Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data

Tian, Haifeng; Li, Wang; Wu, Mingquan; Huang, Ni; Li, Guodong; Li, Xiang; Niu, Zheng

doi:10.3390/rs9060521

Cited by 47 publications

(34 citation statements)

References 42 publications

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“…Lakes are important parts of the hydrosphere, which not only supply water resources needed by humans and the ecological environment, but also maintain the climate system and water cycle [1][2][3][4]. Lakes are very sensitive to climate change or human activities [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Dynamic Changes of the Baiyangdian Lake Surface Based on a Complex Water Extraction Method

Wang

Jiang

et al. 2018

Water

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Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km2 in 1989 and a minimum of 44 km2 in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Dynamic Changes of the Baiyangdian Lake Surface Based on a Complex Water Extraction Method

Wang

Jiang

et al. 2018

Water

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“…This is because every crop has a unique phenology during its period of growth [23], and multi-temporal vegetation indices are closely related to crop phenology [45], making them beneficial for improving classification accuracy. It is difficult to construct whole and similarly shaped curves of vegetation indices using Landsat image at a large scale, as the satellite's revisit cycle is 16 days and images are often impacted by cloud cover [46,47]. Thus, for high spatial resolution images such as Landsat images, it is crucial to determine a way to reduce the dependence on the whole time-series curves of the vegetation indices; this will contribute significantly to the improvement of classification accuracy.Remote Sens.…”

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confidence: 99%

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

et al. 2019

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Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows-a period of low NDVI values and a period of high NDVI values-for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km 2 , with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.Resolution Imaging Spectroradiometer (MODIS) Land Cover Collections [12], and the 1 km Global Land Cover 2000 (GLC2000) [13]). However, more specific information on winter crops is still limited in existing land-cover products [2,14]. On the other hand, the mapping of winter crop planting areas using the traditional agricultural census approach requires a good deal of manpower, money, and time [15]. Further, the timeliness of the traditional approach is poor. For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing. MODIS data was the major data source for large-scale crop mapping in previous studies, due to its high revisit...

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“…Poyang Lake is a shallow and water-carrying seasonal lake, and catchment inflows and water levels observed at the Hukou station along the Yangtze River undergo significant fluctuations. Complex hydrological behaviors controlled by the combined effects of hydraulic interactions between upstream watershed runoff and the downstream Yangtze River and the complex topography of the lake basin bring about sizable seasonal and annual variations in lake water stages, areas, and storage capacities [5,17,18]. A number of researchers have studied area variations [19][20][21] and water area-stage relationships [22].…”

Section: Et Al Adopted Moderate Resolutionmentioning

confidence: 99%

“…As a key indicator used to evaluate a lake's ecosystem status and evolution, the lake inundation area has attracted much attention [4,5]. Over the past several decades, inundation areas of many lakes around the world have undergone pronounced variations or have even vanished as a result of climate change and human activities [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Characteristics and Factors Influencing the Hysteresis of Water Area–Stage Curves for Poyang Lake

Huang

Peng

Liu

et al. 2017

Water

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Flood dynamics of large lake floodplain systems are typically complex. This paper analyses the characteristics and factors that influence the hysteresis of water area-stage curves for Poyang Lake, the largest freshwater lake in China characterized by complex geomorphology and upstream-downstream exchange conditions. For this purpose, a two-dimensional hydrodynamic model (EFDC) based on seven scenarios is established. The results indicate that the area-stage curve presents significant hysteretic characteristics due to different water surface gradients that emerge during the water-rising and water-falling periods. Counter-clockwise, clockwise, and splayed hysteresis directions observed at the northern, southern, and central hydrometric stations, respectively, are found in Poyang Lake for the first time. Upstream catchment inflows and Hukou stage reflecting the downstream condition are the main factors that influence hysteresis. The temporal fluctuation of catchment inflows and Hukou stage has a remarkably positive impact on hysteresis, namely, an increase in fluctuation brings about a larger hysteresis. The effects of magnitude change in the two factors on hysteresis are opposing. Catchment inflows are positively related, while the decline of the Hukou stage will produce a more pronounced hysteresis. The outcomes of this study will benefit the water management of Poyang Lake and other similar large lakes.

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Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data

Cited by 47 publications

References 42 publications

Analysis of the Dynamic Changes of the Baiyangdian Lake Surface Based on a Complex Water Extraction Method

Analysis of the Dynamic Changes of the Baiyangdian Lake Surface Based on a Complex Water Extraction Method

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Characteristics and Factors Influencing the Hysteresis of Water Area–Stage Curves for Poyang Lake

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