Abstract:Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier-MLC), machine learning algorithms (support vector machine-SVM, random forest-RF) and feature extraction (minimum noise fraction (MNF)-transformation) on training datasets of different sizes. Digital images were acquired from 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. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2-15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the OPEN ACCESS Remote Sens. 2015, 7 2047 aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%), but the use of a smaller training dataset (10 training pixels) significantly reduced the accuracy of classification (52.56%). Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC. SVM was not sensitive in the training sample size, which makes it an adequate tool when only a limited number of training pixels are available for some classes.
The spatial complexity of floodplains is a function of several processes: hydrodynamics, flow direction, sediment transportation, and land use. Sediments can bind toxic elements, and as there are several pollution sources, the risk of heavy metal accumulation on the floodplains is high. We aimed to determine whether fluvial forms have a role in metal accumulations. Topsoil samples were taken from point bars and swales in the floodplain of the Tisza River, North-East Hungary. Soil properties and metal concentrations were determined, and correlation and hypothesis testing were applied. The results showed that fluvial forms are important drivers of horizontal metal patterns: there were significant differences (p < 0.05) between point bars and swales regarding Fe, K, Mg, Mn, Cr, Cu, Ni, Pb, and Zn. Vertical distribution also differed significantly by fluvial forms: swales had higher metal concentrations in all layers. General Linear Models had different results for macro and micro elements: macro element concentrations were determined by the organic matter, while for micro elements the clay content and the forms were significant explanatory variables. These findings are important for land managers and farmers because heavy metal concentration has a direct impact on living organisms, and the risk of bioaccumulation can be high on floodplains.
ABSTRACT: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.
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