A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

Pekel, Jean-François; Vancutsem, C.; Bastin, Lucy; Clerici, M.; Vanbogaert, Eric; Bartholomé, Étienne; Defourny, Pierre

doi:10.1016/j.rse.2013.10.008

Cited by 128 publications

(90 citation statements)

References 30 publications

(5 reference statements)

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“…These areas have to be identified, filtered out and interpolated to avoid problems with the subsequent regression. Identification of water bodies is not straightforward [39] since we cannot use an a priori threshold to mask EVI value corresponding to water bodies. In order to have a global and portable method, we use the SWBD dataset (SRTM Water Bodies Data, [40]) to mask water bodies and to remove the corresponding EVI values.…”

Section: Vegetation Index: Evimentioning

confidence: 99%

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

et al. 2015

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Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. Finally, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results OPEN ACCESSRemote Sens. 2015, 7 6455 also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide.

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Section: Vegetation Index: Evimentioning

confidence: 99%

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

et al. 2015

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“…The edge detection technique with the radar and optical satellite images have been described as a more precise method for extracting shorelines/coastlines [28,29]. Pekel et al [30] used HSV transformation of mid-near infrared, near infrared, and red bands of MODIS multi-spectral time series data for developing automated algorithms for near real-time water surface detection, and characterization of their spatial and temporal dynamics. Several researchers have reported better target identification and extraction by the HSV (Hue, Saturation, and Value) color model than by the RGB model [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

et al. 2015

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Abstract:Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. OPEN ACCESSRemote Sens. 2015, 7 13808The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well.

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“…High temporal resolution datasets (e.g., moderate-resolution imaging spectroradiometer (MODIS) data) have been extensively applied to land cover mapping and change detection in a short repetition cycle [29]. However, the coarse spatial resolution (generally larger than 500 m) has limited their practical application at a regional scale for tiny objects such as wadis.…”

Section: Satellite Datamentioning

confidence: 99%

Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia

et al. 2016

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Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous backgrounds are limited for the following reasons: (1) the occurrence of numerous morphological irregularities which disqualify methods based on physical shape; (2) inconspicuous spectral contrast with backgrounds, resulting in frequent false alarms; and (3) the extreme complexity of wadi systems, with numerous tiny tributaries characterized by spectral anisotropy, resulting in a conflict between global and local accuracy. To overcome these difficulties, an automated method for extracting wadis (AMEW) from Landsat-8 Operational Land Imagery (OLI) was developed in order to take advantage of the complementarity between Water Indices (WIs), which is a technique of mathematically combining different bands to enhance water bodies and suppress backgrounds, and image processing technologies in the morphological field involving multi-scale Gaussian matched filtering and a local adaptive threshold segmentation. Evaluation of the AMEW was carried out in representative areas deliberately selected from Jordan, SW Arabian Peninsula in order to ensure a rigorous assessment. Experimental results indicate that the AMEW achieved considerably higher accuracy than other effective extraction methods in terms of visual inspection and statistical comparison, with an overall accuracy of up to 95.05% for the entire area. In addition, the AMEW (based on the New Water Index (NWI)) achieved higher accuracy than other methods (the maximum likelihood classifier and the support vector machine classifier) used for bulk wadi extraction.

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A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

Cited by 128 publications

References 30 publications

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia

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