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Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

  • Ruimeng Wang , Li Pan , Wenhui Niu , Rumeng Li , Xiaoyang Zhao , Xiqing Bian , Chong Yu , Haoming Xia and Taizheng Chen EMAIL logo
Published/Copyright: October 25, 2021
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Open Geosciences
From the journal Open Geosciences Volume 13 Issue 1

Abstract

Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.

Keywords: Landsat imagery; Google Earth Engine; water body extraction; spatiotemporal change; Xiaolangdi Reservoir

1 Introduction

Surface water refers to rivers, lakes, ponds, reservoirs, swamps, glaciers, and other water bodies that exist on the Earth’s surface. They are tremendously important water resources for agriculture, aquaculture, industrial production, and terrestrial ecosystems. Xiaolangdi Reservoir is located in the key position of controlling water and sediment in the lower reaches of the Yellow River, and it is the only control project under Sanmenxia that can obtain large storage capacity [1,2,3]. Xiaolangdi Reservoir is a comprehensive water control project for flood control, ice jam prevention, sediment reduction, runoff regulation, water supply, and power generation in the lower reaches of the reservoir [1,2,4]. It has superior natural conditions and an important strategic position. Timely and accurate acquisition of the reservoir water storage situation is essential to the function of the reservoir [1,3,5,6]. The changes in the scope and area of the Xiaolangdi Reservoir can reflect the comprehensive role of climate and human activities in the water cycle and ecosystem [7].

Remote sensing image data have the characteristics of comprehensive coverage, high revisit frequency, rich information, and low cost, which supplies the possibility for quantitative estimation of long time series of reservoir water body areas [4,8,9,10]. At present, the multisource remote sensing data, Systeme Probatoire d’Observation de la Terre [11], Landsat [12,13,14,15,16,17], Moderate Resolution Imaging Spectroradiometer (MODIS) [18,19,20,21], Sentinel [22,23,24,25], are applied for extracting water body area and change monitoring [26,27,28,29,30,31,32]. The Google Earth Engine (GEE) cloud computing platform is developed by Google. Based on JavaScript or Python algorithm development tools on the web, it can realize online visualization calculation and analytical processing of remote sensing data [14,33,34,35,36,37,38]. GEE has high-performance parallel computing functions, massive remote sensing data, geospatial data, and free use, and it provides a new way for long-term sequence and large-scale remote sensing analysis. It has multi-source remote sensing data sets such as Landsat, MODIS, Sentinel, and other remote sensing data sets [15,16,36,39,40,41,42]. Up to now, GEE was widely used in large-scale geoscientific studies including urban land cover, crop monitoring, wetland monitoring, forest cover mapping, and water body monitoring [43,44].

Water body extraction methods include water body extraction algorithms based on band combination (single-band threshold method, multiband spectral relation method, water index method, and threshold method) and machine learning algorithm (support vector machines, random forest, deep learning) [44,45,46,47]. The water body index can quickly obtain the distribution range of the water body through simple band calculation and threshold processing. For example, Mefeeters et al. [48] proposed the normalized difference vegetation index (NDVI) index, which suppresses vegetation and highlights water information but the suppression effect in buildings was not good. Xu [49] proposed that the modified normalized difference water index (MNDWI) can reduce the interference of soil and buildings, but it is not effective in distinguishing water bodies from vegetation. This study uses a combination of MNDWI, normalized difference vegetation index (NDVI) [50], and enhanced vegetation index (EVI), which can eliminate land noise and decrease wetlands mixed with water bodies and vegetation to improve the accuracy of the water body index.

Based on the GEE platform in this study, 789 remote sensing images of Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+), and Landsat Operational Land Imager (OLI) captured from 1999 to 2019 were obtained by JavaScript programming operation. The method of combining the MNDWI, NDVI, and EVI was used to extract the accuracy of surface water body extraction. The spatiotemporal distribution patterns of the surface water body in the Xiaolangdi Reservoir in recent 21 years were obtained, and three water body types were generated according to water body frequency: maximum water body, seasonal water body, and permanent water body. The inter-annual and intra-annual variation characteristics of the three types of water bodies and the transformation types of the Xiaolangdi Reservoir are analyzed, and the driving force of the change of Xiaolangdi water bodies is analyzed. It provides a scientific basis for ecological protection, water resource management, economic activities, and decision-making and planning in the Xiaolangdi area.

