Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-61753
Koetz, B; Morsdorf, F; Sun, G; Ranson, K J; Itten, K I; Allgöwer, B (2006). Inversion of a lidar waveform model for forest biophysical parameter estimation. Geoscience and Remote Sensing Letters, 3(1):49-53.
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Due to its measurement principle, light detection and ranging (lidar) is particularly suited to estimate the horizontal as well as vertical distribution of forest structure. Quantification and characterization of forest structure is important for the understanding of the forest ecosystem functioning and, moreover, will help to assess carbon sequestration within forests. The relationship between the signal recorded by a lidar system and the canopy structure of a forest can be accurately characterized by physically based radiative transfer models (RTMs). A three-dimensional RTM is capable of representing the complex forest canopy structure as well as the involved physical processes of the lidar pulse interactions with the vegetation. Consequently, the inversion of such an RTM presents a novel concept to retrieve biophysical forest parameters that exploits the full lidar signal and underlying physical processes. A synthetic dataset and data acquired in the Swiss National Park (SNP) successfully demonstrated the feasibility and the potential of RTM inversion to retrieve forest structure from large-footprint lidar waveform data. The SNP lidar data consist of waveforms generated from the aggregation of small-footprint lidar returns. Derived forest biophysical parameters, such as fractional cover, leaf area index, maximum tree height, and the vertical crown extension, were able to describe the horizontal and vertical forest canopy structure.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Geography|
|DDC:||910 Geography & travel|
|Deposited On:||18 Jul 2012 14:04|
|Last Modified:||11 Dec 2013 22:24|
|Free access at:||Publisher DOI. An embargo period may apply.|
|Citations:||Web of Science®. Times Cited: 30|
Scopus®. Citation Count: 34
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