A denoising–classification–retracking method to improve spaceborne estimates of the water level–surface–volume relation over the Urmia Lake in Iran

ABSTRACT Decreasing the volume of the Urmia Lake, as the largest inland water body in Iran, is one of the current environmental and water resource management concerns. This study obtains a reliable spaceborne water level (WL)–area–volume relationship for the Urmia Lake using terrestrial, aerial and satellite-based data. The aim of this study is to improve Urmia Lake’s WL derived from satellite altimetry and, consequently, to more accurately estimate the volume of the lake for the last decade. To this end, improved WL is obtained from the Satellite with Argos and Altika (SARAL/AltiKa) and Jason-2 altimetry missions by performing a post-processing method. The post-processing method includes a denoising, a classification and appropriate retracking algorithms. The results are validated against in situ gauge data and also compared with results from Prototype Innovant de Système de Traitement pour les Applications Côtières et l’Hydrologie (PISTACH) and Prototype on AltiKa for Coastal, Hydrology and Ice (PEACHI) products. The Denoising–Classification–Retracking (DCR) method improves the root mean square error (RMSE) of WL with respect to those of PISTACH and PEACHI by 54% and 24%, respectively. The surface area of the lake is determined from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images based on calculating normalized difference water index (NDWI). The results are validated against the surface area obtained from aerial photogrammetry and Cartosat high resolution image. Moreover, based on bathymetric map a Look-up table including surface area and volume of the lake at specific levels are formed. The obtained surface area is then compared with the values of the Look-up table. The normalized root mean square error between surface extent obtained from proposed method and corresponding values is about 11%. The estimated lake’s volume is compared with the level-volume curve from the bathymetric data. The result showed the RMSE of this comparison is about 0.12 km3. Our validated results show that the lake has lost 75% of its volume from late 2008 to early 2016 but continued with an increase in its volume in May 2017 twice as much as in early 2016. Our results support urgent or long-term restoration plan of Lake Urmia and highlight the important role of spaceborne sensors for hydrological applications.

[1]  F. Massey.  The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[2]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[3]  H. Jay Zwally,et al.  Analysis and retracking of continental ice sheet radar altimeter waveforms , 1983 .

[4]  Duncan J. Wingham,et al.  NEW TECHNIQUES IN SATELLITE ALTIMETER TRACKING SYSTEMS. , 1986 .

[5]  Richard K. Moore,et al.  Radar remote sensing and surface scattering and emission theory , 1986 .

[6]  K. Kelts,et al.  Holocene sedimentology of hypersaline Lake Urmia, northwestern Iran , 1986 .

[7]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[8]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[9]  Ian M. Mason,et al.  A New Global Lakes Database for a Remote Sensing Program Studying Climatically Sensitive Large Lakes , 1995 .

[10]  Bernhard Schölkopf,et al.  Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.

[11]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[12]  Bernhard Schölkopf,et al.  Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.

[13]  Curt H. Davis,et al.  A robust threshold retracking algorithm for measuring ice-sheet surface elevation change from satellite radar altimeters , 1997, IEEE Trans. Geosci. Remote. Sens..

[14]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[15]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[16]  H. Jay Zwally,et al.  Chapter 9 Ice Sheet Dynamics and Mass Balance , 2001 .

[17]  Stephen K. Boss,et al.  INTEGRATED ECHO SOUNDER, GPS, AND GIS FOR RESERVOIR SEDIMENTATION STUDIES: EXAMPLES FROM TWO ARKANSAS LAKES 1 , 2004 .

[18]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[19]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[21]  D. Lettenmaier,et al.  Measuring surface water from space , 2004 .

[22]  J. Crétaux,et al.  Lake studies from satellite radar altimetry , 2006 .

[23]  Xiaoli Deng,et al.  A coastal retracking system for satellite radar altimeter waveforms: Application to ERS‐2 around Australia , 2006 .

[24]  Liu Yuting,et al.  Improved threshold retracker for satellite altimeter waveform retracking over coastal sea , 2006 .

[25]  Seiji Hayashi,et al.  Measuring Water Storage Fluctuations in Lake Dongting, China, by Topex/Poseidon Satellite Altimetry , 2006, Environmental monitoring and assessment.

[26]  Jinyun Guo,et al.  Coastal Gravity Anomalies from Retracked Geosat/GM Altimetry: Improvement, Limitation and the Role of Airborne Gravity Data , 2006 .

[27]  A. Eimanifar,et al.  Urmia Lake (Northwest Iran): a brief review , 2007, Saline systems.

[28]  S. Kotsiantis.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[29]  M. Abbaspour,et al.  Determination of environmental water requirements of Lake Urmia, Iran: an ecological approach , 2007 .

[30]  Xinyu Xu,et al.  Monitoring Level Fluctuations of the Lakes in the Yangtze River Basin from Radar Altimetry , 2008 .

[31]  Stefano Vignudelli,et al.  Coastal Sea Surface Heights from Improved Altimeter Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[32]  H. Akhani,et al.  A late Pleistocene long pollen record from Lake Urmia, Nw Iran , 2008, Quaternary Research.

[33]  Chung-Yen Kuo,et al.  Laurentia crustal motion observed using TOPEX/POSEIDON radar altimetry over land , 2008 .

[34]  Yong Wang,et al.  Improved retracking algorithm for oceanic altimeter waveforms , 2009 .

