ارزیابی و تحلیل بیابان‌زایی با استفاده از روش تحلیل بردار تغییر (منطقه مورد مطالعه: شهرستان قلعه‌گنج)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی منابع طبیعی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران

2 دانشیار دانشکده منابع طبیعی دانشگاه تهران، کرج، ایران

3 استاد پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران

4 استاد پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج،ایران

5 استادیار، گروه ترویج و آموزش کشاورزی، دانشکده اقتصاد و توسعه کشاورزی، دانشگاه تهران، ایران

6 استاد دانشگاه زوریخ، سوئیس

چکیده

بیابان­زایی به عنوان یک تهدید جدی برای محیط­های خشک و نیمه­خشک و حتی نیمه­مرطوب در چند دهه اخیر محسوب شده و مانع اصلی توسعه­پایدار جهانی است، در نتیجه نظارت بر روند تغییرات آن از اقدامات ضروری محسوب می­شود. یکی از روش­های مقرون به صرفه، اغلب رایگان و در دسترس برای نظارت بر تغییرات این مناطق استفاده از سنجش از دور و سامانه اطلاعات جغرافیایی است. در این مطالعه با استفاده از روش تحلیل بردار تغییر اقدام به ارزیابی و تحلیل شدت بیابان‌زایی در بخشی از شهرستان قلعه­گنج در جنوب استان کرمان پرداخته شده­است. بدین منظور ابتدا در گوگل ارث انجین از باندهای تصاویر لندست 8 در دو بازه زمانی 2014 (دوره زمانی اول) و بازه زمانی2020 (دوره زمانی دوم) برای ماه‌های اسفند و فروردین استفاده شده­است. تصحیحات لازم روی آنها اعمال و برای هر دو دوره به صورت جداگانه میانگین­گیری و سپس شاخص‌های EVI و BSI محاسبه شده­است. در مرحله بعد با استفاده از این دو شاخص و روش تحلیل بردار تغییر در محیط نرم‌افزار GIS، به تعیین بزرگی تغییرات و جهت تغییرات بیابان‌زایی در منطقه مورد پرداخته شده است. دستاورد پژوهش حاضر گویای غالبیت روند احیای منطقه در طی سال­های مورد مطالعه می باشد و نتایج کلی حاکی ازین امر است که توسعه کشاورزی و در کنار آن تغییر کاربری اراضی بیشترین تاثیر را بر شاخص­های پایش و روند بیابان­زایی در منطقه داشته­اند؛ بدین صورت که شاهد تخریب اراضی در اطراف مناطق مسکونی هستیم و از سویی رابطه تنگاتنگی بین فعالیت­های کشاورزی و مناطق احیایی در منطقه وجود دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation and analysis of desertification change using change vector analysis method (Region of study: Ghalehgang County)

نویسندگان [English]

  • Fatemeh Narmashiri 1
  • mehdi ghorbani 2
  • Gholamreza Zehtabian 3
  • Hosein Azarnivand 4
  • Amir Alambeigi 5
  • Roland W Scholz 6
1 Ph.D. Student of Natural Resource Engineering, College of Agriculture and Natural Resource, University of Tehran, Iran.
2 Associate Professor, College of Agriculture and Natural Resource, University of Tehran, Iran.
3 Professor, College of Agriculture and Natural Resource, University of Tehran,IranProfessor, College of Agriculture and Natural Resource, University of Tehran,Iran
4 Professor, College of Agriculture and Natural Resource, University of Tehran,Iran, Karaj, Iran
5 Assistant Professor, Department of Agricultural Extension & Education, Faculty of Economics and Agricultural Development, Tehran University, Iran
6 Professor, ETH Zurich, Department of Environmental System Sciences, Zurich, Switzerland
چکیده [English]

Desertification has been considered as a serious threat to arid, semi-arid and even semi-humid climate in recent decades and it is a major obstacle to sustainable global development, so monitoring its changes is urgent. Using remote sensing and GIS is one of the cost-effective, often free and accessible method to monitor changes in these areas. In this study, change vector analysis method was used for evaluation and analysis of desertification change in a part of Ghalehganj county in south of Kerman province. For this purpose, Landsat 8 image bands in two time periods of 2014 (first period) and 2020 period (second period) for March and April was used in Google Earth Engine. Image pre-processing were applied and averaging was done separately for both periods which was followed by calculation of EVI and BSI indices. For the next step, using these two indicators and the change vector analysis method in the GIS software, the magnitude and direction of desertification change trends were determined. The results of the present research indicated the dominance of the reclamation process in the region during the years studied and the overall results indicate that development of cultivated lands and land use change have the greatest impact on monitoring indicators and desertification trends in the region. Thus, degradation of lands around residential areas are witnessed and on the other hand, there is a significant relationship between agricultural activities and rehabilitation areas in the region.

کلیدواژه‌ها [English]

  • Change Vector Analysis
  • Desertification
  • Ghalehgang
  • BSI
  • EVI
  • Akbari, M., karimzadeh, H.R, Modarres, R. and Chakoshi, B., 2007. Assessment and classification of desertification using RS & GIS techniques (Case study: the Arid Region, in the North of Isfahan). Iranian journal of Range and Desert Reseach, 14 (2): 124-142 (In Persian).
  • Allen, T.R. and Kupfer, J.A., 2000. Alication of spherical statistics to change vector analysis of Landsat data: Southern Aalachian spruce-fir forests. Journal of Remote Sensing of Environment, 74: 482–493.
  • Azizi, Z., Najafia, A. and Sohrabia, H., 2008. Forest canopy density estimating, using satellite images. Photogrammetry, Remote Sensing and Spatial Information Sciences, 37: Part B8 (In Persian).
  • Becerril-Pina, R., Dıaz-Delgadoa, C., Mastachi-Lozaa, C.A. and Gonzalez-Sosab, E., 2016. Integration of remote sensing techniques for monitoring desertification in Mexico. Journal of Human and Ecological Risk Assessment, 22 (6): 1323–1340.
  • Becerril-Pina, R., Mastachi-Lozaa, C.A., Gonzalez-Sosab, E., Dıaz-Delgadoa, C. and Ba, K.H.M., 2015. Assessing desertification risk in the semi-arid highlands of central Mexico. Journal of Arid Environments, 120: 4-13.
  • Bezerraa, F.G.S., Aguiara, A.P.D., Alvaláb, R.C.S., Giarollaa, A., Bezerraa, K.R.A., Limac, P.V.P.S., Do Nascimentod, F.R. and Araie, E., 2020. Analysis of areas undergoing desertification, using EVI2 multi-temporal data based on MODIS imagery as indicator. Journal of Ecological Indicators, 117: 106579.
  • Bhavani, M., Hanifar Sangeetha, V., Kalaivani, K., Ulagapriya, K. and Saritha A., 2018. Change detection algorithm for multi-temporal satellite images: A review. International Journal of Engineering and Technology(UAE), 7 (2): 206-209.
  • Carvalho Júnior, O.A., Guimarães, R.F., Gillespie, A.R, Silva, N.C. and Gomes, R.A.T., 2011. A new approach to change vector aAnalysis using distance and similarity measures. Journal of Remote Sensing, 3: 2473-2493.
  • Civco, D.L., Hurd, J.D., Wilson, E.H., Song, M. and Zhang, Z., 2002. A comparison of land use and land cover change detection methods. American Congress on Surveying & Mapping – American Society for Photogrammetry and Remote Sensing 2002 Annual Conference Proceedings.
