بررسی قابلیت داده‌های باند L داده‌های رادار دهانه مصنوعی پالسار برای تهیه نقشه پوشش زمین

نوع مقاله : مقاله پژوهشی

نویسندگان

استادیار، گروه محیط زیست، دانشکدة منابع طبیعی، دانشگاه زابل، زابل، ایران.

چکیده

بررسی­‌های سنجش از دوری برای تهیه نقشه دقیق از تالاب­‌ها، به دلیل تنوع پوشش‌های زمین و شباهت بازتابش بین آنها، همواره با چالش‌هایی روبرو بوده است. داده‌های رادار دهانه مصنوعی به دلیل ایجاد قابلیت نفوذ و عدم وابستگی به زمان و شرایط جوی، قابلیت ویژه‌ای در تشخیص پدیده‌ها دارند. در این مطالعه برای تهیه نقشۀ مناسب از وضعیت پوشش بخشی از تالاب هامون در شرایط ترسالی و خشکسالی، از داده‌های رادار استفاده شده است. به‌همین منظور، در پژوهش حاضر قابلیت باند L داده‌های رادار دهانه مصنوعی برای مطالعه پوشش سطح زمین استفاده شده است. برای بررسی قابلیت تفکیک بین طبقه­‌های پوشش اراضی از شاخص‌های آماری استفاده شد و تصاویر با روش ماشین پشتیبان­‌برداری طبقه­‌بندی شد. نتایج به­‌دست ­آمده از پژوهش حاضر نشان‌دهنده توانایی باند L برای تفکیک پوشش‌های مختلف زمین و نشان­ دادن تفاوت تراکم پوشش گیاهی بر بازتاب باند L است. جدول خطای تهیه ­شده در این مطالعه نیز نشان ­دهنده صحت کلی بیش از 80­% در تهیه نقشه پوشش اراضی منطقه است. علاوه ­براین به دلیل قدرت نفوذ باند L، می‌‌توان آب لایه زیرین گیاهان را تشخیص داد. نتایج این مطالعه نشان داد که تفاوت بازتاب پوشش گیاهی که قسمت کمی از آن داخل آب است، با پوشش گیاهی­ ای که قسمت بیشتر آن درون آب است در باند HV برابر 6 دسی‌بل و در باند HH حدود 2 دسی بل است که نشان­ دهنده توان تفکیک بهتر باند HV در تفکیک این پوشش است.

