scholarly journals Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin

2018 ◽  
Vol 10 (10) ◽  
pp. 1635 ◽  
Author(s):  
Chao Wang ◽  
Mingming Jia ◽  
Nengcheng Chen ◽  
Wei Wang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Minimal Surface ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Google Earth ◽  
Water Dynamics ◽  
Maximal Surface ◽  
Google Earth Engine

Dynamics of surface water is of great significance to understand the impacts of global changes and human activities on water resources. Remote sensing provides many advantages in monitoring surface water; however, in large scale, the efficiency of traditional remote sensing methods is extremely low because these methods consume a high amount of manpower, storage, and computing resources. In this paper, we propose a new method for quickly determining what the annual maximal and minimal surface water extent is. The maximal and minimal water extent in the year of 1990, 2000, 2010 and 2017 in the Middle Yangtze River Basin in China were calculated on the Google Earth Engine platform. This approach takes full advantage of the data and computing advantages of the Google Earth Engine’s cloud platform, processed 2343 scenes of Landsat images. Firstly, based on the estimated value of cloud cover for each pixel, the high cloud covered pixels were removed to eliminate the cloud interference and improve the calculation efficiency. Secondly, the annual greenest and wettest images were mosaiced based on vegetation index and surface water index, then the minimum and maximum surface water extents were obtained by the Random Forest Classification. Results showed that (1) the yearly minimal surface water extents were 14,751.23 km2, 14,403.48 km2, 13,601.48 km2, and 15,697.42 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (2) The yearly maximal surface water extents were 18,174.76 km2, 20,671.83 km2, 19,097.73 km2, and 18,235.95 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (3) The accuracies of surface water classification ranged from 86% to 93%. Additionally, the causes of these changes were analyzed. The accuracy evaluation and comparison with other research results show that this method is reliable, novel, and fast in terms of calculating the maximal and minimal surface water extent. In addition, the proposed method can easily be implemented in other regions worldwide.

Sources and fate of high levels of ammonium in surface water and shallow groundwater of the Jianghan Plain, Central China

2017 ◽  
Vol 19 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Yao Du ◽  
Teng Ma ◽  
Yamin Deng ◽  
Shuai Shen ◽  
Zongjie Lu
Keyword(s):  
Surface Water ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Shallow Groundwater ◽  
Central China ◽  
The Yangtze River ◽  
Jianghan Plain ◽  
The Yangtze River Basin ◽  
Mixed Origin

Ammonium is of anthropogenic, natural and mixed origin in surface water, aquifer and intermediate aquitard between them, respectively, within the Yangtze River Basin.

scholarly journals “3S” TECHNOLOGIES AND APPLICATION FOR DYNAMIC MONITORING SOIL AND WATER LOSS IN THE YANGTZE RIVER BASIN, CHINA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1563-1567
Author(s):  
C. Li ◽  
J. Yao ◽  
R. Li ◽  
Y. Zhu ◽  
H. Yao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Yangtze River ◽  
Soil Loss ◽  
Yangtze River Basin ◽  
Dynamic Monitoring ◽  
The Yangtze River ◽  
Soil And Water Loss ◽  
The Yangtze River Basin ◽  
Remote Sensing Interpretation

Abstract. For China, which has many big rivers, there is an urgent need for efficient dynamic monitoring technology of water and soil loss. However, there are some problems in the current 3S (RS, GIS and GPS) technology for dynamic monitoring water and soil loss. This paper takes the Yangtze River Basin as an example to innovate and optimize the key technologies of the remote sensing interpretation of the water and soil loss dynamic monitoring of the Yangtze River Basin, and overcome the major technical difficulties in the remote sensing interpretation of the dynamic monitoring of water and soil loss. The key technologies include: 1) The establishment of a field investigation platform based on Internet and UAV (Unmanned Aerial Vehicle) for remote sensing interpretation; 2) Near real-time evaluating key factors of soil and water loss based on UAV photogrammetry and digital terrain analysis; 3) Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for remote sensing images pre-processing; 4) An object-oriented land use change update quality control method supported by multi-PC and GIS; and an object-oriented remote sensing image classification system based on random forest, deep learning and transfer learning; 5) Improvement of quantitative change detection method for image vegetation and three-dimensional topography. The results have been successfully applied in the remote sensing interpretation of the dynamic monitoring of water and soil loss in the national key prevention and control area of the Yangtze River Basin. They have been provided a scientific reference for the development planning of The Yangtze River Economic Zone.

scholarly journals Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 2977 ◽  
Author(s):  
Hongwei Zeng ◽  
Bingfang Wu ◽  
Ning Zhang ◽  
Fuyou Tian ◽  
Elijah Phiri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Rainy Season ◽  
Spatiotemporal Analysis ◽  
Google Earth ◽  
Annual Precipitation ◽  
Precipitation Pattern ◽  
Google Earth Engine ◽  
Zambezi River Basin ◽  
Zambezi River

Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation.

scholarly journals Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2091 ◽  
Author(s):  
Dong-Dong Zhang ◽  
Lei Zhang
Keyword(s):  
Surface Water ◽  
Land Cover ◽  
Central Region ◽  
Yangtze River ◽  
Industrial Development ◽  
Landsat Imagery ◽  
Google Earth ◽  
Google Earth Engine ◽  
Main Factors ◽  
Lower Yangtze

Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km2 to 1500.5 km2, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km2. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km2. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km2) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed.

scholarly journals Research and Analysis of Ecological Environment Quality in the Middle Reaches of the Yangtze River Basin between 2000 and 2019

2021 ◽  
Vol 13 (21) ◽  
pp. 4475
Author(s):  
Shengqing Zhang ◽  
Peng Yang ◽  
Jun Xia ◽  
Kunlun Qi ◽  
Wenyu Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Google Earth ◽  
Green Economy ◽  
Ecological Environment ◽  
Eastern Region ◽  
The Yangtze River ◽  
Environment Quality ◽  
The Yangtze River Basin

Ecological environment quality is a long-term continuous concept that is affected by various environmental factors. Its assessment has important implications for implementing the planning and protection of dynamic regional ecosystems. Therefore, this study attempted to obtain these indicators (green, dry, wet, heat) through the Google Earth Engine (GEE) platform, and then coupled the ecological environment quality index in the middle reaches of the Yangtze River Basin (MYRB) between 2000 and 2019, based on the remote sensing ecological index (RSEI). The major results show that: (1) changes in the four indicators in summer were more obvious than those in winter, and the changes were concentrated in the central and northern regions of the MYRB; (2) both the modified normalized difference water index (MNDWI) and normalized differential build-up and bare soil index (NDBI) in summer and winter have higher weighting ratios, implying that water body changes and human activities had a greater impact on the ecological environment; and (3) ecological environment quality in the MYRB between 2000 and 2019 was relatively flat. The ecological conditions began to deteriorate in 2008, and substantial ecological degradation was noted in some areas between 2008 and 2019 (18.7% in the central region, 16.0% in the eastern region). The MYRB has an important position in the Yangtze River economic belt and is an important part of the Yangtze River protection. This research could provide a theoretical basis and decision support for the development and protection of the Yangtze River Basin (YRB) green economy.

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