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Remote Sens. 2009, 1(4), 776-794; doi:10.3390/rs1040776

Discrete Return Lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables

1
The Nature Conservancy, North America Region–Science. 117 E. Mountain Ave, Suite 201. Fort Collins, CO 80524, USA
2
Forest Service, U.S. Department of Agriculture, Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 S. Main St., Moscow, ID 83843, USA
3
Watershed Sciences. Corvallis, OR 97333, USA
4
Department of Forest Resources, College of Natural Resources, University of Idaho, 6th and Line St. Moscow, ID 83844, USA
*
Author to whom correspondence should be addressed.
Received: 1 September 2009 / Revised: 16 October 2009 / Accepted: 20 October 2009 / Published: 27 October 2009
(This article belongs to the Special Issue LiDAR)
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Abstract

Recent years have seen the progression of light detection and ranging (lidar) from the realm of research to operational use in natural resource management. Numerous government agencies, private industries, and public/private stakeholder consortiums are planning or have recently acquired large-scale acquisitions, and a national U.S. lidar acquisition is likely before 2020. Before it is feasible for land managers to integrate lidar into decision making, resource assessment, or monitoring across the gambit of natural resource applications, consistent standards in project planning, data processing, and user-driven products are required. This paper introduces principal lidar acquisition parameters, and makes recommendations for project planning, processing, and product standards to better serve natural resource managers across multiple disciplines.
Keywords: lidar; remote sensing; standards; processing; natural resources; forestry lidar; remote sensing; standards; processing; natural resources; forestry
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Evans, J.S.; Hudak, A.T.; Faux, R.; Smith, A.M.S. Discrete Return Lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables. Remote Sens. 2009, 1, 776-794.

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