کلیدواژه‌ها


  1. Ayehu, G., Tadesse, T., Gessesse, B., Yigrem, Y., & Melesse, A. (2020). Combined use of sentinel-1 sar and landsat sensors products for residual soil moisture retrieval over agricultural fields in the upper Blue Nile basin, Ethiopia.Sensors20(11), 3282.‏
  2. Arnesen, A.S., Silva, T.S., Hess, L.L., Novo, E.M., Rudorff,M., Chapman, B.D. & McDonald, K.C. (2013). Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sensing of Environment, 130, 51–61.
  3. Baghdadi, N., Bernier, P., Gauthier, R., & Neeson, I. (2001). Evaluation of C-band sar data for wetlands mapping. International Journal of Remote Sensing, 22, 71–88.
  4. Banks, S., White, L., Behnamian, A., Chen, Z., Montpetit, B., Brisco, B., & Duffe, J. (2019). Wetland classification with multi-angle/temporal SAR using random forests. Remote Sensing11(670), 1-28.
  5. Baghdadi, N., Cresson., R, El Hajj., M., Ludwig, R., & La Jeunesse, I. (2012). Estimation of soil parameters over bare agriculture areas from c-band polarimetric SAR data using neural networks. Hydrology and Earth System Sciences, 16, 1608–1621.
  6. Betbeder, J., Rapinel, S., Corgne, S., Pottier, E., & Hubert-Moy, L. (2015). TerraSAR-X Dual-pol time-series for mapping of wetland vegetation. ISPRS Photogrammetry and Remote Sensing,107, 90–98.
  7. Bourgeau-Chavez, L.L., Kasischke, E.S., Brunzel, S.M., Mudd, J.P., Smith, K.B., & Frick, L. (2001). Analysis of space-borne SAR data for wetland mapping in Virginia riparian ecosystems. Remote Sensing, 22, 3665–3668.
  8. Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., LiliChabaane, Z., Baghdadi, N., & Mougenot, B. (2019). Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sensing,11(13), 1-20.
  9. Corcione, V., Nunziata, F., Mascolo, L., & Migliaccio, M. (2016). A Study of the use of COSMO-SkyMed SAR Ping Pong polarimetric mode for rice growth monitoring. Remote Sensing, 37(3), 633–647.
  10. Dabboor, M., Howell, S., Shokr, M., & Yackel, J. (2014). The Jeffries–Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data. Remote Sensing35(19), 6859-6873.‏
  11. Dabrowska-Zielinskam, K., Budzynska, M., Tomaszewska, M., Bartold, M., Gatkowska, M., Malek, I., & Napiorkowska, M. (2014). Monitoring wetlands ecosystems using ALOS PALSAR L-Band, HV supplemented by optical data: A case study of Biebrza wetlands in Northeast Poland. Remote Sensing, 6,1605–1633.
  12. El Hajj, M., Baghdadi, N., Belaud, G., Zribi, M., Cheviron, B., Courault, D., & Charron, F. (2014). Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR data. Remote Sensing, 6(10), 10002–10032.
  13. K, M., Tajaddod, M., Ravanbakhsh, M., & Jamalzad.F, F. (2021). Vegetation classification based on wetland index using object-based classification of satellite images. RS and GIS for Natural Resources, 12(3), 1-17. (in Farsi)
  14. Heydarian, P., Rangzan, K., Maleki, S., & Taghizadeh, A. (2014). Land use change detection using post classification comparison LandSat satellite images (Case study: land of Tehran). RS and GIS for Natural Resources, 4(4), 1-10. (in Farsi)
  15. Huang, H., Roy, D.P., Boschetti, L., Zhang, H.K., Yan, L., Kumar, S.S., & Li, J. (2016). Separability analysis of Sentinel-2A Multi-Spectral Instrument (MSI) data for burned area discrimination. Remote Sensing8(10), 1-18.
  16. Hidayat, H., Hoekman, D.H, Vissers, M., Hoitink, A., & Pfister, L. (2012). Flood occurence mapping of the middle Mahakam low land area using satelite Hydrology and Earth System Sciences,16,1805–1816.
  17. Koch, M., Schmid, T., Reyes, M., & Gumuzzio, J. (2012). evaluating full polarimetric C- and L-Band data for mapping wetland conditions in a semi-arid environment in central Spain. Selected Topics in Applied Earth Observations and Remote Sensing, 5, 33–44.
  18. Kiage, L.M., Walker, N.D., Balasubramanian, S., Babin, A., & Barras, J. (2005). Applications of radarsat-1 synthetic aperture radar imagery to assess hurricane-related flooding of coastal Louisiana. Remote Sensing, 26, 5359–5380.
  19. Lang, M.W, Townsend, P.A., & Kasischke, E.S. (2008). Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data. Remote Sensing of Environment,112(10), 3898–3907.
  20. Maleki, S., Soffianian, A.R., Koupaei, S.S., Pourmanafi, S., & Saatchi, S. (2018). Wetland restoration prioritizing, a tool to reduce negative effects of drought; An application of multicriteria-spatial decision support system (MC-SDSS). Ecological Engineering,112, 132-139.
