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تأثیر عامل های طبیعی و انسانی بر تغییر خطی و غیرخطی پوشش گیاهی با تصویرهای ماهواره یی در آبخیز خور- سفیدارک، استان البرز | ||
| پژوهش های آبخیزداری | ||
| مقاله 7، دوره 34، شماره 3 - شماره پیاپی 132، مهر 1400، صفحه 95-114 اصل مقاله (2.27 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2021.352457.1370 | ||
| نویسندگان | ||
| مجید کاظم زاده* 1؛ زهرا نوری1؛ اصغر بیات2؛ سلما ساعدی فرکوش3؛ علی اصغر الیاسی4؛ حسن علی پور5؛ علیرضا منصور فلاح6 | ||
| 1دکترای علوم و مهندسی آبخیز- آب، دانشکده منابع طبیعی، دانشگاه تهران | ||
| 2کارشناس ارشد مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تهران | ||
| 3کارشناس ارشد توسعه روستایی، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه | ||
| 4کارشناسارشد مهندسی محیط زیست- آب و فاضلاب، دانشکده منابع طبیعی دانشگاه آزاد اسلامی واحد بندرعباس | ||
| 5کارشناس ارشد مهندسی آبخیزداری دانشکده منابع طبیعی دانشگاه تهران | ||
| 6کارشناس مهندسی عمران- آب دانشکده فنی و مهندسی دانشگاه آزاد اسلامی واحد کرج | ||
| چکیده | ||
| هدف از این پژوهش بررسی روند تغییر خطی و غیرخطی پوشش گیاهی بر اثر عامل های اقلیمی (بارش، دما و خشکسالی) و انسانی (اقدامهای زیستی آبخیزداری) با تصویرهای ماهواره یی در بازه ی زمانی 20 سال (2000-2019) در آبخیز خور-سفیدارک استان البرز است. شاخص پوشش گیاهی، شاخص خشکسالی و الگوریتم پلیترند با استخراج دورههای زمانی ماهانه بهکاربرده شد. با کاربرد دادههای اقلیمی و اطلاعات اقدامهای آبخیزداری اجراشده، تأثیر عامل های طبیعی (دما، بارش و خشکسالی) و انسانی بر تغییر پوشش گیاهی ارزیابی شد. نتایج نشان داد که در 20 سال گذشته تغییر در 35 % از پوشش گیاهی آبخیز معنیدار (92/6 % افزایشی و 7/4 % کاهشی)، و در 65 % آبخیز نا معنیدار بود. نتیجهها نشان داد که 87/71 % از تغییر پوشش گیاهی منطقه از نوع خطی، و بهترتیب 6/62 % و 5/68 % از تغییر مانده غیرخطی درجهی دو و سه بود. بررسی تأثیر دما و بارش بر پوشش گیاهی نشان داد که بارش منطقه روند معنیداری در دو دهه نداشت، اما روند دما کاهشی معنیدار بود. ارزیابی تأثیر خشکسالی بر تغییر پوشش گیاهی نشان داد که بیشترین کاهش در پوشش گیاهی در سال 2008 اتفاق افتاد، که بر اثر خشکسالی شدید اقلیمی بود. نتیجه اینکه هر چند تغییر پوشش گیاهی در درازمدت بر اثر عامل های اقلیمی نبود، تغییر کوتاه مدت رویدادهای جدی از جمله خشکسالی، بهروشنی تغییر پوشش گیاهی منطقه را توضیح می دهد. از طرف دیگر، نتایج نشان داد که اقدامهای آبخیزداری باعث ایجاد تغییر با الگوی خطی، و تغییر و گسترش منطقههای مسکونی (عامل انسانی) باعث ایجاد الگوی غیرخطی در پوشش گیاهی شد. علت آن این است که اقدامهای زیستی آبخیزداری به شکل خطی و تدریجی باعث افزایش و تقویت پوشش گیاهی منطقه شد، اما تغییر و گسترش زمین منطقههای روستایی باعث کاهش غیرخطی (تغییر ناگهانی و نا تدریجی) پوشش گیاهی شد. | ||
| کلیدواژهها | ||
| اقدامهای آبخیزداری؛ پوشش گیاهی؛ خشک سالی؛ سنجش از دور؛ عامل های طبیعی و انسانی | ||
| عنوان مقاله [English] | ||
| An Evaluation of the Effect of Natural and Human Factors on the Linear and Non-linear Changes in Vegetation Using the Landsat Images of the Khor-Sefidarak Watershed, the Province of Alborz | ||
| نویسندگان [English] | ||
| Majid Kazemzadeh1؛ Zahra Noori1؛ Asghar Bayat2؛ Salma Saedi Farkoush3؛ Ali Asghar Elyasi4؛ Hasan Alipour5؛ Alireza Mansour Fallah6 | ||
| 1Ph.D. in Watershed Science and Engineering, Faculty of Natural Resources, University of Tehran | ||
| 2Graduate of Master of Science in Watershed Engineering, Faculty of Natural Resources, University of Tehran | ||
| 3Graduate of Master of Science in Rural Development from Faculty of Agriculture, Razi University of Kermanshah | ||
| 4Graduate of Master of Science in Environmental Engineering - Water and Wastewater, Faculty of Natural Resources, Islamic Azad University, Bandar Abbas Branch | ||
| 5Graduate of Master of Science in Watershed Engineering, Faculty of Natural Resources, University of Tehran | ||
| 6Graduate of Civil Engineering - Water, Faculty of Engineering, Islamic Azad University, Karaj Branch | ||
| چکیده [English] | ||
