طبقه‌بندی لندفرم‌ها با استفاده از شاخص موقعیت توپوگرافی و بررسی ریسک واقعی فرسایش آنها در مناطق کوهستانی (مطالعة موردی: حوضة آبخیز خارستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموختة دکتری بیابان‌زدایی، دانشکدة منابع طبیعی و علوم زمین، دانشگاه کاشان

2 دانشیار گروه علوم بیابان، دانشکدة منابع طبیعی و علوم زمین، دانشگاه کاشان

3 دانشیار بخش مرتع و آبخیزداری دانشکدة کشاورزی و منابع طبیعی داراب، دانشگاه شیراز

چکیده

یکی از بخش‌های نوین و کم‌سابقه، به‌ویژه در مطالعات داخلی، بررسی کمّی ناهمواری‌هاست. شناخت علمی و کمّی‌گرایانة موقعیت توپوگرافی همواره از مباحثی بوده که در تحقیقات داخلی، چندان به آن توجه نشده است. این مبحث تأثیر بسزایی در تحلیل‌های ژئومورفولوژیک، هیدرولوژی و محیط‌شناسی دارد. وجود انواع لندفرم‌ها و تنوع آنها معمولاً با تغییر شکل و موقعیت زمین کنترل می‌شود؛ بنابراین طبقه‌بندی و شناسایی مناطق گوناگون، با توجه به ویژگی‌های مورفومتری آنها، ضروری است و ازاین‌رو پژوهش حاضر سعی در طبقه‌بندی لندفرم‌ها در منطقة خارستان، واقع در شمال‌غرب استان فارس و عوامل مؤثر در آن دارد. در همین زمینه، در مرحلة اول، از روش شاخص موقعیت توپوگرافی (TPI) به‌منظور طبقه‌بندی لندفرم‌ها و روش کورین برای تعیین کلاس‌های ریسک واقعی فرسایش استفاده شد. همچنین به‌منظور تعیین شاخص تفاضلی نرمال‌شدة پوشش گیاهی (NDVI) از تصاویر ماهوارة لندست 8، مربوط به خرداد 1396، بهره گرفته شد. در مرحلة بعد، رابطة انواع گوناگون لندفرم با فاکتورهای زمینی ارتفاع، شیب، جهت شیب، شاخص خیسی توپوگرافی (TWI)، شاخص ناهمواری زمین (TRI) و NDVI مشخص شد. در مرحلة آخر، وضعیت لندفرم‌های گوناگون در کلاس‌های متفاوت ریسک فرسایش، تعیین شد. نتایج نشان‌دهندة ده گونه لندفرم در منطقة مورد مطالعه بود. بیشترین نوع لندفرم‌ها در منطقة مورد مطالعه، آبراهه و قله‌های مرتفع، به‌ترتیب با مساحت 71/27 و 48/27% بود؛ درصورتی‌که دشت‌های کوچک کمترین مساحت (18/1%) منطقة مورد مطالعه را شامل می‌شد. کلاس‌های لندفرم با شاخص خیسی توپوگرافی در سطح 95% همبستگی معنی‌داری را نشان داد. از نظر توزیع مکانی، بیشترین سطح (71/91%) منطقه را NDVI کلاس 1/0 تا 3/0 دربر می‌گرفت. NDVI بزرگ‌تر از 4/0 در لندفرم‌های آبراهه و دره‌های Uشکل مشاهده شد. ریسک واقعی فرسایش در سه کلاس کم، متوسط و زیاد با مساحت‌های 14/31%، 11/31% و 78/37% طبقه‌بندی شد. در کلاس فرسایش کم، متوسط و زیاد، لندفرم‌های آبراهه و یال‌های مرتفع و قله به‌ترتیب با 44%، 57% و 59% بیشترین سطح را به خود اختصاص دادند.

کلیدواژه‌ها

عنوان مقاله [English]

Classification of Landforms Using Topographic Location Index and Assessment of their Actual Soil Erosion Risk in Mountainous Areas (Case Study: Kharestan Watershed)

نویسندگان [English]

  • Farideh Taripanah 1
  • Abolfazl Ranjbar 2
  • Abbasali Vali 2
  • Marzieh Mokarram 3
1 Ph.D. of Desertification Combating, Desert Control and Management Department, University of Kashan
2 Associate Prof., Dep. of Desert Management, University of Kashan
3 Associate Prof. Dep. of Range and Watershed Management, Darab Compass, Shiraz University
چکیده [English]