2 Materials and methods

2.1 Study area and data

2.1.1 Study area

Xiaolangdi Reservoir is the last Gorge Reservoir in the middle reaches of the Yellow River (Figure 1a); it is 40 km north of Luoyang City, 130 km from the Sanmenxia water control project, and 128 km from Huayuankou Station in Zhengzhou, Henan Province (Figure 1b–d) [1,4,5,6,7,32]. Xiaolangdi Reservoir is a large-scale comprehensive water conservancy project, which mainly focuses on flood control, ice jam prevention, and silt reduction, as well as water supply, irrigation, power generation, harm elimination, and benefit elimination [4,6]. The main project of the Xiaolangdi Reservoir started in September 1991, began to store water in October 1999, and completed the main project by the end of 2001. The total storage capacity of the Xiaolangdi Reservoir is 12.65 × 108 m3, the long-term effective storage capacity is 5.1 × 108 m3, and the control basin area is 69.4 × 104 km2, accounting for 92.3% of the Yellow River Basin Area [3,5]. Xiaolangdi Reservoir’s average annual accumulated precipitation from 1999 to 2019 was 613 mm, and the annual average temperature was 14.14°C.

Figure 1 Map of (a) the Yellow River Basin; (b) location of the Xiaolangdi Reservoir; (c) false color image of the Xiaolangdi Reservoir; and (d) GlobeLand 30 of the Xiaolangdi Reservoir.
Figure 1

Map of (a) the Yellow River Basin; (b) location of the Xiaolangdi Reservoir; (c) false color image of the Xiaolangdi Reservoir; and (d) GlobeLand 30 of the Xiaolangdi Reservoir.

2.1.2 Data sources

This study used all available Landsat TM, ETM+, and OLI surface reflectance images of the entire Xiaolangdi Reservoir in the GEE platform from 1999 to 2019. The Landsat TM and ETM+ surface reflectance datasets were generated from the Landsat ecosystem disturbance adaptive processing system algorithm, and the Landsat OLI surface reflectance products were generated from the Landsat surface reflectance code algorithm [51,52]. For each scene image, the function of the mask algorithm method is used to mask the cloud, cloud shadow, and snow pixels in the data. The high-quality image data after the mask has a good effect and is suitable for change detection of the land satellite data. The total number of these images was 789, including 224 Landsat TM from 1999 to 2013, 326 Landsat ETM+ from 1999 to 2019, and 239 Landsat OLI from 2013 to 2019. The spatial distribution (Figure 2a and b), temporal distribution (Figure 2c), and seasonal distribution (Figure 2d) of the Xiaolangdi Reservoir total observation count from 1999 to 2019.

Figure 2 The Landsat images of the study area from 1999 to 2019: (a) spatial distribution of the number of all Landsat images; (b) spatial distribution of the number of high-quality Landsat images; (c) the temporal distribution of Landsat images; and (d) the seasonal distribution of Landsat images.
Figure 2

The Landsat images of the study area from 1999 to 2019: (a) spatial distribution of the number of all Landsat images; (b) spatial distribution of the number of high-quality Landsat images; (c) the temporal distribution of Landsat images; and (d) the seasonal distribution of Landsat images.

Moreover, the Sentinel-2 images with a spatial resolution of 10 m were used to evaluate the accuracy of the extracted water bodies in the Xiaolangdi Reservoir. The 3 h Global Land Data Assimilation System (GLDAS) from 1999 to 2019 with a spatial resolution of 0.25° was used to analyze the temporal trend of precipitation and temperature in the Xiaolangdi Reservoir [53]. The 2010 Global 30 m land cover remote sensing data product (GlobeLand30) (http://www.globallandcover.com) was used to classify water bodies and non-water bodies [54,55].

