Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sets
2.2.1. Hydrological Datasets
2.2.2. Remote Sensing Datasets
2.3. Methods
2.3.1. Hydrological Analysis
2.3.2. RS Analysis
3. Results and Discussion
3.1. Human Intervention
3.2. Hydrological Drought Analysis
3.3. Remote Sensing Analysis
3.3.1. Accuracy Assessment
3.3.2. Seasonal Water Surface Variation
4. Conclusions
- (i)
- In natural conditions, the lake system is connected to the Black Sea via the Portita, Periboina and Edighiol outlets (Figure 2) and with the St George Arm of the Danube River via a system of canals and marshes. The nine lakes which compose the Razim Sinoe System are interconnected by the above mentioned system of canals. Human intervention has led to a deterioration of the ecosystem of the entire Razim Sinoe system, which has been isolated from the Black Sea, and the connections between the lakes have been cut by sluices. In this context, the water became freshwater. The cannal between Nuntasi-Tuzla Lake and Istria Lake has been silted since 1976. In view of the fact that the Dobrogea region is the driest region of Romania [35] and in order to ensure agricultural development, the state authorities from the period 1968–1975 built an important irrigation system in this region. In the irrigation period, the two tributaries (Nuntasi and Sacele rivers) have supplied the lake constantly, but after 1990, when the irrigation was stopped, the river flow decreased over time reaching its lowest level in the 2004 (2007)–2020 period. In a recent publication, the authors [21] showed that “the precipitation increased starting with 2012 but the evapotranspiration losses are much larger than the precipitation increase”. We can conclude that the budget is negative. It is apparent that the surface lake may be a subject of irreversible changes.
- (ii)
- The analysis of the daily flows of the two rivers during the 2007–2020 period detected several important drought events. Among these, two drought periods with long duration were determined, in 2013 and 2020. In this context, we further investigated if there was any influence of flow decreasing on the lake water surface and if there have been any other similar situations in the past.
- (iii)
- Using the CART model with the seasonal averaged values of RS indices (NDVI, NDWI, MNDWI, WNDWI and WRI) as the input data, we assessed the seasonal water lake surface variation during the period 1984–2021 with over 90% mapping accuracy, user accuracy and overall accuracy. The results of the proposed classification method revealed that the evolution of surface lake water is correlated with human intervention and the hydrological drought identified with the TLM method. Significant decrease during the 2003–2020 period was identified in the surface water lake’s evolution, thus the hydrological drought identified in 2011, 2012, 2013 and 2020 corresponds with the lowest values for water lake surface. In our opinion, the method based on remote sensing data and the CART model is calibrated, due to the results obtained. Unfortunately, we have only a short series of daily records, which limits this study.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Period | Spring | Summer | Autumn |
---|---|---|---|
1984–1999 | 15 | 50 | 25 |
2000–2012 | |||
2000–31 May 2003 | 9 | 8 | 6 |
1 June 2003–2011 | 11 | 29 | 13 |
2013–2021 | 19 | 27 | 21 |
