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 effective 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.
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 Corine 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.