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

Sensors ◽  
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.

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 Monitoring monthly surface water dynamics of Dongting Lake using Sentinal-1 data at 10 m

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4992 ◽  
Author(s):  
Liwei Xing ◽  
Xinming Tang ◽  
Huabin Wang ◽  
Wenfeng Fan ◽  
Guanghui Wang
Keyword(s):  
Surface Water ◽  
Water Distribution ◽  
Remote Sensing Data ◽  
Water Area ◽  
Dongting Lake ◽  
Water Dynamics ◽  
High Temporal Resolution ◽  
Backscattering Coefficient ◽  
Distribution Maps ◽  
Lower Accuracy

High temporal resolution water distribution maps are essential for surface water monitoring because surface water exhibits significant inner-annual variation. Therefore, high-frequency remote sensing data are needed for surface water mapping. Dongting Lake, the second-largest freshwater lake in China, is famous for the seasonal fluctuations of its inundation extents in the middle reaches of the Yangtze River. It is also greatly affected by the Three Gorges Project. In this study, we used Sentinel-1 data to generate surface water maps of Dongting Lake at 10 m resolution. First, we generated the Sentinal-1 time series backscattering coefficient for VH and VV polarizations at 10 m resolution by using a monthly composition method. Second, we generated the thresholds for mapping surface water at 10 m resolution with monthly frequencies using Sentinel-1 data. Then, we derived the monthly surface water distribution product of Dongting Lake in 2016, and finally, we analyzed the inner-annual surface water dynamics. The results showed that: (1) The thresholds were −21.56 and −15.82 dB for the backscattering coefficients for VH and VV, respectively, and the overall accuracy and Kappa coefficients were above 95.50% and 0.90, respectively, for the VH backscattering coefficient, and above 94.50% and 0.88, respectively, for the VV backscattering coefficient. The VV backscattering coefficient achieved lower accuracy due to the effect of the wind causing roughness on the surface of the water. (2) The maximum and minimum areas of surface water were 2040.33 km2in July, and 738.89 km2in December. The surface water area of Dongting Lake varied most significantly in April and August. The permanent water acreage in 2016 was 556.35 km2, accounting for 19.65% of the total area of Dongting Lake, and the acreage of seasonal water was 1525.21 km2. This study proposed a method to automatically generate monthly surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner.

Remote Sensing of Surface Water Dynamics Between 2015 and 2020 in the Prairie Pothole Region 

2021 ◽  
Author(s):  
Stefan Schlaffer ◽  
Marco Chini ◽  
Wouter Dorigo
Keyword(s):  
Surface Water ◽  
Prairie Pothole Region ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Drought Indices ◽  
Prairie Pothole ◽  
Small Water ◽  
Wide Range ◽  
Small Water Bodies

&lt;p&gt;The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (&lt;&amp;#160;1&amp;#160;ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.&lt;/p&gt;

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 Spatial and Temporal Changes in Surface Water Area of Sri Lanka over a 30-Year Period

2020 ◽  
Vol 12 (22) ◽  
pp. 3701
Author(s):  
Deepakrishna Somasundaram ◽  
Fangfang Zhang ◽  
Sisira Ediriweera ◽  
Shenglei Wang ◽  
Junsheng Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Sri Lanka ◽  
Anthropogenic Activities ◽  
Water Area ◽  
Temporal Changes ◽  
Water Bodies ◽  
Water Dynamics ◽  
Annual Growth Rate ◽  
Spatial And Temporal Changes

