Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data

Sulong Zhou; Pengyu Kan; Janet Silbernagel; Jiefeng Jin

doi:10.3390/ijgi9070424

Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9070424 ◽

2020 ◽

Vol 9 (7) ◽

pp. 424 ◽

Cited By ~ 2

Author(s):

Sulong Zhou ◽

Pengyu Kan ◽

Janet Silbernagel ◽

Jiefeng Jin

Keyword(s):

Image Segmentation ◽

Surface Water ◽

Water Surface ◽

Poyang Lake ◽

Google Earth ◽

Water Extraction ◽

Freshwater Lakes ◽

Essential Information ◽

Advantages And Disadvantages ◽

Otsu Method

Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages of a segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are − 14.88 , − 16.93 , − 16.96 and − 16.87 respectively, and the overall accuracy achieves 92 % after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020.

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Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Water ◽

10.3390/w13020138 ◽

2021 ◽

Vol 13 (2) ◽

pp. 138

Author(s):

Zijie Jiang ◽

Weiguo Jiang ◽

Ziyan Ling ◽

Xiaoya Wang ◽

Kaifeng Peng ◽

...

Keyword(s):

Sustainable Development ◽

Surface Water ◽

Essential Element ◽

Water Area ◽

Google Earth ◽

Water Extraction ◽

Natural Ecosystem ◽

The Sustainable Development ◽

Baiyangdian Lake ◽

Google Earth Engine

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

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Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

Scientific Reports ◽

10.1038/s41598-021-04754-y ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Jianfeng Li ◽

Jiawei Wang ◽

Liangyan Yang ◽

Huping Ye

Keyword(s):

Remote Sensing ◽

Cloud Computing ◽

Surface Water ◽

Water Resources ◽

Sri Lanka ◽

Water Resource Management ◽

Google Earth ◽

Water Extraction ◽

Inland Lakes ◽

Lakes And Reservoirs

AbstractSri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

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Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽

10.1371/journal.pone.0253209 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253209

Author(s):

Jianfeng Li ◽

Biao Peng ◽

Yulu Wei ◽

Huping Ye

Keyword(s):

Surface Water ◽

Google Earth ◽

Water Extraction ◽

Practical Significance ◽

Commission Error ◽

Complex Environment ◽

Visual Interpretation ◽

Water Index ◽

Google Earth Engine ◽

Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

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Automatic Extraction of Open Water Using Imagery of Landsat Series

Water ◽

10.3390/w12071928 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1928

Author(s):

Dandan Xu ◽

Dong Zhang ◽

Dan Shi ◽

Zhaoqing Luan

Keyword(s):

Surface Water ◽

Algal Blooms ◽

Water Surface ◽

Near Infrared ◽

Anthropogenic Activities ◽

Eastern China ◽

Water Extraction ◽

Open Surface ◽

Infrared Band ◽

Shortwave Infrared

Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.

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High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations

Sensors ◽

10.3390/s20174872 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4872

Author(s):

Haifeng Tian ◽

Jian Wang ◽

Jie Pei ◽

Yaochen Qin ◽

Lijun Zhang ◽

...

Keyword(s):

Surface Water ◽

Poyang Lake ◽

Water Area ◽

Water Extraction ◽

Water Dynamics ◽

Lake Area ◽

Lake Surface ◽

Spatiotemporal Resolution ◽

High Spatiotemporal Resolution ◽

Resolution Mapping

Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km2 on 2 March 2017 to 1042.57 km2 on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km2 (i.e., increasing from 474.70 km2 up to 708.12 km2). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales.

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Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine

Remote Sensing ◽

10.3390/rs11151824 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1824 ◽

Cited By ~ 7

Author(s):

Haoming Xia ◽

Jinyu Zhao ◽

Yaochen Qin ◽

Jia Yang ◽

Yaoping Cui ◽

...

Keyword(s):

Surface Water ◽

River Basin ◽

Water Surface ◽

Water Area ◽

Google Earth ◽

The Past ◽

Huai River Basin ◽

Huai River ◽

Google Earth Engine ◽

The Huai River Basin

The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages.

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Long-Term Dynamic of Poyang Lake Surface Water: A Mapping Work Based on the Google Earth Engine Cloud Platform

Remote Sensing ◽

10.3390/rs11030313 ◽

2019 ◽

Vol 11 (3) ◽

pp. 313 ◽

Cited By ~ 25

Author(s):

Yingbing Wang ◽

Jun Ma ◽

Xiangming Xiao ◽

Xinxin Wang ◽

Shengqi Dai ◽

...

Keyword(s):

Surface Water ◽

Water Body ◽

Poyang Lake ◽

Detection Algorithm ◽

Google Earth ◽

Lake Basin ◽

Inundation Frequency ◽

The Past ◽

Water Detection ◽

Google Earth Engine

In recent years, the shrinkage of Poyang Lake, the largest freshwater lake in China, has raised concerns for society. The regulation of the Three Gorges Dam (TGD) has been argued to be a cause of the depletion of the lake by previous studies. However, over the past few decades, the lake’s surface water dynamic has remained poorly characterized, especially before the regulation of the TGD (2003). By calculating the inundation frequency with an index- and pixel-based water detection algorithm on Google Earth Engine (GEE), this study explored the spatial–temporal variation of the lake during 1988–2016 and compared the differences in Poyang Lake’s water body between the pre- and post-TGD periods. The year-long water body area of the lake has shown a significant decreasing trend over the past 29 years and has shifted to a smaller regime since 2006. The inundation frequency of the lake has also generally decreased since 2003, particularly at the central part of the lake, and the effects of this trend have been most severe in the spring and autumn seasons. The lake’s area has shown significant correlation with the precipitation of the Poyang Lake Basin on an inner-annual scale. The drivers of and relevant factors relating to the inter-annual variation of the lake’s surface water should be further investigated in the future.

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