Evaluating Ecological Degradation of Kosalan Protected Area using Remote Sensing and GIS

Document Type : Research Article


1 MSc in Environmental Sciences, Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

2 Associate Professor, Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran


Protected areas are defined as a pattern of land use in the environmental planning process. However, they are excluded from any physical exploitation or their use is conditional. They are no less important than other lands. This study aimed to evaluate the ecological degradation of Kosalan protected area using remote sensing and GIS techniques. Images of two time periods 1989 and 2020 were prepared. For this purpose, after radiometric and atmospheric corrections of NDVI index, changes of vegetation in the region in the two time periods were evaluated. Then, to model the ecological degradation, eight criteria including distance from the village, distance from the road, landslide points, erosion intensity, slope, direction, altitude and vegetation were used. The criteria were weighted by hierarchical analysis process method and standardized by fuzzy model. Vegetation changes at different thresholds were analyzed. According to the vegetation situation in the region, 3 thresholds of 0.1-3, 0.1-1 and 0.1-1 were used. The results of this evaluation showed that the quality and density of vegetation has decreased a lot during 31 years. The results of ecological degradation modeling showed that the most effective criterion in causing degradation is distance from the village. In general, 50% of the area has a high potential for ecological degradation. The results showed that the greatest potential for ecological degradation was in the central areas of the southeast and center of the region, where the slope and altitude are high. The impact of slope is so high in these places.

Graphical Abstract

Evaluating Ecological Degradation of Kosalan Protected Area using Remote Sensing and GIS


