Investigating the Impacts of Land Use Changes on Soil Erosion in Givi City using the MABAC Multi-Criteria Decision-Making Model and Landsat Satellite Images (OLI-TM Sensors)

Document Type : Research Article

Authors

• Mousa Abedini ¹
• Elnaz Piroozi ²
• Mehdi Faal Naziri ³

¹ Professor in Geomorphology, University of Mohaghegh Ardabili, Ardabil, Iran

² PhD in Geomorphology, University of Mohaghegh Ardabili, Ardabil, Iran

³ PhD Candidate in Geomorphology, University of Mohaghegh Ardabili, Ardabil, Iran

10.22067/geoeh.2022.73137.1121

Abstract

In recent years, the mutual impact of land use change and soil erosion has become a major environmental concern. Therefore, this study tried to investigate the changes in different land uses and evaluate the impacts of land use changes on soil erosion in Givi city. To achieve the goals of the research, first, a land use map was prepared using the object-oriented method for the two periods of 2000 and 2021. Then, according to the natural and human conditions of the area, other effective factors for erosion in the area were identified and the information layers of each of the criteria were prepared by the geographic information system. Evaluation and standardization of layers were done using the fuzzy membership function and weighting of the criteria was done using the CRITIC method. The final analysis and modeling were done using the MABAC method as one of the Multi-Criteria Decision-Making (MCDM) methods. The results showed that the largest area in 2000 is related to good and medium pastures, recording 313.172 and 283.144 square kilometers, respectively. In 2021, the biggest areas are related to poor pastures and barren lands, with 335.077 and 329.815 square kilometers, respectively. According to the 2000 erosion zoning map, 16.34% and 20.36% of the city and according to the 2021 erosion zoning, 22.92% and 25.58% of the city’s area are in very high and high erosions, respectively. Decreasing good and average pastures and lands with dense vegetation and turning them into agricultural areas, poor pastures, and man-made and barren lands have had the greatest impact on soil erosion.
 

Keywords

• Soil Erosion
• Land Use Changes
• MABAC Model
• Object-oriented Method
• Givi City

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References

 

اصغری­سراسکانرود، صیاد؛ پالیزبان، دلنیا؛ پیروزی، الناز؛ 1397. بررسی مقایسه­ای تغییرات کاربری اراضی و تأثیر آن بر میزان فرسایش با استفاده از روش طبقه­بندی شی­گرا در حوضه سیمینه‌رود بوکان، پژوهش­های فرسایش محیطی، سال هشتم، شماره 1، صص 109-94.  https://www.20.1001.1.22517812.1397.8.1.6.3

بنیاد امیر، اسلام؛ حاجی قادری، طه؛ 1386. تهیه نقشه جنگل­های طبیعی استان زنجان با استفاده از داده­های سنجنده ETM + ماهواره لندست 7، مجله علوم و فنون کشاورزی و منابع طبیعی، جلد 11، شماره 42، صص 638-627. https://www.20.1001.1.24763594.1386.11.42.51.5

رضائی­مقدم، محمدحسین؛ رضایی بنفشه‌درق، مجید؛ فیضی­زاده، بختیار؛ نظم­فر، حسین؛ 1389. طبقه­بندی پوشش اراضی/کاربری اراضی بر اساس تکنیک شی­گرا و تصاویر ماهواره­ای، مطالعه موردی: استان آذربایجان غربی، پژوهش‌های آبخیزداری (پژوهش و سازندگی)، دوره 23، شماره 2، صص 32-19.

https://www.sid.ir/paper/200608/fa

رضائی­مقدم، محمدحسین؛ مختاری، داود؛ سمندر، نسرین؛ 1399. استخراج و ارزیابی تغییرات کاربری اراضی با به‌کارگیری الگوریتم SVM با کرنل چندجمله‌ای و روش حداکثر احتمال در محدودۀ حوضه آبریز اوجان­چای بستان‌آباد، جغرافیا و مخاطرات محیطی، دوره 9، شماره 4، صص 44-25.

https://doi.org/10.22067/GEOEH.2021.67231.0

عابدینی، م.، بهرام‌نیا، ف.، مصطفی‌زاده، ر.، پاسبان، ا. ح؛ 1402. بررسی تأثیر تغییرات کاربری اراضی در یک دوره بیست سال بر میزان فرسایش و رسوب حوضه رضی‌چای، جغرافیا و مطالعات محیطی، دوره 12، شماره45. صص 133-114.  20.1001.1.20087845.1402.12.45.7.9

