Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning

Remote Sensing

András Zlinszky⁴  Balázs Deák¹  Adam Kania²  Anke Schroiff³  Norbert Pfeifer⁶  Hermann Heilmeier⁵  Heiko Balzter⁵  Bernhard H཯le⁵ 

[1] MTA-DE Biodiversity and Ecosystem Services Research Group, Egyetem tér 1, Debrecen 4032, Hungary; E-Mail:;ATMOTERM S.A., ul. Łangowskiego 4, Opole 45-031, Poland; E-Mail:;YggdrasilDiemer, Dudenstr. 38, Berlin 10965, Germany; E-Mail:;Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences, Klebelsberg Kuno út 3, Tihany 8237, Hungary;id="af1-remotesensing-01-02991">1Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences, Klebelsberg Kuno út 3, Tihany 8237, Hunga;Vienna University of Technology, Department of Geodesy and Geoinformation, Research Groups Photogrammetry and Remote Sensing, Gußhausstraße 27–29, Vienna 1040, Austria; E-Mail:

关键词: Natura 2000;    conservation status;    Airborne Laser Scanning;    LiDAR;    grasslands;    Pannonic Salt Steppe;    habitat assessment;    habitat quality;   

DOI  :  10.3390/rs70302991

来源: mdpi

【 摘 要 】

Natura 2000 Habitat Conservation Status is currently evaluated based on fieldwork. However, this is proving to be unfeasible over large areas. The use of remote sensing is increasingly encouraged but covering the full range of ecological variables by such datasets and ensuring compatibility with the traditional assessment methodology has not been achieved yet. We aimed to test Airborne Laser Scanning (ALS) as a source for mapping all variables required by the local official conservation status assessment scheme and to develop an automated method that calculates Natura 2000 conservation status at 0.5 m raster resolution for 24 km² of Pannonic Salt Steppe habitat (code 1530). We used multi-temporal (summer and winter) ALS point clouds with full-waveform recording and a density of 10 pt/m². Some required variables were derived from ALS product rasters; others involved vegetation classification layers calculated by machine learning and fuzzy categorization. Thresholds separating favorable and unfavorable values of each variable required by the national assessment scheme were manually calibrated from 10 plots where field-based assessment was carried out. Rasters representing positive and negative scores for each input variable were integrated in a ruleset that exactly follows the Hungarian Natura 2000 assessment scheme for grasslands. Accuracy of each parameter and the final conservation status score and category was evaluated by 10 independent assessment plots. We conclude that ALS is a suitable data source for Natura 2000 assessments in grasslands, and that the national grassland assessment scheme can successfully be used as a GIS processing model for conservation status, ensuring that the output is directly comparable with traditional field based assessments.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.