Modelling habitats in karst landscape by integrating remote sensing and topography data

Abstract: Field mapping is an accurate but also time consuming method of detailed mapping of habitat types. Levels of habitat types are usually hierarchically nested at several levels. Our main research question therefore is: ‘How detailed can be modelling of habitat types with decision trees and digital data in karst landscape?’ Similar to studies in other (non-karst) environments we explored the basic properties of the habitats in Dinaric Karst study region (Classical Karst in Southwest Slovenia) and tested modelling of habitat types at three different levels of detail. To seek for the best set of predictor variables we used Rapid-Eye satellite images, airborne images and digital elevation model. We prepared more than 60 explanatory variables and divided habitat polygons into training and testing samples to validate the results. The results proved that modelling with decision trees in Dinaric Karst landscape does not result in high accuracy at high detailed levels. Due to the presence of mine fields in the large area of Dinaric Karst (e.g. in Croatia and Bosnia and Herzegovina) the field mapping in this area is difficult therefore the findings from this study can be used for further development of mapping through remote sensing.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Modelling habitats in karst landscape by integrating remote sensing and topography data ; volume:10 ; number:1 ; year:2018 ; pages:137-156 ; extent:20 ; Open Geosciences ; 10, Heft 1 (2018), 137-156 (gesamt 20)

Contributors, Places and Time

• Creator: Valjavec, Mateja Breg ; Ciglič, Rok ; Oštir, Krištof ; Ribeiro, Daniela

Further information

• DOI: 10.1515/geo-2018-0011
• URN: urn:nbn:de:101:1-2501051609250.460690682858
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:31 AM CEST