استخراج الگوی زهکشی دامنه ها در نواحی فشرده جنگلی جنوب بهشهر با استفاده از داده های فرکانس پایین ‌راداری ‌

نوع مقاله: مقاله پژوهشی

نویسندگان

1 تربیت مدرس

2 دانشگاه تهران

چکیده

الگوی شبکه زهکشی از بارزترین لندفرم های سطح زمین محسوب می گردد که تحت تاثیر فرآیندهای دامنه ای شکل می گیرند و گسترش مکانی این عارضه به میزان عملکرد این فرآیندها بستگی دارد. هدف از این پژوهش قابلیت سنجی داده های فرکانس پایین راداری در استخراج الگوی زهکشی دامنه ها در نواحی فشرده جنگلی می باشد، بدین منظور به ارزیابی مقایسه ای مدل های رقومی ارتفاعی ASTER، SRTM و مدل رقومی ارتفاعی حاصل از داده های فرکانس پایین راداری PALSAR در استخراج شبکه های زهکشی پرداخته شده است. ابتدا مدل های رقومی ارتفاعی در محیط Archydro اصلاح و شبکه های زهکشی در محیط ArcGIS10.2 استخراج گردید. جهت استخراج شبکه های زهکشی از آستانه های سلولی ۱۰۰، ۵۰۰، ۱۰۰۰ و ۲۰۰۰ استفاده گردید. طبق نتایج مدل رقومی ارتفاعی داده های فرکانس پایین راداری PALSAR با آستانه سلولی ۱۰۰ با استخراج ۸۰۲۵۳۲۶ متر آبراهه بهترین دقت را نسبت به مدل های رقومی و آستانه های سلولی دیگر داشته است. نتایج بررسی تراکم زهکشی نیز نشان داد که در مدل رقومی ارتفاعی حاصل از داده های راداری PALSAR ۷۱/ ۱۰۲۶ کیلومتر مربع (۸۳/۷۰ درصد) از منطقه مطالعاتی در طبقه تراکم خیلی زیاد ( بیش از ۸) قرار گرفته است که این موضوع بیانگر کارایی بالاتر مدل رقومی ارتفاعی حاصل از داده های فرکانس پایین راداری PALSAR در استخراج شبکه های زهکشی می باشد. نتایج بررسی تراکم زهکشی استخراج شده در مناطق جنگلی فشرده نشان داد که از کل مساحت ۰۶/۷۱۷ کیلومتر مربع جنگل های فشرده در منطقه، بر اساس نتایج حاصل از مدل رقومی ارتفاعی فرکانس پایین راداری، ۶۲/۷۳ درصد (۹۷/۵۲۰ کیلومتر مربع) از مساحت جنگل های فشرده در کلاس تراکم زهکشی بسیار بالا (بالاتر از ۸) قرار گرفته است در حالی این مساحت در مدل رقومی ASTER، تنها ۰۳۳/۰ درصد (۲۴۰/۰ کیلومتر مربع) و در مدل رقومی ارتفاعی SRTM، ۶۷۲/۰ درصد (۸۲۷/۴ کیلومتر مربع) می باشد که این موضوع نشانگر توانایی داده های فرکانس پایین راداری در نفوذ از مناطق جنگلی و استخراج شبکه های زهکشی زیر جنگل با دقت بالا می باشد.
 

کلیدواژه‌ها


عنوان مقاله [English]

Extraction of Hillsides Drainage pattern in density forest Behshahr area using Low-frequency radar

چکیده [English]

Introduction
Synthetic aperture radar (SAR) systems have been widely used in the past two decades to produce high-resolution mapping and other remote sensing applications (Calabro et al. 2010; Sun et al. 2011). The ability of penetrating to the cloud , snow dry soil as well as day and-night operation made the SAR systems with more capability compared to optical imagery (after Karjalainen et al. 2012). SAR data are widely applied for several studies geophysical and geographical approach forestry and vegetation, biomass measurements, soil moisture, natural hazards and etc.  (Lardeux et al. 2011,Herrera et al. 2013). The present study deal with morphological landform Identification over the area covered with dense forest.  Where is the landform assessing and mapping almost appeared as big task due to difficulty of observing true the optical images.   The high penetration potential of SAR signals through the vegetation cover can be obtained using the L band of the ALOS PALSAR satellite with nearly 24 cm signal wavelength (Herrera et al. 2013. Furuta et al. 2005)  Furthermore, it is mention that  ALOS PALSAR data is very useful for producing accurate digital elevation models (DEMs) and deformation monitoring, as well as disaster monitoring and hazard prevention. Topography is a key controlling factor in the operation of a variety of natural processes (Montgomery and Brandon 2002). Hence it needs to be quantitatively analyzed (Lague et al. 2003), to ascertain the relative efficacy of its constituents and operative mechanisms, and to gauge the response of geomorphic systems to different stimuli (Ahmed et al. 2010). Rivers are one of the most sensitive elements of the landscape (Smedberg et al. 2009). The systematic evaluation of land surfaces and drainage pattern characteristics remains as a main study object  in geomorphology. Consequently in geo-morphometry (Prasannakumar et al. 2013). Digital elevation models (DEMs) have been frequently used for the above morphometric analysis of river basins through the extraction of topographic parameters and stream. The biggest advantage of DEMs over traditional topographical maps is the rehabilitee, coverage and data multiplying.  Due to their wide applicability and rehabilitee, DEMs have been used in a variety of studies where terrain and drainage factors play prominent roles. The aim of this research is Extraction of Hillsides Drainage pattern in density forest area using Low-frequency radar data.

