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• UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

  169-178

  László Bertalan

  Zoltán Túri

  Gergely Szabó

  Views:

  264

  A remarkable badland valley is situated near Kazár, NE-Hungary, where rhyolite tuff outcrops as greyish white cliffs and white barren patches. The landform is shaped by gully and rill erosion processes. We performed a preliminary state UAS survey and created a digital surface model and ortophotograph. The flight was operated with manual control in order to perform a more optimal coverage of the aerial images. The overhanging forests induced overexposed photographs due to the higher contrast with the bare tuff surface. The multiresolution segmentation method allowed us to classify the ortophotograph and separate the tuff surface and the vegetation. The applied methods and final datasets in combination with the subsequent surveys will be used for detecting the recent erosional processes of the Kazár badland.

• Analysis of multitemporal aerial images for fenyőfő Forest change detection

  89-100

  Shukhrat Shokirov

  Géza Király

  Views:

  237

  This study evaluated the use of 40 cm spatial resolution aerial images for individual tree crown delineation, forest type classification, health estimation and clear-cut area detection in Fenyőfő forest reserves in 2012 and 2015 years. Region growing algorithm was used for segmentation of individual tree crowns. Forest type (coniferous/deciduous trees) were distinguished based on the orthomosaic images and segments. Research also investigated the height of individual trees, clear-cut areas and cut crowns between 2012 and 2015 years using Canopy Height Models. Results of the research were examined based on the field measurement data. According to our results, we achieved 75.2% accuracy in individual tree crown delineation. Heights of tree crowns have been calculated with 88.5% accuracy. This study had promising result in clear cut area and individual cut crown detection. Overall accuracy of classification was 77.2%, analysis showed that coniferous tree type classification was very accurate, but deciduous tree classification had a lot of omission errors. Based on the results and analysis, general information about forest health conditions has been presented. Finally, strengths and limitations of the research were discussed and recommendations were given for further research.