2.2 Methods

2.2.1 Water body extraction algorithm

The study used an MNDWI (equation (1)), NDVI (equation (2)), and EVI (equation (3)). If a pixel meets the following criteria: EVI < 0.1 and (MNDWI > EVI or MNDWI > NDVI), then it was classified as a water body [14,16,36,56]:

(1) MNDWI = ( ρ Green − ρ Swirl ) ( ρ Green + ρ Swirl ) ,

(2) NDVI = ( ρ Nir − ρ Red ) ( ρ Nir + ρ Red ) ,

(3) EVI = 2.5 × ρ Nir − ρ Red ρ Nir + 6 × ρ Red − 7.5 × ρ Blue + 1 ,

where ρ Red, ρ Green, ρ Blue, ρ Nir, and ρ Swirl are the reflectance of the red band (0.63–0.69 μm), green band (0.52–0.6 μm), blue band (0.45–0.52 μm), near-infrared band 1 (0.77–0.9 μm), and shortwave infrared band 1 (1.55–1.75 μm), respectively.

2.2.2 Change analysis of the surface water body

According to the long-term surface water bodies in the Xiaolangdi Reservoir, the water frequency (WF, equation (1)) ranges from 0 to 100%, According to WF, surface water body can be divided into three types: maximum water body (WF ≥ 25%), seasonal water body (25% < WF ≤ 75%), and permanent water body (WF ≥ 75%) [14,36,40].

(4) WF = ∑ N Water ∑ N Good × 100 % ,

where ∑ N Water represents the number of times that all images were identified as water bodies during the year, and ∑ N Good represents the number of high-quality images during the year.

2.2.3 Dynamic degree of the surface water body

The changes in the surface water body mainly include type, area, dynamic degree, and time and space. The dynamic degree of a single surface water body expresses the change of a given surface water body type within a certain period. The surface water body change transfer matrix describes the surface water body area change [57].

  1. The dynamic degree of a single surface water body is expressed by the formula (equation (5))

    (5) K = S b − S a S a × 1 T × 100 % ,

    where S a and S b are the areas of surface water body types at the beginning and end of the study, and T is the length of the study period.

  2. Surface water body transfer matrix.

The transfer matrix of the surface water body is the mutual conversion relationship between surface water body types in the same area in different periods. By establishing the transfer matrix of the surface water body in two periods, the area changes in different surface water body types in this period are analyzed (equation (6)):

(6) S i j = S 11 S 12 … S 1 n S 21 S 22 … S 2 n … … … … S n 1 S n 2 … S n n ,

where S ij is the state of the surface water body at the beginning and end of the study, and n is the number of surface water body types.

2.2.4 Accuracy assessment

The confusion matrix is widely used in the accurate evaluation of remote sensing classification data, and it is an important method for the accurate evaluation of the surface water body data [57]. The confusion matrix with the producer accuracy (PA, equation (7)), the user accuracy (UA, equation (8)), the overall accuracy (OA, equation (9)), and the Kappa coefficient (Kappa, equation (10)) are as follows:

(7) PA = S i j S i × 100 %

(8) UA = S i j S j × 100 %

(9) OA = S total n

(10) Kappa = n × S total − ∑ i = 1 r S i S j n 2 − ∑ i = 1 r S i S j

where S total is the sum of correctly classified pixels, n is the sum of validation pixels, r is the number of rows, S ij is the observation in row i, column j; S i is a marginal total of row i; and S j is a marginal total of column j.

3 Results

3.1 Accuracy of water body mapping in the Xiaolangdi Reservoir

In this study, Sentinel-2 MSI images with a spatial resolution of 10 m were used to verify the water body extraction results of Landsat 30 m spatial resolution. We have considered the 2 km buffer zone of the Landsat Xiaolangdi Reservoir in 2019 as the verification area to ensure the uniform distribution of verification points in water and non-water bodies. We used 2010 Global 30 m land cover remote sensing data product (GlobeLand30) (http://www.globallandcover.com) for the classification of data. The surface cover was divided into the water body and non-water body, and 5,000 verification points were randomly generated, including 1,072 water and 3,928 non-water verification points (Figure 3). Table 1 shows the water body extraction accuracy of the Xiaolangdi Reservoir. The overall accuracy was 98.86%, the producer’s accuracy was 95.20%, and the Kappa coefficient was 0.96. It shows that the water body extraction accuracy was relatively high and can be further analyzed.

