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Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors / Niels Lindgren in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

Public [article]  inCanadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp Titre : Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors Titre original : Assimilation de données de volume de bois à l’aide d’une séquence de données de télédétection provenant de différents capteurs Type de document : Article/Communication Auteurs : Niels Lindgren, Auteur ; Hakan Olsson, Auteur ; Kenneth Nyström, Auteur ; Mattias Nyström, Auteur ; Göran Stahl, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Betula (genre) [Termes IGN] capital sur pied [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] étalonnage des données [Termes IGN] filtre de Kalman [Termes IGN] forêt boréale [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Quercus (genre) [Termes IGN] Suède [Termes IGN] volume en bois Résumé : (auteur) Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58°27′N, 13°39′E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE). Numéro de notice : A2022-144 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/07038992.2021.1988542 Date de publication en ligne : 17/10/2021 En ligne : https://doi.org/10.1080/07038992.2021.1988542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99985 [article]

Inflation of wood resources in European forests: The footprints of a big-bang / Jean-Daniel Bontemps in Plos one, vol 16 n° 11 (November 2021)  

Public [article]  inPlos one > vol 16 n° 11 (November 2021) . - n° e0259795 Titre : Inflation of wood resources in European forests: The footprints of a big-bang Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur Année de publication : 2021 Projets : ARBRE / AgroParisTech (2007 -), LUE / Université de Lorraine Article en page(s) : n° e0259795 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] anthropisation [Termes IGN] capital sur pied [Termes IGN] changement climatique [Termes IGN] dynamique de la végétation [Termes IGN] exploitation forestière [Termes IGN] exportation [Termes IGN] gestion forestière [Termes IGN] impact sur l'environnement [Termes IGN] politique forestière [Termes IGN] politique publique [Termes IGN] puits de carbone [Termes IGN] ressources forestières [Termes IGN] Union Européenne [Termes IGN] volume en bois [Vedettes matières IGN] Foresterie Résumé : (auteur) The current increase in European forest resources forms a singularity across the globe. Whether this trend will persist, and how biological and economic trends feature it form crucial issues to green economy challenges and C sequestration. The present screening of Forest Europe 2015 statistics explored the features, inertia and limits of this expansion, and its relationships with countries’ development, forest management and trade, intense in this area of the world. Persisting footprint of past demographic pressure on forests was identified, with opposed traces on their area and growing stock density. Steady growing stock (GS) increases, proportional to GS, not density-limited, and sustained by forest area increases, supported the view of an inflationary forest dynamic. Economic development and liberalism fostered both forest exploitation and production, yielding no significant impact on GS changes. Wood exports exerted a tension on forest exploitation and GS changes, thus lowering GS inflation but providing a resource security margin in the face of expected climate threats. Conflicting a common view, GS inflation and moderate felling-to-increment ratios make increased use of wood resources and C sequestration reconcilable, and GS expansion timely for ongoing EU forest policy processes. Anticipated adverse impacts of ongoing climate change were not clearly identified in these statistics. Numéro de notice : A2021-871 Affiliation des auteurs : LIF (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1371/journal.pone.0259795 Date de publication en ligne : 24/11/2021 En ligne : https://doi.org/10.1371/journal.pone.0259795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99130 [article]

A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 97 (May 2021) . - n° 102303 Titre : A new small area estimation algorithm to balance between statistical precision and scale Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Jean-Pierre Renaud , Auteur ; Ankit Sagar , Auteur ; Olivier Bouriaud , Auteur Année de publication : 2021 Projets : LUE / Université de Lorraine, DIABOLO / Packalen, Tuula, ARBRE/CHM-era / Jolly, Anne Article en page(s) : n° 102303 Note générale : bibliographie This research was funded by The French Environmental Management Agency (ADEME), grant number 16-60-C0007. The methods and algorithms for processing photogrammetric data were supported by DIABOLO project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Ankit Sagar received the financial support of the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, through the project Impact DeepSurf. Langues : Anglais (eng) Descripteur : [Termes IGN] arbre BSP [Termes IGN] capital sur pied [Termes IGN] données auxiliaires [Termes IGN] données de terrain [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier national (données France) [Termes IGN] réduction d'échelle [Termes IGN] seuillage [Termes IGN] surface terrière [Vedettes matières IGN] Sylviculture Résumé : (auteur) Combining national forest inventory (NFI) data with auxiliary information allows downscaling and improving the precision of NFI estimates for small domains, where normally too few field plots are available to produce reliable estimates. In most situations, small domains represent administrative units that could greatly vary in size and forested area. In small and poorly sampled domains, the precision of estimates often drop below expected standards. To tackle this issue, we introduce a downscaling algorithm generating the smallest possible groups of domains satisfying prescribed sampling density and estimation error. The binary space partitioning algorithm recursively divides the population of domains in two groups while the prescribed precision conditions are fulfilled. The algorithm was tested on two major forest attributes (i.e. growing stock and basal area) in an area of 7,500 km2 dominated by hardwood forests in the centre of France. The estimation domains consisted in 157 municipalities. The field data included 819 NFI plots surveyed during a 5 years period. The auxiliary data consisted in 48 metrics derived from a forest map, photogrammetric models and Landsat images. A model-assisted framework was used for estimation. For each forest attribute, the best model was selected using a best-subset approach using a Bayesian Information Criteria. The retained models explained 58% and 41% of the observed variance for the growing stocks and basal areas respectively. The performance of the algorithm was evaluated using a minimum of 3 NFI points per domain and estimation errors varying from 10 to 50%. For a target estimation error set to 10%, the algorithm led to a limited number of estimation domains ( The algorithm provides a flexible estimation framework for small area estimation. The key advantages of the approach are relying on its capacity to produce estimations based on a preselected precision threshold and to produce results over the whole area of interest, avoiding areas without any estimates. The algorithm could also be used on any kind of polygon layers (not only administrative ones), provided that the field sampling design enable estimation. This makes the proposed algorithm a convenient tool notably for decision makers and forest managers. Numéro de notice : A2021-067 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2021.102303 Date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.jag.2021.102303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96992 [article]