احسنی، نبی؛ اولادی، جعفر؛ قصریانی، فرهنگ؛ درویش، محمد؛ 1386. معرفی شیوه‌ای برای اعمال مدیریت پایدار بر سرزمین برمبنای معیارهای IUCN استان کردستان، منطقه کوسالان مریوان. فصلنامة علمی- پژوهشی تحقیقات مرتع و بیابان ایران. شماره 4. جلد . صص 558-539.
آرخی، صالح؛ محمودیان، عبدالرحمان؛ عمادالدین، سمیه؛ 1400. پیش‌بینی خطر تخریب جنگل با استفاده از سیستم اطلاعات جغرافیایی و مدل رگرسیون لجستیک (مطالعه موردی: شهرستان سردشت). جغرافیا و مخاطرات محیطی. (4)10.https://doi.org/ 10.22067/geoeh.2021.70743.1070
امامی، حسن؛ شهریاری، حسن؛ 1398. کمی سازی عوامل محیطی و انسانی در وقوع آتش‌سوزی جنگل با روش‌های RS و GIS، مناطق حفاظت‌شده ارسباران. فصلنامه علمی- پژوهشی اطلاعات جغرافیایی سپهر. (112)28. صص 35-53.https://doi.org/10.22131/sepehr.2020.38606
روشن، سجاد؛ 1397. وسعت و وضعیت مناطق حفاظت‌شده و ارتباط آن‌ها در مقیاس سیمای سرزمین با استفاده از نظریه گراف در جهت افزایش پایداری و ارائه راهکارهای مدیریت محیط‌زیستی (مطالعه موردی زیستگاه مرکزی زاگرس). پایان‌نامه. دانشگاه تهران.https://env.ut.ac.ir
سپهر، حسین؛ مخدوم، مجید؛ فریادی، شهرزاد؛ رمضانی مهریان، مجید؛ 1394. ارزیابی کیفیت سرزمین در مناطق حفاظت‌شده با استفاده از مدل تخریب )مطالعه موردی: مجموعه حفاظت شده توران(. پژوهش­های محیط‌زیست. سال 6. شماره 11 . بهار و تابستان. از صص 119 -130.
شیرمحمدی، ایمان؛ جهانی، علی؛ اعتماد، وحید؛ ضرغام، نصرت‌الله؛ مخدوم فرخنده، مجید؛. 1395. ارزیابی آثار محیط‌زیستی توسعه بر منطقه حفاظت‌شده کرکس با استفاده از مدل تخریب. پژوهش‌های محیط‌زیست. 7(14). صص 91-102.
مالچفسکی یاچک؛ 1385. سامانه اطلاعات جغرافیایی و تحلیل تصمیم چند معیاره. اکبر پرهیزکار و عطا غفاری گیلانده تهران: سمت. https://samt.ac.ir/fa/book/837
مجله دیده‌بان محیط‌زیست و حیات‌وحش ایران؛ 1391. منطقه حفاظت شده کوسالان و شاهو. http://www.iew.ir/1391/06/14/1574
نظام فر، سرمستی؛ علوی پناه، کاظم؛ 1400. بررسی امکان کالیبراسیون سنجنده‌های LISSIII و ASTER با استفاده از نمکزارهای مناطق خشک ایران. فصلنامه علوم و تکنولوژی محیط‌زیست. (3)23. 117-131.
Bharathkumar, L., Mohammed-Aslam, M. A., 2015. Crop Pattern Mapping of Tumkur Taluk Using NDVI Technique: A Remote Sensing and GIS Approach. Aquatic Procedia, 4, 1397–1404. https://doi.org/10.1016/j.aqpro.2015.02.181
Eddy, I. M., Gergel, S. E., Coops, N. C., Henebry, G. M., Levine, J., Zerriffi, H., Shibkov, E., 2017. Integrating remote sensing and local ecological knowledge to monitor rangeland dynamics. Ecological Indicators, 82, 106-116. https://doi.org/10.1016/j.ecolind.2017.06.033
Feng, R., Wang, F., Wang, K., 2021. Spatial-temporal patterns and influencing factors of ecological land degradation-restoration in Guangdong-HongKong-Macao Greater Bay Area. Science of The Total Environment, 794, 148671.
Fraser, E. D., Dougill, A. J., Hubacek, K., Quinn, C. H., Sendzimir, J., Termansen, M., 2011. Assessing vulnerability to climate change in dryland livelihood systems: conceptual challenges and interdisciplinary solutions. Ecology and Society, 16(3), 12p.‏
Gillespie, T.W., Kelm, S.O., Dong, C., Willis, K.S., Okin, G.S., MacDonald, G.M., 2018. Monitoring changes of NDVI in protected ateas of southern California. Ecological Indicators, 88, 485-494. https://doi.org/10.1016/j.ecolind.2018.01.031
Gooshbor, L., Bavaghar, M. P., Amanollahi, J., Ghobari, H., 2016. Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing. Plant Protection Science, 52(4), 270-276. http://pps.agriculturejournals.cz/artkey/pps-201604-0008
Granados, L., Pizaro, M., Cayuela, L., Domingo, D., Gomez, D., Gracia, M.B., 2022. Long-term monitoring of NDVI changes by remote sensing to assess the vulnerability of threatened plants. Biological Conservation, 265, 109428. https://doi.org/10.1016/j.biocon.2021.109428
Hellwig, N., Anschlag, K., Broll, G., 2016. A fuzzy logic based method for modeling the spatial distribution of indicators of decomposition in a high mountain environment. Arctic, Antarctic, and Alpine Research, 48(4), 623-635.‏ https://doi.org/10.1657/AAAR0015-073
Jiang, L., Huang, X., Wang, F., Liu, Y., An, P., 2018. Method for evaluating ecological vulnerability under climate change based on remote sensing: A case study. Ecological indicators, 85, 479-486. https://doi.org/10.1016/j.ecolind.2017.10.044
