Vol. 16, No. 1, 1-8, 2017

Integration of remote sensing data into forest inventory in close-to-nature forests: An initial case study in Smolnícka Osada, Slovakia
Ivan Sačkov, Maroš Sedliak, Ladislav Kulla, and Tomáš Bucha

Abstract
The initial case study concerns the first assessment of possibilities of integrating remote sensing into forest inventory in Slovak close-to-nature forests. Aerial images and airborne laser scanning data were used to estimate forest stand characteristics, such as the number of trees, mean tree height, mean tree diameter, and growing stock. eCognition software for tree species classification and reFLex software for individual tree detection was used. The accuracy of the assessment was conducted in the ProSilva object Smolnícka Osada (Eastern Slovakia, Central Europe), which has been under selective management for more than 60 years. The ALS data were obtained with a Leica RCD30 scanner from an average height of 1,034 m with a point density of 4 echoes per m². The ground reference data contained the measured positions and dimensions of 924 trees in 35 plots distributed across the region. It was found that the difference between the remote-based results and ground data was -50% for the number of trees, 8% for mean height and diameter, and -28% for growing stock.

View Full Text (pdf file, 1.1 MB) previous page
DOI: 10.12760/01-2017-1-07

History
Submitted: 21 Oct 2016
Revised: 19 Dec 2016
Accepted: 20 Dec 2016
Published: 20 Feb 2017
Responsible editor: Bogdan Zagajewski

Citation
Sačkov I, M Sedliak, L Kulla & T Bucha, 2017. Integration of remote sensing data into forest inventory in close-to-nature forests: An initial case study in Smolnícka Osada, Slovakia. EARSeL eProceedings, 16(1): 1-8
EARSeL-logo

EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France

   
BIS-logo

BIS-Verlag
BIS Library and Information System, Carl von Ossietzky University of Oldenburg

   
Scopus-logo

Indexed in Scopus

   
DOAJ logo

DOAJ
Directory of Open Access Journals

   
SPERPA/RoMEO logo

SHERPA/RoMEO
Opening access to research

   
JournalGuide logo

JournalGuide
Find the best journal for your research

Creative Commons License

ISSN 1729-3782