Methodology
Dense forests of Behshahr South are a rainforest which is geographically located at a latitude of 36° 25’ 30” up to 36° 43’ 30” N and longitude 53° 04’ 15” up to 53° 54’ 15” E. The Average elevation of the area is 704.58 m above mean sea level. The rainfall received over the basin area varies from 700 to 1000 mm annually. This paper provides a comparative study of different available or derived DEMs (SRTM, ASTER, ALOS-POLSAR), through extraction of stream networks and their eventual comparison. A flowchart schematically shows the methodology followed for the extraction of drainage networks from DEMs in a GIS environment (Fig. 2). The DEMs of the study area is first preprocessed through the operations of Flow Direction, sink dems and filling the data gaps, pit removal–depression filling, and finding outlet cells in an iterative manner. Pit removal and depression filling is a method of filtering the digital elevation data. This is done to overcome any data voids that may be present in the DEM tile and to also ensure proper channel network connectivity. Sometimes, there are some pixels in the continuous array of digital data where the value of the pixel is abnormally low or high in comparison to other neighbouring cells. These are known as data sinks or spikes respectively and these are inherent in any DEM. These need to be removed before carrying out any sort of analysis in the data. After extraction of drainage networks to use Digital Elevation Models, The ability of Digital Elevation Models were evaluated in the extraction of drainage networks.  And in the last step, Drainage density was calculated in each of the land covers.
Results and discussion
The average elevation with a standard deviation of the study area extracted from the different DEMs have been calculated and subsequently presented in Table 2 and figure 3. The results showed that PALSAR digital elevation model has the lowest standard deviation. Figures 4,5 and tables 3,4 depict the comparisons of drainage networks derived from the different DEMs with respect to total stream lengths and Drainage density respectively. It is observed that the maximum lengths of streams and maximum area of high Drainage dendity are generated by PALSAR DEM. Integration of land use and drainage density map also showed that in PALSAR DEM, 73.62 percentage (520.97 K2) of dense forest area have been located in very high Drainage density class. While, in ASTER and SRTM DEMS only 0.033 and 0.672 percentage of dense forest area have been located in very high Drainage density class, respectively.
Conclusion
Digital Elevation Models (DEMs) have been a subject of increasing attention and utilization in the last few decades because of the relative ease in delineation, extraction and calculation of various drainage and terrain morphometric parameters from them. The present study was carried out in order to find the best possible DEM for extraction the drainage pattern in density forest area. After analyzing the different parameters derived from these DEMs, it can be said that the DEM derived from the Low-frequency radar datasets is relatively more accurate and consistent than ASTER and SRTM DEMS. The results showed that Low-frequency radar datasets have High capability for penetration through the vegetation cover and extraction of drainage pattern.

کلیدواژه‌ها [English]

