بررسی تغییرات پوشش گیاهی در فصول مختلف رویش با استفاده از تصاویر ماهواره‌ای و ارتباط آن با تغییرات دما (منطقه مورد مطالعه: شمال شهرستان داراب)
محورهای موضوعی : مدیریت مرتعمرضیه مکرم 1 , ملیحه مزین 2 , محمد فرجی 3 , کتایون موسوی 4
1 - دانشگاه شیراز
2 - مرتع و آبخیزداری، دانشگاه خاتم الانبیا، خوزستان، ایران
3 - گروه مرتع و آبخیزداری، دانشگاه خاتم الانبیا، خوزستان، ایران
4 - گروه مرتع و آبخیزداری، دانشگاه خاتم الانبیا، خوزستان، ایران
کلید واژه: همبستگی, دما, رگرسیون, شهرستان داراب, شاخص پوشش گیاهی,
چکیده مقاله :
این مطالعه با هدف بررسی تغییرات تاج پوشش گیاهی با استفاده از تصاویر ماهواره‌ای و ارتباط آن با شاخص‌های پوشش گیاهی NDVI)، VIN، SS و RVI) و اطلاعات اقلیمی در مکان‌های مختلف مرتعی در شمال شهرستان داراب واقع در استان فارس انجام شد. برای این منظور از تصاویر ماهواره لندست 8 ETM + (2015) و ASTER (2005) استفاده شد. ابتدا جهت پیش پردازش، تصحیحات هندسی و اتمسفری بر روی تصاویر اعمال گردید. سپس با توجه به شاخص‌های پوشش گیاهی استخراج شده از تصاویر لندست، میزان همبستگی پوشش گیاهی با شاخص‌های گیاهی بررسی گردید. برای این منظور از 39 نقطه از منطقه به طور تصادفی نمونه برداری شد. در این مطالعه با استفاده از تکنیک سنجش از دور و تصاویر ماهواره‌ای وضعیت پوشش گیاهی و میزان دما در منطقه بررسی شد. به منظور بررسی پوشش گیاهی منطقه مورد مطالعه از شاخص‌های پوشش گیاهی (NDVI، RVI، SS و VIN) استفاده شد. همچنین به منظور بررسی ارتباط بین شاخص‌های پوشش گیاهی و دما از آنالیز رگرسیون استفاده شد. نتایج حاصل از همبستگی و رگرسیون خطی نشان داد که در اغلب موارد (بیش از 90 درصد) ارتباط معناداری بین شاخص‌های پوشش گیاهی و میزان دما در منطقه مورد مطالعه وجود دارد.
This research is studying the herbal covering crown change using climate information, satellite pictures, morphometric characteristics and its relation with herbal covering index and drought index is done in different pasture places in North part of Darab in Fars province. For this purpose, Landsat satellite pictures in 3 time periods of 2005 and 2015 was chosen and processing. It used ETM+8(2015) and aster (2005) for this goal. At first for pre-processing, the mathematical and Atmospheric scanning from pictures was taken and then with regarding the type of sampling from Landsat ETM+8(2015) pictures was used for surveying the modality scale of herbal covering with herbal index. For this purpose sampling was done from 39 points of area. In this research, the temperature measure and herbal covering was done with satellite pictures and for measurement technique. For surveying herbal coverage index and climate parameters is using climate parameters from regression Analysis. The result of correlation and linear regression relation was shown that there is meaningful relation between NDVI index and temperature measurement in this area, so that with increasing the temperature amount of herbal covering index is increasing. The results were shown that there is a meaningful relation between NDVI index and temperature in study area.
References:
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9. Yin G, Hu Z, Chen X, Tiyip T. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land. 2016; 8(3), 375-388.
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14. Hosseini SZ, Kappas M, Propastin P. Estimating Relationship between Vegetation Dynamic and Precipitation in Central Iran. Toledo. Spain. 2011.
15. Fang J, Piao S, Tang Z, Peng C, Ji W. Interannual Variability in Net Primary Production and Precipitation. Science. 2001; 293(5536): 1723a-1724a.
16. Wellens J. Monitoring and modeling rangeland vegetation in Tunisia using satellite and meteorological data. PhD thesis, University of Reading. 1993.
17. Crlppen RE, Blom RG. Unveiling the lithology of vegetated terrains in remotely sensed imagery. Photogrammetric Engineering & Remote Sensing. 2001; 67(8): 935-943.
18. Wu C, Niu Z, Tang Q, Huang W. Estimating chlorophyll content from hyper spectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology. 2008; 148: 1230-1241.
19. Buyantuyev A, Wu J, Gries C. Estimating vegetation cover in an urban environment based on Landsat ETM+ imagery: A case study in Phoenix. USA. International Journal of Remote Sensing. 2007; 28(2):269-291.
20. Song X. Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data. International Journal of Remote Sensing. 2004; 25(16): 3105-3111.
_||_References:
1. Calera A, Martinez C, and Melia J. A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. International Journal of Remote Sensing. 2001; 22(17): 3357-3362.
2. Cohen WB, Maiersperger TK, Gower ST, Turner DP. An improved strategy for regression of biophysical variables and Land sat ETM+ Data. Remote Sensing of Environment. 2003;84:561-571.
3. Gurgel HC, Ferreira NJ. Annual and Interannual Variability of NDVI in Brazil and its Connections with Climate. International Journal of Remote Sensing. 2003; 24(18): 3595–3609.
4. Liang EY, Shao XM, He JC. Relationships between tree growth and NDVI of grassland in the semiarid grassland of north China. International Journal of Remote Sensing. 2005; 26(13): 2901–2908.
5. Hosseini SZ, Kappas M, Propastin P. Estimating Relationship between Vegetation Dynamic and Precipitation in Central Iran. Toledo. Spain. 2011.
6. Hadian F, Hosseini SZ, Seyyed Hassani M. Monitoring vegetation changes in Kermanshah, using NOVAA and AVHRR satellite imagery and rainfall data. 1393; No (1): 46-62. [Persian].
7. Jabbari S, Khajeh Aldin SJ, Jafari R, Soltani S. Investigating the percentage of changes in vegetation cover in pastures in Isfahan, Semirom, using satellite imagery. Journal of Applied Ecology. 1393; No (10): 27-80. [Persian].
8. Mansuri S, Sepehri A, Farrokhzadeh B. Evaluating drought impact on vegetation in pastures in Golestan, using MODIS satellite imagery. Faculty of Rangeland, Watershed Management, Fisheries and Environment. MA thesis. 1394. [Persian].
9. Yin G, Hu Z, Chen X, Tiyip T. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land. 2016; 8(3), 375-388.
10. Chen Z, Jiang WG, Tang ZH, Jia K. Spatial-Temporal Pattern of Vegetation Index Change and the Relationship to Land Surface Temperature in Zoige. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; 849-852.
11. Zhu Z, Piao S, Myneni RB, Huang M, Zeng Z, Canadell JG, Cao C. Greening of the Earth and its drivers. Nature climate change. 2016; 6(8), 791-795.
12. Bostani A, Salari Sardari FA, Adeli J. Investigating climate instability effects on water supplies. (Case study: Darab city). National conference on water crisis management. Islamic Azad University of Marvdasht. 1388. [Persian].
13. Bostani A, Salari Sardari FA, Adeli J. