A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

Wang, Xiaobiao; Xie, Shuguang; Zhang, Xueliang; Chi, Cheng; Guo, Hao; Du, Jun; Duan, Zheng

doi:10.1016/j.jag.2018.01.018

Cited by 86 publications

(60 citation statements)

References 67 publications

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“…To recognise water colour anomalies by extracting the hue angle of a water body from a Sentinel-2 image, it is necessary to conduct pre-processing, such as atmospheric correction, band resampling, and water body extraction. Sen2Cor and SNAP software developed by the European Space Agency (ESA) were used for the atmospheric correction and band resampling of the Sentinel-2 image data to gain the surface reflectance data with a spatial resolution of 10 m. There are two main methods for extracting water body based on remote-sensing data: (1) through water body indices, such as modified normalised difference water index (MNDWI), normalised difference water index (NDWI), automated water extraction index (AWEI), multiband water index (MBWI), and multispectral water index (MuWI) [24][25][26][27][28][29][30], and (2) through classification methods, such as support vector machine (SVM), maximum likelihood method, decision tree, and random forest [31,32]. The advantages of using water body indices to extract water bodies include easy application and rapid results; however, they have limited band information, show large errors of water body extraction results, and have complex calculations of optimal thresholds, in which each scene image must match an optimal threshold.…”

Section: Overview Of the Study Areamentioning

confidence: 99%

Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

et al. 2020

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As polluted water bodies are often small in area and widely distributed, performing artificial field screening is difficult; however, remote-sensing-based screening has the advantages of being rapid, large-scale, and dynamic. Polluted water bodies often show anomalous water colours, such as black, grey, and red. Therefore, the large-scale recognition of suspected polluted water bodies through high-resolution remote-sensing images and water colour can improve the screening efficiency and narrow the screening scope. However, few studies have been conducted on such kinds of water bodies. The hue angle of a water body is a parameter used to describe colour in the International Commission on Illumination (CIE) colour space. Based on the measured data, the water body with a hue angle greater than 230.958° is defined as a water colour anomaly, which is recognised based on the Sentinel-2 image through the threshold set in this study. The results showed that the hue angle of the water body was extracted from the Sentinel-2 image, and the accuracy of the hue angle calculated by the in situ remote-sensing reflectance Rrs (λ) was evaluated, where the root mean square error (RMSE) and mean relative error (MRE) were 4.397° and 1.744%, respectively, proving that this method is feasible. The hue angle was calculated for a water colour anomaly and a general water body in Qiqihar. The water body was regarded as a water colour anomaly when the hue angle was >230.958° and as a general water body when the hue angle was ≤230.958°. High-quality Sentinel-2 images of Qiqihar taken from May 2016 to August 2019 were chosen, and the position of the water body remained unchanged; there was no error or omission, and the hue angle of the water colour anomaly changed obviously, indicating that this method had good stability. Additionally, the method proposed is only suitable for optical deep water, not for optical shallow water. When this method was applied to Xiong’an New Area, the results showed good recognition accuracy, demonstrating good universality of this method. In this study, taking Qiqihar as an example, a surface survey experiment was conducted from October 14 to 15, 2018, and the measured data of six general and four anomalous water sample points were obtained, including water quality terms such as Rrs (λ), transparency, water colour, water temperature, and turbidity.

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Section: Overview Of the Study Areamentioning

confidence: 99%

Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

et al. 2020

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“…The construction of water index is based on the spectral characteristics of water, so that the calculated index can expand the difference between water and other objects, and then extract water according to the appropriate threshold. Normalized difference water index(NDWI), modified normalized difference water index(MNDWI), automated water extraction index(AWEI) (Feyisa et al, 2014) and multi-band water index(MBWI) (Wang et al, 2018), these water indices come from first to last, each with advantages and disadvantages, as listed from Eq1-Eq5.…”

Section: Water Extraction Indexmentioning

confidence: 99%

Extraction of Rivers and Lakes on Tibetan Plateau Based on Google Earth Engine

Qiao

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.</p>

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“…Also, pixels with ice/snow or clouds may show a high value, which is prone to be mistaken with surface water using two-band method. Accordingly, using multi-band indices may have advantages compared with indices that use limited number of bands for identifying surface water (Wang et al 2018;Ji et al 2015). In recent years, some studies have been done to extend new indices for water detection.…”

Section: Index Equation Ndwimentioning

confidence: 99%

“…Moradi, Sahebi, and Shokri (2017) proposed Modified Optimization Water Index (MOWI) for Landsat-8 OLI/TIRS, in which they used all spectral potential of Landsat 8 for water detection on lakes and dams in Iran. Wang et al (2018) proposed Multi-Band Water Index (MBWI) for Landsat 8 images, maximizing the spectral difference between water and non-water surfaces using pure pixels and the K-means cluster method to automatically extract surface water. Mentioned water indices are presented in Table 1.…”

Section: Index Equation Ndwimentioning

confidence: 99%

“…Therefore, timely monitoring and access to data of salty lakes are essential for policy making, decision making processes, and continuous water resource management. Remote sensing satellites can provide a practical approach to map lakes systematically due to the low-cost, reliable information, wide coverage, repeatable observations, multiband imagery and even real-time or near real time observations at both local and global scales (Sheng et al 2016;Wang et al 2018). Landsat 8 Operational Land Imager (OLI) has currently attracted more attention due to high-quality images.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Water Index (Swi) for Salty Water From Landsat 8 Oli/Tirs

Yousefian

Sahebi

Shokri

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Monitoring natural resources is one of the most important tasks in earth observation and remote sensing satellites. Water resources play a crucial role in the life of human on the planet. Among the water resources, salty lakes are of particular importance in biological, physical and environmental issues. In this study, a new Salty Water Index (SWI) for Landsat 8 Operational Land Imager (OLI) images is proposed based on salty lakes by particle swarm optimization (PSO), where water doesn’t combine by cloud, shadow, and salty areas. SWI is implemented on four famous and important salty lakes with the proper distribution of the whole world and different Salinity, including Lake Assal, Great Salt Lake, Eyre Lake, and Lake Urmia. The performance of SWI is compared with other water indices by overall accuracy, f-score, kappa coefficient, and standard deviation to mean ratio. Results show the efficiency of SWI on all cases due to 0.0055 Standard deviation to mean for SWI compared to 0.0395, 0.0255, 0.0873, 0.0214, 0.0524, 0.0408 and 0.0375 for NDWI, MNDWI, AWEIsh, AWEI, WRI, MOWI, and MBWI, respectively. Also, Effectiveness criteria (E) determines the efficiency of each band of Landsat 8. In this regard, results show the high performance of Green and Near IR band in all conditions and relatively proper performance of some other bands based on a special condition of each case study. The proposed method is also suggested to readers to obtain novel spectral indices of other classes and other sensors.

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A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

Cited by 86 publications

References 67 publications

Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

Extraction of Rivers and Lakes on Tibetan Plateau Based on Google Earth Engine

A Novel Water Index (Swi) for Salty Water From Landsat 8 Oli/Tirs

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