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Auteur Shaohui Zhang |
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Affiner la recherche Interroger des sources externesModelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information / Shaohui Zhang in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)
Titre : Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information Type de document : Article/Communication Auteurs : Shaohui Zhang, Auteur ; Cédric Vega Année de publication : 2022 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 103072 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] empreinte
[Termes IGN] gestion forestière
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation de la forêt
[Termes IGN] placette d'échantillonnage
[Termes IGN] Sologne (France)
[Termes IGN] variogramme
[Termes IGN] volume en boisRésumé : (auteur) The French National Forest Inventory provides detailed forest information up to large national and regional scales. Forest inventory for small areas of interest within a large population is equally important for decision making, such as for local forest planning and management purposes. However, sampling these small areas with sufficient ground plots is often not cost efficient. In response, small area estimation has gained increasing popularity in forest inventory. It consists of a set of techniques that enables predictions of forest attributes of subpopulation with the help of auxiliary information that compensates for the small field samples. Common sources of auxiliary information usually come from remote sensing technology, such as airborne laser scanning and satellite imagery. The newly launched NASA’s Global Ecosystem Dynamics Investigation (GEDI), a full waveform Lidar instrument, provides an unprecedented opportunity of collecting large-scale and dense forest sample plots given its sampling frequency and spatial coverage. However, the geolocation uncertainty associated with GEDI footprints create important challenges for their use for small area estimations. In this study, we designed a process that provides NFI measurements at plot level with GEDI auxiliary information from nearby footprints. We demonstrated that GEDI RH98 is equivalent to NFI dominant height at plot level. We stressed the importance of pairing NFI plots with nearby GEDI footprints, based on not only the distance in between but also their similarities, i.e., forest heights and forest types. Subsequently, these NFI-GEDI pairs were used for small area estimations following a two-phase sampling scheme. We showcased that, with an adequate sample size, small area estimation with GEDI auxiliary data can improve the accuracy of forest volume estimates. Numéro de notice : A2022-786 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.103072 Date de publication en ligne : 22/10/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101890
in International journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103072[article]
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 / 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 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 Unit-level small area estimation of forest inventory with GEDI auxiliary information in France / Shaohui Zhang (2021)
Titre : Unit-level small area estimation of forest inventory with GEDI auxiliary information in France Type de document : Mémoire Auteurs : Shaohui Zhang, Auteur ; Jean-Pierre Renaud Editeur : Paris, Nancy, ... : AgroParisTech (2007 -) Année de publication : 2021 Autre Editeur : Nancy, Metz : Université de Lorraine Note générale : Mémoire de stage, MASTER AETPF Agrosciences, Environnement, Territoire, Paysage, Forêt Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données multisources
[Termes IGN] estimation statistique
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] inventaire forestier (techniques et méthodes)
[Vedettes matières IGN] Inventaire forestierRésumé : (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. 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. This study aims to explore the possibility of using GEDI auxiliary data to improve the precision 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 showed that the precision of forest volume estimates was significantly improved at both the whole study area and individual department levels. Numéro de notice : 13877 Affiliation des auteurs : non IGN Thématique : FORET Nature : Mémoire masters divers Organisme de stage : LIF (IGN) DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99219 Documents numériques
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