétection utilisés en foresterie, tels que le LiDAR et l'imagerie satellite et aéroportée, sont plus compétitifs que les drones dès qu'il s'agit de couvrir de grandes surfaces (plusieurs milliers d'hectare). C'est pourquoi nous pensons que les drones resterons un outil d'analyse de petites surfaces (dizaines voire centaines d'hectares), plus utiles à des fins de recherches scientifiques qu'à une utilisation en gestion forestière. Note de contenu : 1 Introduction
2 A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery
3 Modélisation de la canopée forestière par photogrammétrie depuis des images acquises par drone
4 Discrimination of deciduous tree species from time series of unmanned aerial system imagery
5 Conclusion et perspectivesNuméro de notice : 17355 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : thèse de doctorat : sciences agronomiques et ingénierie biologique : Université Paris-Est : 2016 Organisme de stage : ENSG (IGN) nature-HAL : Thèse DOI : sans En ligne : https://theses.hal.science/tel-01539627v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83787 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 Gini coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure / Rubén Valbuena in Ecological indicators, vol 60 (January 2016)
[article]
Titre : Gini coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure Type de document : Article/Communication Auteurs : Rubén Valbuena, Auteur ; Kalle Eerikäinen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur Année de publication : 2016 Article en page(s) : pp 574 - 585 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] coefficient de Gini
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] hauteur des arbres
[Termes IGN] parc naturel national
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] surveillance forestière
[Termes IGN] sylvicultureRésumé : (auteur) In this study, two forest sites located in Finland were compared by means of predictions of Gini coefficient (GC) obtained from airborne laser scanning (ALS). We discuss the potential of the proposed method for identifying differences in structural complexity in relation with the management history of forests. The first study site (2200 ha), the Koli National Park (NP), includes areas where human intervention was restricted after 1907, in addition to forests which were protected only after the 1990s. The second study site in the municipality of Kiihtelysvaara (800 ha) has been under intensive management. These are commercial forests which include areas with different types of ownership: a large estate owned by an industrial company together with smaller private properties. We observed that GC predictions may be used to evaluate the effects of management practice on forest structure. Conservation and commercial forests showed significant differences, with the old-protected area of Koli having the highest, and the most intensively managed area in Kiihtelysvaara the lowest GC values. The effect of management history was revealed, as the 1990s’ extensions of Koli NP were more similar to unprotected areas than to forests contained within the original borders of the 1907s’ state property. Yet, their conservation status for almost two decades has been sufficient for developing significant differences against the outside of the NP. In Kiihtelysvaara, we found significant differences in GC according to the type of ownership. Moreover, the ALS predictions of GC also detected differences near lakeshores, which are driven by limitations on logging governed by Finnish law. Estimating this indicator with ALS remote sensing allowed to observe its spatial distribution and to detect peculiarities which would otherwise be unavailable from field plot sampling. Consequently, the method presented appears to be well suited for monitoring the effects of management practice, as well as verifying its compliance with legal restrictions. Numéro de notice : A2016-338 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecolind.2015.08.001 En ligne : http://dx.doi.org/10.1016/j.ecolind.2015.08.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81022
in Ecological indicators > vol 60 (January 2016) . - pp 574 - 585[article]Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach / Joanne C. White in Canadian Journal of Forest Research, vol 45 n° 11 (November 2015)PermalinkComparison of linear mixed effects model and generalized model of the tree height-diameter relationship / Z. Adamec in Journal of forest science, vol 61 n° 10 (October 2015)PermalinkForest height estimation by means of Pol-InSAR data inversion : The role of the vertical wavenumber / Florian Kugler in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkHigh-resolution forest canopy height estimation in an African blue carbon ecosystem / David Lagomasino in Remote sensing in ecology and conservation, vol 1 n° 1 (October 2015)PermalinkStand density, tree social status and water stress influence allocation in height and diameter growth of Quercus petraea (Liebl.) / Raphaël Trouvé in Tree Physiology, vol 35 n° 10 (October 2015)PermalinkExtraction of structural and dynamic properties of forests from polarimetric-interferometric SAR data affected by temporal decorrelation / Marco Lavalle in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkModeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar / Qi Chen in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)PermalinkUnderstanding the effects of ALS pulse density for metric retrieval across diverse forest types / Phil Wilkes in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)PermalinkApport de variables issues de la segmentation d'arbres sur données Lidar aéroporté pour l'estimation des variables dendrométriques de placettes forestières / Ana Cristina André in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)PermalinkFORESTIMATOR : un plugin QGIS d'estimation de la hauteur dominante et du site index de peuplements résineux à partir de Lidar aérien / Laurent Dedry in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)Permalink