Search

Poyang Lake and Dongting Lake are the two largest freshwater lakes in China. They play a critical ecological role in regional sustainable development, which is closely related to water occurrence (WO). This study aimed to characterize the spatiotemporal changes, ecological impacts, and influencing factors of WO in the two lakes. First, this study utilized the Google Earth Engine (GEE) platform and all available Landsat images to retrieve the time series of WO in Poyang Lake and Dongting Lake on a two-year scale from 1989 to 2020. Then, the study explored the landscape variation intensity, landscape structure, and their ecological impacts on net primary production (NPP) and total nitrogen (TN) based on the landscape transfer rate (LTR), landscape indices, and geostatistical connectivity analysis. Finally, the study investigated the natural and human factors that influence the changes in WO. The major conclusions include the following: (1) the mean WO showed a significantly decreasing trend in the two lakes from 1989 to 2020. According to the LTR, the landscape variation in Dongting Lake was more intense before 2002, while after 2002, the landscape variation in Poyang Lake was more intense. (2) The landscape structure of WO gradually fragmented and diversified, along with decreasing hydrological connectivity in the two lakes. (3) The increase in NPP in Poyang Lake and the increase in TN in Dongting Lake were closely correlated with the changes in WO. (4) The upstream runoff showed a significantly positive correlation with the mean WO in Poyang Lake and the annual precipitation showed a significantly positive correlation with the mean WO in Dongting Lake; the influences of natural factors on WO changes became weaker. This study reinforces the understanding of long-term changes in the water body in Poyang Lake and Dongting Lake. [ABSTRACT FROM AUTHOR]

The spatiotemporal changes of open-surface water bodies in the Yangtze River Basin (YRB) have profound influences on sustainable economic development, and are also closely relevant to water scarcity in China. However, long-term changes of open-surface water bodies in the YRB have remained poorly characterized. Taking advantage of the Google Earth Engine (GEE) cloud platform, this study processed 75,593 scenes of Landsat images to investigate the long-term changes of open-surface water bodies in the YRB from 1984 to 2018. In this study, we adopted the percentile-based image composite method to collect training samples and proposed a multiple index water detection rule (MIWDR) to quickly extract the open-surface water bodies. The results indicated that (1) the MIWDR is suitable for the long-term and large-scale Landsat water bodies mapping, especially in the urban regions. (2) The areas of permanent water bodies and seasonal water bodies were 29,076.70 km2 and 21,526.24 km2, accounting for 57.46% and 42.54% of the total open-surface water bodies in the YRB, respectively. (3) The permanent water bodies in the YRB increased along with the decreases in the seasonal water bodies from 1984 to 2018. In general, the total open-surface surface water bodies in the YRB experienced an increasing trend, with an obvious spatial heterogeneity. (4) The changes of open-surface water bodies were associated with the climate changes and intense human activities in the YRB, however, the influences varied among different regions and need to be further investigated in the future. [ABSTRACT FROM AUTHOR]

• Urban water bodies in China showed a significant spatial autocorrelation in spatial distribution. • About 40% and 60% of total cities were detected with a mutation point in urban permanent and seasonal water bodies, respectively. • GDP explained about 13 %∼35 % of spatiotemporal variations of urban water bodies in China. Urban water bodies constitute a vital element within the urban ecosystem. Knowledge of the spatiotemporal changes in urban water bodies and their relationship with economic development in China is critical for sustainable development. However, previous studies usually investigated the spatiotemporal changes of urban water bodies at discrete time points and ignored the relationship between urban water bodies and economic development. Here, this study first corrected freely available water body products based on spatiotemporal majority filter, thereby obtaining an improved yearly urban water body dataset. Then, the research adopted Moran's I and Anselin Local Moran's I to explore the spatial distribution patterns of urban water bodies and employed linear regression and segmented linear regression to investigate the temporal abrupt change and long-term trend of urban water bodies. Finally, we used geodetector to evaluate the relative contributions of GDP to the spatial heterogeneity of trends in urban water bodies. The study revealed a significant spatial autocorrelation in the distribution of urban water bodies, particularly notable in the "High-High" cluster within the Yangtze and Pearl River Deltas. It was observed that approximately 40 % and 60 % of total cities experienced an abrupt change in permanent and seasonal urban water bodies, respectively. Specifically, the majority of these abrupt changes occurred during 2010–2013. When considering both long-term trends and abrupt changes, permanent urban water bodies in Chinese cities exhibited primarily a steadily increasing trend, while seasonal water bodies in Chinese cities displayed two distinct temporal patterns: a steadily increasing trend and a decreasing trend followed by a subsequent increasing trend. Furthermore, it was noted that GDP exhibited a significantly positive correlation with the temporal changes of permanent urban water bodies from 2000 to 2020 at the national scale while it first showed a significantly negative correlation with the temporal changes of seasonal urban water bodies from 2000 to 2012 and then showed a significantly positive correlation from 2012 to 2020 on a national scale. In conclusion, we found that GDP played a more important role in driving the spatial heterogeneity of trends in urban water bodies during 2012–2020 compared to 2000–2012, with the relative contribution of ∼ 32.5 %. [ABSTRACT FROM AUTHOR]

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area. [ABSTRACT FROM AUTHOR]

Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations. [ABSTRACT FROM AUTHOR]