کاربرد روش ژئومورفون‌ها در شناسایی عناصر اشکال زمین ( مطالعه موردی حوضه حبله رود)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری ژئومورفولوژی،‌دانشکده علوم زمین دانشگاه شهید بهشتی

2 دانشگاه شهید بهشتی

3 استاد ژئومورفولوژی دانشکده علوم زمین، دانشگاه شهید بهشتی تهران

10.22034/gmpj.2021.255287.1222

چکیده

شناسایی عناصر اشکال زمین در تحلیل چشم اندازهای ژئومورفولوژی از اهمیت خاصی برخوردار است. و منجر به طبقه‌بندی لندفرم‌ها در مقیاس بزرگ می شود. استخراج الگوها و عناصر ناهمواری اولین گام اساسی در شناسایی لندفرم‌‌ها است در این پژوهش روش ژئومورفون‌ برای استخراج اتوماتیک عناصر لندفرمی بر اساس تشخیص الگوی حاصل از ژئومتری DEM به کار گرفته شده است. ژئومورفون الگویی از مورفولوژی زمین و به عبارتی ساختارهای ریز چشم‌انداز هستند. این روش در حوضه آبریز حبله‌رود پیاده سازی شد. حوضه حبله‌رود از نظر موقعیت جغرافیایی در جنوب رشته کوه البرز بین استان تهران و سمنان واقع شده است. هدف این پژوهش شناسایی عناصر لندفرمی و استخراج الگوی حاکم بر ناهمواری‌ها در منطقه مورد مطالعه است. داده های مورد استفاده در این پژوهش شامل مدل رقومی ارتفاع ALOSPOL SAR 12.5 مربوط به سال 2010، تصاویرLandsat8 برداشت در تاریخ 2019-06-29 و برداشتهای میدانی بوده است. روش ژئومورفون‌ برای تولید شناسایی و استخراج عناصر لندفرم‌ها در حوضه حبله رود به کار گرفته شده است. 10 عنصر غالب از شکل زمین در منطقه مورد مطالعه شامل محدوده‌های مسطح(دشت)، قله، خط الراس،‌شانه خط الراس، خط الراس پهلویی، دامنه، دره کوچک پای دامنه، پای دامنه، ‌دره،‌ گودال(دره عمیق) شناسایی گردید. نتایج مستخرج از این پژوهش اشکال سطح زمین و ماهیت فرایندهایی که در این ناحیه عمل کرده و یا در حال حاضر فعال هستند را آشکار ساخت و منجر به شناسایی عناصر و الگوی ناهمواری‌ها گردید و این عناصر به نوبه خود تفاوت‌ها، شباهت‌ها و ناپایداری‌های ناهمواری‌ها را بیان کرده‌است.

کلیدواژه‌ها


عنوان مقاله [English]

Application of Geomorphons method in identifying landform elements (Case study: Hablehroud Basin)

نویسندگان [English]

  • Zahra Adeli 1
  • Manijeh Ghahroudi Tali 2
  • Hassan Sadough 3
1 , Phd student of geomorphology, Faculty of Earth Sciences, Department of Physical Geography, Shahid Beheshti University, Iran
2 Earth Science faculty
3 Professor, Faculty of Earth Sciences, Department of Physical Geography, Shahid Beheshti University, Iran
چکیده [English]

Introduction
Describing geomorphological environments according to identification and extraction of landform elements is essential in landscape analyses and modeling. In fact, identification of landform elements is the key element of the geomorphological analyses. This process leads to the classification of landforms on a large scale, and extracting patterns and elements, which is the first step towards identification of landforms. Information about landforms is obtained through various models that incorporate visual analysis and quantitative techniques such as geoecosystem techniques. In fact, all these models and techniques are based on finding key elements of the landscape benefitting from geomorphometric science. Identification of landforms on a large scale requires a method for extracting patterns and elements. Because pattern recognition is the first essential step in identifying landforms. Landform classification and extraction extentended their application of DEM in the 1990s. In this study, a novel method for the extraction of landform elements from a DEM based on the principle of pattern recognition is introduced and discussed in detail. At the core of the method is the concept of geomorphon (geomorphologic phonotypes). A general-purpose geomorphometric map — an interpreted map of topography — obtained by generalizing all geomorphon to a small number of the most common landform elements.

