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Advances in Forest Inventory Using Airborne Laser Scanning
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, FI-02431 Masala, Finland
School of Science and Technology, Aalto University, FI-00076 Aalto, Finland
Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland
Department of Geography and Geology, University of Turku, FI-20014 Turku, Finland
* Author to whom correspondence should be addressed.
Received: 15 March 2012; in revised form: 23 April 2012 / Accepted: 25 April 2012 / Published: 3 May 2012
Abstract: We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m2. With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level.
Keywords: individual tree detection; airborne laser scanning; forest inventory; Canopy Height Model; area-based inventory; feature extraction; last pulse; point clouds metrics
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MDPI and ACS Style
Hyyppä, J.; Yu, X.; Hyyppä, H.; Vastaranta, M.; Holopainen, M.; Kukko, A.; Kaartinen, H.; Jaakkola, A.; Vaaja, M.; Koskinen, J.; Alho, P. Advances in Forest Inventory Using Airborne Laser Scanning. Remote Sens. 2012, 4, 1190-1207.
Hyyppä J, Yu X, Hyyppä H, Vastaranta M, Holopainen M, Kukko A, Kaartinen H, Jaakkola A, Vaaja M, Koskinen J, Alho P. Advances in Forest Inventory Using Airborne Laser Scanning. Remote Sensing. 2012; 4(5):1190-1207.
Hyyppä, Juha; Yu, Xiaowei; Hyyppä, Hannu; Vastaranta, Mikko; Holopainen, Markus; Kukko, Antero; Kaartinen, Harri; Jaakkola, Anttoni; Vaaja, Matti; Koskinen, Jarkko; Alho, Petteri. 2012. "Advances in Forest Inventory Using Airborne Laser Scanning." Remote Sens. 4, no. 5: 1190-1207.