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Remotely sensing the species of individual trees / Yifang Shi (2020)  

Public Titre : Remotely sensing the species of individual trees Type de document : Thèse/HDR Auteurs : Yifang Shi, Auteur ; Andrew K. Skidmore, Directeur de thèse ; Tiejun Wang, Directeur de thèse Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2020 Collection : ITC Dissertation num. 376 Importance : 163 p. Format : 21 x 30 cm Note générale : bibliographie Doctor of Philosophy, Faculty of Geo-Information Science and Earth Observation, University of Twente Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Abies alba [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] Bavière (Allemagne) [Termes IGN] chlorophylle [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tempérée [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge couleur [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Leaf Mass per Area [Termes IGN] orthoimage Résumé : (auteur) The accurate identification of tree species is critical for the management of forest ecosystems. Mapping of tree species is an important task as it can assist a wide range of environmental applications, such as biodiversity monitoring, ecosystem services assessment, invasive species detection, and sustainable forest management. Compared to the conventional approaches based on labor-intensive field measurements, remote sensing has supplied a large variety of cutting-edge techniques to accomplish forest inventory. However, individual tree species classification in natural mixed forests, as it is typical in central Europe, is still a challenging task. High spectral and structural intra-species variability and inter-species similarity, due to phenological effects, differences in tree age and openness of canopies, shadowing effects, and environment variability, restrict tree species separability. An in-depth understanding of the relationship between species-specific features and remote sensing observations for tree species classification needs further investigation. This thesis aimed to accurately map the species of individual trees using multi-source remotely sensed data, including aerial photographs, airborne LiDAR and hyperspectral data. The research in the thesis firstly evaluated the performance of geometric and radiometric metrics from airborne LiDAR data under leaf-on and leaf-off conditions for individual tree species discrimination. The results empathized the importance of intensity-related LiDAR metrics for tree species identification under both leaf-on and leaf-off conditions. Then, the thesis examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping. The results showed that the texture features generated from multi-temporal digital CIR orthophotos under different view-illumination conditions are species-specific. Combining these texture features with LiDAR metrics significantly improved the accuracy of individual tree species mapping. To explore more valuable species-specific features, the thesis consequently integrated three plant functional traits (i.e. equivalent water thickness, leaf mass per area and leaf chlorophyll) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species. Three selected plant functional traits were accurately retrieved using radiative transfer model and further improved the accuracy of tree species classification. Eventually, the thesis focused on an important tree species silver fir, and accurately mapped individuals of this species based on one-class classifiers using integrated airborne hyperspectral and LiDAR data. The mapping results provided the references locating the areas with a high occurrence probability of silver fir trees and hence increase the efficiency in subsequent field campaigns for forest management and biodiversity monitoring. This thesis explored the potential of various remotely sensed datasets for individual tree species mapping. The methodologies and findings in this thesis can be applied in the mapping of other tree species, which enriches the knowledge of species-specific characteristics and related remotely sensed signatures. The emerging of UAVs and the upcoming hyperspectral missions such as EnMAP and HySPIRI deliver valuable datasets with multi-scale coverage and revisit observations, which can be used for mapping the diversity of tree species at stand or regional level. Note de contenu : - General introduction - Important LiDAR metrics for discriminating tree species - Improving LiDAR-based tree species mapping using multi-temporal CIR orthophotos - Tree species classification using remotely sensed plant functional traits - Mapping individual silver fir trees in a Norway spruce dominated forest - Synthesis: Mapping individual tree species using multi-source remotely sensed data Numéro de notice : 17671 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : PhD thesis : : University of Twente : 2020 DOI : 10.3990/1.978903654953-0 Date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.3990/1.978903654953-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97985