  • Darvish, M., 2019. An introduction to the method of desertification assessment in Iran using adopted Criteria and Indicators. Iranian Journal of Range and Desert Research, 10 (3): 301-383 (In Persian).
  • Dawelbait, M. and Morari, F., 2012. Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. Journal of Arid Environments, 80: 45-55.
  • Ding, H. and Xingming, H., 2021. Spatiotemporal change and drivers’ analysis of desertification in the arid region of northwest China based on geographic detector. Journal of Environmental Challenges, 4: 100082.
  • Ebrahimian, R. and Alesheikh, A., 2019. A change vector analysis method to monitor drought using landsat data. The international archives of the Photogrammetry, remote sensing and spatial information sciences, volume XLII-4/W18, GeoSpatial Conference: 12–14 October 2019, Karaj, Iran (In Persian).
  • Ebrahimzadeh, S., Bazrafshan, J. and Ghorbani, K.H., 2013. Comparative study between satellite and ground-based drought indices using change vector analysis technique (Case study of Kermanshah province). Journal of Water and Soil, 27 (5):1034-1045 (In Persian).
  • Firouzi, F., Tavosi, T. and Mahmoudi, P., 2019. Investigating the sensitivity of NDVI and EVI vegetation indices to dry and wet years in arid and semi-arid regions (Case study: Sistan plain, Iran). Scientific-Research Quarterly of Geographical Data (SEPEHR), 28 (110):163-179 (In Persian).
  • Fitrianto, A. C., Darmawan, A., Tokimatsu, K. and Sufwandika, M., 2018. Estimating the age of oil palm trees using remote sensing technique. In IOP Conference Series: Earth and Environmental Science, 148 (1): 012020.
  • Hellden, U., 2008. A coupled human-environment model for desertification simulation and impact studies. Global and Planetary Change, 64: 158-168.
  • Hu, Y., Hana, Y. and Zhang, Y., 2020. Land desertification and its influencing factors in Kazakhstan. Journal of Arid Environments, 180: 104203.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Gournal of Remote sensing of Environment, 83(1-2), 195-213.
  • Huete, A., Justice, C. and Van Leeuwen, W., 1999. MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3: 213-227.
  • Jalili, A., 2020. Do's and don'ts in desert ecosystems and selecting the proper management strategy, 5 (2): 3 - Serial Number 21. DOI: 10.22092/irn.2020.121625.(In Persian).
  • Jiang, Z., Huete, A., Didan, K. and Miura, T., 2008. Development of a two-band enhanced vegetation index without a blue band. Journal of Remote Sensing, 112: 3833–3845.
  • Karamesoutia, M., Panagosb, P. and Kosmas, C., 2018. Model-based spatio-temporal analysis of land desertification risk in Greece. Journal of Catena, 167: 266–275.
  • Karavitis, C.A., Tsesmelis, D.E., Oikonomoub, P.D., Kairis, O., Kosmasa, C., Fassouli, V., Ritsema, C., Hessel, R., Jetten, V., Moustakas, N., Todorovic, B., Skondras, N.A., Vasilakou, C.G., Alexandris, S., Kolokytha, E., Stamatakos, D.V., Stricevi, R., Chatzigeorgiadis, E., Brandt, J., Geeson, N. and Quaranta, G., 2020. A desertification risk assessment decision support tool (DRAST). Journal of Catena, 187: 104413.
  • Karnieli, A., Qin, Z., Wu, B., Panov, N. and Yan, F., 2014. Spatio-temporal dynamics of land-use and land-cover in the Mu Us Sandy Land, China, using the change vector analysis technique. Journal of Remote Sensing, 6 (10): 9316-9339.
  • Kavosi, M. and Frarajzadeh, M., 2015. The evaluation of vegetation variations trend using linear regression methods and change vector analysis. Journal of Geography and Environmental Planning, 25 (4): 69-82. (In Persian)
  • Kemp, R., 1994. Technology and the transition to environmental sustainability: The problem of technological regime shifts. Journal of Futures, 26 (10): 1023-1046.
