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A voxel-based approach for canopy structure characterization using full-waveform airborne laser scanning


Leiterer, Reik; Morsdorf, Felix; Torabzadeh, Hossein; Schaepman, Michael E; Mucke, W; Pfeifer, N; Hollaus, M (2012). A voxel-based approach for canopy structure characterization using full-waveform airborne laser scanning. In: Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, Munich, Germany, 22 July 2012 - 27 July 2012, 3399-3402.

Abstract

Forests play a significant role in the global biogeochemical and -physical cycles and particularly the complex three-dimensional forest canopy structure influences the fluxes of energy and matter between the atmosphere and forests. Assessing this structure quantitatively using conventional fieldwork or traditional remote sensing methods is difficult, whereas airborne laser scanning (ALS) systems have proven to be suitable for providing explicit vertical information for large areas. However, most existing ALS based approaches include manual processing steps or need additional data about stand characteristics. To solve these issues, a robust and automatic multi-dimensional clustering method was developed to derive forest canopy structure types (CSTs) based on full-waveform ALS data. The results show that it is possible to develop an automatic, self-sustained and transferable method for: the extraction of CSTs without any previous knowledge about the forest stand; and the extraction of bio-physical parameters based on the resulting CSTs.

Abstract

Forests play a significant role in the global biogeochemical and -physical cycles and particularly the complex three-dimensional forest canopy structure influences the fluxes of energy and matter between the atmosphere and forests. Assessing this structure quantitatively using conventional fieldwork or traditional remote sensing methods is difficult, whereas airborne laser scanning (ALS) systems have proven to be suitable for providing explicit vertical information for large areas. However, most existing ALS based approaches include manual processing steps or need additional data about stand characteristics. To solve these issues, a robust and automatic multi-dimensional clustering method was developed to derive forest canopy structure types (CSTs) based on full-waveform ALS data. The results show that it is possible to develop an automatic, self-sustained and transferable method for: the extraction of CSTs without any previous knowledge about the forest stand; and the extraction of bio-physical parameters based on the resulting CSTs.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:27 July 2012
Deposited On:05 Dec 2012 16:38
Last Modified:07 Dec 2017 17:11
Publisher:IEEE International
ISBN:978-1-4673-1160-1
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/IGARSS.2012.6350691

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