Water Body Extraction Methods Study Based on RS and GIS

Yang, Haibo; Wang, Zongmin; Zhao, Haitao; Guo, Yu

doi:10.1016/j.proenv.2011.09.407

Cited by 84 publications

(43 citation statements)

References 2 publications

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“…By exploiting its multispectral nature [31,45], the algebraic rational of different reflective bands has prevailed over any other single-band indicator. Several indices have been proposed in the literature, including the Normalized Difference Water Index (NDWI) [46][47][48][49], Normalized Difference Water Index 2 (NDWI2) [20,21,[45][46][47], Modified Normalized Difference Water Index (MNDWI) [17][18][19][20][21][50][51][52], and the Normalized Difference Pond Index (NDPI) [52][53][54][55][56]. MNDWI [19,45,57] and NDWI2 perform equally well and generally better than the others.…”

Section: Water Indexmentioning

confidence: 99%

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

Horkaew

Puttinaovarat

2017

IJGI

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Abstract:Reliable water surface extraction is essential for river delineation and flood monitoring. Obtaining such information from fine resolution satellite imagery has attracted much interest for geographic and remote sensing applications. However, those images are often expensive and difficult to acquire. This study proposes a more cost-effective technique, employing freely available Landsat images. Despite its extensive spectrum and robust discrimination capability, Landsat data are normally of medium spatial resolution and, as such, fail to delineate smaller hydrological features. Based on Multivariate Mutual Information (MMI), the Landsat images were fused with Digital Surface Model (DSM) on the spatial domain. Each coinciding pixel would then contain not only rich indices but also intricate topographic attributes, derived from its respective sources. The proposed data fusion ensures robust, precise, and observer-invariable extraction of water surfaces and their branching, while eliminating spurious details. Its merit was demonstrated by effective discrimination of flooded regions from natural rivers for flood monitoring. The assessments we completed suggest improved extraction compared to traditional methods. Compared with manual digitizing, this method also exhibited promising consistency. Extraction using Dempster-Shafer fusion provided a 91.81% F-measure, 93.09% precision, 90.74% recall, and 98.25% accuracy, while using Majority Voting fusion resulted in an 84.91% F-measure, 75.44% precision, 97.37% recall, and 97.21% accuracy.

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Section: Water Indexmentioning

confidence: 99%

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

Horkaew

Puttinaovarat

2017

IJGI

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“…Recent studies have demonstrated the potential of combining satellite imagery and radar altimetry to estimate the volume of water stored in lakes, rivers, and floodplains, and how these volumes change in response to climate variability and/or anthropogenic effects using SAR images, multispectral images, or multi-satellite observations (Frappart et al, 2005(Frappart et al, , 2006b(Frappart et al, , 2008(Frappart et al, , 2012Yesou et al, 2007;Ding and Li, 2011;Haibo et al, 2011;Duan and Bastiaanssen, 2013). Despite the relevance of these results, these techniques have not been applied yet to study small lakes due to the difficulty in collecting synchronously radar altimeter data, highresolution images and consistent ground data.…”

Section: F Baup Et Al: Combining High-resolution Satellite Images Amentioning

confidence: 99%

Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

Baup

Frappart

Maubant

2014

Hydrol. Earth Syst. Sci.

100

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Abstract. This study presents an approach to determining the volume of water in small lakes (< 100 ha) by combining satellite altimetry data and high-resolution (HR) images. In spite of the strong interest in monitoring surface water resources on a small scale using radar altimetry and satellite imagery, no information is available about the limits of the remote-sensing technologies for small lakes mainly used for irrigation purposes. The lake being studied is located in the south-west of France and is only used for agricultural irrigation purposes. The altimetry satellite data are provided by an RA-2 sensor onboard Envisat, and the high-resolution images (< 10 m) are obtained from optical (Formosat-2) and synthetic aperture radar (SAR) antenna (Terrasar-X and Radarsat-2) satellites. The altimetry data (data are obtained every 35 days) and the HR images (77) have been available since 2003 and 2010, respectively. In situ data (for the water levels and volumes) going back to 2003 have been provided by the manager of the lake. Three independent approaches are developed to estimate the lake volume and its temporal variability. The first two approaches (HRBV and ABV) are empirical and use synchronous ground measurements of the water volume and the satellite data. The results demonstrate that altimetry and imagery can be effectively and accurately used to monitor the temporal variations of the lake (R 2 ABV = 0.98, RMSE ABV = 5%, R 2 HRBV = 0.90, and RMSE HRBV = 7.4 %), assuming a time-varying triangular shape for the shore slope of the lake (this form is well adapted since it implies a difference inferior to 2 % between the theoretical volume of the lake and the one estimated from bathymetry). The third method (AHRBVC) combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R 2 AHRBVC = 0.98) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

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“…Additionally, the result is multiplied by a constant value C (e.g., 100) to scale the index, to avoid low decimal numbers. NWI has already been used in different studies such as [33].…”

Section: Modified Normalized Difference Water Index (Mndwi)mentioning

confidence: 99%

Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

2019

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In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).

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Water Body Extraction Methods Study Based on RS and GIS

Cited by 84 publications

References 2 publications

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

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