  • ‎ drainage
  • Radar imagery
  • dense forest
  • Behshahr.‎
  • شیرانی، کورش.، سیف، عبدالله و محمد شریفی کیا. ۱۳۹۳، ارزیابی کارایی سنجنده های ASAR و PALSAR به کمک تداخل سنجی تفاضلی در شناسایی و پایش زمین لغزش ها در زاگرس، مهندسی و مدیریت آبخیز، شماره ۳، ۳۰۱-۲۸۸.
  • حسین زاده، سیدرضا و علیرضا بیدخوری. ۱۳۸۹، سیستم های اطلاعات جغرافیایی (چاپ دوم)، انتشارات جهاد دانشگاهی مشهد.
  • نداف سنگانی، مهوش.، حسین زاده، سیدرضا و مرتضی اکبری. ۱۳۹۴، مقایسه ی اثر روش های مختلف تهیه (DEM) بر خصوصیات مورفومتری شبکه های رودخانه ای (مطالعه موردی: حوضه آبخیز آبغه، استان خراسان رضوی)، علوم و مهندسی آبخیزداری ایران، سال نهم، شماره ۲۰، صص ۶۶-۵۹.
  • رفیعی، مجتبی. ۱۳۹۳، قابلیت نفوذ سیگنال فرکانس پایین (باند L) راداری به منظور شناسایی ناپایداری دامنه ای واقع در اراضی جنگلی فشرده جنوب ساری، پایان نامه کارشناسی ارشد. دانشگاه تربیت مدرس. 115 ص.
  • Ahmed, SA., Chandrashekarappa, KN., Raj, SK., Nischitha, V., Kavitha, G., 2010. Evaluation of morphometric parameters derived from ASTER and SRTM DEM: a study on Bandihole sub-watershed basin in Karnataka. J Indian Soc Remote Sens. 38: 227–238.
  • Anornu, G., Kabo-Bah, A., Kortatsi, B. 2012. Comparability studies of high and low resolution digital elevation models for watershed delineation in the tropics: case of Densu River Basin of Ghana. Int J Coop Stud. 1: 9–14.
  • Bahrami, S. 2013. Analyzing the drainage system anomaly of Zagros basins: implications for active tectonics. Tectonophysics. 608: 914–928.
  • Bishop, MP., James, LA., Shroder, JF., Walsh, SJ. 2012. Geospatial technologies and digital geomorphological mapping: concepts, issues and research. Geomorphology. 137(1): 5–26.
  • Biro, K., Pradhan, B., Suleiman, H., Buchroithner, MF. 2013. Evaluation of TerraSAR-X data for land use/land cover analysis using object-oriented classification approach in the AfricanSahel area, Sudan. J Indian SocRemot Sens. 41: 539–553.
  • Billa, L., Pradhan, B. 2011. Semi-automated procedures for shoreline extraction using singleRadarsat-1 SAR image. Estuarine Coastal Shelf Sci. 95:395–400.
  • Calabro, MD., Schmidt, DA., Roering, JJ. 2010. An examination of seasonal deformation at the Portuguese bend landslide, Southern California, using radar interferometry. J GeophysRes:Earth Surf. 115:1–10.
  • Chen, Z., Zhang, Y., Guindon, B., Esch, T., Roth, A., Shang, J. 2013. Urban land use mapping using high resolution SAR data based on density analysis and contextual information. Can JRemote Sensing. 38:738–749.
  • Das, S., Pravin Patel, P., Senqupta, S. 2016. Evaluation of different digital elevation models for analyzing drainage morphometric parameters in a mountainous terrain: a case study of the Supin–Upper Tons Basin, Indian Himalayas, Springerplus. 5(1): 1544.
  • Dragut, L., Blaschke, T. 2012. Automated classification of landform elements using object-based image analysis. Geomorphology. 81:330–344.
  • Evans, IE. 2012. Geomorphometry and landform mapping: what is a landform? Geomorphology. 137:94–106.
  • Fujita, K., Suzuki, R., Nuimura, T., Sakai, A. 2008. Performance of ASTER and SRTM DEMs, and their potential for assessing glacial lakes in the Lunana region, Bhutan Himalaya. J Glaciol. 54(185):220–228.
  • Furuta, R., Shimada, M., Tadono, T., Watanabe, M. 2005. Interferometric capabilities of ALOS PALSAR and its utilization. Paper presented at: the Fringe 2005 Workshop; Italy.
  • García-Davalillo, J.-C., Herrera, G., Notti, D., Strozzi, T., Álvarez-Fernández, I. 2014. DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: the Tena Valley case study. Landslides 11: 225–246.
  • Garcia, MJL., camarasa, AM. 1999. use of geomorphological units to improve drainage network extraction from a DEM: JAG Journal, 1(3/4) :187-195.
  • Honda, K., Nakanishi, T., Haraguchi, M., Mushiake, N., Iwasaki, T., Satoh, H., Kobori Yamaguchi, Y. 2012. Application of exterior deformation monitoring of dams by DInSARanalysis using ALOS PALSAR. Paper presented at: the IEEE International Geoscience and Remote Sensing Symposium (IGARSS); Munich, Germany.
  • Herrera, G., Guti_errez, F., Garc_ıa-Davalillo, J.C., Guerrero, J., Notti, D., Galve, J.P., Fern_andez- Merodo, J.A., Cooksley, G. 2013. Multi-sensor advanced DInSAR monitoring of very slow landslides: the Tena valley case study (Central Spanish Pyrenees)., Remote Sensing Environ. 128:31–43.