One of the new and unique sections, especially in internal studies, is the quantitative examination of unevenness. The scientific and quantitative study of topographic position has always been one of the topics that have received little attention in domestic research. So, classification and identification of different morphometrically distinct regions are necessary. Thus, the present study aims to classify landforms in the northwest of Fars province, Kharestan region and investigate its factors affecting. In this regard, the Topographic Position Index (TPI) method was used in the first stage to classify landforms, followed by the CORINE method to determine erosion risk classes. Additionally, Landsat 8 satellite images from June 2017 were used to determine the normalized differential vegetation index (NDVI). The next step was to determine the relationship between different types of landforms and terrestrial factors such as height, slope, slope direction, topographic wetness index (TWI), Terrain Ruggedness Index (TRI) and NDVI. Finally, the status of different landforms was determined based on erosion risk classes. Results showed ten different types of landforms existed within the study area. Small plains (1.18%) were the lowest in the study area, while waterways (27.71%) and high peaks (27.48%) were the highest. The TWI was significantly correlated with landform classes at 95% level. Most of the region (91.71%) had NDVI classes of 0.1 to 0.3. Stream and u-shaped valleys were found to have higher NDVI values. Real erosion risk was classified into three classes: low, medium, and high with areas of 31.14, 31.11, and 37.78%. There were 44, 57, and 59% erosion levels in the low, medium, and high erosion classes, respectively.

کلیدواژه‌ها [English]

  • Landform classification
  • Terrain factors
  • NDVI
  • Actual erosion risk
  • Kharestan region