Sri Lanka contains a large number of natural and man-made water bodies, which play an essential role in irrigation and domestic use. The island has recently been identified as a global hotspot of climate change extremes. However, the extent, spatial distribution, and the impact of climate and anthropogenic activities on these water bodies have remained unknown. We investigated the distribution, spatial and temporal changes, and the impacts of climatic and anthropogenic drivers on water dynamics in Dry, Intermediate, and Wet zones of the island. We used Landsat 5 and Landsat 8 images to generate per-pixel seasonal and annual water occurrence frequency maps for the period of 1988–2019. The results of the study demonstrated high inter- and intra-annual variations in water with a rapid increase. Further, results showed strong zonal differences in water dynamics, with most dramatic variations in the Dry zone. Our results revealed that 1607.73 km2 of the land area of the island is covered by water bodies, among this 882.01 km2 (54.86%) is permanent and 725.72 km2 (45.14%) is seasonal water area. Total inland seasonal water increased with a dramatic annual growth rate of 7.06 ± 1.97 km2 compared to that of permanent water (4.47 ± 2.08 km2/year). Sri Lanka has the highest permanent water area during December–February (1045.97 km2), and drops to the lowest in May–September (761.92 km2) when the seasonal water (846.46 km2) is higher than permanent water. The surface water area was positively related to both precipitation and Gross Domestic Product, while negatively related to the temperature. Findings of our study provide important insights into possible spatiotemporal changes in surface water availability in Sri Lanka under certain climate change and anthropogenic activities.

Uncertainty Estimation for a Global Inland Surface Water Time-Series

2021 ◽  
Author(s):  
Stefan Mayr ◽  
Igor Klein ◽  
Martin Rutzinger ◽  
Claudia Kuenzer
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Water ◽  
Fresh Water ◽  
Water Dynamics ◽  
Landsat 8 ◽  
Spatiotemporal Resolution ◽  
Observational Uncertainty ◽  
Moderate Resolution ◽  
Sentinel 2

&lt;p&gt;Fresh water is vital for life on the planet. Satellite remote sensing time-series are well suited to monitor global surface water dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on inland surface water. However, operating on diurnal- and global spatiotemporal resolution comes with certain drawbacks. As the time-series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, data gaps due to cloud coverage or invalid observations have to be interpolated. Furthermore, the moderate resolution of 250 m merely allows coarse pixel based areal estimations of surface water extent. To unlock the full potential of this dataset, information on associated uncertainty is essential. Therefore, we introduce several auxiliary layers aiming to address interpolation and quantification uncertainty. The probability of interpolated pixels to be covered by water is given by consideration of different temporal and spatial characteristics inherent to the time-series. Resulting temporal probability layers are evaluated by introducing artificial gaps in the original time-series and determining deviations to the known true state. To assess observational uncertainty in case of valid observations, relative datapoint (pixel) locations in feature space are utilized together with previously established temporal information in a linear mixture model. The hereby obtained classification probability also reveals sub-pixel information, which can enhance the product&amp;#8217;s quantitative capabilities. Functionality is evaluated in 32 regions of interest across the globe by comparison to reference data derived from Landsat 8 and Sentinel-2 images. Results show an improved accuracy for partially water covered pixels (6.21 %), and that by uncertainty consideration, more comprehensive and reliable time-series information is achieved.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; Fresh water, Landsat 8, MODIS, remote sensing, probability, Sentinel-2, sub-pixel scale, validation, water fraction.&lt;/p&gt;

Constructing Mediterranean wetland open water dynamics using a new 18-year MODIS-derived surface water fraction dataset

2020 ◽  
Author(s):  
Linlin Li ◽  
Anton Vrieling ◽  
Andrew Skidmore ◽  
Tiejun Wang
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Temporal Dynamics ◽  
Open Water ◽  
Water Area ◽  
Water Bodies ◽  
Water Dynamics ◽  
Water Fraction ◽  
Wide Range

