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Airborne scanning LiDAR in a double sampling forest carbon inventory

Authors :
Stephens, Peter R.
Kimberley, Mark O.
Beets, Peter N.
Paul, Thomas S.H.
Searles, Nigel
Bell, Alan
Brack, Cris
Broadley, James
Source :
Remote Sensing of Environment. Feb2012, Vol. 117, p348-357. 10p.
Publication Year :
2012

Abstract

Abstract: To meet Kyoto Protocol obligations, New Zealand is required to estimate forest carbon stock change over the first commitment period (2008–2012). New Zealand has three subcategories of forest, namely: Natural forest; Pre-1990 forest; and Post-1989 forest. The Post-1989 forest carbon inventory undertaken in 2008 used discrete return airborne LiDAR and ground-based measurements of 0.06ha circular plots located on a 4-km×4-km grid. The national carbon stock estimate was based on a double sampling scheme consisting of 246 plots from which both ground and LiDAR data were obtained, supplemented with 46 additional plots assessed using only LiDAR. This paper describes the relationships established between carbon stocks estimated using ground-based measurements and LiDAR metrics. A regression model explaining 74% of the variation in total carbon was developed using LiDAR 30th percentile height (P30ht) and canopy cover (%Cover). The regression estimator improved the precision of the national carbon stock estimate in 2008 by 6% compared to the ground-based estimate. This inventory indicates that it is possible to reduce the cost of obtaining carbon stock estimates to a specified level of precision using a combination of ground-based and LiDAR measurements in a double sampling approach. The theoretical maximum improvement in precision expected in 2012, when additional LiDAR data are expected to be available, is 50–55%. [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
00344257
Volume :
117
Database :
Academic Search Index
Journal :
Remote Sensing of Environment
Publication Type :
Academic Journal
Accession number :
70365314
Full Text :
https://doi.org/10.1016/j.rse.2011.10.009