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The Use of LiDAR in Multi-Scale Fore...

Su, Yanjun.

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The Use of LiDAR in Multi-Scale Forestry Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Use of LiDAR in Multi-Scale Forestry Applications./
Author:	Su, Yanjun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	142 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
Subject:	Remote sensing. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10269424
ISBN:	9780355095593

The Use of LiDAR in Multi-Scale Forestry Applications.
Su, Yanjun.

The Use of LiDAR in Multi-Scale Forestry Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 142 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--University of California, Merced, 2017.

Forest ecosystems are a significant faction of the Earth's landscape, and accurate estimates of forest structures are important for understanding and predicting how forest ecosystems respond to climate change and human activities. Light detection and ranging (LiDAR) technology, an active remote sensing technology, can penetrate the forest canopy and greatly improve the efficiency and accuracy of mapping forest structures, compared to traditional passive optical remote sensing and radar technologies. However, currently, LiDAR has two major weaknesses, the lack of spectral information and the limited spatial coverage. These weaknesses have limited its accuracy in certain forestry applications (e.g., vegetation mapping) and its application in large-scale forest structure mapping. The aim of research described in this dissertation is to develop data fusion algorithms to address these limitations. In this dissertation, the effectiveness of LiDAR in estimating forest structures and therefore monitoring forest dynamics is first compared with aerial imagery by detecting forest fuel treatment activities at the local scale. Then, a vegetation mapping algorithm is developed based on the fusion of LiDAR data and aerial imagery. This algorithm can automatically determine the optimized number of vegetation units in a forest and take both the vegetation species and vegetation structure characteristics into account in classifying the vegetation types. To extend the use of LiDAR in mapping forest structures in areas without LiDAR coverage, a data fusion algorithm is proposed to map fine-resolution tree height from airborne LiDAR, spaceborne LiDAR, optical imagery and radar data in regional scale. Finally, this dissertation further investigates the methodology to integrate spaceborne LiDAR, optical imagery, radar data and climate surfaces for the purpose of mapping national- to global-scale forest aboveground biomass. The proposed data fusion algorithms and the generated regional to global forest structure parameters will have important applications in ecological and hydrologic studies and forest management.

ISBN: 9780355095593Subjects--Topical Terms:

535394
Remote sensing.

The Use of LiDAR in Multi-Scale Forestry Applications.
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500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Advisers: Qinghua Guo; Roger C. Bales.
502 $a Thesis (Ph.D.)--University of California, Merced, 2017.
520 $a Forest ecosystems are a significant faction of the Earth's landscape, and accurate estimates of forest structures are important for understanding and predicting how forest ecosystems respond to climate change and human activities. Light detection and ranging (LiDAR) technology, an active remote sensing technology, can penetrate the forest canopy and greatly improve the efficiency and accuracy of mapping forest structures, compared to traditional passive optical remote sensing and radar technologies. However, currently, LiDAR has two major weaknesses, the lack of spectral information and the limited spatial coverage. These weaknesses have limited its accuracy in certain forestry applications (e.g., vegetation mapping) and its application in large-scale forest structure mapping. The aim of research described in this dissertation is to develop data fusion algorithms to address these limitations. In this dissertation, the effectiveness of LiDAR in estimating forest structures and therefore monitoring forest dynamics is first compared with aerial imagery by detecting forest fuel treatment activities at the local scale. Then, a vegetation mapping algorithm is developed based on the fusion of LiDAR data and aerial imagery. This algorithm can automatically determine the optimized number of vegetation units in a forest and take both the vegetation species and vegetation structure characteristics into account in classifying the vegetation types. To extend the use of LiDAR in mapping forest structures in areas without LiDAR coverage, a data fusion algorithm is proposed to map fine-resolution tree height from airborne LiDAR, spaceborne LiDAR, optical imagery and radar data in regional scale. Finally, this dissertation further investigates the methodology to integrate spaceborne LiDAR, optical imagery, radar data and climate surfaces for the purpose of mapping national- to global-scale forest aboveground biomass. The proposed data fusion algorithms and the generated regional to global forest structure parameters will have important applications in ecological and hydrologic studies and forest management.
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