Monitoring the Fluctuation of Lake Qinghai Using Multi-Source Remote Sensing Data

Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data

Lakes are one of the most important parts of the terrestrial hydrosphere. The long-term series of lake area dynamic data with high spatial-temporal resolution is of great significance to the study of global change of the water environment. Satellite observations (such as Landsat) have provided images since the 1970s, but there were challenges for the construction of long-term sequences of lake area on a monthly temporal scale. We proposed a temporal-spatial interpolation and rule-based (TSIRB) approach on the Google Earth Engine, which aims to achieve automatic water extraction and bimonthly sequence construction of lake area. There are three main steps of this method which include bimonthly image sequence construction, automatic water extraction, and anomaly rectification. We applied the TSIRB method to five typical lakes (covering salt lakes, river lagoons, and plateau alpine lakes), and constructed the bimonthly surface water dataset (BSWD) from 1987 to 2020. The accuracy assessment that was based on a confusion matrix and random sampling showed that the average overall accuracy (OA) of water extraction was 96.6%, and the average Kappa was 0.90. The BSWD sequence was compared with the lake water level observation data, and the results show that the BSWD data is closely correlated with the water level observation sequence, with correlation coefficient greater than 0.87. The BSWD improves the hollows in the global surface water (GSW) monthly data and has advantages in the temporal continuity of surface water data. The BSWD can provide a 30-m-scale and bimonthly series of surface water for more than 30 years, which shows good value for the long-term dynamic monitoring of lakes, especially in areas that are lacking in situ surveying data.

A Novel Method for Mapping Lake Bottom Topography Using the GSW Dataset and Measured Water Level

Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset with long-term measured water levels. This method took advantage of the following feature: the rapid change in water level of a lake’s dynamic inundation area leads to a different water occurrence frequency and, therefore, put forward the concept of lake water level frequency, which refers to the frequency at which the water level is higher than or equal to a specified elevation. As water occurs more frequently in lake bottoms with lower elevations and less frequently in lake bottoms with higher elevations, we assume that lake water level frequency is identical to the water occurrence frequency over a long time. The water level frequency curve of all the measured water level data was generated through the P-III distribution function, and the elevation values from the water level frequency curve were assigned to pixels with the same frequency in the water occurrence map in order to generate the lake bottom topographic map. A case study was conducted on Poyang Lake in China to demonstrate the performance of the method. The derived bottom topographic map of Poyang Lake was verified by four measured sections. The results showed that the proposed method was feasible and could well reflect the bottom topography of Poyang Lake. The absolute error was mostly less than 0.5 m, the mean relative error was 7.4%, and the root mean square error was 0.99 m. The proposed method enriches the mapping means of lake bottom topography and has the potential to become a useful tool with a broad application prospect.

Combining Optical and Radar Satellite Imagery to Investigate the Surface Properties and Evolution of the Lordsburg Playa, New Mexico, USA

Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and meteorological parameters, windblown dust events initiated from Lordsburg Playa, New Mexico, United States, threaten public safety and health through low visibility and exposure to dust emissions. Combining optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas—a potent landform for wind erosion. The optical images provide a long-term data record, while radar images can observe land surface irrespective of clouds, darkness, and precipitation. As a home for optical and radar imagery, powerful algorithms, cloud computing infrastructure, and application programming interface applications, Google Earth Engine (GEE) is an invaluable resource facilitating acquisition, processing, and analysis. In this study, the fractional abundance of soil, vegetation, and water endmembers were determined from pixel mixtures using the linear spectral unmixing model in GEE for Lordsburg Playa. For this approach, Landsat 5 and 8 images at 30 m spatial resolution and Sentinel-2 images at 10–20 m spatial resolution were used. Employing the Interferometric Synthetic Aperture Radar (InSAR) techniques, the playa’s land surface changes and possible sinks for sediment loading from the surrounding catchment area were identified. In this data recipe, a pair of Sentinel-1 images bracketing a monsoon day with high rainfall and a pair of images representing spring (dry, windy) and monsoon seasons were used. The combination of optical and radar images significantly improved the effort to identify long-term changes in the playa and locations within the playa susceptible to hydrological stresses and LULC changes. The linear spectral unmixing algorithm addressed the limitation of Landsat and Sentinel-2 images related to their moderate spatial resolutions. The application of GEE facilitated the study by minimizing the time required for acquisition, processing, and analysis of images, and storage required for the big satellite data.

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

Mechanism of the Rapid Shrinkage of Limboto Lake in Gorontalo, Indonesia

This study explores the mechanisms behind the rapid shrinkage of Limboto Lake, Gorontalo, Indonesia, using remotely sensed imagery and river outcrop investigation data. The results show that more than 70% of the sedimentation resulting in shrinkage is contributed by riverbank erosion causing rivers to drain into the lake during the period 2003–2017. From geological investigation, it is found that the lowland area to the west of Limboto Lake comprises 1 m of flood sediments, followed by at least 5 m of fine-grained inner bay sediments. Severe riverbank erosion is also observed at many points. Hence, it is concluded that the shrinkage of Limboto Lake resulted from rapid-induced rapid erosion of inner bay sediments formed during plate collision which readily flowed into and were deposited in Limboto Lake; this created a delta, especially on the west side of the lake. Accelerated sedimentation caused by river erosion has led to rapid lake shrinkage. This phenomenon could be typical of the transformation of enclosed seas into lakes by the rapid uplifting movement of land in collision zones.

