Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
| Autor: | Lucie Jakešová, Lucie Kupková, Lucie Červená, Renata Sucha |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Geography Planning and Development 0211 other engineering and technologies multispectral data 02 engineering and technology Land cover 01 natural sciences Cohen's kappa Feature (machine learning) Image resolution 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Orthophoto lcsh:Geography. Anthropology. Recreation Vegetation Spectral bands Support vector machine per-pixel classification lcsh:G lcsh:HB848-3697 object based classification vegetation above the tree line Krkonoše Mountains General Earth and Planetary Sciences lcsh:Demography. Population. Vital events |
| Zdroj: | Acta Universitatis Carolinae Geographica, Vol 51, Iss 1, Pp 113-129 (2016) |
| ISSN: | 2336-1980 0300-5402 |
| Popis: | This paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonose Mts. National Park. Two legends were proposed: the detailed one with twelve classes, and simplified legend with eight classes. Aerial orthorectified images (orthoimages) with very high spatial resolution (12.5 cm) and four spectral bands have been examined using the object based classification. Satellite data WorldView-2 (WV-2) with high spatial resolution (2 metres) and eight spectral bands have been examined using object based classification and per-pixel classification. Per-pixel classification has been applied also to the freely available Landsat 8 data (spatial resolution 30 metres, seven spectral bands). Of the algorithms for per-pixel classification, the following classifiers were compared: maximum likelihood classification (MLC), support vector machine (SVM), and neural net (NN). The object based classification utilized the example-based approach and SVM algorithm (all available in ENVI 5.2). Both legends (simplified and detailed ones) show best results in the case of orthoimages (overall accuracy 83.56% and 71.96% respectively, Kappa coefficient 0.8 and 0.65 respectively). The WV-2 classification brought best results using the object based approach and simplified legend (68.4%); in the case of per-pixel classification it was the SVM method (RBF) and detailed legend (60.82%). Landsat data were best classified using the MLC (78.31%). Our research confirmed that Landsat data are sufficient to get a general overview of basic land cover classes above the tree line in the Krkonose Mts. National Park. Based on the comparison of the data with different spectral and spatial resolution we can however conclude that very high spatial resolution is the decisive feature that is essential to reach high overall classification accuracy in the detailed level. |
| Databáze: | OpenAIRE |
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