Skip to main content

Evaluation of digital terrain models generated in forest conditions from airborne laser scanning data acquired in two seasons

Buy Article:

$63.00 plus tax (Refund Policy)

Abstract:

Airborne laser scanners (ALS) provide accurate data for digital terrain model (DTM) construction. Because of a small number of experiences, ALS-based DTMs should be tested widely in a variety of forest environments. In this study, a series of DTMs were produced from ALS data, acquired twice in one year (spring/summer). The study was carried out in a 1000-ha forested area in Poland. Spatial resolutions of output DTMs, season of data acquisition, number of vegetation layers and tree species in the first forest floor were evaluated to assess their influence on the DTM errors. Surveying methods were used to collect coordinates of 95 checkpoints. For various output raster resolutions and seasons of data acquisition, mean errors varied between −0.2 and 0.34 m, and root mean square errors varied from 0.28 to 0.79 m. Errors increased linearly with DTM pixel size, and their variability was significantly higher in DTMs derived from summer data than in DTMs derived from spring data. Effects of seasonality were modified by both forest structure and species composition. One-layer stands were more sensitive to season of data acquisition than were multilayer stands, as were larch and alder stands in comparison to pine and oak stands.

Keywords: Accuracy assessment; DTM; LIDAR; forestry

Document Type: Research Article

DOI: https://doi.org/10.1080/02827581.2011.570781

Affiliations: 1: Department of Forest Management, Geomatics and Forest Economics, Faculty of Forestry,Warsaw University of Life Sciences – SGGW, Warsaw, Poland 2: Department of GIS, Cartography and Remote Sensing, Institute of Geography and Spatial Management,Jagiellonian University, Kraków, Poland

Publication date: 2011-08-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more