| The aim of current study was to study the trend of linear and non-linear changes in vegetation cover under the influences of climatic factors (rainfall, temperature and drought) and human activity (biological watershed management measures) using satellite images over a period of 20 years (2000-2019) on the Khor-Sefidarak Watershed, the Province of Alborz. Vegetation index (NDVI), drought index (SPI) and PolyTrend algorithm were used by extracting the monthly time series. Then, using climatic and implemented watershed management measures data for the basin, the impact of natural (temperature, precipitation and drought) and human factors on vegetation changes was assessed. The results indicated that 35% of the area had significant changes (92.6% increasing and 7.4% decreasing) in vegetation, and 65% of the area had insignificant changes during the last 20 years. The results also indicated that 87.71% of the vegetation changes in the region had experienced a linear type change and the remaining 6.62 and 5.68% haddemonstrated second and third degree nonlinear changes, respectively. Considering the effects of the two climatic variables on vegetation, precipitation did not show a significant trend over the two decades; however the temperature indicated a significant decreasing trend. Assessing the impact of drought on vegetation change, the largest decline in vegetative cover occurred in 2008, which was strongly influenced by severe climatic drought. Although the vegetation changes have not been affected by climatic factors in the long term, short-term changes in extreme events, including drought, strongly justify changes in the region's vegetation. On the other hand, the results indicated that the watershed management measures have caused changes with a linear pattern; however, the changes and expansion of the residential areas (as human factors) have caused a nonlinear pattern in vegetation. Thus the biological watershed management measures have increased and strengthened the vegetation of the region linearly; however, sudden and rapid changes in the rural areas have caused a non-linear decrease in vegetation. | ||
| کلیدواژهها [English] | ||
| Drought, natural and human factors, remote sensing, vegetation, watershed management measures | ||
| مراجع | ||
|
Alavipanah SK. 2003. Application of remote sensing in earth sciences (Soil Sciences). Tehran University Press. 478 p. (In Persian). Baldi G, Nosetto M, Aragón R, Aversa F, Paruelo J, Jobbágy E. 2008. Long-term satellite NDVI data sets: Evaluating their ability to detect ecosystem functional changes in South America. Sensors, 8 (9): 5397–5425. Barbosa HA, Kumar L, Paredes F, Elliott S, Ayuga JG. 2019. Assessment of Caatinga response to drought using Meteosat-SEVIRI normalized difference vegetation index (2008–2016). ISPRS Journal of Photogrammetry and Remote Sensing, 148 (2019): 235–252. Cai H, Yang X, Wang K, Xiao L. 2014. Is forest restoration in the southwest China Karst promoted mainly by climate change or human-induced factors? Remote Sensing, 6 (10): 9895–9910. Gang Y, Zengyun H, Xi Ch, Tashpolat T. 2016. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land, 8(3): 375–388. Geng L, Che T, Wang X, Wang H. 2019. Detecting spatiotemporal changes in vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017. Remote Sensing, 11(103): 1–19. Hajibigloo M, Rashidi M, Mohabbati A. 2017. Hydrological assessment of watershed management on flood characteristics (Case study: watershed upstream of the dam Vushmgir), 8(2): 67–82. (In Persian). Hashemi Dareh Badami S, Nouraeisefat I, Karimi S, Nazari S. 2015. Development trend analysis of urban heat island regarding land use/cover changes using time series of landsat images. Journal of RS and GIS for Natural Resources, 6(3): 15–28. (In Persian). Hoseini Tavasol M, Arzani H, Farajzadeh Asl M, Jafari M, Babaei Kafaei S, Kohandel A .2016. Monitoring of vegetation change in the growing