Improving forest type classification using the vegetation local difference index
| Autor: | Honggan Wu, Chenxin Chen, Zhao Bian, Ping Tang, Shengyang Li |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | International Journal of Remote Sensing. 36:3701-3713 |
| ISSN: | 1366-5901 0143-1161 |
| DOI: | 10.1080/01431161.2015.1047992 |
| Popis: | A spatial feature extraction method was applied to increase the accuracy of land-cover classification of forest type information extraction. Traditional spatial feature extraction applications use high-resolution images. However, improving the classification accuracy is difficult when using medium-resolution images, such as a 30 m resolution Enhanced Thematic Mapper Plus ETM+ image. In this study, we demonstrated a novel method that used the vegetation local difference index VLDI derived from the normalized difference vegetation index NDVI, which were calculated based on the topographically corrected ETM+ image, to delineate spatial features. A simple maximum likelihood classifier and two different ways to use spatial information were introduced in this study as the frameworks to incorporate both spectral and spatial information for analysis. The results of the experiments, where Landsat ETM+ and digital elevation model DEM images, together with ground truth data acquired in the study area were used, show that combining the spatial information extracted from medium-resolution images and spectral information improved both classification accuracy and visual qualities. Moreover, the use of spatial information extracted through the proposed method greatly improved the classification performance of particular forest types, such as sparse woodlands. |
| Databáze: | OpenAIRE |
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