Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations
<p>The study area is located in the upper reach of the Yellow River, with a lot of reservoirs built on it. The overview image shows the location of the study area and the terrains in and around the study area. The background was generated by mosaicking two adjacent Landsat-8 OLI RGB composite images. The locations of newly-built reservoirs/dams in the study area derived from Google Earth imagery are marked in this figure.</p> "> Figure 2
<p>Schematic flowchart of the methodology, including identification of newly dammed reservoirs, extraction of high spatial-resolution reservoirs and estimation of reservoir water volume.</p> "> Figure 3
<p>Plots of original NDWI time series and BFAST-decomposed seasonal, trend and remainder components and BFAST-detected break points of samples in land-to-water areas. In each plot, the four panels from the top to the bottom are original NDWI time series (“Yt”), seasonal component (“St”), trend component (“Tt”) and reminder component (“et”), respectively. The <span class="html-italic">x</span>-axis shows time. The gray vertical dashed lines in the rows of trend components indicate timing of break points within the confidence interval of 95%.</p> "> Figure 4
<p>Plots of NDWI time series and BFAST-decomposed seasonal, trend, and remainder components and BFAST-detected break points of samples in water areas without damming activities in the period of NDWI time series. These sampled plots showed that the values in trend component before the break points (if detected) are higher than those in land-to-water pixels.</p> "> Figure 5
<p>Plots of NDWI time series and BFAST-decomposed seasonal, trend, and remainder components and BFAST-detected break points of samples in land areas. These sampled plots showed that the values in trend component after the break points (if detected) are lower than those in land-to-water pixels.</p> "> Figure 6
<p>Spatial locations and extents and damming dates of true break points detected in Gongboxia Reservoir (<b>a</b>), Suzhi Reservoir (<b>b</b>), Huangfeng Reservoir (<b>c</b>), Jishixia Reservoir (<b>d</b>), and Sigouxia Reservoir (<b>e</b>). They showed the high consistency of locations of pixels detected as true break points and the timing of true break points which correspond to damming dates indicated by Google Earth imagery and documentary materials. The lines in the figures point at the location of dams.</p> "> Figure 6 Cont.
<p>Spatial locations and extents and damming dates of true break points detected in Gongboxia Reservoir (<b>a</b>), Suzhi Reservoir (<b>b</b>), Huangfeng Reservoir (<b>c</b>), Jishixia Reservoir (<b>d</b>), and Sigouxia Reservoir (<b>e</b>). They showed the high consistency of locations of pixels detected as true break points and the timing of true break points which correspond to damming dates indicated by Google Earth imagery and documentary materials. The lines in the figures point at the location of dams.</p> "> Figure 7
<p>Outlines of the Landsat-derived water surface of Gongboxia Reservoir (<b>a</b>), Suzhi Reservoir (<b>b</b>), Huangfeng Reservoir (<b>c</b>), Jishixia Reservoir (<b>d</b>), and Sigouxia Reservoir (<b>e</b>).</p> "> Figure 8
<p>Unfiltered false break points detected in terrestrial areas away from the Yellow River (<b>a</b>), near Liujiaxia reservoir (<b>b</b>), and in the Yellow River (<b>c</b>). From the plots of trend component, what could be discovered is that the values in trend components jumped to a little higher value than the threshold we set. With a little higher value of threshold, these false break points would be effectively eliminated, which is the inherent disadvantage of threshold-based elimination.</p> "> Figure 8 Cont.
<p>Unfiltered false break points detected in terrestrial areas away from the Yellow River (<b>a</b>), near Liujiaxia reservoir (<b>b</b>), and in the Yellow River (<b>c</b>). From the plots of trend component, what could be discovered is that the values in trend components jumped to a little higher value than the threshold we set. With a little higher value of threshold, these false break points would be effectively eliminated, which is the inherent disadvantage of threshold-based elimination.</p> "> Figure 9
<p>Landsat-8 OLI RGB synthesis image of Dahejia Reservoir with the MOD09A1 pixels in its range and the BFAST decomposition result of the pixel at the dam of it. The spatial extents of each MOD09A1 pixel was transparently shown as a chess board to illustrate that all of the pixels in Dahejia Reservoir are combined of different features, which obstructed the effectiveness of BFAST algorithm.</p> "> Figure 10
<p>Timing of break points detected in the time series (<b>a</b>), averages of trend components before break points (<b>b</b>) and averages of trend components after break points (<b>c</b>), influenced by different settings of h parameter sampled in reservoir area. Values in the <span class="html-italic">x</span>-axis indicate different settings of h parameter, values in the <span class="html-italic">y</span>-axis (a) indicate dates of break points detected by BFAST algorithm while values in <span class="html-italic">y</span>-axis (b and c) indicate averages of trend components decomposed by BFAST algorithm. Each line drawn in each diagram indicates their corresponding values of <span class="html-italic">y</span>-axis influenced by different settings of h parameter. They were drawn in different colors to distinguish different sample pixels. The numbers behind each line in legend indicate the IDs of sample pixels. All of the three diagrams share the same legend to diagram (a).</p> "> Figure 10 Cont.
