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Features of Point Clouds Synthesized from Multi-View ALOS/PRISM Data and Comparisons with LiDAR Data in Forested AreasLiDAR waveform data from airborne LiDAR scanners (ALS) e.g. the Land Vegetation and Ice Sensor (LVIS) havebeen successfully used for estimation of forest height and biomass at local scales and have become the preferredremote sensing dataset. However, regional and global applications are limited by the cost of the airborne LiDARdata acquisition and there are no available spaceborne LiDAR systems. Some researchers have demonstrated thepotential for mapping forest height using aerial or spaceborne stereo imagery with very high spatial resolutions.For stereo imageswith global coverage but coarse resolution newanalysis methods need to be used. Unlike mostresearch based on digital surface models, this study concentrated on analyzing the features of point cloud datagenerated from stereo imagery. The synthesizing of point cloud data from multi-view stereo imagery increasedthe point density of the data. The point cloud data over forested areas were analyzed and compared to small footprintLiDAR data and large-footprint LiDAR waveform data. The results showed that the synthesized point clouddata from ALOSPRISM triplets produce vertical distributions similar to LiDAR data and detected the verticalstructure of sparse and non-closed forests at 30mresolution. For dense forest canopies, the canopy could be capturedbut the ground surface could not be seen, so surface elevations from other sourceswould be needed to calculatethe height of the canopy. A canopy height map with 30 m pixels was produced by subtracting nationalelevation dataset (NED) fromthe averaged elevation of synthesized point clouds,which exhibited spatial featuresof roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlationwith RH50 of LVIS data with a slope of 1.04 and R2 of 0.74 indicating that the canopy height derived fromPRISM triplets can be used to estimate forest biomass at 30 m resolution.
Document ID
20150012722
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Ni, Wenjian
(Maryland Univ. College Park, MD, United States)
Ranson, Kenneth Jon
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Zhang, Zhiyu
(Academia Sinica Beijing, China)
Sun, Guoqing
(Maryland Univ. College Park, MD, United States)
Date Acquired
July 7, 2015
Publication Date
May 3, 2014
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 149
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN22283
Funding Number(s)
CONTRACT_GRANT: 40971203
CONTRACT_GRANT: NNX12AD03A
CONTRACT_GRANT: XDA05050100
CONTRACT_GRANT: 91125003
CONTRACT_GRANT: 2013CB733404
CONTRACT_GRANT: 41001208
CONTRACT_GRANT: 41171283
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
LiDARD
ALOSPRISM
Data
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