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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.

No MeSH data available.


Characteristics of the directional 2D Morlet wavelet function. (a) Vertical and lateral views of real and image parts of the Morlet wavelet function, while the directional angle is π/4 and the scale factor is one. (b) Fourier transforms of the conjugate of wavelet function with different scale factors and directional angles, where A's scale factor is three and directional angle is π/4 while B's scale factor is 18 and directional angle is −π/4. (c) For a scale factor between four and 42, determination of Equation (7) is 65536 × (1 ± 0.1%)for a study area composed of 256 × 256 (65536) grid nodes.
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f1-sensors-08-01070: Characteristics of the directional 2D Morlet wavelet function. (a) Vertical and lateral views of real and image parts of the Morlet wavelet function, while the directional angle is π/4 and the scale factor is one. (b) Fourier transforms of the conjugate of wavelet function with different scale factors and directional angles, where A's scale factor is three and directional angle is π/4 while B's scale factor is 18 and directional angle is −π/4. (c) For a scale factor between four and 42, determination of Equation (7) is 65536 × (1 ± 0.1%)for a study area composed of 256 × 256 (65536) grid nodes.

Mentions: The 2D Morlet wavelet function is given by [7](1)φ(r⇀,θ)=π−0.5e−ω0⇀(θ)⋅r⇀e−0.5/r⇀/2,where r⃑ is a location vector; is a directional vector, that is taken to be six in this study to satisfy the admissibility condition [11], and the directional angle θ is positively measured counter-clockwise from due east with a range of π/2 ≥ θ > − π/2. The dilated φ(r⃑) is defined as(2)φλ(r⇀,θ)=π−0.5λe−iω0⇀(θ)⋅r⇀λe−0.5/r⇀λ/2,where λ is a scale factor and the normalized constant is chosen to conserve the norm so that . By setting , θ = 4/, and λ = 1, the real, Re(φλ(r⃑, θ)), and image, Im(φλ(r⃑, θ)), parts of φλ(r⃑, θ) are illustrated in Figure 1(a).


Integrating Remote Sensing Data with Directional Two-Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management
Characteristics of the directional 2D Morlet wavelet function. (a) Vertical and lateral views of real and image parts of the Morlet wavelet function, while the directional angle is π/4 and the scale factor is one. (b) Fourier transforms of the conjugate of wavelet function with different scale factors and directional angles, where A's scale factor is three and directional angle is π/4 while B's scale factor is 18 and directional angle is −π/4. (c) For a scale factor between four and 42, determination of Equation (7) is 65536 × (1 ± 0.1%)for a study area composed of 256 × 256 (65536) grid nodes.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3927515&req=5

f1-sensors-08-01070: Characteristics of the directional 2D Morlet wavelet function. (a) Vertical and lateral views of real and image parts of the Morlet wavelet function, while the directional angle is π/4 and the scale factor is one. (b) Fourier transforms of the conjugate of wavelet function with different scale factors and directional angles, where A's scale factor is three and directional angle is π/4 while B's scale factor is 18 and directional angle is −π/4. (c) For a scale factor between four and 42, determination of Equation (7) is 65536 × (1 ± 0.1%)for a study area composed of 256 × 256 (65536) grid nodes.
Mentions: The 2D Morlet wavelet function is given by [7](1)φ(r⇀,θ)=π−0.5e−ω0⇀(θ)⋅r⇀e−0.5/r⇀/2,where r⃑ is a location vector; is a directional vector, that is taken to be six in this study to satisfy the admissibility condition [11], and the directional angle θ is positively measured counter-clockwise from due east with a range of π/2 ≥ θ > − π/2. The dilated φ(r⃑) is defined as(2)φλ(r⇀,θ)=π−0.5λe−iω0⇀(θ)⋅r⇀λe−0.5/r⇀λ/2,where λ is a scale factor and the normalized constant is chosen to conserve the norm so that . By setting , θ = 4/, and λ = 1, the real, Re(φλ(r⃑, θ)), and image, Im(φλ(r⃑, θ)), parts of φλ(r⃑, θ) are illustrated in Figure 1(a).

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.