<p>Timing of break points detected in the time series (<b>a</b>), averages of trend components before break points (<b>b</b>) and averages of trend components after break points (<b>c</b>), influenced by different settings of h parameter sampled in reservoir area. Values in the <span class="html-italic">x</span>-axis indicate different settings of h parameter, values in the <span class="html-italic">y</span>-axis (a) indicate dates of break points detected by BFAST algorithm while values in <span class="html-italic">y</span>-axis (b and c) indicate averages of trend components decomposed by BFAST algorithm. Each line drawn in each diagram indicates their corresponding values of <span class="html-italic">y</span>-axis influenced by different settings of h parameter. They were drawn in different colors to distinguish different sample pixels. The numbers behind each line in legend indicate the IDs of sample pixels. All of the three diagrams share the same legend to diagram (a).</p> "> Figure 11
<p>Averages of trend components of water area pixels before (left) and after (right) break their point, influenced by different settings of h parameter sampled in water (<b>a</b>) and land (<b>b</b>) area. Values in <span class="html-italic">x</span>-axis indicate different settings of h parameter, while values in <span class="html-italic">y</span>-axis indicate averages of trend components before or after break points. Each line drawn in the diagram indicates average of trend components in each sample pixel influenced by different settings of h parameter. They were drawn in different colors to distinguish different sample pixels. The numbers behind each line in the legend indicate the IDs of sample pixels. The no-data points in the diagrams indicate no break points were detected.</p> "> Figure 12
<p>Extracted newly dammed reservoir pixels when value of threshold was set to −0.10 (<b>a</b>), −0.15 (<b>b</b>), −0.20 (<b>c</b>), −0.25 (<b>d</b>), and −0.30 (<b>e</b>).</p> "> Figure 12 Cont.
<p>Extracted newly dammed reservoir pixels when value of threshold was set to −0.10 (<b>a</b>), −0.15 (<b>b</b>), −0.20 (<b>c</b>), −0.25 (<b>d</b>), and −0.30 (<b>e</b>).</p> "> Figure 12 Cont.
<p>Extracted newly dammed reservoir pixels when value of threshold was set to −0.10 (<b>a</b>), −0.15 (<b>b</b>), −0.20 (<b>c</b>), −0.25 (<b>d</b>), and −0.30 (<b>e</b>).</p> ">
Abstract
:1. Introduction
2. Study Area
3. Data and Method
3.1. Datasets and Preprocessing
3.2. Methods
3.2.1. General Methodology
3.2.2. Identifying Locations and Time of New Reservoir Emergence
3.2.3. Extracting Reservoir Extents and Estimating Impounded Water Volumes
4. Results and Analyses
4.1. Abrupt Changes in NDWI Time Series Due to Damming Activities
4.2. Reservoir Inundation Areas and Water Storages
5. Discussions
6. Summary
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Reservoir | Total Reservoir Capacity (m3) × | Impounded Water V in 2015 (m3) × | Estimated Reservoir Area (m2) | Estimated Water V (m3) | Ratio (to Total Reservoir Capacities, %) | Ratio (to Water V in 2015, %) |
---|---|---|---|---|---|---|
Gongboxia | 6.20 × 108 | 5.36 × 108 | (2.17 ± 0.26) × 107 | (3.99 ± 1.09) × 108 | 64.35 ± 17.51% | 74.44 ± 20.25% |
Suzhi | 4.55 × 107 | 4.10 × 107 | (5.30 ± 0.47) × 106 | (3.85 ± 1.00) × 107 | 84.62 ± 21.98% | 93.09 ± 24.40% |
Huangfeng | 5.90 × 107 | No Data | (2.28 ± 0.30) × 106 | (8.54 ± 3.82) × 106 | 14.47±6.98% | No Data |
Jishixia | 2.64 × 108 | 1.71 × 108 | (6.66 ± 1.28) × 106 | (5.43 ± 3.10) × 107 | 20.57 ± 11.75% | 31.75 ± 18.14% |
Dahejia | 3.90 × 106 | No Data | No Data | No Data | No Data | No Data |
Sigouxia | 4.79 × 107 | No Data | (4.17 ± 0.73) × 106 | (3.33 ± 0.62) × 107 | 69.52 ± 12.95% | No Data |
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Zhang, W.; Pan, H.; Song, C.; Ke, L.; Wang, J.; Ma, R.; Deng, X.; Liu, K.; Zhu, J.; Wu, Q. Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations. Remote Sens. 2019, 11, 25. https://doi.org/10.3390/rs11010025
Zhang W, Pan H, Song C, Ke L, Wang J, Ma R, Deng X, Liu K, Zhu J, Wu Q. Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations. Remote Sensing. 2019; 11(1):25. https://doi.org/10.3390/rs11010025
Chicago/Turabian StyleZhang, Wensong, Hang Pan, Chunqiao Song, Linghong Ke, Jida Wang, Ronghua Ma, Xinyuan Deng, Kai Liu, Jingying Zhu, and Qianhan Wu. 2019. "Identifying Emerging Reservoirs along Regulated Rivers Using Multi-Source Remote Sensing Observations" Remote Sensing 11, no. 1: 25. https://doi.org/10.3390/rs11010025