Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey
| Autor: | Coskun Ozkan, M. Hayri Kesikoglu, U. Haluk Atasever, Filiz Dadaser-Celik |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Conservation of Natural Resources
Support Vector Machine Environmental Engineering Watershed Marsh Turkey 010504 meteorology & atmospheric sciences Water flow Wetland 010501 environmental sciences 01 natural sciences Aster (genus) Ecosystem 0105 earth and related environmental sciences Water Science and Technology geography geography.geographical_feature_category biology biology.organism_classification Water resources Ramsar site Wetlands Environmental science Neural Networks Computer Physical geography Change detection Environmental Monitoring |
| Zdroj: | Water Science and Technology. 80:466-477 |
| ISSN: | 1996-9732 0273-1223 |
| DOI: | 10.2166/wst.2019.290 |
| Popis: | Wetlands are among the most productive ecosystems that provide services ranging from flood control to climate change mitigation. Wetlands are also critical habitats for the survival of numerous plant and animal species. In this study, we used satellite remote sensing techniques for classification and change detection at an internationally important wetland (Ramsar Site) in Turkey. Sultan Marshes is located at the center of semi-arid Develi closed basin. The wetlands have undergone significant changes since the 1980s due to changes in water flow regimes, but changes in recent years have not been sufficiently explored yet. In this study, we focused on the changes from 2005 to 2012. Two multispectral ASTER images with spatial resolution of 15 m, acquired on June 11, 2005 and May 20, 2012, were used in the analyses. After geometric correction, the images were classified into four information classes, namely water, marsh, agriculture, and steppe. The applicability of three classification methods (i.e. maximum likelihood (MLH), multi-layer perceptron type artificial neural networks (ANN) and support vector machines (SVM)) was assessed. The differences in classification accuracies were evaluated by the McNemar's test. The changes in the Sultan Marshes were determined by the post classification comparison method using the most accurate classified images. The results showed that the highest overall accuracy in image classifications was achieved with the SVM method. It was observed that marshes and steppe areas decreased while water and agricultural areas expanded from 2005 to 2012. These changes could be the results of water transfers to the marshes from neighboring watershed. |
| Databáze: | OpenAIRE |
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