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Mapping shorelines to subpixel accuracy using Landsat imagery

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Mapping shorelines to subpixel accuracy using Landsat imagery Ron Abileah (1), Stefano Vignudelli (2), and Andrea Scozzari (3) jOmegak, San Carlos CA, USA, ([email protected]) National Research Council (CNR-IBF), Italy ([email protected]) - PowerPoint PPT Presentation
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Mapping shorelines to subpixel accuracy using Landsat imagery Ron Abileah (1), Stefano Vignudelli (2), and Andrea Scozzari (3) (1) jOmegak, San Carlos CA, USA, ([email protected]) (2) National Research Council (CNR-IBF), Italy ([email protected]) (3) National Research Council (CNR-ISTI), Italy ([email protected]) Introduction 10X image upsampling along x and y Radiance gradient calculation Estimate radiances from pixels along maximum gradient direction Calculate mixture ratio in each pixel (water / land transition) Contour the 0.5 level i.e., individuate sub-pixels nearest to half land / half water conditions Please see the companion poster titled: "Case-studies of potential applications for highly resolved shorelines" The shorelines determined from three different dates are shown superimposed on an image from Google Earth. White lines connect centers of ‘dark’ cells in the original 30-m resolution. Red lines are shorelines determined with the subpixel shoreline method. The subpixel shorelines track the edge of the dam with an accuracy (resolution) of 5-m. To compare shorelines away from the dam we select a subset of Landsat images from days when the water level (known from water level gauges) is close the same level as the day of the Google Earth Image. In example shown here the image is from 7/8/2012 when the water level was 316.2 m. The Landsat shorelines are from three dates when the water level was within +0.2 m of this value. Subpixel shorelines perform best where the two components mixture Lake Shasta is an artificial reservoir in California, United States. It is a lake characterized by a typical bare ground shoreline. The dam provides an unchanging sharp edge feature to test subpixel shoreline mapping. References: Abileah R and Vignudelli S, Bathymetry from Fusion of Multi-temporal Landsat and Radar Altimetry, In Proceedings of 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Trento, Italy, pp. 12-14, July 2011 Vignudelli S, Zaghloul S S, Scozzari A, Abileah R, Lessons learned from comparing gauge levels with Landsat and radar altimetry observations: case-study of lake Nasser (Egypt) International Water Technology Journal, Alexandria, Vol. 2, No. 1, pp. 26-34, 2012 Pardo-Pascual J E et al., Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision, Remote Sens. of Env. 123, 1-11, 2012 Richter R, A fast atmospheric correction algorithm applied to Landsat TM images, International Journal of Remote Sensing Vol. 11, Conclusions: A resolution of 10m is easily achieved and in some cases it is even better than 5m. The proposed method can be used to study long term shoreline changes by exploiting the 30 years of archived world-wide coverage Landsat imagery, with around one revisit per month at the same place. The Landsat imagery collection is free and easily accessible for downloading. The Landsat sensor has the potential of exploiting the spectral band (SWIR) for land-water discrimination. High resolution satellites (e.g., IKONOS 4-m multi-spectral) can more easily map shorelines with high resolution but have very limited revisits and are very expensive. The proposed method can be used in several applications, including reservoir volume, flood inundation, bathymetry, beach erosion. Future work is to apply the method to MODIS (that has 1 day revisit worldwide) starting from 500 m native resolution and trying to get 100 m resolution. The additional resolution obtained by the sub-pixel shoreline method may be useful for critical applications such as tracking inundations. Other future investigations may include the use of three component mixing to improve shoreline accuracy that will require using two or more spectral bands and the application of the proposed method to rivers. Methodology: Second step: individuation of the 50-50 mixing points. Image data is interpolated and up-sampled to ten times the original resolution. The local gradient in radiance is used to find the direction to the shore, thus searching along that path for the interpolated pixel closest to a 50-50 mix. Landsat images with 30m resolution, processed by this method, may thus provide a more accurate shoreline. Compared to similar approaches available in the literature, the method proposed discriminates sub-pixels crossed by the shoreline by using a criteria based on the absolute value of radiance, rather than its gradient. In fact, gradient is used only to assess the search direction. An improved subpixel method to accurately map land- water boundaries with the Landsat multi-spectral satellite is proposed. We demonstrate that the spatial resolution in mapping shorelines and water area can be improved starting with the native 30-m image pixels. Our previous work on shoreline mapping included Lake Nasser, Egypt (Vignudelli, Zaghloul, Scozzari, Abileah, 2012) and Lake George, New York (unpublished). Lake Shasta, California was selected for this study for the following reasons (1) Landsat revisited the place with continuity ensuring a long- term imagery archive; (2) in situ water level gauge data are easily available; (3) this water body has more complex morphology than the ones used in previous investigations. sults: Lake Shasta (US) Subpixel shoreline Subpixel method confused by water spilling over the dam Conventional “dark pixel” land/water discrimination is inadequate to determine shorelines, because they typically miss pixels containing the water/land boundary, due to the land component in the overall received radiance. Landsat ETM provides eight multi-spectral bands and panchromatic. Previous shoreline mapping works used pixel unmixing method with multiple bands. Our proposed improved approach is a variant of the pixel unmixing method (Zhang and Wylie), that uses only the SWIR band. In addition to the well known reasons which involve atmospheric radiative transfer mechanisms, bottom reflection and water colour, SWIR also exhibits minimal adjacency scattering. Richter (1990), Pardo-Pascual et al. (2012) and the NGA Prototype Global Shoreline Data project reached the same conclusion. SWIR provides the best land-water discrimination, as illustrated in the images and histograms andsat multispectral satellite imagery in two steps f each image pixel into land/water categories by conventional ’dark pixel’ method, based on a single SWIR image band The boundary of the water cover map determined in stage 1 underestimates the water cover and true shoreline by up to one pixel. A more accurate shoreline is obtained by connecting the center of pixels with exactly 50-50 mix of water and land
Transcript
Page 1: Mapping shorelines to subpixel accuracy using Landsat imagery