Assessment of Water Storage Change in China’s Lakes and Reservoirs over the Last Three Decades

Lakes and reservoirs are essential elements of the hydrological and biochemical cycles, considered sentinels of global climate change. However, comprehensive quantifications of their water storage changes (∆V) at a large spatiotemporal scale are still rare. Here, we integrated a global surface water dataset and SRTM digital elevation models, both available from Google Earth Engine platform at a spatial resolution of 30 m, to evaluate ∆V for a total of 760 lakes and reservoirs across China at an annual timescale since 1984. The results indicated that (1) the aggregated water storage went through a slight increase of 41.5 Gt (1.7 Gt/yr) during 1985–2005, a significant decrease of 100 Gt (-20.6 Gt/yr) during 2005–2009, and then increased by 136.3 Gt (21.3 Gt/yr) during 2009–2015. (2) The increasing trend was largely attributed to lakes and reservoirs in the Tibetan Plateau Lake Zone, and the decreasing trend was mainly due to the North and Northwest Lake Zone, with little variations observed for the Northeast and Southwest Lake Zones. (3) Qinghai lake was associated with the largest increase (18.3 Gt) and Poyang lake presented the largest decline (-9.2 Gt). The results can help advance our understanding of the impact of climate change and improve future projection.

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

The availability of very high spatial resolution (VHR) remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC) changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees) classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

Estimating the fluctuation of Lake Hulun, China, during 1975-2015 from satellite altimetry data

Lake level, area, and volume changes can describe the fluctuation of water bodies. In this study, ICESat/Hydroweb and Landsat data recorded with irregularly time intervals from 1975 to 2015 were used to examine changes in lake level and area which were combined to indirectly estimate water volume variations of Lake Hulun. The time series of lake level, area, and volume variations of Lake Hulun exhibited a fluctuating trend from 1975 to 1984 and the mean value were about 542.57 m, 2065.76 km², and - 0.045 km³, respectively and an increasing trends showed during 1984-2000. Lake Hulun revealed the fastest decrease in lake level (- 0.42 m/a), volume (- 0.83 km³/a) from 2000 to 2009, and the fastest shrinking in surface area (- 33.88 km²/a) during 2000-2012. There was a seasonal variation of water level and lake volume variations during 2009-2012 and the mean value were 539.98 m and - 5.72 km³, respectively. From 2012 to 2015, a faster increasing trends were shown in water level, area, and volume variations with a rate of 0.73 m/a (the amount of change was 2.92 m), 81.95 km³/a (the amount of change was 327.8 km²), and 0.42 km³/a (the amount of change was1.67 km³), respectively. The lake level and area showed strong correlations for Lake Hulun (R ² = 0.93). The water volume changes were in very good agreement for lake level changes (R ² > 0.99) and surface area variations (R ² = 0.92). Combining with lake level and area changes, the sum of lake volume variation of Lake Hulun was obtained and it showed a positive water budgets of 0.24 km³ during past 40 years. River and groundwater discharge, the pan evaporation, the net pan evaporation, and the water diversion project were reasons for the lake level, area, and volume variations in Lake Hulun. This study demonstrates that remote sensing data can be used as a source of information for monitoring comprehensively the fluctuation of large water bodies.

Analysis of water level variation of lakes and reservoirs in Xinjiang, China using ICESat laser altimetry data (2003-2009)

This study utilizes ICESat Release 33 GLA14 data to analyse water level variation of Xinjiang’s lakes and reservoirs from 2003 to 2009. By using Landsat images, lakes and reservoirs with area larger than 1 km² are numerically delineated with a software tool. Based on ICESat observations, we analyse the characteristics of water level variation in different geographic environments, as well as investigate the reasons for the variation. Results indicate that climatic warming contributes to rising water levels in lakes in mountainous areas, especially for lakes that are recharged by snow and glacial melting. For lakes in oases, the water levels are affected jointly by human activity and climate change, while the water levels of reservoirs are mainly affected by human activity. Comparing the annual average rates of water levels, those of lakes are higher than those of reservoirs in oasis areas. The main reasons for the decreasing water levels in desert regions are the reduction of recharged runoff and high evaporation. By analysing the variation of water levels and water volume in different geologic environments, it is found that water level and volume increased in mountainous regions, and decreased in oasis regions and desert regions. Finding also demonstrate that decreasing volume is greater than increasing volume, which results in decreasing total volume of Xinjiang lakes and reservoirs.