A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes
<p>Study area map showing lakes >0.3 km<sup>2</sup> in Massachusetts where water-level fluctuations were evaluated. The lakes highlighted in orange colors are those with available in situ water-level data; blue dots are lakes with satellite data only.</p> "> Figure 2
<p>Overall study framework including data and processing levels to characterize winter drawdown lakes in Massachusetts (MA). Shapes used in the above flowchart are as follows: tilted rectangles represent data; regular rectangles represent processing and analysis; and small circles are used as connectors and for comparisons. WD = winter drawdown.</p> "> Figure 3
<p>Raw images (<b>a</b>–<b>l</b>) and corresponding surface water area maps (<b>a’</b>–<b>l’</b>) derived from three satellite sensors (Landsat 8-Operational Land Imager (OLI), Sentinel 2-MultiSpectral Instrument (MSI), and Sentinel 1-synthetic aperture radar (SAR)) for Lake Onota, Massachusetts. Dates were selected where environmental conditions presented potential issues: sun glint (<b>a</b>–<b>c</b>; 21 June 2017), clouds (<b>d</b>–<b>f</b>; 30 July 2017), cloud shadow (<b>g</b>–<b>i</b>; 14 December 2017), and ice (<b>j</b>–<b>l</b>; 31 January 2018).</p> "> Figure 4
<p>Surface water area time-series of Lake Otis, a winter drawdown (WD) lake (<b>a</b>), and Leverett Pond, a non-winter drawdown lake (ND) (<b>b</b>) in Massachusetts derived from three sensors, namely Sentinel 1-synthetic aperture radar (SAR), Landsat 8-Operational Land Imager (OLI), and Sentinel 2-MultiSpectral Instrument (MSI). Cloud cover and revisit frequency resulted in less frequent observations derived from optical sensors (Landsat 8OLI and Sentinel 2-MSI) than Sentinel 1-SAR. Red circles highlight outliers caused by ice and cloud cover.</p> "> Figure 5
<p>(<b>a</b>) Example of spatiotemporal variability in surface water area showing drawdown regions (dark regions compared to blue-colored water regions) matching with shallow regions (orange and red colors) in respective bathymetry maps for Lake Onota, Massachusetts (MA), and (<b>b</b>) comparison between water area derived from three sensors (red circle: Landsat 8-Operational Land Imager (OLI); blue square: Sentinel 1-synthetic aperture radar (SAR); and green triangle: Sentinel 2-MultiSpectral Instrument (MSI)) and water level derived from in situ loggers (purple diamond) for Lake Onota.</p> "> Figure 6
<p>(<b>a</b>) Winter drawdown (WD) frequency for 166 lakes across Massachusetts for five years (2016–2020); bubble size and color reflect the frequency. (<b>b</b>) Total number of WD lakes (blue bars) and total annual precipitation (orange bar) and total precipitation between September to December (green bar) for contiguous Massachusetts between 2016–2020.</p> "> Figure 7
<p>Boxplots reporting the minimum and maximum (whiskers), 25th–75th%iles (boxes), medians (horizontal line and label), and outliers (circles) of winter drawdown (WD) metrics: (<b>a</b>) start date, (<b>b</b>) duration, (<b>c</b>) relative magnitude, and (<b>d</b>) % lake area exposed for five years (2016–2020). Start dates range from 1 September to 1 February.</p> "> Figure 8
<p>(<b>a</b>) Comparison between relative winter drawdown (WD) magnitude (Km<sup>2</sup>) and % lake area exposed for five years (2016–2020) with linear regression R<sup>2</sup> reported for each year. Each year’s data (points and trendline) are shown in a different color. The combined data trendline for all five years is indicated by a dashed black line (<b>a</b>). Comparison between WD start date and WD duration for five years (2016–2020) (<b>b</b>). Each year data is shown in different color with different symbols (<b>b</b>).</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. Satellite Data Collection and Preprocessing
2.3. Water Area Mask (Otsu Method)
2.4. Time-Series Analysis and Accuracy Assessment