  • Le Houerou, H.N., 2006. Desertization. In: Lal, R. (Ed.), Soil Science. CRC Press, Boca Raton, Florida, pp. 468-474.
  • Li, B., Tang, H. and Chen, D., 2009. Drought monitoring using the modified temperature/vegetation dryness index, 2nd International Congress on Image and Signal Processing, 17-19 Oct. 2009, China.
  • Li, S. and Chen, X., 2014. A new bare-soil index for rapid mapping developing areas using landsat8 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-4. ISPRS Technical Commission IV Symposium, 14 – 16 May 2014, Suzhou, China.
  • Lorena, R.B., Santos, J.R., Shimabukuro, Y.E., Brown I.F. and Heinrich Kux, H.J., 2002. A change vector analysis technique to monitor land use/land cover in SW Brazilian amazon: Acre state. Proceedings of the International Society for Photogrammetry and Remote Sensing (ISPRS), 1–8.
  • Lu, D., Mausel, P., Brondizio, E. and Moran, E., 2004. Change detection techniques. Journal of Remote Sensing, 25 (12): 2365-2407.
  • Macías, M.J.G., Carbajala, N. and Vargasb, J.T., 2020. Soil deterioration in the southern Chihuahuan Desert caused by agricultural practices and meteorological events. Journal of Arid Environments, 176: 104097.
  • Matsushita, B., Wei, Y., Jin, C., Yuyichi, O. and Guoyn, Q., 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density Cypress forest. Sensors, 7 (11): 2636-2651.
  • MirzaeiZadeh, V., Niknejad, M. and Hojjati, S.M., 2015. Estimation of forest canopy density using FCD. Ecology of Iranian Forests, 3 (5): 75-63 (In Persian).
  • Mzid, N., Pignatti, S., Huang,W. and Casa, R., 2021. An analysis of bare soil occurrence in arable croplands for remote sensing topsoil applications. Journal of Remote Sensing, 13: 474-488.
  • Nateghi, S., Nohegar, A., Ehsani, A.H. and Bazrafshan, O., 2016. Coastal desert land use monitoring using change vector analysis technique during 2001 to 2014 (Case study: Qeshm Island). Iranian Journal of Range and Desert Research, 23 (2): 404-416 (In Persian).
  • Nguyen, C.T., Chidthaisong, A., Diem, P.K. and Huo, L.Z.H., 2021. A modified bare soil index to identify bare land features during agricultural fallow-period in Southeast Asia using Landsat8. Land, 10: 231-245.
  • Rikimaru, A., 2003. Concept of FCD mapping model and semi-expert system. Japan Overseas Forestry Consultants Association. Rep. 72 pp.
  • Salih, A.A.M., Ganawa, El-T. and Elmah, A.A., 2017. Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery. The Egyptian Journal of Remote Sensing and Space Sciences, 20: 21–29.
  • Sepehr, A., Ekhtesasi, M.R. and Almodaresi, S.A., 2012. Development of desertification indicator system based on DPSIR (Take advantages of Fuzzy-TOPSIS). Geography and Environmental Planning Journal, 45 (1): 33-50. (In Persian)
  • Shammi, S.A. and Meng, Q., 2021. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Journal of Ecological Indicators, 121: 107124.
  • Soleimani Sardo, M., Tavili, A., Alipour, A. and Hashemi, S.M., 2017. Evaluation of desertification hazard severity in the Jaz-Murian region. RS & GIS for Natural Resources, 7 (4):31-44 (In Persian).
  • UNCCD, Z.N.L.D., 2012. United Nations convention to combat desertification.
  • Xiaolu, S. and Bo, C.H., 2011. Change detection using change vector analysis from landsat TM Images in Wuhan. Procedia Environmental Sciences, 11: 238 – 244.
  • Zhan, Q., Zhao, W., Yang, M. and Xiong, D., 2021. A long-term record (1995–2019) of the dynamics of land desertification in the middle reaches of Yarlung Zangbo River basin derived from Landsat data. Journal of Geography and Sustainability, 2: 12–21.
  • Zhang, D. and Deng, H., 2020. Historical human activities accelerated climate-driven desertification in China’s Mu Us Desert. Journal of Science of the Total Environment, 708: 134771.