AIRBORNE HYPERSPECTRAL REMOTE SENSING FOR IDENTIFICATION GRASSLAND VEGETATION

In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes

Application of remote sensing in the red sludge environmental disaster in Hungary

One of the largest industrial disasters in Europe took place in the village of Kolontár (Hungary) on October 4th, 2010. Due to a ruptured dam more than 1 million m3 of red sludge flooded the nearby small towns along the Torna river. The spilled material containing a highly alkaline solution (>12 pH) resulted in a complex environmental disaster, requiring a multi-disciplinary approach regarding the assessment and the remediation of the site.The Károly Róbert College has developed a remote sensing protocol, which greatly assists both the domestic and international disaster management (forecast, damage surveying and control). In case of the Hungarian red sludge disaster the primary objective of the hyperspectral remote sensing mission was to estimate the environmental damage, the precise size of the polluted area, the rating of substance concentration in the sludge. 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