2.5. Statewide Classification of WD Lakes
2.6. Statewide WD Lake Frequency (2016–2021) and Precipitation Data
2.7. WD Metrics Calculation
3. Results
3.1. Surface Water Area (Optical vs. SAR)
3.2. Surface Water Area vs. In Situ Water Level
3.3. Classification of WD Lakes
3.4. Statewide WD Classification
3.5. Winter Drawdown Metrics
4. Discussion
4.1. Effectiveness of Sensors for Capturing Water Area and Water-Level Fluctuations
4.2. WD Frequency in MA Lakes
4.3. Variability in WD Characteristics in MA Lakes
4.4. Web Application Interface
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Satellite Sensors | Data Availability | Temporal Resolution | Spatial Resolution | Data Source Link |
---|---|---|---|---|
Landsat 8-OLI | April 2013–present | 16 days | 30 m | https://earthexplorer.usgs.gov/ (accessed on 13 July 2022) |
Sentinel 2-MSI | June 2015–present | 10 days with one sensor (S2A), 5 days with two sensors (S2A, S2B) | 10 m, 20 m, 60 m | https://scihub.copernicus.eu/ (accessed on 13 July 2022) |
Sentinel 1-SAR (GRD) | 2014–present | 12 days with one sensor (S1A), 6 days with two sensors (S1A, S1B) | 10 m, 25 m, 40 m | https://scihub.copernicus.eu/ (accessed on 13 July 2022) |
Lake Type | Sample Size (n) | 12 Months (April to March) | 6 Months (July to December) | 3 Months (October to December) |
---|---|---|---|---|
Training Dataset | ||||
WD | 42 | 94.6% | 99.7% | 99.7% |
ND | 7 | 71.4% | 83.9% | 98.2% |
Overall | 49 | 91.3% | 97.4% | 99.4% |
Test Dataset | ||||
WD | 14 | 94% | 100% | 100% |
ND | 3 | 38% | 62.5% | 87.5% |
Overall | 17 | 75% | 87.5% | 95.8% |
Lake Type | Sample Size (n) | 12 Months (April to March) | 6 Months (July to December) | 3 Months (October to December) |
---|---|---|---|---|
Training Dataset | ||||
WD | 90 | 87.78% | 87.78% | 88.88% |
ND | 50 | 66% | 70% | 74% |
Overall | 140 | 80% | 81.42% | 83.57% |
Test Dataset | ||||
WD | 30 | 86.67% | 76.66% | 76.66% |
ND | 20 | 25% | 25% | 35% |
Overall | 50 | 62% | 56% | 60% |
WD Metrics | Stats | 2016 (n = 73) | 2017 (n = 93) | 2018 (n = 63) | 2019 (n = 84) | 2020 (n = 61) |
---|---|---|---|---|---|---|
WD Start Date | Min | 09/01/2016 | 09/01/2017 | 09/01/2018 | 09/01/2019 | 09/01/2020 |
Max | 01/28/2017 | 01/23/2018 | 01/26/2019 | 01/29/2020 | 01/26/2021 | |
Mean | 10/15/2016 | 10/28/2017 | 11/29/2018 | 11/03/2019 | 11/13/2020 | |
Median | 10/6/2016 | 11/1/2017 | 12/5/2018 | 11/15/2019 | 11/13/2020 | |
±σ (days) | 44 | 38 | 31 | 45 | 45 | |
IQR (days) | 75 | 74 | 35 | 76 | 69 | |
WD Duration (Days) | Min | 89 | 76 | 80 | 78 | 65 |
Max | 299 | 299 | 286 | 300 | 299 | |
Mean | 206 | 182 | 163 | 190 | 183 | |
Median | 205 | 178 | 162 | 194 | 186 | |
±σ | 54 | 49 | 42 | 61 | 56 | |
IQR | 85 | 79 | 47 | 90 | 71 | |
WD Magnitude (Km2) | Min | 0.00 | 0.00 | 0.02 | 0.01 | 0.01 |
Max | 0.87 | 4.42 | 7.55 | 0.69 | 1.46 | |
Mean | 0.16 | 0.24 | 0.39 | 0.11 | 0.19 | |
Median | 0.08 | 0.09 | 0.11 | 0.07 | 0.12 | |
±σ | 0.19 | 0.57 | 1.13 | 0.12 | 0.24 | |
IQR | 0.15 | 0.13 | 0.13 | 0.09 | 0.15 | |
%LAE | Min | 0.03 | 0.18 | 2.04 | 0.12 | 0.86 |
Max | 34.27 | 70.72 | 51.78 | 42.56 | 47.66 | |
Mean | 11.47 | 13.38 | 14.01 | 9.91 | 14.65 | |
Median | 8.87 | 8.61 | 11.32 | 9.29 | 11.83 | |
±σ | 8.42 | 12.66 | 10.55 | 7.60 | 10.94 | |
IQR | 11.02 | 12.48 | 11.94 | 7.99 | 12.57 |
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Kumar, A.; Roy, A.H.; Andreadis, K.M.; He, X.; Butler, C. A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes. Remote Sens. 2024, 16, 947. https://doi.org/10.3390/rs16060947
Kumar A, Roy AH, Andreadis KM, He X, Butler C. A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes. Remote Sensing. 2024; 16(6):947. https://doi.org/10.3390/rs16060947
Chicago/Turabian StyleKumar, Abhishek, Allison H. Roy, Konstantinos M. Andreadis, Xinchen He, and Caitlyn Butler. 2024. "A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes" Remote Sensing 16, no. 6: 947. https://doi.org/10.3390/rs16060947