Faculté des Sciences
Faculté des Sciences

Use of UAS-based LiDAR and multispectral data for estimating vegetation parameters

Petit, Céline ULiège
Promotor(s) : Jonard, François ULiège
Date of defense : 29-Jun-2023/30-Jun-2023 • Permalink :
Title : Use of UAS-based LiDAR and multispectral data for estimating vegetation parameters
Translated title : [fr] Utilisation de données LiDAR et multispectrales grâce à un UAS pour l'estimation de paramètres de végétation
Author : Petit, Céline ULiège
Date of defense  : 29-Jun-2023/30-Jun-2023
Advisor(s) : Jonard, François ULiège
Committee's member(s) : Nascetti, Andrea ULiège
Schmitz, Serge ULiège
Language : English
Number of pages : 82
Keywords : [en] Multispectral
[en] unmanned aircraft system
[en] light detection and ranging
[en] green area index
[en] plant area index
Discipline(s) : Physical, chemical, mathematical & earth Sciences > Earth sciences & physical geography
Target public : Researchers
General public
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en sciences géographiques, orientation géomatique, à finalité spécialisée en geodata-expert
Faculty: Master thesis of the Faculté des Sciences


[en] This master's thesis focuses on estimating vegetation parameters using multispectral and Light Detection and Ranging (LiDAR) sensors mounted on unmanned aerial systems (UAS). The aim was to investigate different methodologies for green area index (GAI) and plant area index (PAI) estimation, specifically in the context of a winter wheat crop over an entire growing season.
We employed three distinct methods for GAI estimation using multispectral data and two methods for PAI estimation using LiDAR data. The GAI estimation involved the use of the Fraction of Vegetation Cover (FVC) approach with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red and Red Edge (NDRRE), and Normalized Difference Red Edge (NDRE) indices. The PAI estimation utilized a modified Beer-Lambert light extinction model with ground and canopy returns and intensity ratios.
Results indicate the potential of LiDAR intensity for estimating PAI. The GAI estimation using NDRRE also shows a promising future by being less affected by saturation and shadow effects compared to the two other indices.
Although the obtained results may not have met the initial expectations, this thesis can serve as a foundation for further exploration and raises new questions that motivate future research in the field of remote sensing-based vegetation parameter estimation.



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  • Petit, Céline ULiège Université de Liège > Mast. scienc. géogr. or. géom. à fin.


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