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Auflistung nach Autor:in "Panov, Pance"

1 - 2 von 2
  • Konferenzbeitrag
    Using Decision Trees to Predict Forest Stand Height and Canopy Cover from LANDSAT and LIDAR Data
    (Managing Environmental Knowledge, 2006) Dzeroski, Saso; Kobler, Andrej; Gjorgjioski, Valentin; Panov, Pance
    The motivation for this study was to improve the consistency and accuracy, and increase the spatial resolution of some of the supporting information to the forest monitoring system in Slovenia by using data mining techniques. Specifically we aim to generate raster maps with 25 m horizontal resolution of forest stand height and canopy cover, for the Kras region of Slovenia. We used predictive models based on multi-temporal Landsat data and calibrated it with high resolution airborne laser scanning (ALS) data. The visual inspection by a forestry expert of the resulting maps showed that the generated maps corresponded to the actual forest cover in the Kras region, both in terms of forest stand height as well as canopy cover.
  • Konferenzbeitrag
    Using Simulation Models and Data Mining to Study Co-Existence of GM/Non-GM Crops at Regional Level
    (Managing Environmental Knowledge, 2006) Ivanovska, Aneta; Panov, Pance; Colbach, Nathalie; Debeljak, Marko; Dzeroski, Saso; Messean, Antoine
    Genetically-modified (GM) crops increased their share in EU agriculture, so the adventitious presence of GM varieties in non-GM seeds and crops has become an issue and poses the problem of their co-existence with conventional and organic crops. Therefore, there is a need to propose appropriate measures at the farm and regional levels to minimize adventitious presence of GM crops. Outputs from the previously developed GENESYS model for gene flow between cropped and volunteer oilseed rape were used to make rule-based models that predict the rates of adventitious presence of GM seeds in the central field of a large-risk field pattern. Data aggregation was carried out to investigate if the regional variables improve the prediction quality of the rule-based model.