Land Cover Classification of Anzali Wetland Using Fusion of Sentinel 1 and ALOS/PALSAR 2 Images

Document Type : Research Article

Authors

1 master of remote sensing and geographic information system, faculty of geography, Tehran university

2 Faculty of Geography University of Tehran

Abstract

Anzali Wetland in Iran as one of the most valuable wetlands registered in the Ramsar Convention is being destroyed by environmental factors and human activities. In the last two decades, among various satellite images, radar images have played a special role in wetland monitoring. Radar is an all-weather sensor and it is sensitive to surface roughness and moisture, they serve as a valuable source for quick and accurate monitoring of wetlands. However, similarities in backscattering coefficients of different wetland classes and relatively difficult processing – in comparison to optical images- are the most important factors that limit their application. In this study, the capabilities of SAR images in the classification of Anzali wetland and the three main land use classes around the wetland (i.e. agricultural lands, reeds, and built-up areas) were evaluated. Two radar images; Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) and Sentinel 1 captured in 2018 were used. The texture parameters of the two images have been extracted. The images and their extracted texture layers have been fused by the feature-level method and further classified by the random forest method. The overall accuracy of feature-level fusion is equal to 75% and the kappa coefficient is equal to 0.62. The evaluation results related to producer and user accuracy are 100% and 83.33%, respectively, show the high capability of radar images in the classification and detection of wetlands. However, some errors have been observed in the separation of agricultural lands, reeds, and built-up areas.