Figure 3 Verification points of the water body and non-water body.
Figure 3

Verification points of the water body and non-water body.

Table 1

The confusion matrix was employed for an accurate evaluation of this study

Landsat Sentinel-2 MSI Total UA
Water No-Water
Water 972 8 980 99.18%
No-Water 49 3971 4,020 98.78%
Total 1,021 3,979 5,000 OA = 98.86%
PA 95.20% 99.80% Kappa = 0.96

3.2 Spatiotemporal distribution of the maximum water body in the Xiaolangdi Reservoir

3.2.1 Inter-annual variation of the maximum water body area

The maximum water body area of the Xiaolangdi Reservoir changes greatly from 1999 to 2019. Figure 4 shows that the minimum value of the maximum water body area of the Xiaolangdi Reservoir from 1999 to 2019 was 29.14 km2 (1999), and the maximum was 241.41 km2 (2017), with an area ratio of 1:8.28 and a difference of 212.27 km2. From 1999 to 2005, the area of the maximum water body showed an upward trend of 30 km2/year. From 2005 to 2019, the area of the maximum water body showed a relatively flat change, showing an upward trend of 2.57 km2/year. Since the construction of the Xiaolangdi Reservoir was completed at the end of 2001, the area of the maximum water body around 2002 was quite different. The average water body area was divided into two phases: 1999–2019 and 2002–2019. This study was consistent with the changing trend of the Xiaolangdi water body researched by Yang et al. [58].

Figure 4 Inter-annual variation of the maximum water body area.
Figure 4

Inter-annual variation of the maximum water body area.

3.2.2 Monthly variation of the maximum water body area

Figure 5a and b shows the monthly distribution of the average maximum water body from February to July and August to January from 2002 to 2019. The maximum water body area of the Xiaolangdi Reservoir has a minimum value of August (106.72 km2) and a maximum value of March (230.52 km2). The area ratio was 1:2.16, with a difference of 123.8 km2. It shows that the area of the largest water body fluctuates greatly during the year.

Figure 5 The monthly distribution of the average maximum water body from 2002 to 2019: (a) February to July and (b) August to January.
Figure 5

The monthly distribution of the average maximum water body from 2002 to 2019: (a) February to July and (b) August to January.

3.3 Spatiotemporal distribution of the seasonal and permanent water bodies in Xiaolangdi Reservoir

Before and after the completion of the Xiaolangdi Reservoir, the seasonal and permanent water bodies have undergone major changes. Before the completion of the Xiaolangdi Reservoir in 1999, the seasonal water body area and permanent water body area were the smallest, 10.51 and 18.64 km2, respectively (Figure 6a). After the completion of the Xiaolangdi Reservoir in 2019, the seasonal water body area and permanent water body area were 61.39 and 145.76 km2, respectively (Figure 6b). Table 2 shows the comparison of the water body area changes in the Xiaolangdi Reservoir in the four periods of 1999–2004, 2005–2009, 2010–2014, and 2015–2019 in this study. The maximum of seasonal and permanent water body area was 2015–2019, and the minimum of seasonal and permanent water body area was 1999–2004. The area of seasonal and permanent water bodies from 1999 to 2019 showed an upward trend, with an increase of 41.32 and 85.35%, respectively. The area of the permanent water body has increased significantly, and the total water body area of the Xiaolangdi Reservoir shows an upward trend, with an increase of 74.29%.

Figure 6 Spatial distribution of seasonal and permanent water body: (a) 1999 and (b) 2019.
Figure 6

Spatial distribution of seasonal and permanent water body: (a) 1999 and (b) 2019.