مراجع

Ahmadi Mirghaed, F., Souri, B., Mohammadzadeh, M., Salmanmahiny, A. & Mirkarimi, S.H., 2018, Evaluation of the Relationship between Soil Erosion and Landscape Metrics across Gorgan Watershed in Northern Iran, Environmental Monitoring and Assessment, 190(11), P. 643.
Al-Husban, Y., 2019, Landforms Classification of Wadi Al-Mujib Basin in Jordan, Based on Topographic Position Index (TPI), and the Production of a Flood Forecasting Map, Dirasat, Human and Social Sciences, 46(3), PP. 23-42.
Baboli, H. & Negahban, S., 2018, Investigation of Fermi Characteristics of Land Surface Based on Morphometric Indices and Using GIS (Case Study: Fahlian Watershed), Geography (Quarterly Scientific Research and International Journal of Geographical Society of Iran), 19(68), PP. 102-117.
Blaszczynski, J.S., 1997, Landform Characteriza-tion with Geographic Information System, Photogrammetric Engineering and Remote Sensing, 63(2), PP. 183-191.
Burr, D., Baker, V.R. & Carling, P., 2009, Megaflooding on Earth Andmars, Cambridge, UK; New York: Cambridge University Press.
Chalmers, A.C., Erskine, W.D., Keene, A.F. & Bush, R.T., 2012, Relationship between Vegetation, Hydrology and Fluvial Landforms on an Unregulated Sand-Bed Stream in the Hunter Valley, Australia, Austral Ecology, 37(1), PP. 193-203.
De Reu, J., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., De Smedt, P. & Crombé, P., 2013, Application of the Topographic Position Index to Heterogeneous Landscapes, Geomorphology, 186, PP. 39-49. 
Denguz, O. & Akgul, S., 2004, Soil Erosion Risk Assessment of the Golbasi Environmental Protection Area and Its Vicinity Using the CORINE Model, Turk. J. Agric, 29, PP. 439-448.
Florinsky, V., Eilers, R.G., Manning, G. & Fuller, L.G., 2002, Prediction of Soil Properties by Digital Terrain Modelling, Environ Model Softwar, 17, PP. 295-311.
Gerçek, D., 2010, Object-Based Classification of Landforms Based on their Local Geometry and Geomorphometric Context, PhD Diss., University of Middle East Technical.
Guber, F., Baruck, J., Geitner, C., 2017, Algoritm Vs Surveeyors: A Comparison of Automated Landform Delineation and Surveyed Topographic Position from Soil Mapping in an Alpine, Environment Geoderma, 308, PP. 9-25.
Hilker, T., Lyapustin, A.I., Tucker, C.J., Hall, F.G., Myneni, R.B., Wang, Y. & Sellers, P.J., 2014, Dynamics and Rainfall Sensitivity of the Amazon, Proc, Natl. Acad. Sci., 111, PP. 16041-16046.
Hoersch, B., Braun, G., & Schmidt, U., 2003, Relation between Landform and Vegetation in Alpine Regions of Wallis, Switzerland, A Multiscale Remote Sensing and GIS Approach, Computers Environment and Urban Systems.
Howey, M.C.L. & Clark, M., 2018, Analyzing Landform Patterns in the Monumental Landscape of the Northern Great Lakes, 1200–1600 CE, Journal of Archaeological Science: Reports, 19, PP. 886-893.
Kosmas, C., Tsara, M., Moustakas, N. & Karavitis, C., 2003, Identification of Indicators for Desertification, Ann. Arid Zone, 42, PP. 393-416.
Kubota, Y., Murata, H. & Kikuzawa, K., 2004, Effects of Topographic Heterogeneity on Tree Species Richness and Stand Dynamics in a Subtropical Forest in Okinawa Island, Southern Japan, Journal of Ecology, 92, PP. 230-240.
Lindenmayer, D.B. & Fischer, J., 2006, Habitat Fragmentation and Landscape Change An Ecological And Conservation Synthesis, Island Press, USA.
Lindsay, C. & Rassel, I., 2015, An Integral Image Approach to Performing Muilti-Scale Topographic Position Index, Analysis Geomorphology, 3, PP. 51-61.
Liu, H., Zheng, L. & Yin, S.H., 2018, Multi-Perspective Analysis of Vegetation Cover Changes and Driving Factors of Long Time Series Based on Climate and Terrain Data in Hanjiang River Basin, China, Arabian Journal of Geosciences, 11, PP. 509 -524.
Metelka, V., Baratoux, L., Jessell, M.W., Barth, A., Ježek, J. & Naba, S., 2018, Automated Regolith Landform Mapping Using Airborne Geophysics and Remote Sensing Data, Burkina Faso, West Africa, Remote Sensing of Environment, 204, PP. 964-978.
Mokarram, M. & Sathyamoorthy, D., 2016, Relationship between Landform Classification and Vegetation (Case Study: Southwest of Fars Province, Iran), Open Geosci, 8, PP. 302-309.
Moravej, K. & Karimian Eghbal, M., 2012., Comparison of Automated and Manual Landform Delineationin Semi Detailed Soil Survey Procedure, Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Ir, African Journal of Agricultural Research, 7(17), PP. 2592-2600.
Newman, D.R., Lindsay, J.B. & Cockburn, J.M.H, 2018, Evaluating Metrics of Local Topographic Position for Multiscale Geomorphometric Analysis, Geomorphology, 312, PP. 40-50. 
Pakoksung, K. & Takagi, M., 2015, Remote Sensing Data Application for Ood Modeling, J. Appl. Surv. Technol., 26, PP. 115-122.
Pakoksung, K. & Takagi, M., 2016, Digital Elevation Models on Accuracy Validation and Bias Correction in Vertical, Modeling Earth Syst. Environ., 2(11).
Pfeffer, K.E., Pebesma, J. & Burroug, P.A., 2003, Mapping Alpine Vegetation Using Vegetation Observations and Topographic Attributes, Landscape Ecology, 18, PP. 759-776.
Pfiffner, A.& Kühni O.A., 2001, The Relief of the Swiss Alps and Adjacent Areas and its Relation to Lithology and Structure—Topographic Analysis from 250-M DEM, Geomorphology, 41, PP. 285-307.
Pike, R.J., Evans, I.S., Hengl, T., 2009. Geomorphometry - Concepts, Software, Applications. Developments in Soil Science. In: Geomorphometry: a brief guide. Edited by: Hengl, T., Reuter, H.I. (Eds.). Elsevier. PP. 330.
Riley, S.J., DeGloria, S.D. & Elliot., R., 1999, A Terrain Ruggedness Index that Quantifies Topographic Heterogeneity, Intermountain Journal of Sciences, 5, PP. 1-4.
Salvacion, A.R., 2016, Terrain Characterization of Small Island Using Publicly Available Data and Open- Source Software: A Case Study of Marinduque, Philippines, Model. Earth Syst. Environ., 2, P. 31.
Schaetzl, R.J., 2013, Catenas and Soils, In: John F. Shroder & Pope, G.A. (Ed.s), Treatise on Geomorphology, Vol. 4, Weathering and Soils Geomorphology, San Diego: Academic Press, PP. 145-158.
Stage, A.R. & Salas, C., 2007, Interactions of Elevation, Aspect and Slope in Models of Forest Species Composition and Producti-vity, Forest Science, 53(4), PP. 486-492.
Wang, G., Zhou, K., Sun, L., Qin, Y. & Li, M., 2010, Study on the Vegetation Dynamic Change and R/S Analysis in the Past Ten Years in Xinjiang, Remote Sens. Technol. Appl., 25, PP. 84-90.
Weiss, A.D., 2001, Topographic Positions and Landforms Analysis, ESRI International User Conference, San Diego, CA, 3, PP. 9-13.
Wilson, J.P. & Gallant, J.C., 2000, Terrain Analysis, New York: John Wiley and Sons.
Yuan, Z.Q., Fang, C., Zhang, R., Li, F.M., Javaid, M.M. & Janssens, I.A., 2019, Topographic Influences on Soil Properties and Aboveground Biomass in lucerne-Rich Vegetation in a Semi-Arid Environment, Geoderma, 344, PP. 137-143.
Zawawi, A., Shiba, M., Janatun, N. & Jemali, N., 2014, Landform Classification for Site Evaluation and Forest Planning: Integration between Scientific Approach and Traditional Concept, Sains Malaysiana, 43(3), PP. 349-358.

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