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Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 12 (June-2 2021) . - n° 2398 Titre : Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data Type de document : Article/Communication Auteurs : Claire Teillet, Auteur ; Benjamin Pillot, Auteur ; Thibault Catry, Auteur ; Laurent Demagistri, Auteur ; Dominique Lyszczarz, Auteur ; Marc Lang, Auteur ; Pierre Couteron, Auteur ; Nicolas Barbier, Auteur ; Arsène Adou Kouassi, Auteur ; Quentin Gunther, Auteur ; Nadine Dessay, Auteur Année de publication : 2021 Projets : GeoSud / , TOSCA / Article en page(s) : n° 2398 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Bouaké [Termes IGN] Brasilia [Termes IGN] caractérisation [Termes IGN] empreinte [Termes IGN] image Pléiades-HR [Termes IGN] image Sentinel-MSI [Termes IGN] paysage urbain [Termes IGN] texture d'image [Termes IGN] zone urbaine Résumé : (auteur) Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited. Numéro de notice : A2021-505 Affiliation des auteurs : ENSG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13122398 date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.3390/rs13122398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98125 [article]

Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-waveform signals / Tristan Allouis in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 6 n° 2 part 3 (April 2013)  

Public [article]  inIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 6 n° 2 part 3 (April 2013) . - pp 924 - 934 Titre : Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-waveform signals Type de document : Article/Communication Auteurs : Tristan Allouis, Auteur ; Sylvie Durrieu, Auteur ; Cédric Vega , Auteur ; Pierre Couteron, Auteur Année de publication : 2013 Article en page(s) : pp 924 - 934 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] biomasse aérienne [Termes IGN] carte de la végétation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] forme d'onde pleine [Termes IGN] modèle mathématique [Termes IGN] Pinus (genre) [Termes IGN] signal lidar [Termes IGN] volume en bois Résumé : (auteur) The diameter at breast height (DBH) is the most extensively measured parameter in the field for estimating stem volume and aboveground biomass of individual trees. However, DBH can not be measured from airborne or spaceborne light detection and ranging (LiDAR) data. Consequently, volume and biomass must be estimated from LiDAR data using other tree metrics. The objective of this paper is to examine whether full-waveform (FW) LiDAR data can improve volume and biomass estimation of individual pine trees, when compared to usual discrete-return LiDAR data. Sets of metrics are derived from canopy height model (CHM-only metrics), from the vertical distribution of discrete-returns (CHM+DR metrics), and from full-waveform LiDAR data (CHM+FW metrics). In each set, the most relevant and non-collinear metrics were selected using a combination of methods using best subset and variance inflation factor, in order to produce predictive models of volume and biomass. CHM-only metrics (tree height and tree bounding volume [tree height x crown area] provided volume and biomass estimates of individual trees with an error (mean error ± standard deviation) of 2% ± 26% and -15% ±49%, which is equivalent to previous studies. CHM+FW metrics did not improve stem volume estimates (5% ± 31%), but they increased the accuracy of aboveground biomass estimates ( -4%±31%). The approach is limited by the delineation of individual trees. However, the results highlight the potential of full-waveform LiDAR data to improve aboveground biomass estimates through a better integration of branch and leaf biomass than with discrete-return LiDAR data. Numéro de notice : A2013-053 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/JSTARS.2012.2211863 date de publication en ligne : 27/09/2012 En ligne : http://doi.org/10.1109/JSTARS.2012.2211863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84586 [article]