Method
In order to examine the practical application of the introduced geomorphon method, it was used to generate a geomorphometric map of Halberd watershed, located in the south of Alborz mountain between latitudes 35-57-22 N and longitudes 83-8-53 E positioned between Semnan and Tehran provinces. A DEM ALOSPOL SAR 12.5-2010-as input and landsat8 image -29-06-2019 were used respectively and observations were filed as the following step. This process is applied in SAGA7.5 and ArcGIS10.5 software and Google Earth. The results state that using geomorphon to map landscapes has some desirable properties. First, it must calculate differential geometry-based terrain. Second, the method can identify specific landforms having different sizes and it establishes a finite, absolute set of possible landforms so no landform is too rare to be found. Finally, geomorphon is calculated using a scale-flexible procedure. This map was obtained through generalizing all geomorphon to the most common landform elements. The pattern arises from a comparison of a focus pixel with its eight neighbors. Starting from the one located to the east and continuing counterclockwise. For example, a tuple [+,-,-,-, 0, +, +, +] describes one possible pattern of relative measures, {higher, lower, lower, lower, equal, higher, higher, higher} for pixels surrounding the focus pixel. It is important to stress that the neighboring entities are not immediate neighbors of the focus pixel in the grid. But, pixels are determined from the line-of-sight principle along the eight principal directions. The results are defined according to the values of two parameters: search radius (L) and relief threshold (d). The search radius is also defined as the allowable distance for the calculation of zenith and nadir study. Subsequently, the resulting geomorphon map was adapted to the photos taken from the field.
Results and discussion
Geomorphon map includes the 10 most common landform elements namely: peak, ridge, shoulder, spur, slope, hollow, foot slope, valley, pit, and flat obtained from 498 patterns. In the geomorphon map, a pattern of various landscapes has been created. Due to the distribution of landforms, Hablehroud catchment has different features such as steep valleys and narrow flood plains in the northern, central and southern parts. According to the results, the greatest percentage of extraction of landform element was related to the slope and the least percentage to the flat. In the next step, the compliance of the geomorphon map extracted from the images of the conducted field study. The results showed a match between landform elements and the surface. The location of a pit within the valley and the density of hollows on the slope have also shown a good adaptation.
Conclusion
Geomorphon was introduced and examined as a novel perspective on how to approach quantitative terrain analysis. The method grew from our desire to develop a robust and efficient tool for identification and extraction of landform elements from DEMs. Investigation of geomorphometric variables in Hableroud watershed showed that Geomorphon identifies both earth properties and landforms with a single scan of the DEM. The results of this study revealed the landform and the nature of the processes that has been present or are currently active in this area. It was shown that the evolution of unevenness in Shoulder, Spur, Hollow on the slope are more advanced on Ridge and valley. In addition, in geomorphon comprising complex shapes, the evolution of the elements was determined to be more. Thus, the automatic extraction of landform elements lead to a pattern of roughness. Furthermore, the identification of their elements, in turn, have expressed the differences, similarities and instabilities of the roughness.

کلیدواژه‌ها [English]

  • Geomorphon
  • Landform elements
  • pattern
  • Hableroud
  • بهرامی، حسین ـ احمد نوحه‌گر ـ وفا، محمودی (۱۳۹۲) طبقه بندی خودکار لندفرمهای حوضة آبخیز با استفاده از سیستم اطلاعات جغرافیایی مطالعة موردی: حوضة آبخیز بروجن در استان چهارمحال و بختیار،‌پژوهش‌های کمی ژئومورفولوژی شماره ۳،‌ ۱۷-۳۰
  • بیرانوند حجت- عبداله سیف(۱۳۹۹) شناسایی،‌طبقه بندی و مورفومتری سیرک‌های یخچالی ارتفاعات جوپار کرمان ‌پژوهش‌های کمی ژئومورفولوژی شماره ۴،‌ ۸۰-۶۳
  • حجتی مجید- مرضیه مکرم (۱۳۹۵) استفاده از مدل زیر پیکسل جاذبه attraction به منظور طبقه‌بندی لندفرم‌ها،‌پژوهش‌های کمی ژئومورفولوژی شماره ۴،‌ ۵۵-۴۰
  • رعیتی شوازی،‌ منیره-امیر کرم،‌ حمیدرضا غفاریان،‌عادل سپهر (۱۳۹۶) مقایسه کارایی برخی الگوریتم‌های طبقه‌بندی در مطالعه تغییرات لندفرم‌های بیابانی دشت یزد-اردکان،‌ ‌پژوهش‌های کمی ژئومورفولوژی شماره ۱،‌ ۵۷-۷۳
  • فرج‌زاده، منوچهر،‌۱۳۷۸،‌طرح آمایش استان تهران،‌ مطالعات آب وزرات نیرو
  • مددی،‌ عقیل- مظفری، حسن (۱۳۹۷) مقایسه و ارزیابی روش‌های طبقه‌بندی نظارت شده در استخراج و آشکار‌سازی تغییرات لندفرم‌های ژئومورفولوژی حوضه آبریز سجاسرود،‌ ‌پژوهش‌های کمی ژئومورفولوژی شماره ۳،‌ ۱۷-۳۰
  • مکرم،‌ مرضیه- نگهبان، سعید(۱۳۹۳) طبقه‌بندی لندفرمها با استفاده از شاخص موقعیت توپوگرافی(TPI) مطالعه موردی: منطقه جنوبی شهرستان داراب، فصلنامه علمی- پژوهشی اطلاعات جغرافیایی سپهر  شماره ۹۲

 

 