&lt;p&gt;Wetlands are among the most biodiverse ecosystems in the world, due largely to their dynamic hydrology. Frequent observations by satellite sensors such as the Moderate Resolution Imaging Spectrometer (MODIS) allow for monitoring the seasonal, inter-annual and long-term dynamics of surface water extent. However, existing MODIS-based studies have only demonstrated this for large water bodies despite the ecological importance of smaller-sized wetland systems. In this paper, we constructed the temporal dynamics of surface water extent for 340 individual water bodies in the Mediterranean region between 2000 and 2017, using a previously developed 8-day 500 m MODIS surface water fraction (SWF) dataset. These water bodies has a wide range of size, specifically 0.01 km&lt;sup&gt;2&lt;/sup&gt; and larger. We then compared the water extent time series derived from MODIS SWF with those derived from a Landsat-based dataset. Results showed that MODIS- and Landsat-derived water extent time series showed a high correlation (r = 0.81) for more dynamic water bodies. Our MODIS SWF dataset can also effectively monitor the variability of very small water bodies (&lt;1 km&lt;sup&gt;2&lt;/sup&gt;) when comparing with Landsat data as long as the temporal variability in their surface water area was high. We conclude that MODIS SWF is a useful product to help understand hydrological dynamics for both small and larger-sized water bodies, and to monitor their seasonal, intermittent, inter-annual and long-term changes.&lt;/p&gt;

Spatial and Temporal Responses of Lakes in Northern Mongolia to Climate Change

2021 ◽  
Author(s):  
Alexander Orkhonselenge ◽  
Dashtseren Gerelsaikhan ◽  
Tuyagerel Davaagatan
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Air Temperature ◽  
Summer Precipitation ◽  
Water Dynamics ◽  
Lake Area ◽  
Mongolian Altai ◽  
Surface Water Resources ◽  
Northern Mongolia ◽  
Lake Basins

&lt;p&gt;Lakes play a valuable role in the surface water resources of Mongolia. Understanding surface water dynamics and climate change over various spatiotemporal scales from local to regional are essential in Mongolia today. This study presents how lakes in the Mongolian Altai, Khuvsgul, and Khentii Mountain Ranges at high latitudes in northern Mongolia responded to the climate change during the past 50 years. The temporal trend shows that the lakes had extended in the area during the first three decades but reduced during the last two decades. However, Lakes Khoton and Khurgan in the Mongolian Altai and Lake Khangal in the Khentii increased in the area during 1970&amp;#8211;2000 and since 2010, but decreased from 2000 to 2010. Lake Tolbo in the Mongolian Altai dropped in the area during 1970&amp;#8211;2000, and continuously increased since 2000. Whereas Lakes Erkhel and Khargal in the Khuvsgul and Lake Gurem in the Khentii extended in 1970&amp;#8211;2000 but reduced during 2000&amp;#8211;2020. The spatial trend in lake area changes shows similar patterns for glacial lakes at an elevation above 2000 m a.s.l. in the Mongolian Altai and for tectonic and fluvial lakes at an elevation below 1500 m a.s.l. in the Khuvsgul and Khentii. Anomalies of seasonal variations in air temperature and precipitation in the lake basins show that the Lake Khangal basin in the Khentii is warmer and wetter than other lake basins. Moreover, the Lake Khargal basin in the Khuvsgul is cooler in winter and autumn but warmer in spring and summer compared to the basins. Whereas Lakes Tolbo, Khoton, and Khurgan basins in the Mongolian Altai are drier than others. The correlation analysis shows that hydrological dynamics of Lake Khargal in the Khuvsgul are strongly dependent on summer precipitation (r = 0.71), and autumn (r = 0.67) and summer (r = 0.47) air temperatures. However, the linear regression shows that the lake area is moderately related to the summer precipitation (R&lt;sup&gt;2&lt;/sup&gt; = 0.5318) and the autumn air temperature (R&lt;sup&gt;2&lt;/sup&gt; = 0.4555). Overall, the lakes in northern Mongolia show the distinct responses of hydrological dynamics to the changing climate depending on their physiographic conditions.&lt;/p&gt;

High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015

2020 ◽  
Vol 3 (7) ◽  
pp. 564-570 ◽  
Author(s):  
Xiaoping Liu ◽  
Yinghuai Huang ◽  
Xiaocong Xu ◽  
Xuecao Li ◽  
Xia Li ◽  
...  
Keyword(s):  
Urban Change ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution ◽  
Resolution Mapping
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