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Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data / Wai Yeung Yan in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 152 - 165 Titre : Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data Type de document : Article/Communication Auteurs : Wai Yeung Yan, Auteur ; Karin Y. Van Ewijk, Auteur ; Paul M. Treitz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 152 - 165 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] artefact [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] correction d'image [Termes IGN] correction radiométrique [Termes IGN] couvert forestier [Termes IGN] délignage [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tempérée [Termes IGN] intensité lumineuse [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Ontario (Canada) [Termes IGN] peuplement mélangé [Termes IGN] restauration d'image [Termes IGN] semis de points Résumé : (auteur) In order to use the airborne LiDAR intensity in conjunction with the height-derived information for forest modeling and classification purposes, radiometric correction is deemed to be a critical pre-processing requirement. In this study, we implemented a LiDAR scan line correction (LSLC) and an overlap-driven intensity correction (OIC) to remove the stripe artifacts that appeared within the individual flight lines and overlapping regions of adjacent flight lines of a multispectral LiDAR dataset. We tested the effectiveness of these corrections in various land/forest cover types in a temperate mixed mature forest in Ontario, Canada. Subsequently, we predicted three plot level forest attributes, i.e., basal area (BA), quadratic mean diameter (QMD), and trees per hectare (TPH), using different combinations of height and intensity metrics derived from the multispectral LiDAR data to determine if LiDAR intensity data (corrected and uncorrected) improved predictions over models that utilize LiDAR height-derived information only. The results show that LSLC can reduce the intensity banding effect by 0.19–23.06% in channel 1 (1550 nm) and 4.79–66.87% in channel 2 (1064 nm) at the close-to-nadir region. The combined effect of LSLC and OIC is notable particularly at the swath edges. After implementing both methods, the intensity homogeneity is improved by 5.51–12% in channel 1, 6.37–42.93% in channel 2, and 6.48–33.77% in channel 3 (532 nm). Our results further demonstrate that BA and QMD predictions in our study area gained little from additional LiDAR intensity metrics. Intensity metrics from multiple LiDAR channels and intensity normalized difference vegetation index (NDVI) metrics did improve TPH predictions up to 7.2% in RMSE and 1.8% in Bias. However, our lowest TPH prediction errors (%RMSE) were still approximately 10% larger than for BA and QMD. We observed only minimal differences in plot level BA, QMD, and TPH predictions between models using original and corrected intensity. We attribute this to: (i) the lower effectiveness of radiometric correction in forest versus grassland, bare soil and road land cover types, and (ii) the effect of spatial resolution on intensity noise. Numéro de notice : A2020-640 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.001 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96063 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020113	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Predicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model / Paulina T. Marczak in Remote sensing, vol 12 n° 1 (January 2020)  

Public [article]  inRemote sensing > vol 12 n° 1 (January 2020) . - 29 p. Titre : Predicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model Type de document : Article/Communication Auteurs : Paulina T. Marczak, Auteur ; Karin Y. Van Ewijk, Auteur ; Paul M. Treitz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 29 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] changement climatique [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] diamètre des arbres [Termes IGN] données lidar [Termes IGN] forêt tempérée [Termes IGN] modèle de croissance végétale [Termes IGN] Ontario (Canada) [Termes IGN] peuplement forestier [Termes IGN] photo-interprétation [Termes IGN] puits de carbone [Termes IGN] rendement [Termes IGN] semis de points Résumé : (auteur) Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p Numéro de notice : A2020-222 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010201 Date de publication en ligne : 06/01/2020 En ligne : https://doi.org/10.3390/rs12010201 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94934 [article]