Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery
Abstract
:1. Introduction
- To evaluate the performance of various WMRs based on the SI, MI, and TB categorization within GEE.
- To investigate long-term spatiotemporal changes in SWA in Iran over a 32-year period using Landsat 5, 7, and 8 data.
- To examine the long-term correlation between environmental variables (such as precipitation and temperature) and SWA change.
2. Study Area
3. Data and Methodology
3.1. Satellite Data
3.1.1. Landsat Imageries
3.1.2. Global Human Settlement Layer (GHSL)
3.1.3. SRTM DEM
3.1.4. FLDAS
3.1.5. NOAA AVHRR
3.1.6. USGS Spectral Library V7
3.1.7. Other Datasets
3.2. Framework
3.2.1. Pre-Processing
3.2.2. Feature Extraction and Water Mapping Rules
3.2.3. Accuracy Assessment and Further Analysis
4. Results
4.1. Performance Evaluation of Different Water Mapping Methods
4.2. Long-Term Changes of SWA
4.3. Water Frequency Map
4.4. Correlation with Environmental Variables
5. Discussion
5.1. WMRs
5.2. SWA in Iran
5.3. Flood and Drought Events
5.4. Uncertainties and Future Trends
6. Conclusions
- Preliminary results revealed that, from the twelve WMRs (of different water mapping rules of SI, MI, and TB), those providing a higher separation between the two target classes (water and non-water) lead to higher overall classification accuracy;
- The results also indicate that methods using the NIR band can achieve higher accuracy than those using only SWIR or in combination with NIR with SWIR (NIR + SWIR) bands. Among the twelve WMRs from this study, the MI-based method WMR #7 (EVI < 0.1 and (NDWI > NDVI or NDWI > EVI)) was selected as the most accurate approach to surface water mapping;
- Of the five major basins that cover Iran, only the Persian Gulf Basin had an upward trend for SWA. In contrast, other basins experienced a downward trend in SWA;
- There was a declining trend for total SWA from 1990 to 2021 due to drought. Prior to 2000, Iran experienced higher SWA values, but since 2000, SWA in 2015 has declined to less than 50% when compared to the wettest year (1992);
- An analysis of the environmental variables through the same period (1990–2021) also confirmed overall SWA trends. Precipitation (P) and NDVI experienced an overall downward trend (direct correlation with SWA), but temperature (T) showed a general rising tendency (inverse correlation).
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AWEI | Automatic Water Extraction Index (for shaded images with dark surfaces (AWEIsh) and shadowless images (AWEInsh)) |
DEM | Digital Elevation Model |
DNN | Deep Neural Network |
ESRI | Environmental Systems Research Institute |
EVI | Enhanced Vegetation Index |
FLDAS | Famine Early Warning Systems Network Land Data Assimilation System |
GEE | Google Earth Engine |
GRD | Ground Range Detected |
LBV | L: general radiance level, B: visible-infrared radiation balance, V: radiance variation vector |
MI | Multi-Index |
MODIS | Moderate Resolution Imaging Spectroradiometer |
NDWI | Normalized Difference Water Index |
OLI | OLI Operational Land Imager |
P | Annual mean precipitation rate |
QA | Quality Attribute map |
Rotw | Wetness component of TC transformation derived by transforming Principal Components (PC)-based rotated axes |
RS | Remote Sensing |
SC | Supervised Classification |
splib07b | Spectral library version 7 |
