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Fusion of optical, radar and waveform LiDAR observations for land cover classification / Huiran Jin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 187 (May 2022) . - pp 171 - 190 Titre : Fusion of optical, radar and waveform LiDAR observations for land cover classification Type de document : Article/Communication Auteurs : Huiran Jin, Auteur ; Giorgos Mountrakis, Auteur Année de publication : 2022 Article en page(s) : pp 171 - 190 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse comparative [Termes IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion d'images [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat-TM [Termes IGN] image multitemporelle [Termes IGN] occupation du sol Résumé : (Auteur) Land cover is an integral component for characterizing anthropogenic activity and promoting sustainable land use. Mapping distribution and coverage of land cover at broad spatiotemporal scales largely relies on classification of remotely sensed data. Although recently multi-source data fusion has been playing an increasingly active role in land cover classification, our intensive review of current studies shows that the integration of optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) observations has not been thoroughly evaluated. In this research, we bridged this gap by i) summarizing related fusion studies and assessing their reported accuracy improvements, and ii) conducting our own case study where for the first time fusion of optical, radar and waveform LiDAR observations and the associated improvements in classification accuracy are assessed using data collected by spaceborne or appropriately simulated platforms in the LiDAR case. Multitemporal Landsat-5/Thematic Mapper (TM) and Advanced Land Observing Satellite-1/ Phased Array type L-band SAR (ALOS-1/PALSAR) imagery acquired in the Central New York (CNY) region close to the collection of airborne waveform LVIS (Land, Vegetation, and Ice Sensor) data were examined. Classification was conducted using a random forest algorithm and different feature sets in terms of sensor and seasonality as input variables. Results indicate that the combined spectral, scattering and vertical structural information provided the maximum discriminative capability among different land cover types, giving rise to the highest overall accuracy of 83% (2–19% and 9–35% superior to the two-sensor and single-sensor scenarios with overall accuracies of 64–81% and 48–74%, respectively). Greater improvement was achieved when combining multitemporal Landsat images with LVIS-derived canopy height metrics as opposed to PALSAR features, suggesting that LVIS contributed more useful thematic information complementary to spectral data and beneficial to the classification task, especially for vegetation classes. With the Global Ecosystem Dynamics Investigation (GEDI), a recently launched LiDAR instrument of similar properties to the LVIS sensor now operating onboard the International Space Station (ISS), it is our hope that this research will act as a literature summary and offer guidelines for further applications of multi-date and multi-type remotely sensed data fusion for improved land cover classification. Numéro de notice : A2022-228 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.010 Date de publication en ligne : 17/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100214 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022051	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2022053	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2022052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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]