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Mapping aquatic vegetation of the Rakamaz-Tiszanagyfalui Nagy-Morotva using hyperspectral imagery
Published October 9, 2010

Rapid development in remote sensing technologies provides more and more reliable methods for environmental assessment. For most wetlands, it is difficult to walk-in without disturbing the endangered species living there; therefore, application of opportunities provided by remote sensing has a great importance in population-mapping. One effectiv...e tool of vegetation pattern estimation is hyperspectral remote sensing, which can be used for association and species level mapping as well, due to high ground resolution. The Rakamaz-Tiszanagyfalui Nagy-morotva is an oxbow lake, located in the north-eastern part of Hungary. For this study, a wetland area of 1.17 km2 containing the original water bad and shoreline was selected. For the image analysis, images taken by an AISA DUAL system hyperspectral sensor were used. At the same time, 7 main vegetation classes were separated, which are typical for the sample plot designated on the test site. Classification was performed by the master areas signed by the most common associations of the Rakamaz-Tiszanagyfalui Nagy-morotva with determined spectrums. During the image analysis, SAM classification method was used, where radian values were optimized by the results of classification performed at the control area.

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Studying floodplain roughness in an Upper Tisza study area
Published July 14, 2021

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.

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Preliminary analysis of red mud spill based on aerial imagery, Hungary
Published June 17, 2011

One of the largest industrial spills in Europe occurred in the village of Kolontár (Hungary) on October 4, 2010. The primary objective of the hyperspectral remote sensing mission was to monitor that is necessary in order to estimate the environmental damage, the precise size of the polluted area, the rating of substance concentration in the mu...d, and the overall condition of the flooded district as soon as possible. The secondary objective was to provide geodetic data necessary for the high-resolution visual information from the data of an additional Lidar survey, and for the coherent modeling of the event. For quick assessment and remediation purposes, it was deemed important to estimate the thickness of the red mud, particularly the areas where it was deposited in a thick layer. The results showed that some of the existing tools can be easily modified and implemented to get the most out of the available advanced remote sensing data.

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Analysis of multitemporal aerial images for fenyőfő Forest change detection
Published October 14, 2016

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/decidu...ous 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.

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Land cover analysis based on descriptive statistics of Sentinel-2 time series data
Published December 20, 2018

In our paper we examined the opportunities of a classification based on descriptive statistics of NDVI
throughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 images
in 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, in
accordance with Co...rine Land Cover (CLC) nomenclature. The result of classification had a 76.2% overall
accuracy. We described the reasons for the disagreement in case of the most remarkable errors. 

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Spatial distribution of vegetation cover in Erbil city districts using high-resolution Pléiades satellite image
Published June 30, 2018

Green spaces are playing an essential role for ecological balance and for human health in the city as well.
They play a fundamental role in providing opportunities for relaxation and enjoying the beauty of nature
for the urban population. Therefore, it is important to produce detailed vegetation maps to assist planners
in designing str...ategies for the optimisation of urban ecosystem services and to provide a suitable plan
for climate change adaptation in one fast growing city. Hence, this research is an investigation using 0.5
m high-resolution multispectral Pléiades data integrated with GIS data and techniques to detect and
evaluate the spatial distribution of vegetation cover in Erbil City. A supervised classification was used
to classify different land cover types, and a normalised difference vegetation index (NDVI) was used
to retrieve it for the city districts. Moreover, to evaluate the accessibility of green space based on their
distance and size, a buffer zone criterion was used. The results indicate that the built-up land coverage
is 69% and vegetation land cover is 14%. Regarding NDVI results, the spatial distribution of vegetation
cover was various and, in general, the lowest NDVI values were found in the districts located in the city
centre. On the other hand, the spatial distribution of vegetation land cover regarding the city districts was
non-equal and non-concentric. The newly built districts and the districts far from the Central Business
District (CBD) recorded the lowest vegetation cover compared with the older constructed districts.
Furthermore, most of the districts have a lack of access to green spaces based on their distance and size.
Distance and accessibility of green areas throughout the city are not equally distributed. The majority of
the city districts have access to green areas within radius buffer of two kilometres, whereas the lowest
accessibility observed for those districts located in the northeast of the city in particular (Xanzad,
Brayate, Setaqan and Raperin). Our study is one of the first investigations of decision-making support
of the spatial planning in a fast-growing city in Iraq and will have a utilitarian impact on development
processes and local and regional planning for Erbil City in the future.

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The developement of red mud flood environmental information system and the methodology for the spatial analysis of the degraded area
Published May 4, 2015

The red mud disaster occurred on 4th October 2010 in Hungary has raised the necessity of rapid intervention and drew attention to the long-term monitoring of such threat. Both the condition assessment and the change monitoring indispensably required the prompt and detailed spatial survey of the impact area. It was conducted by several research ...groups - independently - with different recent surveying methods. The high spatial resolution multispectral aerial photogrammetry is the spatially detailed (high resolution) and accurate type of remote sensing. The hyperspectral remote sensing provides more information about material quality of pollutants, with less spatial details and lower spatial accuracy, while LIDAR ensures the three-dimensional shape and terrain models. The article focuses on the high spatial resolution, multispectral electrooptical method and the evaluation methodology of the deriving high spatial resolution ortho image map, presenting the derived environmental information database

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Unsupervised classification of high resolution satellite imagery by self-organizing neural network
Published October 16, 2010

The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispe...ctral image information can be taken after a resampling step as neural network inputs, and then the derived network weights are able to evaluate the whole image with acceptable thematic accuracy.

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