scholarly journals Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3010 ◽  
Author(s):  
Ruimeng Wang ◽  
Haoming Xia ◽  
Yaochen Qin ◽  
Wenhui Niu ◽  
Li Pan ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Body ◽  
Vegetation Index ◽  
Water Area ◽  
Google Earth ◽  
Body Area ◽  
Hetao Plain ◽  
Google Earth Engine

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

scholarly journals Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

2021 ◽  
Vol 13 (1) ◽  
pp. 1290-1302
Author(s):  
Ruimeng Wang ◽  
Li Pan ◽  
Wenhui Niu ◽  
Rumeng Li ◽  
Xiaoyang Zhao ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Vegetation Index ◽  
Yellow River ◽  
Google Earth ◽  
Body Type ◽  
Body Area ◽  
Surface Water Body ◽  
Google Earth Engine ◽  
Xiaolangdi Reservoir

Abstract Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.

scholarly journals Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2822
Author(s):  
Jiahao Chen ◽  
Tingting Kang ◽  
Shuai Yang ◽  
Jingyi Bu ◽  
Kexin Cao ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Water Area ◽  
Google Earth ◽  
Water Bodies ◽  
Tarim River ◽  
Tarim River Basin ◽  
Open Surface ◽  
Body Area ◽  
The Tarim River Basin

The Tarim River Basin (TRB), located in an arid region, is facing the challenge of increasing water pressure and uncertain impacts of climate change. Many water body identification methods have achieved good results in different application scenarios, but only a few for arid areas. An arid region water detection rule (ARWDR) was proposed by combining vegetation index and water index. Taking computing advantages of the Google Earth Engine (GEE) cloud platform, 56,284 Landsat 5/7/8 optical images in the TRB were used to detect open-surface water bodies and generated a 30-m annual water frequency map from 1992 to 2019. The interannual changes and trends of the water body area were analyzed and the impacts of climatic and anthropogenic drivers on open-surface water body area dynamics were examined. The results show that: (1) ARWDR is suitable for long-term and large-scale water body identification, especially suitable for arid areas lacking vegetation. (2) The permanent water area was 2093.63 km2 and the seasonal water area was 44,242.80 km2, accounting for 4.52% and 95.48% of the total open-surface water area of he TRB, respectively. (3) From 1992 to 2019, the permanent and seasonal water bodies of the TRB all showed an increasing trend, with obvious spatial heterogeneity. (4) Among the effects of human activities and climate change, precipitation has the largest impact on the water area, which can explain 65.3% of the change of water body area. Our findings provide valuable information for the entire TRB’s open-surface water resources planning and management.

scholarly journals 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 ◽  
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.

scholarly journals Satellite Imageries and Field Data of Macrophytes Reveal a Regime Shift of a Tropical Lake (Lake Ziway, Ethiopia)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 396
Author(s):  
Yohannes Tefera Damtew ◽  
Boud Verbeiren ◽  
Aymere Awoke ◽  
Ludwig Triest
Keyword(s):  
Surface Water ◽  
Water Level ◽  
Regime Shift ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Aquatic Vegetation ◽  
Meteorological Data ◽  
Water Area ◽  
Ethiopian Rift

Lake Ziway is one of the largest freshwater lakes located in the central Ethiopian rift valley. The lake shoreline is dominated by macrophytes which play an important role in immobilizing run-off pollution, stabilize sediments and support biodiversity. Monitoring the spatio-temporal changes of great lakes requires standardized methods. The aim of this study was to assess the current and long-term trends of macrophyte distribution, surface water area and the water level of Lake Ziway using remote sensing images from 1986 to 2016 with additional hydro-meteorological data. A supervised image classification with classification enhancement using Normalized Difference Aquatic Vegetation Index (NDAVI) and Normalized Difference Vegetation Index (NDVI) was applied. The classification based on NDAVI revealed eight target classes which were identified with an overall producer’s accuracy of 79.6%. Contemporary open water and macrophyte fringes occupied most of the study area with a total area of 407.4 km2 and 60.1 km2, respectively. The findings also revealed a regime shift in the mean water level of the lake and a decline in macrophyte distribution. The long-term water surface area of Lake Ziway also decreased between 1986 and 2016. The changes in water level could be explained by climate variability in the region and strong anthropogenic disturbance. A decline in water level was also associated with lowered surface water area, lakeward retreated macrophyte fringes and enhanced landward encroachment of mudflats, and resulted in a succession of macrophytes with semi-terrestrial vegetations.

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

2019 ◽  
Vol 11 (15) ◽  
pp. 1824 ◽  
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.

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

2019 ◽  
Vol 11 (3) ◽  
pp. 313 ◽  
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.

scholarly journals Downscaling MODIS Surface Reflectance to Improve Water Body Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xianghong Che ◽  
Min Feng ◽  
Hao Jiang ◽  
Jia Song ◽  
Bei Jia
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Terrestrial Ecosystems ◽  
Water Area ◽  
Water Bodies ◽  
Tibet Plateau ◽  
Surface Reflectance ◽  
Discrimination Ability ◽  
Automated Method ◽  
Landsat 7

Inland surface water is essential to terrestrial ecosystems and human civilization. Accurate mapping of surface water dynamic is vital for both scientific research and policy-driven applications. MODIS provides twice observation per day, making it perfect for monitoring temporal water dynamic. Although MODIS provides two bands at 250 m resolution, accurately deriving water area always depends on observations from the spectral bands with 500 m resolution, which limits its discrimination ability over small lakes and rivers. The paper presents an automated method for downscaling the 500 m MODIS surface reflectance (SR) to 250 m to improve the spatial discrimination of water body extraction. The method has been tested at Co Ngoin and Co Bangkog in Qinghai-Tibet plateau. The downscaled SR and the derived water bodies were compared to SR and water body mapped from Landsat-7 ETM+ images were acquired on the same date. Consistency metrics were calculated to measure their agreement and disagreement. The comparisons indicated that the downscaled MODIS SR showed significant improvement over the original 500 m observations when compared with Landsat-7 ETM+ SR, and both commission and omission errors were reduced in the derived 250 m water bodies.

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

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.

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

PLoS ONE ◽  
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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