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Integrating Remote Sensing Data with Directional Two-Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management

View Article: PubMed Central - PubMed

ABSTRACT

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. As a case study, this study analyzed NDVI images derived from SPOT HRV images before and after the ChiChi earthquake (7.3 on the Richter scale) that hit the Chenyulan basin in Taiwan, as well as images after two large typhoons (xangsane and Toraji) to delineate the spatial patterns of landslides caused by major disturbances. Disturbed spatial patterns of landslides that followed these events were successfully delineated using 2D wavelet analysis, and results of pattern recognitions of landslides were distributed simultaneously to other agents using geography markup language. Real-time information allows successive platforms (agents; to work with local geospatial data for disaster management. Furthermore, the proposed is suitable for detecting landslides in various regions on continental, regional, and local scales using remotely sensed data in various resolutions derived from SPOT HRV, IKONOS, and QuickBird multispectral images.

No MeSH data available.


Geography of the study area.
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f3-sensors-08-01070: Geography of the study area.

Mentions: Field data were collected from the Chenyulan watershed. The Chenyulan watershed, located in central Taiwan, is an intermountain watershed, and has average altitude of 1540 m and area of 449 km2. The Chenyulan stream which follows the Chenyulan fault flows from south to north and elongates the watershed in the same direction. Different uplifting along the fault has generated abundant fractures throughout the watershed, resulting in an average slope of 62.5% and relief of 585 m/km2 in the watershed. Moreover, the main course of the Chenyulan stream has a gradient of 6.1%, and over 60% of its tributaries have gradients exceeding 20%. The special geological and geographical characteristics of the watershed result in frequent landsides and debris flows [18]. At 01:47′12.6″ on Sep. 21, 1999, the ChiChi earthquake (7.3 on the Richter scale), caused by motion of the Chelungpu thrust fault, struck central Taiwan. The earthquake epicenter was located at 23.87°N and 120.75°E, around 30 km north-northwest of the Chenyulan watershed [19]. One year the earthquake, typhoon Xangsane brushed the eastern side of the watershed as it moved from south to north and dropped 127-270 mm of total rainfall in the watershed between Oct. 31, and Nov. 1, 2000 [20]. Another typhoon, Toraji, which brought intense rainfall over a short duration exceeding 300 years return period, hit the watershed as it moved east to west on July 28-31, 2001. After crossing Taiwan, typhoon Toraji degraded into a tropical storm; however, the typhoon dropped 339-757 mm of rainfall in the watershed [21]. Sediments already loosened by the ChiChi earthquake bore the torrential rainfall brought by the two typhoons. Consequently, a large quantity of loose sediment promoted massive debris flows during subsequent typhoons. The Taiwan Water and Soil Conservation Bureau announced debris flood warnings for several tributaries of the Chenyulan stream after typhoon Toraji. Location of landslides was shifted from mid-hills to hilltops in central Taiwan by the ChiChi earthquake, and hilltop landslides triggered serious debris flows [22]. The study area 3.2×3.2 km2 (256×256 pixels) was selected upstream of the largest debris flood in the watershed (Figure 3).


Integrating Remote Sensing Data with Directional Two-Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management
Geography of the study area.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927515&req=5

f3-sensors-08-01070: Geography of the study area.
Mentions: Field data were collected from the Chenyulan watershed. The Chenyulan watershed, located in central Taiwan, is an intermountain watershed, and has average altitude of 1540 m and area of 449 km2. The Chenyulan stream which follows the Chenyulan fault flows from south to north and elongates the watershed in the same direction. Different uplifting along the fault has generated abundant fractures throughout the watershed, resulting in an average slope of 62.5% and relief of 585 m/km2 in the watershed. Moreover, the main course of the Chenyulan stream has a gradient of 6.1%, and over 60% of its tributaries have gradients exceeding 20%. The special geological and geographical characteristics of the watershed result in frequent landsides and debris flows [18]. At 01:47′12.6″ on Sep. 21, 1999, the ChiChi earthquake (7.3 on the Richter scale), caused by motion of the Chelungpu thrust fault, struck central Taiwan. The earthquake epicenter was located at 23.87°N and 120.75°E, around 30 km north-northwest of the Chenyulan watershed [19]. One year the earthquake, typhoon Xangsane brushed the eastern side of the watershed as it moved from south to north and dropped 127-270 mm of total rainfall in the watershed between Oct. 31, and Nov. 1, 2000 [20]. Another typhoon, Toraji, which brought intense rainfall over a short duration exceeding 300 years return period, hit the watershed as it moved east to west on July 28-31, 2001. After crossing Taiwan, typhoon Toraji degraded into a tropical storm; however, the typhoon dropped 339-757 mm of rainfall in the watershed [21]. Sediments already loosened by the ChiChi earthquake bore the torrential rainfall brought by the two typhoons. Consequently, a large quantity of loose sediment promoted massive debris flows during subsequent typhoons. The Taiwan Water and Soil Conservation Bureau announced debris flood warnings for several tributaries of the Chenyulan stream after typhoon Toraji. Location of landslides was shifted from mid-hills to hilltops in central Taiwan by the ChiChi earthquake, and hilltop landslides triggered serious debris flows [22]. The study area 3.2×3.2 km2 (256×256 pixels) was selected upstream of the largest debris flood in the watershed (Figure 3).

View Article: PubMed Central - PubMed

ABSTRACT

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. As a case study, this study analyzed NDVI images derived from SPOT HRV images before and after the ChiChi earthquake (7.3 on the Richter scale) that hit the Chenyulan basin in Taiwan, as well as images after two large typhoons (xangsane and Toraji) to delineate the spatial patterns of landslides caused by major disturbances. Disturbed spatial patterns of landslides that followed these events were successfully delineated using 2D wavelet analysis, and results of pattern recognitions of landslides were distributed simultaneously to other agents using geography markup language. Real-time information allows successive platforms (agents; to work with local geospatial data for disaster management. Furthermore, the proposed is suitable for detecting landslides in various regions on continental, regional, and local scales using remotely sensed data in various resolutions derived from SPOT HRV, IKONOS, and QuickBird multispectral images.

No MeSH data available.