[35]  Manman Zhang.  Satellite Radar Altimetry for Inland Hydrologic Studies , 2009 .

[36]  Stefano Vignudelli,et al.  Coastal sea surface heights from improved altimeter data in the Mediterranean Sea , 2010 .

[37]  Abdolreza Karbassi,et al.  Environmental Impacts of Desalination on the Ecology of Lake Urmia , 2010 .

[38]  Jinyun Guo,et al.  Optimized Threshold Algorithm of Envisat Waveform Retracking over Coastal Sea , 2010 .

[39]  C. Medina,et al.  Water volume variations in Lake Izabal (Guatemala) from in situ measurements and ENVISAT Radar Altimeter (RA-2) and Advanced Synthetic Aperture Radar (ASAR) data products , 2010 .

[40]  Jean-Yves Tourneret,et al.  Shape classification of altimetric signals using anomaly detection and bayes decision rule , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[41]  Jean-François Crétaux,et al.  Lakes Studies from Satellite Altimetry , 2011 .

[42]  Bradley Doorn,et al.  From Research to Operations: The USDA Global Reservoir and Lake Monitor , 2011 .

[43]  Stefano Vignudelli,et al.  A COMPLETELY REMOTE SENSING APPROACH TO MONITORING RESERVOIRS WATER VOLUME , 2011 .

[44]  D. Lettenmaier,et al.  Global monitoring of large reservoir storage from satellite remote sensing , 2011 .

[45]  Xiaoli Deng,et al.  The Retracking Technique on Multi-Peak and Quasi-Specular Waveforms for Jason-1 and Jason-2 Missions near the Coast , 2012 .

[46]  Ozgur Kisi,et al.  Forecasting Water Level Fluctuations of Urmieh Lake Using Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System , 2012 .

[47]  A. Melesse,et al.  Bathymetric Mapping for Lake Hardibo in Northeast Ethiopia Using Sonar , 2012 .

[48]  M. Tajrishy,et al.  Using satellite data to extract volume–area–elevation relationships for Urmia Lake, Iran , 2013 .

[49]  Wim G.M. Bastiaanssen,et al.  Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data , 2013 .

[50]  Gh.R. Roshan,et al.  Statistical Modeling of Future Lake Level under Climatic Conditions, Case study of Urmia Lake (Iran) , 2013 .

[51]  F. Frappart,et al.  Combining high-resolution satellite images and altimetry to estimate the volume of small lakes , 2013 .

[52]  Shuanggen Jin,et al.  Large-scale variations of global groundwater from satellite gravimetry and hydrological models, 2002–2012 , 2013 .

[53]  Jean-Yves Tourneret,et al.  Parameter Estimation for Peaky Altimetric Waveforms , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Yongwei Sheng,et al.  Monitoring decadal lake dynamics across the Yangtze Basin downstream of Three Gorges Dam , 2014 .

[55]  罗甫林 Luo Fu-Lin,et al.  Classification of Hyperspectral remote sensing images using correlation neighbor LLE , 2014 .

[56]  Ali Selamat,et al.  Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery , 2014, Remote. Sens..

[57]  Zheng Duan,et al.  Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data , 2014, Remote. Sens..

[58]  S. Setegn.  Water Resources Management for Sustainable Environmental Public Health , 2015 .

[59]  N. Sneeuw,et al.  A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran , 2015 .

[60]  Chao Wang,et al.  High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral Imagery , 2015, Remote. Sens..

[61]  K. Madani,et al.  Aral Sea syndrome desiccates Lake Urmia: Call for action , 2015 .

[62]  Khaled Zoroufchi Benis,et al.  Forecasting Surface Area Fluctuations of Urmia Lake by Image Processing Technique , 2015 .

[63]  Nicolas Picot,et al.  Using SARAL/AltiKa to Improve Ka-band Altimeter Measurements for Coastal Zones, Hydrology and Ice: The PEACHI Prototype , 2015 .

[64]  Anuar Ahmad,et al.  A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[65]  S. Nanda.  Multiple scatterer retracking and interferometric swath processing of CryoSat-2 data for ice sheet elevation changes , 2015 .

[66]  S. Basu,et al.  Shape Classification of AltiKa 40-Hz Waveforms using Linear Discriminant Analysis and Bayes Decision Rule in the Gujarat Coastal Region , 2015 .

[67]  Konstantinos N. Topouzelis,et al.  Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data , 2016, ISPRS Int. J. Geo Inf..

[68]  Alireza Taravat,et al.  A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes , 2016 .

[69]  Xiaohua Tong,et al.  Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery , 2016, Remote. Sens..

[70]  J. Crétaux,et al.  Lake Volume Monitoring from Space , 2016, Surveys in Geophysics.

[71]  Feng Gao,et al.  Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery , 2016 .

[72]  Gulcan Sarp,et al.  Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey , 2017 .

[73]  B. Voosoghi,et al.  An estimation of tropospheric corrections using GPS and synoptic data: Improving Urmia Lake water level time series from Jason-2 and SARAL/AltiKa satellite altimetry , 2018 .

[74]  B. Voosoghi,et al.  The Inflection-Point Retracking Algorithm: Improved Jason-2 Sea Surface Heights in the Strait of Hormuz , 2018 .

[75]  F. Frappart,et al.  Influence of recent climatic events on the surface water storage of the Tonle Sap Lake. , 2018, The Science of the total environment.