Publication Date:
2012-11-21
Description:
ABSTRACT Accurate and cost-effective mapping of karst rocky desertification (KRD) is still a challenge at the regional and national scale. Visual interpretation has been utilized in majority of studies, while automated method based on pixel has been investigated repeatedly at present. A object-based method coupling with Support Vector Machine (SVM) was developed and tested by ETM + images in three selected counties (Liujiang, Changshun, Zhenyuan) with different karst landscape in SW China. The method provided a strategy of defining mapping unit. It combined ETM + images and ancillary data including elevation, slope and Normalized Difference Vegetation Index images. A sequence of scale parameters estimation, image segmentation, training data sampling, SVM parameters tuning, and object classification had been performed to achieve the mapping. A quantitative and semi-automated approach was used to estimate scale parameters for segmenting object in an optimal scale. We calculated sum of area-weighted standard deviation (WS), rate of change for WS, local variance (LV) and rate of change for LV at each scale level, and the threshold of above index indicated the optimal segment level and merge level. The KRD classification results had overall accuracies of 85.50%, 84.00% and 84.86% for Liujiang, Changshun and Zhenyuan respectively, and Kappa coefficients are up to 0.8062, 0.7917 and 0.8083 respectively. This approach mapped six classes of KRD and offered a visually appealing presentation. Moreover, it proposed a conceptual and size-variable object from the classification standard of KRD. The results demonstrate that the application of our method provides an efficient approach for the mapping KRD. Copyright © 2012 John Wiley & Sons, Ltd.
Print ISSN:
1085-3278
Electronic ISSN:
1099-145X
Topics:
Geography
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Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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