SQMK | Sequential Mann–Kendall |
SVM | Support Vector Machine |
SW | Inland Surface Waters |
TC | Tasseled-Cap transformation |
TD | Transformed Divergence |
TM | Thematic Mapper |
UA | User Accuracy |
USGS | United States Geological Survey |
WMR | Water Mapping Rule |
WI2006, WI2015 | Water Index |
ANDWI | Augmented NDWI |
API | Application Programming Interface |
AVHRR | Advanced Very High-Resolution Radiometer |
ESA | European Space Agency |
ETM+ | Enhanced Thematic Mapper Plus |
Fmask | Fmask Function of the mask |
GHSL | Global Human Settlement Layer |
JM | Jefferies-Matusita |
JRC | Joint Research Center |
L5, L7, L8 | Landsat 5, 7, 8 |
LSWI | Land Surface Water Index |
ME | Middle East |
MNDWI | Modified NDWI |
NASA | National Aeronautics and Space Administration |
NDVI | Normalized Difference Vegetation Index |
NIR | Near Infrared |
NOAA | National Oceanic and Atmospheric Administration |
OA | Overall Accuracy |
Orthow | Wetness component of TC transformation derived by orthogonalization techniques such as Gram-Schmidt |
RB | Rule-Based |
RF | Random Forest |
SI | Single-Index |
SLC | Scan Line Corrector |
SRTM | Shuttle Radar Topographic Mission |
SWA | Surface Water Area |
SWIR | Short-Wave Infra-Red |
T | Annual mean temperature |
TB | Transformation-Based |
WFM | Water Frequency map |
Appendix A
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Basin | Total Area [km2] | Percentage Inside Iran | Mean Altitude (Above Geoid) [m] |
---|---|---|---|
Caspian Sea | 346,896 | 50.5% | 1369 |
Persian Gulf | 1,279,083 | 33.5% | 982 |
Lake Urmia | 51,739 | 100% | 1735 |
Central Plateau | 825,124 | 100% | 1350 |
Easter Border 1 | 565,734 | 18.25% | 1188 |
Qareh-Qum 2,* | 461,141 | 9.5% | 1210 |
Cat | Nu | Feature Space | Required Bands | Criteria | Reference |
---|---|---|---|---|---|
SI | 1 | NDWI | G, NIR | [36] | |
2 | MNDWI | G, SWIR1 | [37] | ||
3 | G, NIR, SWIR1, SWIR2 | [38] | |||
4 | G, R, NIR, SWIR1, SWIR2 | [41] | |||
5 | All 6 bands | [5] | |||
MI | 6 | B, G, R, NIR, SWIR1 | [11,31,34] | ||
7 | B, G, R, NIR | [42] | |||
8 | B, G, R, NIR, SWIR1 | [42] | |||
9 | All 6 bands | [7] | |||
TB | 10 | Rotw | All 6 bands | [47] | |
11 | Orthow | All 6 bands | [48] | ||
12 | B, V | G, R, NIR, SWIR1 | [49] |
Year | Method | |||||||
---|---|---|---|---|---|---|---|---|
7 vs. 1 | 7 vs. 11 | |||||||
Water | Non-Water | Water | Non-Water | |||||
χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | χ2 | p-Value | |
2018 | 6.87 | 0.01 | 8.07 | 0.01 | 46.71 | 0.001 | 7.91 | 0.01 |
2019 | 6.19 | 0.02 | 13.05 | 0.001 | 32.82 | 0.001 | 6.92 | 0.01 |
2020 | 1.78 | 0.1 | 26.46 | 0.001 | 57.46 | 0.001 | 6.34 | 0.02 |
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Taheri Dehkordi, A.; Valadan Zoej, M.J.; Ghasemi, H.; Jafari, M.; Mehran, A. Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery. Remote Sens. 2022, 14, 4491. https://doi.org/10.3390/rs14184491
Taheri Dehkordi A, Valadan Zoej MJ, Ghasemi H, Jafari M, Mehran A. Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery. Remote Sensing. 2022; 14(18):4491. https://doi.org/10.3390/rs14184491
Chicago/Turabian StyleTaheri Dehkordi, Alireza, Mohammad Javad Valadan Zoej, Hani Ghasemi, Mohsen Jafari, and Ali Mehran. 2022. "Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery" Remote Sensing 14, no. 18: 4491. https://doi.org/10.3390/rs14184491