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Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2147 Titre : Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band Type de document : Article/Communication Auteurs : Xing Peng, Auteur ; Xinwu Li, Auteur ; Yanan Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande L [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données TomoSAR [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image 3D [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] itération [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] Suède [Termes descripteurs IGN] tomographie radar Résumé : (auteur) Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m. Numéro de notice : A2021-441 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13112147 date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.3390/rs13112147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97828 [article]

Model-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4635 - 4653 Titre : Model-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing Type de document : Article/Communication Auteurs : Michael L. Benson, Auteur ; Pierce Leland, Auteur ; Katleen Bergen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4635 - 4653 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] Canada [Termes descripteurs IGN] canopée [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] lasergrammétrie [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] structure d'un peuplement forestier Résumé : (auteur) One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m 2 and 1.68 m respectively. Numéro de notice : A2021-423 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3018638 date de publication en ligne : 09/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3018638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97778 [article]

Weak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests / Thomas Asbeck in European Journal of Forest Research, vol 140 n° 3 (June 2021)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 3 (June 2021) . - pp 721 - 731 Titre : Weak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests Type de document : Article/Communication Auteurs : Thomas Asbeck, Auteur ; Julian Frey, Auteur Année de publication : 2021 Article en page(s) : pp 721 - 731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Abies alba [Termes descripteurs IGN] composition d'un peuplement forestier [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Forêt-Noire, massif de la [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] modélisation de la forêt [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure d'un peuplement forestier Résumé : (auteur) Understanding the relationship of stand structural complexity and forest management is relevant to create desired stand structures by adapting management strategies under changing disturbance scenarios and climatic conditions. To overcome difficulties in differentiating between strict categories of silvicultural practices and to describe the impact of forest management more appropriate, we used a continuous indicator of forest management intensity (ForMI). The ForMI consists of three components including volumes of natural deadwood, non-native tree species and harvested trees. There are a great number of approaches to quantify stand structure; here we used the recently established stand structural complexity index (SSCI) which represents a density-dependent as well as vertical measure of complexity based on the distribution of points in 3D space inventoried by terrestrial laser scanning. The data collection took place in 135 one-hectare plots managed under close-to-nature forest management (CTNFM) located in the Black Forest, Germany. We build generalized additive models to test the relationship of the SSCI with the ForMI. The model results did not prove a significant relationship between the SSCI and the ForMI, but components of the ForMI showed significant relationships to the SSCI. Our results indicate that the relationship between stand structural complexity and forest management intensity is, while plausible, not trivial to demonstrate. We conclude that forest managers have a relatively wide range of choices in CTNFM to adapt forests within a similar range of management intensity as presented here to future challenges, since management intensity does not change the forest structure drastically. Numéro de notice : A2021-431 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10342-021-01361-4 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1007/s10342-021-01361-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97793 [article]

Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method / Hongliang Lu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 99 - 118 Titre : Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method Type de document : Article/Communication Auteurs : Hongliang Lu, Auteur ; Heng Zhang, Auteur ; Huaitao Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 99 - 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande P [Termes descripteurs IGN] Chine [Termes descripteurs IGN] compensation [Termes descripteurs IGN] données TomoSAR [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] erreur de phase [Termes descripteurs IGN] Guyane (département français) [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] ligne de base [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] tomographie radar [Termes descripteurs IGN] triangulation de Delaunay Résumé : (auteur) Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency’s campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation. Numéro de notice : A2021-271 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.022 date de publication en ligne : 14/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.022 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97329 [article]

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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 descripteurs IGN] arbre BSP [Termes descripteurs IGN] capital sur pied [Termes descripteurs IGN] données auxiliaires [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] réduction d'échelle [Termes descripteurs IGN] seuillage [Termes descripteurs 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]

Refinement of interferometric SAR parameters using digital terrain model as an external reference / Jyunpei Uemoto in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

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Towards silviculture guidelines to produce large-sized silver birch (betula pendula roth) logs in Western Europe / Héloïse Dubois in Forests, vol 12 n° 5 (May 2021)  

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An iterative-mode scan design of terrestrial laser scanning in forests for minimizing occlusion effects / Linyuan Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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The impact of drought stress on the height growth of young norway spruce full-sib and half-sib clonal trials in Sweden and Finland / Haleh Hayatgheibi in Forests, vol 12 n° 4 (April 2021)  

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Tree extraction and estimation of walnut structure parameters using airborne LiDAR data / Javier Estornell in International journal of applied Earth observation and geoinformation, vol 96 (April 2021)  

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Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model / Francisco Mauro in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

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Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)  

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Forest height estimation using a single-pass airborne L-band polarimetric and interferometric SAR system and tomographic techniques / Yue Huang in Remote sensing, Vol 13 n° 3 (February 2021)  

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Long-term tree species population dynamics in Swiss forest reserves influenced by forest structure and climate / A.S. Mathys in Forest ecology and management, vol 481 (February 2021)  

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A quantitative assessment of rockfall influence on forest structure in the Swiss Alps / Christine Moos in European Journal of Forest Research, vol 140 n° 1 (February 2021)  

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