RSI | Formula | Observation |
---|---|---|
NDVI | NDVI = (NIR − Red)/(NIR + Red) | Water has negative value |
NDWI | NDWI = (Green − NIR)/(Green + NIR) | Water has positive value |
MNDWI | MNDWI = (Green − MIR)/(Green + MIR) | Water has positive value |
WNDWI | WNDWI = (Green – a ∙ NIR − (1 − a) ∙ SWIR)/(Green + a ∙ NIR + (1 − a) ∙ SWIR) | a [0;1] 1 |
WRI | WRI = (Green + Red)/(NIR + MIR) | Water is >1 |
Data | Satellite | Date | Cloud Cover (%) |
---|---|---|---|
D1 | Landsat 5 TM | 16 July 1988 | 2.00 |
D2 | Landsat 5 TM | 3 September1994 | 4.00 |
D3 | Landsat 5 TM | 28 May 1999 | 14.00 |
D4 | Landsat 7 ETM+ | 7 June 2000 | 0.00 |
D5 | Landsat 5 TM | 22 March 2004 | 0.00 |
D6 | Landsat 5 TM | 18 September 2011 | 3.00 |
D7 | Landsat 8 OLI | 10 September 2014 | 10.30 |
D8 | Landsat 8 OLI | 08 May 2015 | 0.83 |
D9 | Landsat 8 OLI | 29 July 2016 | 1.16 |
Hydrometric Station | Subseries Period | Average (m3/s) | Observation |
---|---|---|---|
Nuntasi | 1965–1980 | 0.466 | hydraulic work construction and irrigation system operation |
1981–1989 | 0.637 | ||
1990–1997 | 0.410 | gradual disrupting of irrigation system | |
1998–2006 | 0.229 | ||
2007–2020 | 0.069 | ||
Sacele | 1965–1996 | 0.103 | |
1997–2003 | 0.095 | id. | |
2004–2020 | 0.033 |
Hydrometric Station | EFQ (%) | Qo (m3 s−1) |
---|---|---|
Nuntasi | 95 | 0.03 |
90 | 0.03 | |
80 | 0.04 | |
75 | 0.05 | |
Sacele | 95 | 0.01 |
90 | 0.01 | |
80 | 0.02 | |
75 | 0.02 |
Date Start | Date End | Length Period (Days) |
---|---|---|
7/20/2008 | 7/22/2008 | 3 |
9/28/2011 | 9/30/2011 | 3 |
9/27/2012 | 9/29/2012 | 3 |
6/13/2013 | 6/30/2013 | 18 |
7/2/2013 | 7/2/2013 | 1 |
7/23/2013 | 8/30/2013 | 39 |
8/15/2019 | 8/16/2019 | 2 |
8/29/2019 | 9/1/2019 | 4 |
8/3/2020 | 9/14/2020 | 43 |
9/16/2020 | 9/30/2020 | 15 |
10/7/2020 | 10/20/2020 | 14 |
Data No. | Class | UA (%) | PA (%) | OA (%) | Kappa |
---|---|---|---|---|---|
D1 | Water | 95.74 | 100.00 | 98.21 | 0.96 |
Non-Water | 100.00 | 97.31 | |||
D2 | Water | 100.00 | 98.11 | 99.09 | 0.98 |
Non-Water | 98.27 | 100.00 | |||
D3 | Water | 98.36 | 100.00 | 99.01 | 0.98 |
Non-Water | 100.00 | 97.61 | |||
D4 | Water | 96.77 | 100.00 | 98.03 | 0.96 |
Non-Water | 100.00 | 95.23 | |||
D5 | Water | 100.00 | 98.36 | 99.09 | 0.98 |
Non-Water | 98.03 | 100.00 | |||
D6 | Water | 98.50 | 100.00 | 99.06 | 0.98 |
Non-Water | 100.00 | 98.00 | |||
D7 | Water | 98.07 | 100.00 | 99.03 | 0.98 |
Non-Water | 100.00 | 98.11 | |||
D8 | Water | 98.21 | 100.00 | 99.03 | 0.98 |
Non-Water | 100.00 | 97.95 | |||
D9 | Water | 97.95 | 100.00 | 99.09 | 0.98 |
Non-Water | 100.00 | 98.38 |
Date | Class | UA (%) | PA (%) | OA (%) | Kappa |
---|---|---|---|---|---|
20 August 2012 | Water | 95.75 | 95.55 | 95.74 | 0.91 |
Non-Water | 95.94 | 95.91 | |||
23 September 2012 | Water | 96.07 | 98.00 | 96.66 | 0.94 |
Non-Water | 97.91 | 95.91 |
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Șerban, C.; Maftei, C.; Dobrică, G. Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania. Water 2022, 14, 556. https://doi.org/10.3390/w14040556
Șerban C, Maftei C, Dobrică G. Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania. Water. 2022; 14(4):556. https://doi.org/10.3390/w14040556
Chicago/Turabian StyleȘerban, Cristina, Carmen Maftei, and Gabriel Dobrică. 2022. "Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania" Water 14, no. 4: 556. https://doi.org/10.3390/w14040556