Mapping Dynamics of Inundation Patterns of Two Largest River-Connected Lakes in China: A Comparative Study

Wu, Guojun; Liu, Yuanbo

doi:10.3390/rs8070560

Cited by 24 publications

(21 citation statements)

References 34 publications

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“…Precipitation is among the important factors that drive the dynamics of surface water areas for southwest Poyang Lake, and these conclusions are consistent with previous studies [ 10 , 56 ]. However, Figure 3 illustrates that the correlation between the surface water area and the accumulated precipitation is very low in some periods.…”

Section: Discussionsupporting

confidence: 93%

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

Tian

Wang

Pei

et al. 2020

Sensors

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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.

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Section: Discussionsupporting

confidence: 93%

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

Tian

Wang

Pei

et al. 2020

Sensors

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“…Feng et al () used a time series of MODIS data to assess the surface water area of Poyang Lake from 2000 to 2010 and found that the lake area had been extremely variable over this period (Figure ). Feng et al () and Wu and Liu () both compared the pattern of inundation dynamics in Poyang Lake and Dongting Lake using time series MODIS data. Han et al () monitored the wetland change of Poyang Lake over the last four decades using Landsat data series.…”

Section: Spatio‐temporal Monitoringmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

513

267

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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite-based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio-temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio-temporal fusion have been developed to alleviate this contradiction. Spatio-temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.Plain Language Summary Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

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“…The rates of decrease were small in spring and winter at −4.05 km 2 /a and −0.88 km 2 /a, respectively. These characteristics are related to precipitation, temperature conditions, the operation of the Three Gorges Reservoir (TGR), and the main flood season of the Yangtze River in the Dongting Lake region [73,91]. The seasonal and interannual changes in the water area of Dongting Lake from 2001 to 2017 are shown in Figure 5.…”

Section: Analysis Of the Interannual And Seasonal Changes In The Watementioning

confidence: 99%

“…This lake represents an important regional fishery and tourism resource; correspondingly, the existence and stability of the lake play irreplaceable roles in the development of the regional economy and the balance of the regional environment. However, due to intensified human activities and climate change, the occurrence of algal blooms and the reduction in the water surface area have considerably impacted Dongting Lake and its surrounding environment [72][73][74][75][76]. Additionally, these factors have limited the supply of freshwater to the surrounding population [77,78].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

et al. 2020

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Significant water quality changes have been observed in the Dongting Lake region due to environmental changes and the strong influence of human activities. To protect and manage Dongting Lake, the long-term dynamics of the water surface and algal bloom areas were systematically analyzed and quantified for the first time based on 17 years of Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The traditional methods (index-based threshold algorithms) were optimized by a dynamic learning neural network (DL-NN) to extract and identify the water surface area and algal bloom area while reducing the extraction complexity and improving the extraction accuracy. The extraction accuracy exceeded 94.5% for the water and algal bloom areas, and the analysis showed decreases in the algal bloom and water surface areas from 2001–2017. Additionally, the variations in the water surface and algal bloom areas are greatly affected by human activities and climatic factors. The results of these analyses can help us better monitor human contamination in Dongting Lake and take measures to control the water quality during certain periods, which is crucial for future management. Moreover, the traditional methods optimized by the DL-NN used in this study can be extended to other inland lakes to assess and monitor long-term temporal and spatial variations in algal bloom areas and can also be used to acquire baseline information for future assessments of the water quality of lakes.

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Mapping Dynamics of Inundation Patterns of Two Largest River-Connected Lakes in China: A Comparative Study

Cited by 24 publications

References 34 publications

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

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

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

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