Keywords

Main Subjects

References

[1].    Fickas KC, Cohen WB, Yang Z. Landsat-based monitoring of annual wetland change in the Willamette Valley of Oregon, USA from 1972 to 2012. Wetl Ecol Manag. 2016;24(1):73–92.
[2].    Jones K, Lanthier Y, van der Voet P, van Valkengoed E, Taylor D, Fernández-Prieto D. Monitoring and assessment of wetlands using Earth Observation: The GlobWetland project. J Environ Manage. 2009;90(7):2154–69.
[3].    Ottinger M, Clauss K, Kuenzer C. Large-scale assessment of coastal aquaculture ponds with sentinel-1 time series data. Remote Sens. 2017;9(5):440.
[4].    Ghahraman A, Atar F. Anzali wetland in danger of death (an ecologic-floristic research). J Environ Stud. 2003;28:1–38. [Persian]
[5].    Tavakoli B, Sabetraftar K. Determination of relationships between pollution indices with socioeconomic and ecological factors in watershed area of Anzali wetland. J Environ Stud. 2003;28:51_57.
[6].    Nicholls RJ. Coastal flooding and wetland loss in the 21st century: changes under the SRES climate and socio-economic scenarios. Glob Environ Chang. 2004;14(1):69–86.
[7].    Guo M, Li J, Sheng C, Xu J, Wu L. A review of wetland remote sensing. Sensors. 2017;17(4):777.
[8].    Mahdavi S, Maghsoodi Y. Fundamentals of radar remote sensing. K.N.Toosi University of Technology; 2016. 290 p. [Persian]
[9].    Mohammadimanesh F, Salehi B, Mahdianpari M, Brisco B, Motagh M. Wetland water level monitoring using interferometric synthetic aperture radar (InSAR): A review. Can J Remote Sens. 2018;44(4):247–62.
[10]. Chen Y, Qiao S, Zhang G, Xu YJ, Chen L, Wu L. Investigating the potential use of Sentinel-1 data for monitoring wetland water level changes in China’s Momoge National Nature Reserve. PeerJ. 2020;8:e8616.
[11]. Ottinger M, Kuenzer C. Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review. Remote Sens. 2020;12(14):2228.
[12]. Jensen K, McDonald K, Podest E, Rodriguez-Alvarez N, Horna V, Steiner N. Assessing L-band GNSS-reflectometry and imaging radar for detecting sub-canopy inundation dynamics in a tropical wetlands complex. Remote Sens. 2018;10(9):1431.
[13]. Davidson NC, Fluet-Chouinard E, Finlayson CM. Global extent and distribution of wetlands: trends and issues. Mar Freshw Res. 2018;69(4):620–7.
[14]. Mitchell AL, Milne AK, Tapley I. Towards an operational SAR monitoring system for monitoring environmental flows in the Macquarie Marshes. Wetl Ecol Manag. 2015;23(1):61–77.
[15]. Xie C, Shao Y, Xu J, Wan Z, Fang L. Analysis of ALOS PALSAR InSAR data for mapping water level changes in Yellow River Delta wetlands. Int J Remote Sens. 2013;34(6):2047–56.
[16]. van Genderen JL, Pohl C. Geometric aspects of remote sensing data fusion. InACRS 1993: Proceedings of the 14th Asian conference on remote sensing (ACRS): October 12-17 1993, Teheran, Iran, pp. I-3-1-I-3-5 1993 (pp. I-3).
[17]. Castanedo F. A review of data fusion techniques. Sci world J. 2013;2013.
[18]. Wang J, Shang J, Brisco B, Brown RJ. Comparison of multidate RADAR and multispectral optical satellite data for wetland detection in the Great Lakes region. Proc Geomatics era RADARSAT. 1997.
[19]. Lin Y, Yue C. China’s new national rules on wetland protection. Available SSRN 2517481. 2014.
[20]. Amani M, Mobasheri MR. A parametric method for estimation of leaf area index using landsat ETM+ data. GIScience Remote Sens. 2015;52(4):478–97. [Persian]
[21]. LaRocque A, Phiri C, Leblon B, Pirotti F, Connor K, Hanson A. Wetland mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in southern New Brunswick, Canada. Remote Sens. 2020;12(13):2095.
[22]. Kaplan G, Avdan U. Sentinel-1 AND Sentinel-2 Data Fusion For Wetlands Mapping: BALIKDAMI, TURKEY. Int Arch Photogramm Remote Sens Spat Inf Sci. 2018;42(3).
[23]. Evans TL, Costa M, Telmer K, Silva TSF. Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. IEEE J Sel Top Appl Earth Obs Remote Sens. 2010;3(4):560–75.
[24]. Banks S, White L, Behnamian A, Chen Z, Montpetit B, Brisco B, et al. Wetland classification with multi-angle/temporal SAR using random forests. Remote Sens. 2019;11(6):670.
[25]. Jahncke R, Leblon B, Bush P, LaRocque A. Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data. Int J Appl earth Obs Geoinf. 2018;68:139–56.
[26]. Clausi DA, Yue B. Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery. IEEE Trans Geosci Remote Sens. 2004;42(1):215–28.
[27]. Javedan Kherad A, EsmailiSari A, Bahramifar N. Investigation of persistent organic pollutants residue in sediments of international Anzali wetland, Iran. J Environ Stud. 2011;37(57):35–44. [Persian]
[28]. Modaberi H, Shokoohi, A R. Using eco-hydrologic methods in determining Anzali wetland environmental water requirement. IRAN-WATER Resour Res. 2019;15:91_104. [Persian]
[29]. Quegan S, Yu JJ. Filtering of multichannel SAR images. IEEE Trans Geosci Remote Sens. 2001;39(11):2373–9.
[30]. Richards JA. Remote sensing with imaging radar. Vol. 1. Springer; 2009.
[31]. Sim CK, Abdullah K, MatJafri MZ, Lim HS. Land cover classification using ALOS imagery for Penang, Malaysia. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing; 2014. p. 12025.
[32]. Lee J-S, Wen J-H, Ainsworth TL, Chen K-S, Chen AJ. Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans Geosci Remote Sens. 2008;47(1):202–13.
[33]. Attarchi S. Efficiency evaluation of SAR-derived indices in urban impervious surfaces extraction using full polarimetric image. Geogr Urban Plan Res. 2019;7(4):837–54. [Persian]
[34]. Shokri M, Sahebi MR. Fusion of yynthetic aperture radar data and optic images based on curvelet transform. J Geomatics Sci Technol. 2017;7(2):127–38. [Persian]
[35]. Kandaswamy U, Adjeroh DA, Lee M-C. Efficient texture analysis of SAR imagery. IEEE Trans Geosci Remote Sens. 2005;43(9):2075–83.
[36]. Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;(6):610–21.
[37]. Pal M. Random forest classifier for remote sensing classification. Int J Remote Sens. 2005;26(1):217–22.
[38]. Breiman L. Random forests Mach learn. 2001; 45: 5–32.
[39]. Zakeri H, Yamazaki F, Liu W. Texture analysis and land cover classification of Tehran using polarimetric synthetic aperture radar imagery. Appl Sci. 2017;7(5):452.
[40]. Fallah A, Kalbi S, Shataee S, Karami O. Determinate ASTER satellite data capability and classification and regression tree and random forest algorithm for forest type mapping. For Wood Prod. 2015;67(4):573–84.
[41]. Sohrabi Mofrad M, Bakhtyari Kia M. Detecting impervious urban surfaces using the textural properties of Radar imagery. Spat Plan. 2020;10(1):85–104. [Persian]
[42]. Adeli S, Salehi B, Mahdianpari M, Quackenbush LJ, Brisco B, Tamiminia H, et al. Wetland monitoring using SAR data: A meta-analysis and comprehensive review. Remote Sens. 2020;12(14):2190.
[43]. Boon PI, Schofield NJ, Brock MA, Bunn SE. National wetlands R&D program: Scoping review. 1997;
[44]. Brisco B. Mapping and monitoring surface water and wetlands with synthetic aperture radar. Remote Sens Wetl Appl Adv. 2015;119–36.
[45]. White L, Brisco B, Pregitzer M, Tedford B, Boychuk L. RADARSAT-2 beam mode selection for surface water and flooded vegetation mapping. Can J Remote Sens. 2014;40(2):135–51.
[46]. Lillesand T, Kiefer RW, Chipman J. Remote sensing and image interpretation. John Wiley & Sons; 2015.
Iranian journal of Ecohydrology
Volume 8, Issue 3
October 2021
Pages 611-622

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History

  • Receive Date: 31 January 2021
  • Revise Date: 13 July 2021
  • Accept Date: 15 June 2021
  • First Publish Date: 13 July 2021
  • Publish Date: 23 September 2021

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