Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/101112

Estimating Canopy Bulk Density (CBD) Using Discrete-Return LiDAR and Hyperspectral Imagery

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Item Summary

Title:Estimating Canopy Bulk Density (CBD) Using Discrete-Return LiDAR and Hyperspectral Imagery
Authors:Lindquist, Mahany C.
Contributors:Chen, Qi (advisor)
Geography and Environment (department)
Keywords:canopy bulk density
LiDAR
hyperspectral
remote sensing
wildfires
Date Issued:Dec 2014
Publisher:[Honolulu] : [University of Hawaii at Manoa], [December 2014]
Abstract:The objective of this research is to estimate canopy bulk density (CBD) using discretereturn LiDAR data and hyperspectral imagery. This research will seek to answer the following questions:
How accurate is LiDAR data in estimating CBD in comparison to using hyperspectral data?
To what extent does the fusion of LiDAR and hyperspectral data improve the accuracy of CBD estimation?
Based on previous studies, it is expected that combining these two data types will improve CBD estimation. Exploring these two types of remote sensing data, and using different methodologies is important in finding alternatives in terms of data used to predict CBD that could help foresters and fire scientists better understand the wildfire phenomenon, and in turn, assist them in making informed decisions.
Description:MA University of Hawaii at Manoa 2014
Includes bibliographical references (leaves 55–60).
Pages/Duration:v, 60 leaves
URI/DOI:http://hdl.handle.net/10125/101112
Rights:All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: M.A. - Geography


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