Quantifying forest above ground carbon content using LiDAR remote sensing

Patenaude, G., Hill, R.A., Milne, R., Rowland, C.S. and Dawson, T.P. (2004) Quantifying forest above ground carbon content using LiDAR remote sensing Remote Sensing of Environment, 93, (3), pp. 368-380. (doi:10.1016/j.rse.2004.07.016).


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The UNFCCC and interest in the source of the missing terrestrial carbon sink are prompting research and development into methods for carbon accounting in forest ecosystems. Here we present a canopy height quantile-based approach for quantifying above ground carbon content (AGCC) in a temperate deciduous woodland, by means of a discrete-return, small-footprint airborne LiDAR. Fieldwork was conducted in Monks Wood National Nature Reserve UK to estimate the AGCC of five stands from forest mensuration and allometric relations. In parallel, a digital canopy height model (DCHM) and a digital terrain model (DTM) were derived from elevation measurements obtained by means of an Optech Airborne Laser Terrain Mapper 1210. A quantile-based approach was adopted to select a representative statistic of height distributions per plot. A forestry yield model was selected as a basis to estimate stemwood volume per plot from these heights metrics. Agreement of r=0.74 at the plot level was achieved between ground-based AGCC estimates and those derived from the DCHM. Using a 20×20 m grids superposed to the DCHM, the AGCC was estimated at the stand level and at the woodland level. At the stand level, the agreement between the plot data upscaled in proportion to area and the LiDAR estimates was r=0.85. At the woodland level, LiDAR estimates were nearly 24% lower than those from the upscaled plot data. This suggests that field-based approaches alone may not be adequate for carbon accounting in heterogeneous forests. Conversely, the LiDAR 20×20 m grid approach has an enhanced capability of monitoring the natural variability of AGCC across the woodland.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.rse.2004.07.016
ISSNs: 0034-4257 (print)
Keywords: carbon, forest, LiDAR

ePrint ID: 58530
Date :
Date Event
15 November 2004Published
Date Deposited: 14 Aug 2008
Last Modified: 16 Apr 2017 17:36
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/58530

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