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GISAli.ahmadi2012@ut.ac.ir
GISArman.samadi@ut.ac.ir
MNDWI)
MNDWI
Lausch & )
(Herzog, 2002
Geist) (et
al., 2006
.(Ozesmi and Bauer, 2002)
Lausch & )
(Herzog, 2002
.(Davis and Richard, 1994)
Lu, )
.(Mausel, Brondízio, & Moran, 2004
.(Ozesmi & Bauer, 2002)
SPOT,TM
(Guariglia et al., 2006)
(Kurt, Karaburun, & Demirci, 2010)
(Gong, Gong, Zhao, & Li, 2007)
.(de Roeck, Miya, Verhoest, Batelaan, & Brendonck, 2007 )
1) TOPEX / Poseidon historical archive
2) OSTM interim GDR 20hz altimetry
3) Jason-3 interim GDR 20hz altimetry
https://fa.wikipedia.org/wiki
USGS
USGS
ENVI
ETMSLC Off
extension(Gapfill)
Ozturk
Sesli 2015
NDWI(Gautam, Gaurav, Murugan, & Annadurai, 215)
Normalized Difference Water Index NDWI= (Green-NIR) / (Green+ NIR)
(Gautam et al., 2015) MNDWI
Modified Normalized Difference Water Index MNDWI= (Green-MIR) / (Green + MIR)
(Feyisa, Meilby, Fensholt, & Proud, 2014) AWEI
Automated Water Extraction Index (0.25*NIR+2.75*SWIR2) -SWIR1) -= 4 *(Green
nsh
AWEI
https://earthexplorer.usgs.gov
MNDWI
ETM+TM
ENVI
ArcGIS
RGB و
AWEI, NDWI, MNDWI
OLI
TOPEX, OSTMJeson1
Jeson1
OSTM
References
de Roeck, E., Miya, M., Verhoest, N., Batelaan, O., & Brendonck, L. (2007). Integrating Remote Sensing and
Wetland Ecology: a Case Study on South African Wetlands. In 2007 International Workshop on the Analysis
of Multi-temporal Remote Sensing Images (pp. 1–5). IEEE.
https://doi.org/10.1109/MULTITEMP.2007.4293033
Davis, Richard A., Jr. (1994). The Evolving Coast. New York: Scientific American Library. pp. 101, 107.
Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated Water Extraction Index: A new technique
for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35.
https://doi.org/10.1016/j.rse.2013.08.029
Gautam, V. K., Gaurav, P. K., Murugan, P., & Annadurai, M. (2015). Assessment of Surface Water Dynamicsin
Bangalore Using WRI, NDWI, MNDWI, Supervised Classification and K-T Transformation. Aquatic
Procedia, 4(Icwrcoe), 739–746. https://doi.org/10.1016/j.aqpro.2015.02.095
Geist, H., McConnell, W., Lambin, E. F., Moran, E., Alves, D., & Rudel, T. (2006). Causes and Trajectories of
Land-Use/Cover Change. Land-Use and Land-Cover Change. https://doi.org/10.1007/3-540-32202-7_3
Gong, Z., Gong, H., Zhao, W., & Li, X. H. Z. (2007). Using RS and GIS to monitoring Beijing wetland resources
evolution. In 2007 IEEE International Geoscience and Remote Sensing Symposium (pp. 4596–4599). IEEE.
https://doi.org/10.1109/IGARSS.2007.4423881
Guariglia, A., Buonamassa, A., Losurdo, A., Saladino, R., Trivigno, M. L., Zaccagnino, A., & Colangelo, A. (2006).
A multisource approach for coastline mapping and identification of shoreline changes. Annals of Geophysics,
49(1), 295–304. https://doi.org/10.4401/ag-3155
Kurt, S., Karaburun, A., & Demirci, A. (2010). Coastline changes in Istanbul between 1987 and 2007. Scientific
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Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: Issues
of scale, resolution and interpretability. Ecological Indicators, 2(1–2), 3–15. https://doi.org/10.1016/S1470-
160X(02)00053-5
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of
Remote Sensing, 25(12), 2365–2401. https://doi.org/10.1080/0143116031000139863
Ozesmi, S. L., & Bauer, M. E. (2002). Satellite remote sensing of wetlands. Wetlands Ecology and Management,
10(5), 381–402. https://doi.org/10.1023/A:1020908432489
Abstract
Wetlands are very special and important environments, both for environmental sustainability and for
animals and humans, especially if the wetland has fresh water, which adds to its importance and value.
Hamoon is thebiggest lake in the south-east of the Iran country, both in terms of its size and ecologically.
In this study, we tried to study the changes, area and volume of the wetland during the 30-year period. For
this purpose, Landsat satellite images were used to study the changes in the area and the area of the
wetland along with rainfall data and altimetry data. In this study, three different indicators were used and
after comparing them, the (MNDWI) index was selected as the best indicator in terms of maximum
synchrony with ground reality. Studies show that the lake has undergone dramatic changes over the years,
and these changes show a strong correlation with the altimetry data of the Kajaki Dam and rainfall data.
Reduced rainfall, global warming, rising pressure on the lake and mismanagement can be considered as
the main causes of these changes.
Keywords
Hamoon Lake, MNDWI, Altimetry data, Landsat images