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 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.

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 Developing a Method for Assessing Environmental Sustainability Based on the Google Earth Engine Platform

2021 ◽  
Author(s):  
Xianwang Xia ◽  
Chentai Jiao ◽  
Shixiong Song ◽  
Ling Zhang ◽  
Xingyun Feng ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Environmental Sustainability ◽  
Google Earth ◽  
Urban Agglomeration ◽  
Environmental Index ◽  
The Sustainable Development ◽  
The Past ◽  
Environmental Deterioration ◽  
Google Earth Engine

Abstract Environmental sustainability is the foundation and of great significance for the sustainable development of urban agglomerations. Taking the Beijing-Tianjin-Hebei urban agglomeration as an example, we developed a method to effectively assess long-term regional environmental sustainability based on the Google Earth Engine (GEE) platform. We used the GEE to obtain 5206 Landsat remote sensing images in the region from 1983 to 2016 and developed the comprehensive environmental index (CEI) to assess regional environmental sustainability based on the theme-oriented framework proposed by the United Nations Commission on Sustainable Development. We found that the environmental sustainability of the urban agglomeration showed a trend of first rising, then falling, and then rising again in the past 30 years. The average CEI increased from 0.621 to 0.631 from 1985 to 1990, dropped to the lowest value of 0.618 in 2000, and then rose to the highest value of 0.672 in 2015. In particular, the extent of areas in which environmental sustainability improved (56% of the region) was greater than the extent of areas in which environmental deterioration occurred. The environmental sustainability of Hengshui, Xingtai and Cangzhou in the southeast of the region has been significantly improved. The method proposed in this study provides an automatic, rapid and extensible way to assess regional environmental sustainability and provides a scientific reference for improving the sustainability of the regional ecological environment.

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

2020 ◽  
Vol 9 (7) ◽  
pp. 424 ◽  
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.

scholarly journals ICT sustainable management: towards a better alignment with the sustainable development strategy

2020 ◽  
Vol 7 (71) ◽  
pp. 165-181
Author(s):  
Rachid Hba ◽  
Mohammed Abdou Janati Idrissi ◽  
Majid Kaissar El Ghaib ◽  
Abdellah El Manouar
Keyword(s):  
Sustainable Development ◽  
Information And Communication Technologies ◽  
Development Strategy ◽  
Sustainable Management ◽  
Management Strategies ◽  
Essential Element ◽  
The Sustainable Development ◽  
Information And Communication ◽  
Innovation Approach ◽  
The Impact

The impact of Information and Communication Technologies (ICT) in terms of Sustainable Development (SD) has led to a great transformation in business management, which positions innovation as an essential element of differentiation for the new market conquest. These ICT, that are particularly adapted, allow creating value for the company in a sustainable way and more aligned with the global SD strategy while ensuring to bring competitive advantage and anticipate new economic, social and environmental problems. To implement an innovation approach that gives organizations the means to develop sustainable management strategies, allowing them to increase overall performance, we proposed the "ICT Sustainable Management" model which is designed according to a Green IT (GIT) and Corporate Social Responsibility (CSR) concepts in order to seek new transition ways for management serving SD. Our model constitutes a first research step to lead reflection on next-generation SD-oriented management models. For this reason, we consider it more appropriate to approve the validity of our model through its application on different categories of companies that use ICT. Analysis of the results of this application test has demonstrated the validity of our model at the scale of five companies. This initial validation will be supported by improvements and new tests that will be extremely useful to improve the model and to advance knowledge in this new field of research.

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.

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