  • Alin Mihu-Pintilie, Ionut Cristi Nicu,2019.GIS-based Landform Classification of Eneolithic Archaeological Sites in the Plateau-plain Transition Zone (NE Romania): Habitation Practices vs. Flood Hazard Perception, Remote Sensing and GIS for Environmental Analysis and Cultural Heritage, 11, 915
  • Baker, M., Saavedra, D., & Norton, M. (2018). Scope# 10: Methodology for developing high-resolution stream and waterbody datasets for the Chesapeake Bay watershed.‏
  • Cui, X., Xing, Z., Yang, F., Fan, M., Ma, Y., & Sun, Y. (2020). A method for multibeam seafloor terrain classification based on self-adaptive geographic classification unit. Applied Acoustics157, 107029.‏
  • de Souza Robaina, L. E., Trentin, R., de Cristo, S. S. V., & Sccoti, A. A. V. (2017). Application of the concept of geomorphons to the landform classification in Tocantins state, Brazil. Raega-O Espaço Geográfico em Análise41, 37-48.‏
  • Di Stefano, M., & Mayer, L. A. (2018). An automatic procedure for the quantitative characterization of submarine bedforms. Geosciences8(1), 28.‏
  • Fernando M. G. Martins,Helena M. Fernandez,Antonio Jordán &Lorena Zavala , 2015. Classification of landforms in Southern Portugal (RIA Formosa Basin), Journal of map
  • K. Summerella,c,e, J. Vazeb,e, N.K. Tutejab,e, R.B. Graysonc,e, and T.I. Dowlingd, 2003. Development of an objective terrain analysis based method for delineating the major landforms of catchments
  • Gawrysiak, L., & Kociuba, W. (2020). Application of geomorphons for analysing changes in the morphology of a proglacial valley (case study: The Scott River, SW Svalbard). Geomorphology371, 107449.‏
  • Heyns, A. M., du Plessis, W., Curtin, K. M., Kosch, M., & Hough, G. (2021). Analysis and exploitation of landforms for improved optimisation of camera-based wildfire detection systems. Fire Technology, 1-35.‏
  • Hoffmeister, D., & Kramm, T., 2018. Suitability of different digital elevation models for landform classification methods and further geomorphometric analysis in the Atacama Desert. In EGU, General Assembly Conference Abstracts (p. 13326).‏
  • Ian S. Evans, 2012.Geomorphometry and landform mapping: What is a landform, geomorpholgy137, 94-106
  • Jaroslaw Jasiewicz, Pawel Netzel, Tomasz F. Stepinski, 2014. Landscape similarity, retrieval, and machine mapping of physiographic units, Geomorphology 221, 104-112
  • Jarosław Jasiewicz, Tomasz F. Stepinski, 2013. Geomorphons- a pattern recognition approach to classification and mapping of landforms: Geomorphology 182, 147-156
  • Lucian Drăguţ, Thomas Blaschke, 2006. Automated classification of landform elements using object-based image analysis, Geomorphology 81, 330–344
  • Luo, W., & Liu, C. C., 2018. Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides, 15(3), 465-474.‏
  • Mercy W. Ngunjiri, Zamir Libohova, Phillip R. Owens, Darrell G. Schulze, 2020. Landform pattern recognition and classification for predicting soil types of the Uasin Gishu Plateau, Kenya, Catena188, 104390
  • Pál, Marton, & Albert, G., 2021. The use of geomorphons in geodiversity assessment. In EGU, General Assembly Conference Abstracts (pp. EGU21-1363).‏
  • Philip Verhagen Lucian Drǎguţ, 2012. Object-based landform delineation and classification from DEMs for archaeological predictive mapping, Archaeological Science 39
  • Tanja Kramm 1, Dirk Hoffmeister 1, Constanze Curdt, Sedigheh Maleki, Farhad Khormali 2 and Martin Kehl, 2017 . Accuracy Assessment of Landform Classification Approaches on Different Spatial Scales for the Iranian Loess Plateau. Geo information.
  • Tomasz F. Stepinski, Jarosław Jasiewicz, 2011. Geomorphons - a new approach to classification of Geomorphetery
  • Trevan Flynn, Andrei Rozanov, Freddie Ellis, Willem de Clercq, Cathy Clarke, 2020. Farm-scale soil patterns derived from automated terrain classification, Catena185, 104311
  • Trevan Flynn, Andrei Rozanov, Willem de Clercq, Benjamin Warr, Cathy Clarke, 2019. Semi-automatic disaggregation of a national resource inventory into a farm scale soil depth class map, Geoderma 337, 1136-1145
  • Wu-fan Zhao, Li-yang Xiong, Hu Ding , Guo-an Tang, 2017. Automatic recognition of loess landforms using Random Forest method, Mountain Science1,4, 885–897
  • Yan, G., Cheng, H., Teng, L., Xu, W., Jiang, Y., Yang, G., & Zhou, Q. (2020). Analysis of the Use of Geomorphic Elements Mapping to Characterize Subaqueous Bedforms Using Multibeam Bathymetric Data in River System. Applied Sciences10(21), 7692.‏
  • Zamir Libohova, Hans E. Winzeler, Brad Lee, Philip J. Schoeneberger, Jyotishka Datta Phillip R. Owens, 2016. Geomorphons- Landform and property predictions in a glacial moraine in Indiana landscapes,Catena 142, 66-76