عابدینی، م.، جوادی علی‌بابالو، س.، مصطفی‌زاده، ر.، پاسبان، ا.ح. 1401. ارتباط شاخص‌های پوشش گیاهی و ژئومورفیک با مقادیر فرسایش و رسوب در حوضه آبریز کوزه‌توپراقی، هیدروژئومورفولوژی، دوره ، شماره 9 صص 128-105. https://civilica.com/doc/1581841

فیضی­زاده، بختیار؛ جعفری، فیروز؛ نظم­فر، حسین؛ 1387. کاربرد داده­های سنجش‌ازدور در آشکار­سازی تغییرات کاربری­های اراضی شهری (مطالعه موردی فضای سبز شهر تبریز)، هنر­های زیبا، شماره 34، صص 24-17. https://www.sid.ir/paper/5716/fa

مزبانی، مهدی؛ رضائی­مقدم، محمدحسین؛ حجازی، اسد­اله؛ 1400. ارزیابی خطر فرسایش خاک در کاربری‌های اراضی با استفاده از معادله اصلاح شده جهانی فرسایش خاک (مطالعه موردی: حوضه آبریز سیکان)، جغرافیا و مخاطرات محیطی، دوره 10، شماره 1، صص 63-41.https://doi.org/10.22067/GEOEH.2021.67238.0

میاحی، جاسم؛ اسکندری دامنه، هادی؛ زراسوندی، علیرضا؛ 1400. ارزیابی اثر تغییر پوشش اراضی بر روی روند فرسایش خاک تالاب هور العظیم در جنوب غرب ایران، مخاطرات محیط طبیعی، دوره 10، شماره 27، صص 122-107. https://doi.org/10.22111/JNEH.2020.33269.1626

 

Aher P, Adinarayana J, & Gorantiwar SD. 2013. Prioritization of watersheds using multi-criteria evaluation through the fuzzy analytical hierarchy process. Agric Eng Int CIGR J, 15(1): 11–1. https://cigrjournal.org/index.php/Ejounral/article/view/2282

Aiello A, Adamo M, & Canora, F .2015. Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy. Catena, 131: 174–185. https://doi.org/10.1016/j.catena.2015.04.003

Ajaykumar K, Jawee, TH, Kale S, Umrikar, B, & Rabindranath SR. 2019. Identification of erosion-prone areas using modified morphometric prioritization method and sediment production rate. a remote sensing and GIS approach, Geomatics, Natural Hazards and Risk, 10(1): 986-1006. https://doi.org/10.1080/19475705.2018.1555189

Alinezhad A, & Khalili J. 2019. New Methods and Applications in Multiple Attribute Decision Making (MADM). International Series in Operations Research & Management Science, vol 277, Springer, Cham.  https:// doi.org/10.1007/978-3-030-15009-9

Arab-Ameri A, Pourghasemi H.R, & Cerda A. 2018. Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models, Science of The Total Environment, 613-614: 1385-1400. https://doi.org/10.1016/j.scitotenv.2017.09.210

Baumgertel A, Luki´c S, Belanovi´c, Simi´c S, & Kadovi´c R. 2019. Identifying Areas Sensitive toWind Erosion—A Case Study of the AP Vojvodina (Serbia). Appl. Sci., 9: 1-12. https://doi.org/10.3390/app9235106

Bou-kheir R, Cerdan O, & Abdelah C. 2006. Regional soil erosion risk mapping in Lebanon. Geomorphology, 82: 347-359. https://doi.org/10.1016/j.geomorph.2006.05.012

Congalton R.G, & Green K. 2009. Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Boca Raton, Florida, 137. https://doi.org/10.1201/9781420055139

Coppin P, Jonckheere I, Nackaerts K, Muys B, & Lambin E. 2004. Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9): 1565-1596. https://doi.org/10.1080/0143116031000101675

Georgiou D, Mohammed E.S, & Rozakis, S. 2015. Multi-criteria decisionmaking on the energy supply configuration of autonomous desalination units. Renew. Energy, 75: 459–467. https://doi.org/10.1016/j.renene.2014.09.036

Hoseini H. 2019. Use fuzzy interface systems to optimize land suitability evaluation for surface and trickle irrigation, Information Processing in Agriculture, 6 (1): 11-19. https://doi.org/10.1016/j.inpa.2018.09.003

Jabbar M.T, & Zhou X. 2011. Eco-environmental change detection by using remote sensing and GIS techniques: a case study Basrah province, south part of Iraq. Journal of Environ Earth Sci, 64: 1397–1407. https://doi.org/10.1007/s12665-011-0964-5

Kenneth M, & Gunter, M. 2012.  Monitoring Land-Use Change in Nakuru Kenya Using Multi-Sensor Satellite Data, advance remote sensing, 1: 74-78. https://doi.org/10.4236/ars.2012.13008