Table 2

Water body area of the Xiaolangdi Reservoir in four periods

Type Area (km2) Percentage change
1999–2004 2005–2009 2010–2014 2015–2019 1999–2019 (%)
Seasonal 26.21 29.02 32.41 38.54 41.32
Permanent 94.09 166.90 177.55 180.36 85.35
Total 120.30 195.92 209.97 218.91 74.29

3.4 Conversions of water bodies in the Xiaolangdi Reservoir

Seasonal and permanent water bodies are wetland ecologically staggered zones, and changes in water bodies are indispensable for the movement, living range, and migration of species, and for sustainable development. Figure 7a and b shows the conversion among non-water body, seasonal water body, and permanent water body in the Xiaolangdi Reservoir. During the period of 1999–2004 and 2005–2009, the increased seasonal water body was mainly transformed from the permanent water body, with a conversion area of 7.29 km2, of which 10.42 km2 of the seasonal water body was converted into a non-water body. In the period 2005–2009 and 2010–2014, the increased seasonal water bodies were mainly the conversion of permanent water bodies. The conversion area was 6.70 km2, and only 3.29 km2 of non-water bodies were converted into seasonal water bodies. The increased permanent water body was mainly transformed from non-water body and seasonal water body, with conversion areas of 2.69 and 2.27 km2, respectively. During the period of 2010–2014 and 2015–2019, the increased seasonal water bodies were mainly due to the transformation of permanent water bodies, with a conversion area of 1.98 km2, and only 0.96 km2 of non-water bodies converted into seasonal water bodies (Figure 7c). The seasonal water body area increase from 1999 to 2019 was 15.97 km2, which was mainly formed by the permanent water body transformation. The reduced area of seasonal water bodies was 14.80 km2, of which 67.56% of seasonal water bodies were converted into non-water bodies; the reduced permanent water body area was 19.53 km2, of which 75.39% was caused by non-water bodies.

Figure 7 Water body conversion types of the Xiaolangdi Reservoir in the period 1999–2019: (a) spatial patterns of water body transformation; (b) water body transformation matrix; and (c) water body transformation in different periods.
Figure 7

Water body conversion types of the Xiaolangdi Reservoir in the period 1999–2019: (a) spatial patterns of water body transformation; (b) water body transformation matrix; and (c) water body transformation in different periods.

4 Discussion

Many factors affect the water body area changes in the reservoir, which are mainly the changes in precipitation and recharge water in the area and the influence of water consumption for production and domestic use. To determine the changes in the water body area and related factors of the Xiaolangdi Reservoir, we selected 11 indicators that may affect the water body area change (Table 3). It includes three items of nature and seven items of human factors. Pearson correlation analysis is carried out by using SPSS software. The spatial scope includes the Henan Province (Mengjin County, Xin’an County, Mianchi County, and Jiyuan City) and the Shanxi Province (Xia County and Yuanqu County) around the reservoir. The natural factor data are derived from the GLDAS data in the GEE platform and the annual runoff data are from the Yellow River Conservancy Commission of the Ministry of Water Resources. The data of anthropogenic factors mainly refer to the statistical yearbooks of Henan and Shanxi provinces from 1999 to 2019 to analyze the correlation between the reservoir body area and the main economic and social indicators.

Table 3

Changes in the water body area and related factor statistics of the Xiaolangdi Reservoir

Water body area of the Xiaolangdi Reservoir
Pearson correlation Significant N
Runoff 0.731 0.266 15
Precipitation −0.007 0.975 21
Temperature −0.061 0.794 21
Population −0.670*** 0.001 21
GDP 0.677*** 0.001 21
Gross value of the primary industry 0.684*** 0.001 21
Gross value of the secondary industry 0.700*** 0.000 21
Gross value of the tertiary industry 0.532** 0.013 21
Per capita GDP 0.661*** 0.001 21
Grain sown area 0.237 0.301 21

Note: “**” and “***” Significance levels at 0.05, and 0.01, respectively. N = 15 (2005–2019); N = 21 (1999–2019).

