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 Spatiotemporal Change Analysis of Long Time Series Inland Water in Sri Lanka Based on Remote Sensing Cloud Computing

2021 ◽  
Author(s):  
Jianfeng Li ◽  
Jiawei Wang ◽  
Liangyan Yang ◽  
Huping Ye
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Surface Water ◽  
Water Resources ◽  
Sri Lanka ◽  
Google Earth ◽  
Change Analysis ◽  
Index Method ◽  
Inland Lakes ◽  
Lakes And Reservoirs

Abstract Sri Lanka is an important hub connecting Asia-Africa-Europe maritime routes, with abundant rainfall but uneven spatiotemporal distribution, and obvious seasonal water shortage. Monitoring water area change in its inland lakes and reservoirs plays an important role in guiding the development and utilization of water resources. In this study, a rapid extraction model of surface water based on Google Earth Engine remote sensing cloud computing platform was constructed. On the basis of evaluating the optimal spectral water index method, the spatiotemporal variation of typical reservoir and inland lakes and reservoirs in Sri Lanka were analyzed. The results showed that AWEIsh can accurately identify the water boundary, with an overall accuracy of 99.14%, which was suitable for the extraction of surface water in Sri Lanka. The area of Maduru Oya Reservoir showed an overall increasing trend on the basis of small fluctuations from 1988 to 2018, and the monthly area of reservoir fluctuated greatly in 2017, thus water resources management in dry zone should pay more attention to seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased in different degrees, mainly concentrated in arid provinces such as Northern Province, North Central Province, Western Province, etc., and the amount of surface water resources increased as a whole.

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 Online Command Area Water Resource Management System

2020 ◽  
Vol 5 (2) ◽  
pp. 70-74
Author(s):  
P. Pallavi ◽  
Shaik Salam
Keyword(s):  
Remote Sensing ◽  
Resource Management ◽  
Water Resources ◽  
Water Resource ◽  
Water Resource Management ◽  
Management Practices ◽  
Web Gis ◽  
Command Area ◽  
Space Technology ◽  
Server Side

Water is an important, but often ignored element in sustainable development by now it has been clear that urgent action is needed to avoid global water crisis. Water resource management is the activity of planning, developing, distributing and managing the optimum use of water resources. Successful management of water resources requires accurate knowledge of their resource distribution to meet up the competing demands and mechanisms to make good decisions using advanced recent technologies.Towards evolving comprehensive management plan in suitable conservation and utilization of water resources space technology plays a crucial role in managing country’s available water resources. Systematic approaches involving judicious combination of conventional server side scripting programming and remote sensing techniques pave way for achieving optimum planning and operational of water resources projects.   new methodologies and 24/7 accessible system need to be built, these by reducing the dependency on complex infrastructure an specialist domain Open source web GIS systems have proven their rich in application of server side scripting and easy to use client application tools. Present study and implementation aims to provide wizard based or easily driven tools online for command area management practices. In this large endeavour modules for handling remote sensing data, online raster processing, statistics and indices generation will be developed.

Contribution of urbanization and climate variability on surface water depletion across USA watersheds

2021 ◽  
Author(s):  
Serena Ceola ◽  
Irene Palazzoli
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Resources ◽  
Climate Variability ◽  
Water Loss ◽  
The United States ◽  
Future Climate Change ◽  
Integrated Analysis ◽  
World Population ◽  
Surface Water Resources

<p>Surface water resources are extremely vulnerable to climate variability and are seriously threatened by human activities. The depletion of surface water is expected to rapidly increase due to the combination of future climate change and world population growth projections. Under this scenario, the impacts of climate and human dynamics on surface water resources represent a global issue, requiring the definition of adequate management strategies that prevent water crisis and guarantee equitable access to freshwater resources. Remote sensing provides data that allow to monitor environmental change processes, such as changes in climatic conditions, land use, and spatial allocation of human settlements and activities. Although many products describing surface water dynamics and urban growth obtained from satellite imagery are available, an integrated analysis of such geospatial information has not been performed yet. Here, we explore the driving role of the variation in key climatic variables (e.g.,  precipitation, temperature, and soil moisture) and the extent of urban areas in the depletion of surface water across the watersheds in the United States by using data derived from remote sensing images and performing a correlation analysis. From our preliminary results, we observe that there is a positive correlation between surface water loss and the level of urbanization in each basin of our study area, meaning that surface water loss increases with the extent of urban area. On the contrary, we find that the correlation between surface water loss and precipitation has a counter-intuitive trend which needs to be further examined.</p>

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.

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

scholarly journals A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

2018 ◽  
Author(s):  
Tim Busker ◽  
Ad de Roo ◽  
Emiliano Gelati ◽  
Christian Schwatke ◽  
Marko Adamovic ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Altimetry ◽  
Google Earth ◽  
Water Levels ◽  
Water Volume ◽  
Lake Area ◽  
Reservoir Volume ◽  
Long Time ◽  
Lakes And Reservoirs ◽  
Computationally Intensive

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R2 > 0.8 and an average R2 of 0.91. For these lakes and for lakes with a nearly constant lake area (coefficient of variation 

scholarly journals An Integrative Approach for Environmental Assessment and Water Resources Management Using Direct Current Resistivity (DC), Geographic Information System (GIS), Remote Sensing, and Gain and Loss Method

2021 ◽  
Author(s):  
Dina Ragab Desouki Abdelmoneim
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Dc Resistivity ◽  
Seepage Rate ◽  
Loss Method ◽  
Shallow Groundwater Table

Sustainable water resource management is a crucial national and global issue (Currell et al., 2012). In arid areas, groundwater is often the major source of water or at least a crucial supplement to other freshwater resources for agriculture, industry and domestic consumption (Vrba and Renaud, 2016). The complexity associated with groundwater-surface water interactions creates uncertainty about water resource sustainability in semi-arid environments, especially with urbanization and population growth. Flood irrigation in the early 1900s increased the shallow groundwater table in the Treasure Valley (TV), but with increasing irrigation efficiencies, they have been declining since the 1960s with a mean decline rate of about 2.9-3.9x10^-9 (m/s) (Contor et al., 2011). Quantifying how much surface water is being exchanged with the shallow groundwater table through canals in the TV is necessary for gaining a better understanding of groundwater-surface water interactions in this heavily managed system. This knowledge would help evaluate alternative management options for achieving sustainable management of existing water resources. The key objectives of this project are to determine the seepage rate through some canal reaches in the TV, evaluate the integration of the gain and loss method, remote sensing, GIS, hydrogeophysical simulation, and direct current (DC) resistivity geophysical methods for water resource management. We hypothesize that the underlying lithology and size of canals affect the magnitude of the seepage rate. Flow measurements were collected weekly between July and August 2020 in canal reaches representing different sizes and lithological units to determine the seepage rate using the reach gain/loss method. Canal variability and measurement uncertainty were included in seepage estimation for the entire TV using 3 alternative scaling approaches. DC resistivity was used as a complementary method to monitor the seepage effect on the shallow GW aquifer over 2 months. This research evaluates to what extent canal size and its underlying lithology affects the seepage rate, and how the integration of methods may provide additional insight into groundwater exchange-surface water.

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