
NSF Org: |
EAR Division Of Earth Sciences |
Recipient: |
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Initial Amendment Date: | August 20, 2012 |
Latest Amendment Date: | July 12, 2013 |
Award Number: | 1226145 |
Award Instrument: | Continuing Grant |
Program Manager: |
Leonard E. Johnson
EAR Division Of Earth Sciences GEO Directorate for Geosciences |
Start Date: | September 1, 2012 |
End Date: | September 30, 2013 (Estimated) |
Total Intended Award Amount: | $219,000.00 |
Total Awarded Amount to Date: | $219,000.00 |
Funds Obligated to Date: |
FY 2013 = $0.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
921 S 8TH AVE POCATELLO ID US 83201-5377 (208)282-2592 |
Sponsor Congressional District: |
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Primary Place of Performance: |
ID US 83209-0002 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
Instrumentation & Facilities, GEOINFORMATICS |
Primary Program Source: |
01001314DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.050 |
ABSTRACT
This collaborative project between Idaho State University (ISU), Utah State University (USU), and USFS Rocky Mountain Research Station (RMRS) will develop the next generation of analytical and processing tools for new airborne and ground-based LiDAR monitoring data. The tools will help earth scientists and natural resource managers exploit these data streams to address some of our most pressing environmental questions and management challenges. The software tools will target Earth Science-specific analysis of 3-D point cloud data from platforms like airborne LiDAR (ALS), ground-based LiDAR (TLS), multi-beam SONAR (MBS), and/or Structure from Motion (SfM). The tool development will scale-up existing documented and proven algorithms to be more accessible, as well as build new algorithms that are necessary to address emerging challenges such as change detection analysis from repeat surveys, improved bare earth surface generation, and novel fusion of point clouds collected from different platforms (e.g. ALS and TLS). The new software will be developed collaboratively between geoscientists and computer scientists to optimize performance when handling the large and often problematic data volumes associated with point clouds. This work will provide a suite of community software that will permit innovative and potentially transformative data analysis with the potential to enhance our understanding of numerous earth surface processes.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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