Mapping shorelines to subpixel accuracy using Landsat imagery

Ron Abileah (1), Stefano Vignudelli (2), and Andrea Scozzari (3)

(1) jOmegak, San Carlos CA, USA, ([email protected])(2) National Research Council (CNR-IBF), Italy ([email protected])(3) National Research Council (CNR-ISTI), Italy ([email protected])

Introduction

10X image upsamplingalong x and y

Radiance gradientcalculation

Estimate radiancesfrom pixels along

maximum gradient direction

Calculate mixture ratioin each pixel

(water / land transition)

Contour the 0.5 level

i.e., individuate sub-pixelsnearest to half land / half water

conditions

Please see the companion poster titled: "Case-studies of potential applications for highly resolved

shorelines"

The shorelines determined from three different dates are shown superimposed on an image from Google Earth. White lines connect centers of ‘dark’ cells in the original 30-m resolution. Red lines are shorelines determined with the subpixel shoreline method. The subpixel shorelines track the edge of the dam with an accuracy (resolution) of 5-m.To compare shorelines away from the dam we select a subset of Landsat images from days when the water level (known from water level gauges) is close the same level as the day of the Google Earth Image. In example shown here the image is from 7/8/2012 when the water level was 316.2 m. The Landsat shorelines are from three dates when the water level was within +0.2 m of this value.Subpixel shorelines perform best where the two components mixture model is adequate. Less well in: (1) coves, (2) were adjacency radiance may be a factor, (3) where mixture is possibly of three components.

Lake Shasta is an artificial reservoir in California, United States.It is a lake characterized by a typical bare ground shoreline. The dam provides an unchanging sharp edge feature to test subpixel shoreline mapping.