  21. Maleki, S., Soffianian, A.R., Koupaei, S.S., Saatchi, S., Pourmanafi, S., & Sheikholeslam, F. (2016). Habitat mapping as a tool for water birds’ conservation planning in an arid zone wetland: the case study Hamoun wetland. Ecological Engineering, 95, 594-603.
  22. Maleki, S., Baghdadi, N., & Rahdari, V. (2020). Which water bird groups need greater habitat conservation measures in a wetland ecosystem? Ecological Engineering143,1-10.
  23. Maleki, S., Baghdadi, N., Soffianian, A., El Hajj, M., & Rahdari, V. (2020). Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland. Remote Sensing41(6), 2277-2302.‏
  24. Martinisro, S., & Rieke, C. (2015). Backscatter analysis using multi-temporal and multi-frequency SAR data in the context of flood mapping at river Saale, Germany. Remote Sensing, 7, 32-52.
  25. Marti-Cardona, B., Lopez-Martinez, C., Dolz-Ripolles, J., & Bladè-Castellet, E. (2010). A SAR polarimetric, multi-incidence angle and multitemporal characterization of Donana wetlands for flood extent monitoring. Remote Sensing of Environment,114, 2802–2815.
  26. Morandeira, NS., Grings, F., Facchinetti, C., & Kandus, P. (2016). Mapping plant functional types in floodplain wetlands: an analysis of C-band polarimetric SAR data from RADARSAT-2. Remote Sensing8(3), 1-17.
  27. Mitsch, W.J., & Gosselink, J.G. (2007). Wetlands. Hoboken, John Wiley & Sons.
  28. Magagi, R., Bernier, M., & Ung, C.H. (2002). Quantitative analysis of radarsat SARdata over a sparse forest canopy. IEEE Transactions on Geoscience and Remote Sensing, 40, 1301−1313.
  29. Novo, E.M., Costa, M.P.F., Mantovani, J.E., & Lima, L.B.T. (2002). Relationship between Macrophyte Stand Variables and Radar Backscatter at L and C Band, Tucuruí reservoir, Brazil. Remote Sensing, 23, 1241–1260.
  30. Pal, M. (2005). Random forest classifier for remote sensing classification. Remote Sensing, 26, 217-222.
  31. Pham, T., Yoshino, K., & Kaida, N. (2017). Monitoring mangrove forest changes in cat ba biosphere reserve using ALOS PALSAR imagery and a GIS-based support vector machine algorithm. In International Conference on Geo-Spatial Technologies and Earth Resources, Springer, Cham.‏
  32. Pelletier, C., Valero, S., Inglada, J., Champion, N., & Dedieu, G. (2016). Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote Sensing of Environment, 187,156-168.
  33. (2016). The list of wetlands of international importance. Ramsar.
  34. Reschke, J., Bartsch, A., Schlaffer, S., & Schepaschenko, D. (2012). Capability of C-Band SAR for operational wetland monitoring at high latitudes. Remote Sensing, 4, 2923–2943.
  35. Santoro, M., Pantze, A., Fransson, J.E., Dahlgren, J., & Persson, A. (2012). Nation-wide clear-cut mapping in Sweden using ALOS PALSAR strip images. Remote Sensing, 4,1693–1715.
  36. Sand, M., & Rieke, S. (2015). Backscatter analysis using multi-temporal and multi-frequency SAR data in the context of flood mapping at river Saale. Germany. Remote Sensing, 7, 7732–7752.
  37. Shamohammadi, Z., & Maleki, S. (2011). The Life of Human. Jahad Daneshgahi, Iran (in Farsi).
  38. Shen, G., Liao, J., Guo, H., & Liu, J. (2015(. Poyang Lake wetland vegetation biomass inversion using polarimetric RADARSAT-2 synthetic aperture radar data. Applied Remote Sensing, 9, 451-455.
  39. Taft, O.W., Haig, S.M., & Kiilsgaard, C. (2003). Use of radar remote sensing (RADARSAT) to map winter wetland habitat for shorebirds in an agricultural landscape. Environment Management, 33, 750-763.
  40. Touzi, R., Deschamp, B., & Rother, G. (2007). Wetland characterization using polarimetric RADARSAT-2 capability. Canadian Journal of Remote Sensing, 33(1), 56-67.
  41. Wilusz, D.C., Zaitchik, B.F., Anderson, M.C., Hain, C.R., Yilmaz, M.T., & Mladenova, I.E. (2017). Monthly flooded area classification using low resolution SAR imagery in the Sudd wetland from 2007 to 2011. Remote Sensing of Environment,194, 205–218.
  42. Widis, D.C., BenDor, T.K., & Deegan, M. (2015). Prioritizing wetland restoration sites: a review and application to a large- scale coastal restoration program. Ecological Restoration, 33, 358–377.
  43. Zhang, M., Li, Z., Tian, B., Zhou, J., & Tang, P. (2016). The Backscattering characteristics of wetland vegetation and water-level changes detection using multi-mode SAR. Applied Earth Observation and Geoinformation, 45, 1–13.