In Taiwan, earthquakes have long been recognized as a major cause of landslides that are wide spread by floods brought by typhoons followed. Distinguishing between landslide spatial patterns in different disturbance regimes is fundamental for disaster monitoring, management, and land-cover restoration. To circumscribe landslides, this study adopts the normalized difference vegetation index (NDVI), which can be determined by simply applying mathematical operations of near-infrared and visible-red spectral data immediately after remotely sensed data is acquired. In real-time disaster monitoring, the NDVI is more effective than using land-cover classifications generated from remotely sensed data as land-cover classification tasks are extremely time consuming. Directional two-dimensional (2D) wavelet analysis has an advantage over traditional spectrum analysis in that it determines localized variations along a specific direction when identifying dominant modes of change, and where those modes are located in multi-temporal remotely sensed images. Open geospatial techniques comprise a series of solutions developed based on Open Geospatial Consortium specifications that can be applied to encode data for interoperability and develop an open geospatial service for sharing data. This study presents a novel approach and framework that uses directional 2D wavelet analysis of real-time NDVI images to effectively identify landslide patterns and share resulting patterns via open geospatial techniques.

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