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Automated Tree-Level Forest Quantifi...

Hamraz, Hamid.

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Automated Tree-Level Forest Quantification Using Airborne Lidar.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated Tree-Level Forest Quantification Using Airborne Lidar./
作者:	Hamraz, Hamid.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	139 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10954415
ISBN:	9780438239043

Automated Tree-Level Forest Quantification Using Airborne Lidar.
Hamraz, Hamid.

Automated Tree-Level Forest Quantification Using Airborne Lidar. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 139 p.

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of Kentucky, 2018.

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of &sim;170. Our deep learning experiments showed high classification accuracies (&sim;82% coniferous and &sim;90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level.

ISBN: 9780438239043Subjects--Topical Terms:

523869
Computer science.

Automated Tree-Level Forest Quantification Using Airborne Lidar.
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