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 (géométrie) [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 [Termes IGN] semis de points 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]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 137 (March 2018) . - pp 163 - 174 Titre : Important LiDAR metrics for discriminating forest tree species in Central Europe Type de document : Article/Communication Auteurs : Yifang Shi, Auteur ; Tiejun Wang, Auteur ; Andrew K. Skidmore, Auteur ; Marco Heurich, Auteur Année de publication : 2018 Article en page(s) : pp 163 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Allemagne [Termes IGN] arbre (flore) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Europe centrale [Termes IGN] forêt tempérée [Termes IGN] morphologie mathématique Résumé : (Auteur) Numerous airborne LiDAR-derived metrics have been proposed for classifying tree species. Yet an in-depth ecological and biological understanding of the significance of these metrics for tree species mapping remains largely unexplored. In this paper, we evaluated the performance of 37 frequently used LiDAR metrics derived under leaf-on and leaf-off conditions, respectively, for discriminating six different tree species in a natural forest in Germany. We firstly assessed the correlation between these metrics. Then we applied a Random Forest algorithm to classify the tree species and evaluated the importance of the LiDAR metrics. Finally, we identified the most important LiDAR metrics and tested their robustness and transferability. Our results indicated that about 60% of LiDAR metrics were highly correlated to each other (|r| > 0.7). There was no statistically significant difference in tree species mapping accuracy between the use of leaf-on and leaf-off LiDAR metrics. However, combining leaf-on and leaf-off LiDAR metrics significantly increased the overall accuracy from 58.2% (leaf-on) and 62.0% (leaf-off) to 66.5% as well as the kappa coefficient from 0.47 (leaf-on) and 0.51 (leaf-off) to 0.58. Radiometric features, especially intensity related metrics, provided more consistent and significant contributions than geometric features for tree species discrimination. Specifically, the mean intensity of first-or-single returns as well as the mean value of echo width were identified as the most robust LiDAR metrics for tree species discrimination. These results indicate that metrics derived from airborne LiDAR data, especially radiometric metrics, can aid in discriminating tree species in a mixed temperate forest, and represent candidate metrics for tree species classification and monitoring in Central Europe. Numéro de notice : A2018-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.02.002 Date de publication en ligne : 07/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89442 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018033	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018032	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 13 - 25 Titre : Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Simon D. Jones, Auteur ; Tiejun Wang, Auteur ; Marco Heurich, Auteur ; Xi Zhu, Auteur ; Yifang Shi, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle de visée [Termes IGN] Bavière (Allemagne) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] instrument aéroporté [Termes IGN] parc naturel régional [Termes IGN] placette d'échantillonnage [Termes IGN] structure d'un peuplement forestier Résumé : (Auteur) Gap fraction (Pgap) and vertical gap fraction profile (vertical Pgap profile) are important forest structural metrics. Accurate estimation of Pgap and vertical Pgap profile is therefore critical for many ecological applications, including leaf area index (LAI) mapping, LAI profile estimation and wildlife habitat modelling. Although many studies estimated Pgap and vertical Pgap profile from airborne LiDAR data, the scan angle was often overlooked and a nadir view assumed. However, the scan angle can be off-nadir and highly variable in the same flight strip or across different flight strips. In this research, the impact of off-nadir scan angle on Pgap and vertical Pgap profile was evaluated, for several forest types. Airborne LiDAR data from nadir (0°∼7°), small off-nadir (7°∼23°), and large off-nadir (23°∼38°) directions were used to calculate both Pgap and vertical Pgap profile. Digital hemispherical photographs (DHP) acquired during fieldwork were used as references for validation. Our results show that angular Pgap from airborne LiDAR correlates well with angular Pgap from DHP (R2 = 0.74, 0.87, and 0.67 for nadir, small off-nadir and large off-nadir direction). But underestimation of Pgap from LiDAR amplifies at large off-nadir scan angle. By comparing Pgap and vertical Pgap profiles retrieved from different directions, it is shown that scan angle impact on Pgap and vertical Pgap profile differs amongst different forest types. The difference is likely to be caused by different leaf angle distribution and canopy architecture in these forest types. Statistical results demonstrate that the scan angle impact is more severe for plots with discontinuous or sparse canopies. These include coniferous plots, and deciduous or mixed plots with between-crown gaps. In these discontinuous plots, Pgap and vertical Pgap profiles are maximum when observed from nadir direction, and then rapidly decrease with increasing scan angle. The results of this research have many important practical implications. First, it is suggested that large off-nadir scan angle of airborne LiDAR should be avoided to ensure a more accurate Pgap and LAI estimation. Second, the angular dependence of vertical Pgap profiles observed from airborne LiDAR should be accounted for, in order to improve the retrieval of LAI profiles, and other quantitative canopy structural metrics. This is especially necessary when using multi-temporal datasets in discontinuous forest types. Third, the anisotropy of Pgap and vertical Pgap profile observed by airborne LiDAR, can potentially help to resolve the anisotropic behavior of canopy reflectance, and refine the inversion of biophysical and biochemical properties from passive multispectral or hyperspectral data Numéro de notice : A2018-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89432 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018023	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 77 - 87 Titre : Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Marco Heurich, Auteur ; Tiejun Wang, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Allemagne [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt alpestre [Termes IGN] hauteur des arbres [Termes IGN] lever topographique [Termes IGN] normalisation [Termes IGN] relief Résumé : (Auteur) As an important metric for describing vertical forest structure, the plant area index (PAI) profile is used for many applications including biomass estimation and wildlife habitat assessment. PAI profiles can be estimated with the vertically resolved gap fraction from airborne LiDAR data. Most research utilizes a height normalization algorithm to retrieve local or relative height by assuming the terrain to be flat. However, for many forests this assumption is not valid. In this research, the effect of topographic normalization of airborne LiDAR data on the retrieval of PAI profile was studied in a mountainous forest area in Germany. Results show that, although individual tree height may be retained after topographic normalization, the spatial arrangement of trees is changed. Specifically, topographic normalization vertically condenses and distorts the PAI profile, which consequently alters the distribution pattern of plant area density in space. This effect becomes more evident as the slope increases. Furthermore, topographic normalization may also undermine the complexity (i.e., canopy layer number and entropy) of the PAI profile. The decrease in PAI profile complexity is not solely determined by local topography, but is determined by the interaction between local topography and the spatial distribution of each tree. This research demonstrates that when calculating the PAI profile from airborne LiDAR data, local topography needs to be taken into account. We therefore suggest that for ecological applications, such as vertical forest structure analysis and modeling of biodiversity, topographic normalization should not be applied in non-flat areas when using LiDAR data. Numéro de notice : A2017-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86992 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017102	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017103	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants / Fangyuan Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)  

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3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction / Xi Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

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Effect of slope on treetop detection using a LiDAR Canopy Height Model / Anahita Khosravipour in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)  

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