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Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 108 - 123 Titre : Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits Type de document : Article/Communication Auteurs : Tawanda W. Gara, Auteur ; Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 108 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf mass per area [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV  0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services. Numéro de notice : A2019-493 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.005 date de publication en ligne : 11/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93725 [article]

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Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model / Roshanak Darvishzadeh in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 79 (July 2019) . - pp 58-70 Titre : Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model Type de document : Article/Communication Auteurs : Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; Haidi Abdullah, Auteur ; Elias Cherenet, Auteur Année de publication : 2019 Article en page(s) : pp 58-70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse multibande [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] modèle d'inversion [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] réflectance végétale [Termes descripteurs IGN] spectrophotométrie [Termes descripteurs IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM. Numéro de notice : A2019-460 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.03.003 date de publication en ligne : 08/03/2019 En ligne : https://doi.org/10.1016/j.jag.2019.03.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93577 [article]

CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 415 - 424 Titre : CNN-based dense image matching for aerial remote sensing images Type de document : Article/Communication Auteurs : Shunping Ji, Auteur ; Jin Liu, Auteur ; Meng Lu, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chine [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] Munich [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] Stuttgart [Termes descripteurs IGN] ville [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Dense stereo matching plays a key role in 3D reconstruction. The capability of using deep learning in the stereo matching of remote sensing data is currently uncertain. This article investigated the application of deep learning–based stereo methods in aerial image series and proposed a deep learning–based multi-view dense matching framework. First, we applied three typical convolutional neural network models, MC-CNN, GC-Net, and DispNet, to aerial stereo pairs and compared the results with those of the SGM and a commercial software, SURE. Second, on different data sets, the generalization ability of each network is evaluated by using direct transfer learning with models pretrained on other data sets and by fine-tuning with a small number of target training data. Third, we present a deep learning–based multi-view dense matching framework where the multi-view geometry is introduced to further refine matching results. Three sets of aerial images as the main data sets and two open-source sets of street images as auxiliary data sets are used for testing. Experiments show that, first, the performance of deep learning–based stereo methods is slightly better than traditional methods. Second, both the GC-Net and the MC-CNN have demonstrated good generalization ability and can obtain satisfactory results on aerial images using a pretrained model on several available stereo benchmarks. Third, multi-view geometry constraints can further improve the performance of deep learning–based methods, which is better than that of the multi-view–based SGM and SURE. Numéro de notice : A2019-246 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.415 date de publication en ligne : 01/06/2019 En ligne : https://doi.org/10.14358/PERS.85.6.415 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93002 [article]

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Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 208 - 220 Titre : Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Tiejun Wang, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 208 - 220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] angle [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] modèle numérique de surface de la canopée Mots-clés libres : inclinaison longitudinale  Leaf inclination angle  leaf angle distribution Résumé : (Auteur) Leaf inclination angle and leaf angle distribution (LAD) are important plant structural traits, influencing the flux of radiation, carbon and water. Although leaf angle distribution may vary spatially and temporally, its variation is often neglected in ecological models, due to difficulty in quantification. In this study, terrestrial LiDAR (TLS) was used to quantify the LAD variation in natural European beech (Fagus Sylvatica) forests. After extracting leaf points and reconstructing leaf surface, leaf inclination angle was calculated automatically. The mapping accuracy when discriminating between leaves and woody material was very high across all beech stands (overall accuracy = 87.59%). The calculation accuracy of leaf angles was evaluated using simulated point cloud and proved accurate generally (R2 = 0.88, p  Numéro de notice : A2019-075 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.005 date de publication en ligne : 15/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92162 [article]

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 19 - 28 Titre : Simultaneous chain-forming and generalization of road networks Type de document : Article/Communication Auteurs : Susanne Wenzel, Auteur ; Dimitri Bulatov, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme de Douglas-Peucker [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] axe médian [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] Graz [Termes descripteurs IGN] itération [Termes descripteurs IGN] mise à jour automatique [Termes descripteurs IGN] Munich [Termes descripteurs IGN] objet géographique linéaire [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] polyligne [Termes descripteurs IGN] primitive géométrique [Termes descripteurs IGN] relation topologique [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] squelettisation [Termes descripteurs IGN] zone urbaine [Vedettes matières IGN] Généralisation Résumé : (auteur) Streets are essential entities of urban terrain and their automatic extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological, and semantic aspects. Given a binary image representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. Thereby, we aim at a fusion of raw segments to longer chains which better match to the intuitive perception of what a street is. We propose a two-step approach for simultaneous chain-forming and generalization. First, we obtain an over-segmentation of the raw polylines. Then, a model selection approach is applied to decide whether two neighboring segments should be fused to a new geometric entity. For this purpose, we propose an iterative greedy optimization procedure in order to find a strong minimum of a cost function based on a Bayesian information criterion. Starting at the given initial raw segments, we thus can obtain a set of chains describing long alleys and important roundabouts. Within the procedure, topological attributes, such as junctions and neighborhood structures, are consistently updated, in a way that for the greedy optimization procedure, accuracy, model complexity, and topology are considered simultaneously. The results on two challenging datasets indicate the benefits of the proposed procedure and provide ideas for future work. Numéro de notice : A2019-026 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.19 date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.19 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91962 [article]

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Estimation and uncertainty of the mixing effects on Scots pine—European beech productivity from national forest inventories data / Sonia Condés in Forests, vol 9 n° 9 (September 2018)  

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Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests / Nina Amiri in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

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Experiences with the QDaedalus system for astrogeodetic determination of deflections of the vertical / Markus Hauk in Survey review, vol 49 n° 355 (October 2017)  

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An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)  

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Modellbasierte Transformation von 3D-Gebäudemodellen nach INSPIRE / Klement Aringer in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, Vol 141 n° 3 (Mai - Juni 2016)  

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Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation / Przemyslaw Polewski in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

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Persistent scatterers at building facades – Evaluation of appearance and localization accuracy / Stefan Gernhardt in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)  

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Maximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas / Michael Schmitt in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)

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Radargrammetric registration of airborne multi-aspect SAR data of urban areas / Michael Schmitt in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)

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