Jiang, L., Liu, Y., Wu, S., Yang, C., 2021. Analyzing ecological environment change and associated driving factors in China based on NDVI time series data. Ecological Indicators, 129, 107933. https://doi.org/10.1016/j.ecolind.2021.107933
Jing, Y., Zhang, F., He, Y., Johnson, V. C., Arikena, M., 2020. Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecological Indicators, 110, 105874.
Li, M., Yu, H., Meng, B., Sun, Y., Zhang, J., Zhang, H., Wu, J., Yi, S., 2021. Drought reduces the effectiveess of ecological projects: Perspectives from the inter-annual variability of vegetation index. Ecological Indicators, 130, 108154.
Li, Q., Zhou, y., Yi, S., 2022. An integrated approach to constructing ecological security patterns and identifying ecological restoration and protection areas: A case study of Jingmen, China. Ecological Indicators, 137, 108723. https://doi.org/10.1016/j.ecolind.2022.108723
Liu, S., Zhang, J., Zhang, J., Guo, Y., 2022. Simultaneously tackling ecological degreadation and poverty challenges: Evidence from desertifies areas in northern China. Science of The Total Environment, 815, 152927. https://doi.org/10.1016/j.scitotenv.2022.152927
Mahmood, K., Batool, A., Faizi, F., Chaudhry, M. N., Ul-Haq, Z., Rana, A. D., Tariq, S., 2017. Bio-thermal effects of open dumps on surroundings detected by remote sensing—Influence of geographical conditions. Ecological indicators, 82, 131-142.
Malano, H.M., Gao, G., 1992. Ranking and Classification of irrigation system performance using fuzzy set theory: case studies in Australia and China. Irrigation and Drainage Systems, 6, 129-148. https://link.springer.com/article/10.1007/BF01102973
Manzo, C., Mei, A., Zampetti, E., Bassani, C., Paciucci, L., Manetti, P., 2017. Top-down approach from satellite to terrestrial rover application for environmental monitoring of landfills. Science of The Total Environment, 584, 1333-1348.
Nematollahi, S., Fakheran, S., Jafari, A., Pourmanafi, S., Kienast, F., 2022. Applying a systematic conservation planning tool and ecological risk index for spatial prioritization and optimization of protected area networks in Iran. Journal for Nature Conservation. 66, 126144. https://doi.org/10.1016/j.jnc.2022.126144
Pravalie, R., Sirodoev, I., Nita, I., Patriche, C., Dumitrascu, M., Rosca, B., Tiscovsci, A., Bandoc, G., Savulescu, L., Manoiu, V., Birsan, M., 2022. NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987-2018. Ecological Indicators, 136, 108629. https://doi.org/10.1016/j.ecolind.2022.108629
Shen, Ge., 2019. Remote sensing and evaluation of the wetland ecological degradation process of the Zoige Plateau Wetland in China, Ecological Indicators, 104, 48-59. 
Slonecker, T., Fisher, G. B., Aiello, D. P., Haack, B., 2010. Visible and infrared remote imaging of hazardous waste: a review. Remote Sensing, 2(11), 2474-2508.
Tan, J., Li, A., Lei, G., Bian, J., Zhang, Z., 2019. A novel and direct ecological risk assessment index for environmental degradation based on response curve approach and remotely sensed data. Ecological indicators, 98, 783-793. https://doi.org/10.1016/j.ecolind.2018.11.038
Usman, O., Lorember, T., Jelilov, G., Isik, A., Ike, G.N., Sarkodie, S.A., 2021. Towards mitigating ecological degredation in G-7 countries: accounting for economic effect dynamics, renewable energy consumption, and innovation. Heliyon, 7(12), e08592. https://doi.org/10.1016/j.heliyon.2021.e08592
Walz, U., 2011. Landscape structure, landscape metrics and biodiversity. Living reviews in landscape research, 5(3), 1-35.‏ https://doi.org/10.12942/lrlr-2011-3
Xu, H., Wang, M., Shi, T., Guan, H., Fang, C., Lin, Z., 2018. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological indicators, 93, 730-740.
Zhang, X., Liu, K., Li, X., Wang, S., Wang, J., 2022. Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China. Physics and Chemistry of the Earth, Parts A/B/C, 125, 103106. https://doi.org/10.1016/j.pce.2022.103106
  • Receive Date: 23 January 2022
  • Revise Date: 20 March 2022
  • Accept Date: 31 March 2022
  • First Publish Date: 31 March 2022