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• Studying floodplain roughness in an Upper Tisza study area

  85-90

  Róbert Vass

  Zoltán Túri

  Views:

  180

  Floods slowing down due to the significant decrease of the gradient have considerable sediment accumulation capacity in the floodplain. The grade of accumulation is further increased if the width of the floodplain is not uniform as water flowing out of the narrow sections diverge and its speed is decreased. Surface roughness in a study area of 492 hectares in the Upper Tisza region was analysed based on CIR (color-infrared) orthophotos from 2007. An NDVI index layer was created first on which object-based image segmentation and threshold-based image classification were performed. The study area is dominated by land cover / land use types (grassland-shrubs, forest) with high roughness values. It was concluded that vegetation activity based analyses on their own are not enough for determining floodplain roughness.

• Analysis of the connection between urban land cover and census districts using geoinformatical methods

  52-67

  László Henits

  László Mucsi

  Views:

  47

  Remote sensing resources are usually used in research to better understand urban built-up density, spatial structure and the processes of change. Based on results of image segmentation, landscape metrics indexes, texture and pattern may be analyzed beside the spatial changes in urban reflectance. Social processes within the settlement can be analyzed efficiently, although the census data may also be connected to the urban land cover data through geoinformation systems. On the research project different parameters of urban segments, i.e. patch number, mean patch area, total patch area, total patch perimeter, patch density and edge density, formations that make up the urban pattern were analyzed. Urban functional districts of different built-up density were separated using appropriate indexes, and extending the database with spectral content made it possible to review district boundaries and to mark new boundaries due to these changes.

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

  169-178

  László Bertalan

  Zoltán Túri

  Gergely Szabó

  Views:

  193

  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:

  223

  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.