As shown in Table 3, (1) the water body area of the Xiaolangdi Reservoir is weakly, negatively correlated with precipitation and temperature, which shows that the influence of natural factors on the change of the water body area is not obvious. (2) The annual variation of runoff of the Xiaolangdi water station shows that the average runoff from 1987 to 2015 is 244.4 × 108 m3, the runoff from 2005 is 221.3 × 108 m3, and the runoff from 2019 is 459.2 × 108 m3. The runoff into the reservoir increased gradually from 2005 to 2019. The direct reason for the increasing area of the Xiaolangdi Reservoir in recent 20 years is the gradual increase of the runoff into the reservoir. (3) There was a weak positive correlation between the sown area of grain and the water body area. The correlation analysis showed that the increase of the sown area of grains and the water body area were associated, which indicated that the important reason for the change of the water area was the change of the cultivated area. (4) With the rapid development of society and economy, the demand for water resources in the Xiaolangdi area is increasing. Irrigation accounts for most of the total water withdrawals because wheat and corn are the main crops, and irrigation requires a lot of groundwater. The total population of the Xiaolangdi area in 2019 is nearly 74,000 less than that in 1999. There is a positive correlation between total population and water consumption. The decrease of population means the decrease of water consumption, which is the indirect driving force of the increase of water body area; (5) the water body area of the Xiaolangdi Reservoir is strongly and positively correlated with the gross domestic product (GDP), the gross value of the primary industry, the gross value of the secondary industry, the gross value of the tertiary industry, and per capita GDP of the Xiaolangdi area. The GDP of the Xiaolangdi Reservoir area was 30.83 × 108 yuan in 1999, 113.17 × 108 yuan in 2010, and 157.72 × 108 yuan in 2019, which is about five times that of 1999. After the completion of the Xiaolangdi Reservoir, the secondary industry developed rapidly, accounting for 58.81% in 1999, 68.18% in 2010, and 80.13% in 2019. The development of the Xiaolangdi tourism industry has driven the development of the surrounding economy, increased the surrounding fiscal revenue, promoted local employment, and improved the living standard of surrounding residents.

5 Conclusion

This study investigated the inter-annual and intra-annual spatial changes in the water body area in the Xiaolangdi Reservoir from 1999 to 2019 based on all available Landsat TM, ETM+, and OLI images in the GEE platform. The overall accuracy of Xiaolangdi Reservoir’s water body extraction was 98.86%, the producer’s accuracy was 95.20%, and the Kappa coefficient was 0.96, indicating that the water body extraction results are more accurate and suitable for water body extraction in the Xiaolangdi Reservoir. The water bodies were classified into the maximum water body, seasonal water body, and permanent water body according to the inundation frequency. The maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal and permanent water bodies have uneven spatiotemporal distribution. In the conversion of water body types, the seasonal water body area increased by the Xiaolangdi Reservoir from 1999 to 2019 was 15.97 km2, which was mainly formed by permanent water body conversion, and the reduced permanent water body area was 19.53 km2, of which 75.39% was caused by non-water conversion. The change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population, and it was positively correlated with seven indicators such as runoff and regional GDP.

The results of this study provide a scientific basis for the ecological protection, water resources management, and decision-making planning of the Xiaolangdi Reservoir in the Yellow River Basin. However, there are some shortcomings. The mixed pixels at the edge of the water body are the main reason for the classification error of the pixels. The next step can be combined with deep learning, neural network, and other algorithms for water body research.

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Acknowledgments

We thank the editors and the anonymous reviewers for their valuable comments and suggestions.

  1. Funding information: This research was funded by “Henan Provincial Department of Science and Technology Research Project (212102310019),” “Natural Science Foundation of Henan (202300410531),” “Youth Science Foundation Program of Henan Natural Science Foundation (202300410077),” “the major project of Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education (2020M19),” “College Students’ Innovative Entrepreneurial Training Plan Program (202010475141),” “Dabieshan National Observation and Research Field Station of Forest Ecosystem at Henan,” and “Data Center of Middle & Lower Yellow River Regions, National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://henu.geodata.cn, accessed on 14 October 2020).”

  2. Author contributions: This research was carried out under the cooperation of all authors. H.X., and T.C. provided the writing ideas for the research; R.W., and H.X. completed data collection, analysis and wrote the paper; and L.P., W.N., R.L., X.Z., X.B., C.Y., T.C., Y.S., and H.X. contributed to the discussion and revision of the paper. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

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Received: 2021-06-07
Revised: 2021-09-27
Accepted: 2021-10-02
Published Online: 2021-10-25

