Next Article in Journal
Post-Fire Seedling Recruitment and Morpho-Ecophysiological Responses to Induced Drought and Salvage Logging in Pinus halepensis Mill. Stands
Previous Article in Journal
Forest Ecosystem Services: Issues and Challenges for Biodiversity, Conservation, and Management in Italy
Article Menu

Export Article

Open AccessArticle
Forests 2015, 6(6), 1839-1857; doi:10.3390/f6061839

Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction

1
Department of Forest Sciences, University of Helsinki, Helsinki FI-00014, Finland
2
Centre of Excellence in Laser Scanning Research, Finnish Geodetic Institute, Masala FI-02431, Finland
3
School of Forest Sciences, University of Eastern Finland, Joensuu FI-80101, Finland
4
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Masala FI-02431, Finland
5
Department of Real Estate, Planning and Geoinformatics, Aalto University, Aalto FI-00076, Finland
6
Civil engineering and building services, Helsinki Metropolia, University of Applied Sciences, Helsinki FI-00079, Finland
7
Department of Geography and Geology, University of Turku, Turku FI-20014, Finland
*
Author to whom correspondence should be addressed.
Academic Editor: Eric Jokela
Received: 14 April 2015 / Revised: 5 May 2015 / Accepted: 25 May 2015 / Published: 28 May 2015
View Full-Text   |   Download PDF [3249 KB, uploaded 28 May 2015]   |  

Abstract

The demand for cost-efficient forest aboveground biomass (AGB) prediction methods is growing worldwide. The National Land Survey of Finland (NLS) began collecting airborne laser scanning (ALS) data throughout Finland in 2008 to provide a new high-detailed terrain elevation model. Similar data sets are being collected in an increasing number of countries worldwide. These data sets offer great potential in forest mapping related applications. The objectives of our study were (i) to evaluate the AGB component prediction accuracy at a resolution of 300 m2 using sparse density, leaf-off ALS data (collected by NLS) derived metrics as predictor variables; (ii) to compare prediction accuracies with existing large-scale forest mapping techniques (Multi-source National Forest Inventory, MS-NFI) based on Landsat TM satellite imagery; and (iii) to evaluate the accuracy and effect of canopy height model (CHM) derived metrics on AGB component prediction when ALS data were acquired with multiple sensors and varying scanning parameters. Results showed that ALS point metrics can be used to predict component AGBs with an accuracy of 29.7%–48.3%. AGB prediction accuracy was slightly improved using CHM-derived metrics but CHM metrics had a more clear effect on the estimated bias. Compared to the MS-NFI, the prediction accuracy was considerably higher, which was caused by differences in the remote sensing data utilized. View Full-Text
Keywords: remote sensing; forest inventory; LiDAR remote sensing; forest inventory; LiDAR
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kankare, V.; Vauhkonen, J.; Holopainen, M.; Vastaranta, M.; Hyyppä, J.; Hyyppä, H.; Alho, P. Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction. Forests 2015, 6, 1839-1857.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top