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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 IGN] Bavière (Allemagne) [Termes IGN] canopée [Termes IGN] classification par forêts aléatoires [Termes IGN] écosystème forestier [Termes IGN] hétérogénéité spatiale [Termes IGN] image Sentinel-MSI [Termes IGN] indice foliaire [Termes IGN] Leaf Mass per Area [Termes IGN] photosynthèse [Termes 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 Affiliation des auteurs : non IGN 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 IGN] analyse multibande [Termes IGN] bande rouge [Termes IGN] bande spectrale [Termes IGN] Bavière (Allemagne) [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] image RapidEye [Termes IGN] image Sentinel-MSI [Termes IGN] modèle d'inversion [Termes IGN] Picea abies [Termes IGN] réflectance végétale [Termes IGN] spectrophotométrie [Termes 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 Affiliation des auteurs : non IGN 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]

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 IGN] angle [Termes IGN] Bavière (Allemagne) [Termes IGN] croissance végétale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Fagus sylvatica [Termes IGN] feuille (végétation) [Termes 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 Affiliation des auteurs : non IGN 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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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 265 - 274 Titre : Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests Type de document : Article/Communication Auteurs : Nina Amiri, Auteur ; Przemyslaw Polewski, Auteur ; Marco Heurich, Auteur ; Peter Krzystek, Auteur ; Andrew K. Skidmore, Auteur Année de publication : 2018 Article en page(s) : pp 265 - 274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier local [Termes IGN] lasergrammétrie [Termes IGN] Pinophyta [Termes IGN] segmentation [Termes IGN] semis de points [Vedettes matières IGN] Inventaire forestier Mots-clés libres : Bavarian Forest National Park Résumé : (auteur) The development of new approaches to individual tree crown delineation for forest inventory and management is an important area of ongoing research. The increasing availability of high density ALS (Airborne Laser Scanning) point clouds offers the opportunity to segment the individual tree crowns and deduce their geometric properties with a high level of accuracy. Top-down segmentation methods such as normalized cut are established approaches for delineation of single trees in ALS point clouds. However, overlapping crowns and branches of nearby trees frequently cause over- and under-segmentation due to the difficulty of defining a single criterion for stopping the partitioning process. In this work, we investigate an adaptive stopping criterion based on the visual appearance of trees within the point clouds. We focus on coniferous trees due to their well-defined crown shapes in comparison to deciduous trees. This approach is based on modeling the coniferous tree crowns with elliptic paraboloids to infer whether a given 3D scene contains exactly one or more than one tree. For each processed scene, candidate tree peaks are generated from local maxima found within the point cloud. Next, paraboloids are fitted at the peaks using a random sample consensus procedure and classified based on their geometric properties. The decision to stop or continue partitioning is determined by finding a set of non-overlapping paraboloids. Experiments were performed on three plots from the Bavarian Forest National Park in Germany. Based on validation data from the field inventory, results show that our approach improves the segmentation quality by up to 10% across plots with different properties, such as average tree height and density. This indicates that the new adaptive stopping criterion for normalized cut segmentation is capable of delineating tree crowns more accurately than a static stopping criterion based on a constant Ncut threshold value. Numéro de notice : A2018-670 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.006 date de publication en ligne : 29/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90405 [article]

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Connecting infrared spectra with plant traits to identify species / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 139 (May 2018) . - pp 183 - 200 Titre : Connecting infrared spectra with plant traits to identify species Type de document : Article/Communication Auteurs : Maria F. Buitrago, Auteur ; Andrew K. Skidmore, Auteur ; Thomas A. Groen, Auteur ; Christoph A. Hecker, Auteur Année de publication : 2018 Article en page(s) : pp 183 - 200 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse discriminante [Termes IGN] arbre (flore) [Termes IGN] bande infrarouge [Termes IGN] biochimie [Termes IGN] caractérisation [Termes IGN] espèce végétale [Termes IGN] signature spectrale [Termes IGN] teneur en eau de la végétation Résumé : (Auteur) Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 µm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified species-specific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 µm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing. Numéro de notice : A2018-116 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.03.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89552 [article]

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Important LiDAR metrics for discriminating forest tree species in Central Europe / Yifang Shi in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 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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The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data / Alby D. Rocha in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

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Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

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Retrieval of leaf area index in different plant species using thermal hyperspectral data / Elnaz Neinavaz in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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Changes in thermal infrared spectra of plants caused by temperature and water stress / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)  

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Generating pit-free canopy height models from airborne lidar / Anahita Khosravipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)  

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Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data / Abel Ramoelo in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)

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vol 26 n° 11-12 - november - december 2012 - Second special issue on spatial ecology [Suivi de] Reflections on geographic information science : special issue in honor of Michael Goodchild (Bulletin de International journal of geographical information science IJGIS) / Laffan Shawn

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The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass / T.M. Basuki in Geocarto international, vol 27 n° 4 (July 2012)  

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