Kidane M, Bezie A, Kesete N, & Tolessa T. 2019. The impact of land use and 0land cover (LULC) dynamics on soil erosion and sediment yield in Ethiopia. Heliyon, 5 (12): 1-13. https://doi.org/10.1016/j.heliyon.2019.e02981

Kogo B.K, Kumar L, & Koech R. 2020. Impact of Land Use/Cover Changes on Soil Erosion in Western Kenya. Sustainability, 12, 9740: 1-17. https://doi.org/10.3390/su12229740

Li M, Li T, Zhu L, Meadows ME, Zhu W, & Zhang S. 2021. Effect of Land Use Change on Gully Erosion Density in the Black Soil Region of Northeast China from 1965 to 2015: A Case Study of the Kedong County. Front. Environ. Sci, 9: 652-933. https://doi.org/10.3389/fenvs.2021.652933

Lillesand ‎T.M, Kiefer R.W, & Chipman W.J. 2008. Remote sensing and Image Interpretation, New York: John Wiley & Sons, Inc., 6^th Ed, 812. https://www.wiley.com/en-us/Remote+Sensing+and+Image+Interpretation%2C+7th+Edition-p-9781119128465

Masselink R, Temme A.J.A.M, Giménez R, Casalí J, & Keesstra, S.D. 2017. Assessing hillslope-channel connectivity in an agricultural catchment using rare-earth oxide tracers and random forests models. Cuadernos de Investigación Geográfica. 43 (1), 19-39. https://doi.org/10.18172/cig.3169

Mendoza M.E. 2011. Analysing land cover and land use change processes at watershed level: A multitemporal study in the Lake Cuitzeo Watwrshed, Mexico (1975-2003). Applied Geography, 31(1): 237-250. https://doi.org/10.1016/j.apgeog.2010.05.010

Nekhay O, Arriaza M, & Boerboom L. 2009. Evaluation of erosion risk using Analytic Network Process and GIS: a case study from Spanish mountain olive plantations. J Environ Manag, 90(10): 3091-3104. https://doi.org/10.1016/j.jenvman.2009.04.022

Okumura M, & Araujo A.G. 2014. Long-term cultural stability in hunter–gatherers: a case study using traditional and geometric morphometric analysis of lithic stemmed bifacial points from Southern Brazil. J. Archaeol. Sci, 45: 59–71. https://doi.org/10.1016/j.jas.2014.02.009

Ozşahin E, & Eroglu I. 2019. Soil Erosion Risk Assessment due to Land Use/Land Cover Changes (LULCC) in Bulgaria from 1990 to 2015, Alinteri J. of Agr. Sci, 34(1): 1-8. https://doi.org/10.28955/alinterizbd.444193

Santos J. C. N, Andrade E. M, Medeiros P. H. A, & Joao M. 2017. Land use impact on soil erosion at different scales in the Brazilian semi-arid. Revista Ciencia Agronomica, 48(2): 251-260. https://doi.org/10.5935/1806-6690.20170029 

Singh G, & Panda RK. 2017. Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using USLE, GIS and remote sensing: A case study in the Kapgari watershed, India. Int Soil Water Conserv Res, 5: 202–211. https://doi.org/10.1016/j.iswcr.2017.05.006

Subhatu A, Lemann T, Hurni K, Portner B, Kassawmar T, Zeleke G, & Hurni H. 2017. Deposition of eroded soil on terraced croplands in Minchet catchment, Ethiopian Highlands. Int Soil Water Conserv Res, 5: 212–220. https://doi.org/10.1016/j.iswcr.2017.05.008

Tachizuka S, Hung T, Ochi S, & Yasuoka Y. 2002. Monitoring of Long-term Urban Expansion by the use of Remote Sensing Image from Different Sensor, Asian conference on Remote sensing, Kathmandu, Nepal, November 2002: 1-7. https://api.semanticscholar.org/CorpusID:195777719

Tuş A, & Aytaç Adalı E. 2019. The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem, opsearch, 56: 528–538. https://doi.org/10.1007/s12597-019-00371-6

Xiaolu S, & Cheng B. 2011. Change Detection Using Change Vector Analysis from Landsat TM Images in Wuhan, Procedia Environmental Sciences, 11 (1): 238 – 244. https://doi.org/10.1016/j.proenv.2011.12.037

Wang J, Wei G, Wei C, & Wei Y. 2020. MABAC method for multiple attribute group decision making under q-rung orthopair fuzzy environment, Defence Technology, 16 (1): 208-216. https://doi.org/10.1016/j.dt.2019.06.019

Wessels K.J, Prince S.D, Frost P.E, & Van Z. D. 2004. Assessing the effects of human induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Journal of Remote Sensing Environment, 9 (1): 47–67. https://doi.org/10.1016/j.rse.2004.02.005