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

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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

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2014 -2024 IGN

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