Applications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)  

Public Titre : Applications of remote sensing data in mapping of forest growing stock and biomass Type de document : Monographie Auteurs : Jose Aranha, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 276 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-0365-0569-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] capital sur pied [Termes IGN] carte forestière [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] foresterie [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image Landsat-OLI [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image SPOT 6 [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Pinus massoniana [Termes IGN] puits de carbone [Termes IGN] service écosystémique [Termes IGN] système d'information géographique [Termes IGN] Thaïlande Résumé : (éditeur) This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques. Note de contenu : 1- Finer resolution estimation and mapping of mangrove biomass using UAV LiDAR and WorldView-2 data 2- Nondestructive estimation of the above-ground biomass of multiple tree species in boreal forests of China using Terrestrial Laser Scanning 3- Estimating forest aboveground carbon storage in Hang-Jia-Hu using Landsat TM/OLI data and random morest Model 4- Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms 5- Comparative analysis of seasonal Landsat 8 images for forest aboveground biomass estimation in a subtropical forest 6- Estimating urban vegetation biomass from Sentinel-2A image data 7- Estimation of forest biomass in Beijing (China) using multisource remote sensing and forest inventory data 8- Spatially explicit analysis of trade-offs and synergies among multiple ecosystem services in Shaanxi Valley basin 9- Influence of site-specific conditions on estimation of forest above ground biomass from airborne laser scanning 10- Multi-sensor prediction of stand volume by a hybrid model of support vector machine for regression kriging 11- Applying LiDAR to quantify the plant area index along a successional gradient in a tropical forest of Thailand 12- Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification 13- Evaluation of different algorithms for estimating the growing stock volume of pinus massoniana plantations using spectral and spatial information from a SPOT6 image Numéro de notice : 15305 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0569-5 En ligne : https://doi.org/10.3390/books978-3-0365-0569-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99903

High resolution mapping of forest resources and prediction reliability using multisource inventory approach / Ankit Sagar (2021)    

Public Titre : High resolution mapping of forest resources and prediction reliability using multisource inventory approach Type de document : Article/Communication Auteurs : Ankit Sagar , Auteur ; Cédric Vega , Auteur ; Christian Piedallu, Auteur ; Olivier Bouriaud , Auteur ; Jean-Pierre Renaud , Auteur Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : ARBRE / 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, INCA 2021, 41th Indian National Cartographic Association international conference, Cartography for self-reliant India 27/10/2021 29/10/2021 Chandigarh Inde open access proceedings Projets : DEEPSURF / Pironon, Jacques Importance : pp 219 - 221 Langues : Anglais (eng) Descripteur : [Termes IGN] capital sur pied [Termes IGN] données multisources [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] ressources forestières [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) [introduction] National forest inventory (NFI) provides precise forest resource estimates at national up to regional scale but could not support local estimates with high precision because of inadequate number of field plots. The forest managers and stakeholders prefer local estimates at fine spatial resolution (Chirici et al. 2020). Multi source-national forest inventory (MS-NFI) opens the possibility for wall-to-wall mapping of forest attributes with good precision at high spatial resolution. MS-NFI rely on the combination of NFI data with auxiliary data (remote sensing data, thematic map, etc.), and in many cases, this combination is modelled through a non-parametric k-nearest neighbour (k-NN) approach. k-NN is capable in predicting several attributes in a single model with a low prediction bias. The major drawbacks of k-NN are its inability to predict beyond the range of training data (Magnussen et al. 2010), the lack of well-established variance estimator (McRoberts et al. 2011) and its decreasing performance with increasing dimensionality. The estimation maps for the forest resources are important (Tomppo et al. 2008; Chirici et al., 2020), but their prediction uncertainties have also to be taken into consideration. Methods have been proposed recently to map the prediction uncertainty (Esteban et al, 2019) and these maps have been included into an inferential framework (Saarela et al, 2020). In this study we propose a method building upon bootstrap model-based estimator (McRoberts et al. 2011) to estimate forest attributes of interest at pixel level and address the problem of extrapolation and precision of estimation by providing maps for both at high spatial resolution. For sake of concision, results were presented for growing stock volume (GSV) only. Numéro de notice : C2021-031 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1986 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1986 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98995

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