Assessing the Utility of UAV-Borne Hyperspectral Image and Photogrammetry Derived 3D Data for Wetland Species Distribution Quick Mapping
Refereed conference paper presented and published in conference proceedings


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AbstractLightweight unmanned aerial vehicle (UAV) loading with novel sensors offered a low cost and risk approach for data acquisition in complex environment. This study assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area of Hong Kong. Multiple feature reduction methods and different classifiers were compared. The best result was obtained when transformed components from minimum noise fraction (MNF) and DSM were combined in support vector machine (SVM) classifier. Wavelength regions at chlorophyll absorption green peak, red, red edge and Oxygen absorption at near infrared were identified for better species discrimination. In addition, input of DSM data reduces overestimation of low plant species and misclassification due to the shadow effect and inter-species morphological variation. This study establishes a framework for quick survey and update on wetland environment using UAV system. The findings indicate that the utility of UAV-borne hyperspectral and derived tree height information provides a solid foundation for further researches such as biological invasion monitoring and bio-parameters modelling in wetland.
Acceptance Date23/08/2017
All Author(s) ListQ.S. Li, F.K.K. Wong, T. Fung
Name of Conference4th ISPRS International Conference on Unmanned Aerial Vehicles in Geomatics, UAV-g 2017
Start Date of Conference04/09/2017
End Date of Conference07/09/2017
Place of ConferenceBonn
Country/Region of ConferenceGermany
Proceedings TitleInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – ISPRS Archies
Year2017
Volume Number42
Issue Number2W6
PublisherISPRS
Place of PublicationBonn
Pages209 - 215
ISSN1682-1750
LanguagesEnglish-United Kingdom
KeywordsDSM, Feature Reduction, Hyperspectral, Photogrammetric Point Cloud, Species Mapping, UAV

Last updated on 2020-28-06 at 01:13