References:Abileah R and Vignudelli S, Bathymetry from Fusion of Multi-temporal Landsat

and Radar Altimetry, In Proceedings of 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Trento, Italy, pp. 12-14, July 2011

Vignudelli S, Zaghloul S S, Scozzari A, Abileah R, Lessons learned from comparing gauge levels with Landsat and radar altimetry observations: case-study of lake Nasser (Egypt) International Water Technology Journal, Alexandria, Vol. 2, No. 1, pp. 26-34, 2012

Pardo-Pascual J E et al., Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision, Remote Sens. of Env. 123, 1-11, 2012

Richter R, A fast atmospheric correction algorithm applied to Landsat TM images, International Journal of Remote Sensing Vol. 11, No. 1, 1990

Ji L, Zhang L, Wylie B, Analysis of Dynamic Thresholds for the Normalized Difference Water Index, Photogrammetric Engineering & Remote Sensing Vol. 75, No. 11, pp. 1307–1317, November 2009

Conclusions:A resolution of 10m is easily achieved and in some cases it is even better than 5m. The proposed method can be used to study long term shoreline changes by exploiting the 30 years of archived world-wide coverage Landsat imagery, with around one revisit per month at the same place. The Landsat imagery collection is free and easily accessible for downloading. The Landsat sensor has the potential of exploiting the spectral band (SWIR) for land-water discrimination. High resolution satellites (e.g., IKONOS 4-m multi-spectral) can more easily map shorelines with high resolution but have very limited revisits and are very expensive. The proposed method can be used in several applications, including reservoir volume, flood inundation, bathymetry, beach erosion. Future work is to apply the method to MODIS (that has 1 day revisit worldwide) starting from 500 m native resolution and trying to get 100 m resolution. The additional resolution obtained by the sub-pixel shoreline method may be useful for critical applications such as tracking inundations. Other future investigations may include the use of three component mixing to improve shoreline accuracy that will require using two or more spectral bands and the application of the proposed method to rivers.

Methodology:

Second step: individuation of the 50-50 mixing points. Image data is interpolated and up-sampled to ten times the original resolution. The local gradient in radiance is used to find the direction to the shore, thus searching along that path for the interpolated pixel closest to a 50-50 mix. Landsat images with 30m resolution, processed by this method, may thus provide a more accurate shoreline. Compared to similar approaches available in the literature, the method proposed discriminates sub-pixels crossed by the shoreline by using a criteria based on the absolute value of radiance, rather than its gradient. In fact, gradient is used only to assess the search direction.

An improved subpixel method to accurately map land-water boundaries with the Landsat multi-spectral satellite is proposed. We demonstrate that the spatial resolution in mapping shorelines and water area can be improved starting with the native 30-m image pixels. Our previous work on shoreline mapping included Lake Nasser, Egypt (Vignudelli, Zaghloul, Scozzari, Abileah, 2012) and Lake George, New York (unpublished). Lake Shasta, California was selected for this study for the following reasons (1) Landsat revisited the place with continuity ensuring a long-term imagery archive; (2) in situ water level gauge data are easily available; (3) this water body has more complex morphology than the ones used in previous investigations.

Results: Lake Shasta (US)

Subpixel shoreline

Subpixel method confused by water spilling over the dam

Conventional “dark pixel” land/water discrimination is inadequate to determine shorelines, because they typically miss pixels containing the water/land boundary, due to the land component in the overall received radiance. Landsat ETM provides eight multi-spectral bands and panchromatic. Previous shoreline mapping works used pixel unmixing method with multiple bands. Our proposed improved approach is a variant of the pixel unmixing method (Zhang and Wylie), that uses only the SWIR band. In addition to the well known reasons which involve atmospheric radiative transfer mechanisms, bottom reflection and water colour, SWIR also exhibits minimal adjacency scattering. Richter (1990), Pardo-Pascual et al. (2012) and the NGA Prototype Global Shoreline Data project reached the same conclusion. SWIR provides the best land-water discrimination, as illustrated in the images and histograms

The method is applied to Landsat multispectral satellite imagery in two steps

First step: classification of each image pixel into land/water categories by conventional ’dark pixel’ method, based on a single SWIR image band

The boundary of the water cover map determined in stage 1 underestimates the water cover and true shoreline by up to one pixel. A more accurate shoreline is obtained by connecting the center of pixels with exactly 50-50 mix of water and land

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