season by using satellite imagery and its relationship with climatic factors (Case study: Alborz Province), 22(4): 615–624. (In Persian). Huang S, Xiaosong LI, Bingfang WU, Liang P. 2012. The distribution and drivers of land degradation in the three-north shelter forest region of China during 1982–2006. Acta Geographica Sinica, 67 (5): 589–598. (In Chinese). Hua L, Wang H, Sui H, Wardlow B, Hayes M, Wang J. 2019. Mapping the spatial-temporal dynamics of vegetation response lag to drought in a semi-arid region. Remote Sensing, 11 (1873): 1–22. Huang K, Zhang Y, Zhu J, Liu Y, Zu J, Zhang J. 2016. The influences of climate change and human activities on vegetation dynamics in the qinghai-tibet plateau. Remote Sensing, 8 (10): 850–876. Jamali S, Seaquist J, Eklundh L, Ardö J. 2014. Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote Sensing of Environment, 141 (0): 79–89. Jiang L, Jiapaer G, Bao A, Guo H, Ndayisaba F. 2017. Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of the Total Environment, 599 (0): 967–980. Jiang W, Yuan L, Wang W, Cao R, Zhang Y, Shen W. 2015. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51 (0): 117–126. Karpouzos D K, Kavalieratou S, Babajimopoulos C.2010. Nonparametric trend analysis of precipitation data in Pieria Region (Greece). European Water, 30 (30): 31–40. Kazemzadeh M, Malekian A. 2016. Spatial characteristics and temporal trends of meteorological and hydrological droughts in northwestern Iran. Natural Hazards, 80(1): 191–210. Kazemzadeh M, Malekian A, Moghaddamnia A, Khalighi Sigaroudi S. 2017. Shift changes and heterogeneity analysis of hydro-climatic variables (A case study: Aji Chai Watershed). Iranian Journal of Ecohydrology. 4(1): 163–175. (In Persian). Kendall MG.1975. Rank correlation measures. Charles Griffin. London. 202 p. Kim JY, Rastogi G, Do Y, Kim DK, Muduli PR, Samal RN, Pattnaik AK, Joo GJ. 2015. Trends in a satellite-derived vegetation index and environmental variables in a restored brackish lagoon. Global Ecology and Conservation, 4 (0): 614–624. Liu J, Kuang W, Zhang Z, Xu X, Qin Y, Ning J, Zhou W, Zhang S, Li R, Yan C, Wu S, Shi X, Jiang N, Yu D, Pan X, Chi W. 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J Geogr Sci, 24 (2): 195–210. Liu Zh, Liu Y, Li Y. 2018. Anthropogenic contributions dominate trends of vegetation cover change over the farming-pastoral ecotone of northern China. Ecological Indicators, 95 (0):370–378. Mann HB. 1945. Non-parametric tests against trend. Econometrica, 13 (0): 245–259. Mckee TB, Doesken NJ, Kleist J. 1993. The relationship of drought frequency and duration to time scale. 8Th Congress on Applied Climatology. United States. Mercier A, Betbeder J, Baudry J, Roux VL, Spicher F, Lacoux J, Roger D, Hubert-Moy L. 2020. Evaluation of sentinel-1 & 2 times series for predicting wheat and rapeseed phenological stages. ISPRS Journal of Photogrammetry and Remote Sensing, 163 (0): 231–256. Mirahsani M. 2017. Time series analysis to identify the trend of land degradation based on explaining effective biophysical characteristics with emphasis on spatial-temporal monitoring of drought and land cover / land use changes. Ph.D. Thesis, Gorgan University of Agricultural Sciences and Natural Resources. 200 p. (In Persian). Mirahsani M, Salman Mahiny A, Soffianian A, Mohamadi J, Modarres R, Jafari R, Pourmanafi S. 2019. Evaluation of trend in vegetation variations using time series images and mann-kendall test over Gavkhuni Basin. Journal of Environmental Studies, 45(1): 99–114. (In Persian). Morawitz DF, Blewett TM, Cohen A. et al. 2006. Using NDVI to assess vegetative land cover change in central Puget Sound. Environ Monit Assess, 114 (1): 85–106. Nateghi S, Nohegar A, Ehsani A, Bazrafshan O. 2017. Evaluating the vegetation changes upon vegetation index by using remote sensing, 24(4): 778–790. (In Persian). Ning J, Liu J, Kuang W, Xu X, Zhang S, Yan C, Li R, Wu S, Hu Y, Du G, Chi W, Pan T, Ning J. 2018. Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. J. Geogr. Sci, 28 (5): 547–562. Rokni K, Musa TA. 2019. Normalized difference vegetation change index: A technique for detecting vegetation changes using landsat imagery. Catena, 178 (0): 59–63. Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC. 1974. Monitoring the vernal dvancement of retrogradation of natural vegetation; Type III, Final Report; NASA/GSFC. Greenbelt, MD, USA. 200 p. Sun B, Li Z, Gao W, Zhang Y, Gao Z, Song Z, Tian X. 2019. Identification and assessment of the factors driving vegetation degradation/regeneration in drylands using synthetic high spatiotemporal remote sensing Data—A case study in Zhenglanqi, Inner MongoliaChina. Ecological Indicators, 107 (0): 1–16. Tang X, Cui Y, Li N, Fu Y, Liu X, Run Y, Li M, Zhao G, Dong J. 2020. Human activities enhance radiation forcing through surface albedo associated with vegetation in Beijing. Remote Sensing, 12(5): 1–17. Tong X, Brandt M, Yue Y, Horion S, Wang K, Keersmaecker WD, Tian F, Schurgers G, Xiao X, Luo Y. 2018. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustainab, 1 (1): 44–50. Tucker CJ, Townshend JR G, Goff TE. 1985. African land-over classification using satellite data. Science, 227: 369–375. Tucker CJ. 1979. Red and photographic infrared linear combanations for monitoring vegetation. Remote Sensing of Environment, 8: 127–150. United States Geological survey. 2019. Published satellite images (online) Available at: http://www.Earthexplorer.usgs.gov. Accessed: 15 May 2020.Wang J, Xie Y, Wang, X, Dong J, Bie, Q. 2019. Detecting patterns of vegetation gradual changes (2001–2017) in Shiyang River Basin, Based on a Novel Framework. Remote Sensing, 11(0): 1–24. Wang H, Liu G, Li Z, Ye X, Fu B, Lv Y. 2018. Impacts of drought and human activity on vegetation growth in the grain for green program region, China. Chinese Geographical Science, 28(3): 470–481. Wang J, Wang K, Zhang M, Zhang C. 2015. Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecol. Eng, 81: 451–461. Wang Z, Duanyang XU, Yang H, Ding X, Dajing LI, University BF. 2017. Impacts of climate change and human activities on vegetation dynamics in Inner Mongolia 1981–2010. Prog. Geog, 36 (8): 1025–2032. Wu J, Feng Y, Zhang X, Wurst S, Tietjen B, Tarolli P, Song C. 2017. Grazing exclusion by fencing non-linearly restored the degraded alpine grasslands on the Tibetan Plateau. Scientific Reports, 7(1): 1–9. Xie Z, Phinn SR, Game ET, Pannell DJ, Hobbs RJ, Briggs PR, McDonald-Madden E. 2019. Using landsat observations (1988–2017) and google earth engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation. Remote Sensing of Environment, 232 (0): 1–15. Zeng B, Yang TB. 2009. Natural vegetation responses to warming climates in Qaidam Basin 1982–2003. International Journal of Remote Sensing, 30 (21): 5685–5701. Zhang Y, Zhang C, Wang Z, Chen Y, Gang C, An R, Li J. 2016. Vegetation dynamics and its driving forces from climate change and human activities in the three-river source region, China from 1982 to 2012. Science of the Total Environment, 563 (564): 210–220. Zhang Zh, Coillie FV, De Clercq EM, Ou X, Wulf RD. 2013. Mountain vegetation change quantification using surface landscape metrics in Lancang watershed, China. Ecological Indicators, 31 (0):49–58. Zhao YS. 2003. Principles and methods of analysis of remote sensing applications. Science Press. Beijing, pp. 374–376. Zhao A, Zhang A, Liu J, Feng L, Zhao Y. 2019. Assessing the effects of drought and “Grain for Green” Program on vegetation dynamics in China's Loess Plateau from 2000 to 2014. Catena, 175 (0): 446–455. Zheng J, Fang X, Wu S. 2018. Recent progress of climate change research in physical geography studies from China. Prog. Geog, 37 (1): 16–27. | ||
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