  • Karjalainen, M., Kankare, V., Vastaranta, M., Holopainen, M., Hyypp€a, J. 2012. Prediction of plot-level forest variables using Terrasar-X Stereo SAR data. Remote Sensing Environ.117:338–347.
  • Kirby, E., Whipple, KX. 2012. Expression of active tectonics in erosional landscapes. J Struct Geol. 44:54–75.
  • Lague, D., Crave, A., Davy, P. 2003. Laboratory experiments simulating the geomorphic response to tectonic uplift. J Geophys Res Solid Earth. 108(B1):ETG 3-1–ETG 3-20.
  • Lardeux, C., Frison, PL., Tison, C., Souyris, JC., Stoll, B., Fruneau, B., Rudant, JP. 2011. Classification of tropical vegetation using multifrequency partial SAR polarimetry. IEEE GeosciRemote Sensing Lett. 8:133–137.
  • Minar, J., Evans, IS. 2008. Elementary forms for land surface segmentation: the theoretical basis of terrain analysis and geomorphological mapping. Geomorphology. 95(3–4):236–259.
  • Millaresis, GC., Argialas, DP. 2000. Extraction and delineation of alluvial fans from digital elevationmodels and Landsat Thematic Mapper images. Photogrammetric Engineering and Remote Sensing, 66,1093-1101.
  • Montgomery, DR., Brandon, MT. 2002. Topographic controls on erosion rates in tectonically active mountain ranges. Earth Planet Sci Lett. 201:481–489.
  • NeamahJebur, M., Pradhan, B., Tehrani, M.S. 2013. Using ALOS PALSAR derived high-‎resolution DInSAR to detect slow-moving landslides in tropical forest: Cameron Highlands, ‎Malaysia, Geomatics, Natural Hazards and Risk, http://dx.doi.org/10.1080/19475705.2013.860407.‎
  • NeamahJebur, M., Pradhan, B., Tehrani, M.S. 2014. Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia, using L-band InSAR technique, Geosciences Journal, 18, 1: 61 – 68.
  • Paik, K., Kuma,r P. 2010. Optimality approaches to describe characteristic fluvial patterns on landscapes. Philos Trans R Soc B Biol Sci. 365(1545):1387–1395.
  • Phillips, JD. 2007. The perfect landscape. Geomorphology. 284:159–169.
  • Prasannakumar, V., Vijith, H., Geetha, N. 2013. Terrain evaluation through the assessment of geomorphometric parameters using DEM and GIS: case study of two major sub-watersheds in Attapady, South India. Arab J Geosci. 6(4):1141–1151.
  • Pradhan, B., Hagemann, U., Tehrany, M., Prechtel, N. 2013. An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. ComputGeosci. (Article online first available).
  • Saran, S., Sterk, G., Peters, P., Dadhwal, VK. 2009. Evaluation of digital elevation models for delineation of hydrological response units in a Himalayan watershed. GeoCarto Int. 25:105–122.
  • Smith, M. J. and pain, C.F. 2009: Applications of remote sensing in geomorphology: progress in physical Geography, 33(4) pp. 568-582.
  • Siart, C., Bubenzer, O., Eitel, B. 2009. Combining digital elevation data (SRTM/ASTER), high resolution satellite imagery (Quickbird) and GIS for geomorphological mapping: a multi-component case study on Mediterranean karst in Central Crete. Geomorphology. 112(1–2):106–121.
  • Smedberg, E., Humborg, C., Jakobsson, M., Morth, C-M. 2009. Landscape elements and river chemistry as affected by river regulation—a 3-D perspective. Hydrol Earth Syst Sci. 13:1597–1606. doi: 10.5194/hess-13-1597-2009.
  • Strozzi, T., Farina, P., Corsini, A., Ambrosi, C., Thüring, M., Zilger, J., Wiesmann, A., Wegmüller, U., Werner, C. 2005. Survey and monitoring of landslide displacements by means of L-band satellite SAR interferometry. Landslides, 2:193-201.
  • Solleiro-Rebolledo, E., Sycheva, S., Sedov, S. 2011. McClung de Tapia E, Rivera-Uria Y, Salcido-Berkovich C, Kuznetsova A. Fluvial processes and paleopedogenesis in the Teotihuacan Valley, Mexico: responses to late Quaternary environmental changes. Quat Int. 233:40–52.
  • Sun, G., Ranson, KJ., Guo, Z., Zhang, Z., Montesano, P., Kimes, D. 2011. Forest biomass mappingfrom LiDAR and Radar synergies. Remote Sensing Environ. 115:2906–2916.
  • Tucker, GE., Catani, F., Rinaldo, A., Bras, RL. (2001). Statistical analysis of drainage density fromdigital terrain data. Geomorphology, 36: 187-202.
  • Whittaker, AC. 2012. How do landscapes record tectonics and climate? Lithosphere. 4(2):160–164.