© 2021 Ruimeng Wang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  3. Spatiotemporal change of land use for deceased in Beijing since the mid-twentieth century
  4. Geomorphological immaturity as a factor conditioning the dynamics of channel processes in Rządza River
  5. Modeling of dense well block point bar architecture based on geological vector information: A case study of the third member of Quantou Formation in Songliao Basin
  6. Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
  7. Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
  8. Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data
  9. Spatiotemporal evolution of single sandbodies controlled by allocyclicity and autocyclicity in the shallow-water braided river delta front of an open lacustrine basin
  10. Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation
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  35. Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
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https://doi.org/10.1515/geo-2020-0305
Keywords for this article
Landsat imagery; Google Earth Engine; water body extraction; spatiotemporal change; Xiaolangdi Reservoir
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Lithopetrographic and geochemical features of the Saalian tills in the Szczerców outcrop (Poland) in various deformation settings
  3. Spatiotemporal change of land use for deceased in Beijing since the mid-twentieth century
  4. Geomorphological immaturity as a factor conditioning the dynamics of channel processes in Rządza River
  5. Modeling of dense well block point bar architecture based on geological vector information: A case study of the third member of Quantou Formation in Songliao Basin
  6. Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
  7. Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
  8. Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data
  9. Spatiotemporal evolution of single sandbodies controlled by allocyclicity and autocyclicity in the shallow-water braided river delta front of an open lacustrine basin
  10. Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation
  11. Impulse noise treatment in magnetotelluric inversion
  12. Application of multivariate regression on magnetic data to determine further drilling site for iron exploration
  13. Comparative application of photogrammetry, handmapping and android smartphone for geotechnical mapping and slope stability analysis
  14. Geochemistry of the black rock series of lower Cambrian Qiongzhusi Formation, SW Yangtze Block, China: Reconstruction of sedimentary and tectonic environments
  15. The timing of Barleik Formation and its implication for the Devonian tectonic evolution of Western Junggar, NW China
  16. Risk assessment of geological disasters in Nyingchi, Tibet
  17. Effect of microbial combination with organic fertilizer on Elymus dahuricus
  18. An OGC web service geospatial data semantic similarity model for improving geospatial service discovery
  19. Subsurface structure investigation of the United Arab Emirates using gravity data
  20. Shallow geophysical and hydrological investigations to identify groundwater contamination in Wadi Bani Malik dam area Jeddah, Saudi Arabia
  21. Consideration of hyperspectral data in intraspecific variation (spectrotaxonomy) in Prosopis juliflora (Sw.) DC, Saudi Arabia
  22. Characteristics and evaluation of the Upper Paleozoic source rocks in the Southern North China Basin
  23. Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques
  24. Input/output inconsistencies of daily evapotranspiration conducted empirically using remote sensing data in arid environments
  25. Geotechnical profiling of a surface mine waste dump using 2D Wenner–Schlumberger configuration
  26. Forest cover assessment using remote-sensing techniques in Crete Island, Greece
  27. Stability of an abandoned siderite mine: A case study in northern Spain
  28. Assessment of the SWAT model in simulating watersheds in arid regions: Case study of the Yarmouk River Basin (Jordan)
  29. The spatial distribution characteristics of Nb–Ta of mafic rocks in subduction zones
  30. Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
  31. Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
  32. Detection and modeling of soil salinity variations in arid lands using remote sensing data
  33. Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images
  34. Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China
  35. Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
  36. Comparing LiDAR and SfM digital surface models for three land cover types
  37. East Asian monsoon during the past 10,000 years recorded by grain size of Yangtze River delta
  38. Influence of diagenetic features on petrophysical properties of fine-grained rocks of Oligocene strata in the Lower Indus Basin, Pakistan
  39. Impact of wall movements on the location of passive Earth thrust
  40. Ecological risk assessment of toxic metal pollution in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, NW China
  41. Seasonal color matching method of ornamental plants in urban landscape construction
  42. Influence of interbedded rock association and fracture characteristics on gas accumulation in the lower Silurian Shiniulan formation, Northern Guizhou Province
  43. Spatiotemporal variation in groundwater level within the Manas River Basin, Northwest China: Relative impacts of natural and human factors
  44. GIS and geographical analysis of the main harbors in the world
  45. Laboratory test and numerical simulation of composite geomembrane leakage in plain reservoir
  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
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