| Summary: | 碩士 === 國立中央大學 === 遙測科技碩士學位學程 === 107 === Optical remote sensing data has been used to assist the preparation of landslide inventory based on its high distinguishability of spectrum characteristic. However, its applicability can be limited when the applied image is contaminated by cloud. Synthetic Aperture Radar (SAR) signals can penetrate cloud and identify land surface changes by examining their backscattering characteristics which are useful information for landslide detection. Therefore, SAR-based emergency mapping has recognizable potential to design, implement, and evaluate disaster risks and recovery programs. The major objective in this study is to develop a method that can integrate both optical and SAR remote sensing data for an effective and rapid landslide detection, and aims to maintain high accuracy during cloudy weather conditions.
Accordingly, to integrate advantages of both remote sensing data in landslide detection, decision-level image fusion approach in an object-based image analysis (OBIA) framework is designed and tested in this study. The Laonong River watershed in southern Taiwan is selected as the study site, where heavy rainfall induced large-scale landslides and caused severe damages during Typhoon Morakot in 2009. Specifically, two indices, Normalized Difference Vegetation Index difference (NDVIdiff) for optical data and Normalized Difference Sigma-naught Index (NDSI) for SAR data, were separately calculated by using pre- and post-event Formosat-2 images and L-band ALOS-PALSAR images respectively, and then were applied in the OBIA to generate six texture indices (mean, standard deviation, contrast, entropy, homogeneity and dissimilarity) for both optical and SAR data. Decision-level fusion is adopted in this study when pixel-level and feature-level fusion are difficult to practice due to co-registration issue between optical and SAR images over mountainous area. In the study, considering the successful application of machine-learning algorithm in image classification in recent years, Artificial Neural Network (ANN) classifier is therefore applied. Overall, five experiments were performed and simulated: (1) ANN-NDVIdiff, (2) ANN-NDSI, (3) ANN-All, (4) ANN-Cloud and (5) ANN-Mosaic.
Finally, landslide detection results obtained by ANN-All show higher overall accuracy and Kappa coefficient than other experiments, indicating the significance of applicability of optical-SAR fused data in landslide mapping task. In other hand, result of ANN-NDSI indicates that SAR image can detect landslide areas in approximately location, especially performs well for large scale landslides. SAR image can instead of optical image during cloudy weather situation for emergency landslide mapping.
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