Ground and Surface Water Extraction

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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Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽

10.3390/s18082580 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2580 ◽

Cited By ~ 42

Author(s):

Tri Acharya ◽

Anoj Subedi ◽

Dong Lee

Keyword(s):

Surface Water ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Pure Water ◽

Confusion Matrix ◽

Threshold Value ◽

Environmental Noise ◽

Water Extraction ◽

Landsat 8 ◽

Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

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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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Automated Surface Water Extraction Combining Sentinel-2 Imagery and OpenStreetMap Using Presence and Background Learning (PBL) Algorithm

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2019.2936406 ◽

2019 ◽

Vol 12 (10) ◽

pp. 3784-3798 ◽

Cited By ~ 2

Author(s):

Zhiqiang Zhang ◽

Xinchang Zhang ◽

Xin Jiang ◽

Qinchuan Xin ◽

Zurui Ao ◽

...

Keyword(s):

Surface Water ◽

Water Extraction ◽

Sentinel 2

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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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Characterization and Management Concerns of Water Resources around Pallikaranai Marsh, South Chennai

Reconsidering the Impact of Climate Change on Global Water Supply, Use, and Management - Advances in Environmental Engineering and Green Technologies ◽

10.4018/978-1-5225-1046-8.ch007 ◽

2017 ◽

pp. 102-121 ◽

Cited By ~ 1

Author(s):

Avantika Bhaskar ◽

G. Babu Rao ◽

Jayshree Vencatesan

Keyword(s):

Climate Change ◽

Surface Water ◽

Water Shortage ◽

Water Bodies ◽

Water Extraction ◽

Water Crisis ◽

Surrounding Water ◽

Groundwater Levels ◽

Natural Wetlands ◽

The Government

Pallikaranai is one of the last remaining natural wetlands of Chennai. This marsh collects floodwater and increases groundwater levels in the region. The present study characterizes the water sources available around Pallikaranai Marsh. Groundwater was found to be the main source of water in the study area, extracted through domestic wells as well as commercially through a large number of agricultural wells. Direct surface water extraction from wetlands by private tankers was also observed in some areas. Acute water shortage and inefficient water supply by the government has led to thriving of tanker market in this area. Shrinking of the marsh and surrounding water bodies owing to construction, dumping of waste and encroachment accompanied by over-extraction of groundwater is driving this area towards extreme water crisis especially in event of climate change. Conservation of wetlands and evolving norms for sustainable water extraction of groundwater especially by commercial entities is recommended.

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FY-3D/MERSI Global Surface Water Extraction Based on DNN Method

10.1109/igarss47720.2021.9554029 ◽

2021 ◽

Author(s):

Kuanle Bao ◽

Jinlong Fan ◽

Wenbo Xu ◽

Chunliang Zhao ◽

Wenhui Du

Keyword(s):

Surface Water ◽

Water Extraction ◽

Global Surface

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Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽

10.3390/s19122769 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2769 ◽

Cited By ~ 8

Author(s):

Tri Dev Acharya ◽

Anoj Subedi ◽

Dong Ha Lee

Keyword(s):

Machine Learning ◽

Surface Water ◽

Recursive Partitioning ◽

Learning Algorithms ◽

High Elevation ◽

Machine Learning Algorithms ◽

Water Extraction ◽

Support Vector ◽

Landsat 8 ◽

Water Index

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

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