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

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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.

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(a) Point clumps with different densities were generated in 4 ′A′ regions each with area of 20 × 25 on a random background ′B′ with area of 256 × 256. The background randomness has a point density of 0.025 (1639/65536). Spectrum maps of clumps with (b) 2-fold, (c) 4-fold, (d) 6-fold, (e) 8-fold, and (f) 24-fold point densities relative to the background were used to verify the performance of directional 2D Morlet wavelet analysis in separating true patterns from random fluctuations. The dashed polygons represent shapes and scales of point clumps. For a recognizable pattern, the thick solid line, that represents a normalized mean spectrum of one, is expected to be totally contained in the dashed polygon.
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f5-sensors-08-01070: (a) Point clumps with different densities were generated in 4 ′A′ regions each with area of 20 × 25 on a random background ′B′ with area of 256 × 256. The background randomness has a point density of 0.025 (1639/65536). Spectrum maps of clumps with (b) 2-fold, (c) 4-fold, (d) 6-fold, (e) 8-fold, and (f) 24-fold point densities relative to the background were used to verify the performance of directional 2D Morlet wavelet analysis in separating true patterns from random fluctuations. The dashed polygons represent shapes and scales of point clumps. For a recognizable pattern, the thick solid line, that represents a normalized mean spectrum of one, is expected to be totally contained in the dashed polygon.

Mentions: Point clumps with different densities were generated in 4 regions with an area of 20 × 25 grid nodes for each on a random background with an area of 256 × 255 grid nodes (Figure 5(a)). The background randomness has a point density of 0.025 (1639/65536), whereas point clumps have 2-, 4-, 6-, 8-, and 24-fold point densities relative to the background. Spectrum maps were employed to assess the performance of the directional 2D Morlet wavelet analysis in separating true patterns from random fluctuations. Mean spectrums in the spectrum maps were normalized using the square mean of background randomness, . As a point density in a clump increases, the number of points in clumps that are restricted increases. As the number of random points decreases, the degree to which a normalized mean spectrum deviates from one increases; one is the expected value of a normalized mean spectrum for a spatial randomness. As the density in clumps increases, the strength of a pattern increases both visually and analytically (Figures 5(b)–(f)). Clumps with 8-fold point density relative to the background are visually recognizable in a spectrum map (Figure 5(e)).


Integrating Remote Sensing Data with Directional Two-Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management
(a) Point clumps with different densities were generated in 4 ′A′ regions each with area of 20 × 25 on a random background ′B′ with area of 256 × 256. The background randomness has a point density of 0.025 (1639/65536). Spectrum maps of clumps with (b) 2-fold, (c) 4-fold, (d) 6-fold, (e) 8-fold, and (f) 24-fold point densities relative to the background were used to verify the performance of directional 2D Morlet wavelet analysis in separating true patterns from random fluctuations. The dashed polygons represent shapes and scales of point clumps. For a recognizable pattern, the thick solid line, that represents a normalized mean spectrum of one, is expected to be totally contained in the dashed polygon.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3927515&req=5

f5-sensors-08-01070: (a) Point clumps with different densities were generated in 4 ′A′ regions each with area of 20 × 25 on a random background ′B′ with area of 256 × 256. The background randomness has a point density of 0.025 (1639/65536). Spectrum maps of clumps with (b) 2-fold, (c) 4-fold, (d) 6-fold, (e) 8-fold, and (f) 24-fold point densities relative to the background were used to verify the performance of directional 2D Morlet wavelet analysis in separating true patterns from random fluctuations. The dashed polygons represent shapes and scales of point clumps. For a recognizable pattern, the thick solid line, that represents a normalized mean spectrum of one, is expected to be totally contained in the dashed polygon.
Mentions: Point clumps with different densities were generated in 4 regions with an area of 20 × 25 grid nodes for each on a random background with an area of 256 × 255 grid nodes (Figure 5(a)). The background randomness has a point density of 0.025 (1639/65536), whereas point clumps have 2-, 4-, 6-, 8-, and 24-fold point densities relative to the background. Spectrum maps were employed to assess the performance of the directional 2D Morlet wavelet analysis in separating true patterns from random fluctuations. Mean spectrums in the spectrum maps were normalized using the square mean of background randomness, . As a point density in a clump increases, the number of points in clumps that are restricted increases. As the number of random points decreases, the degree to which a normalized mean spectrum deviates from one increases; one is the expected value of a normalized mean spectrum for a spatial randomness. As the density in clumps increases, the strength of a pattern increases both visually and analytically (Figures 5(b)–(f)). Clumps with 8-fold point density relative to the background are visually recognizable in a spectrum map (Figure 5(e)).

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.


Related in: MedlinePlus