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Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 n° 1 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 n° 1 (2016) . - pp 795 - 807 Titre : Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation Type de document : Article/Communication Auteurs : Alyssa Endres, Auteur ; Giorgos Mountrakis, Auteur ; Huiran Jin, Auteur ; Wei Zhuang, Auteur ; Ioannis Manakos, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 795 - 807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] biomasse aérienne [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] feuillu [Termes IGN] fusion de données [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat Résumé : (auteur) Aboveground forest biomass estimation is an integral component for climate change, carbon stocks assessment, biodiversity and forest health. LiDAR (Light Detection And Ranging), specifically NASA’s Laser Vegetation Imaging Sensor (LVIS), PALSAR (Phased Array type L-band Synthetic Aperture Radar), and Landsat data have been previously used in biomass estimation with promising results when used individually. In this manuscript, all three products are jointly utilized for the first time to assess their importance for deciduous biomass estimation. Results indicate that LVIS inputs are ranked as most important followed by PALSAR inputs. Particularly for PALSAR, scenes acquired in May and August were ranked higher compared to other months. Numéro de notice : A2016-827 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164942 En ligne : http://dx.doi.org/10.5721/EuJRS20164942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82707 [article]

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data / Wei Zhuang in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 95 (September 2014) . - pp 81 – 92 Titre : An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data Type de document : Article/Communication Auteurs : Wei Zhuang, Auteur ; Giorgos Mountrakis, Auteur Année de publication : 2014 Article en page(s) : pp 81 – 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] empreinte [Termes IGN] filtrage numérique d'image [Termes IGN] forêt [Termes IGN] forme d'onde [Termes IGN] groupe [Termes IGN] identification automatique [Termes IGN] onde lidar [Termes IGN] surface du sol [Termes IGN] télémétrie laser aéroporté [Termes IGN] traitement de données Résumé : (Auteur) Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors. Numéro de notice : A2014-474 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74051 [article]

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