Estimating Canopy Bulk Density (CBD) Using Discrete-Return LiDAR and Hyperspectral Imagery
Date
2014-12
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[Honolulu] : [University of Hawaii at Manoa], [December 2014]
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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).
Includes bibliographical references (leaves 55–60).
Keywords
canopy bulk density, LiDAR, hyperspectral, remote sensing, wildfires
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v, 60 leaves
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Theses for the degree of Master of Arts (University of Hawaii at Manoa). Geography.
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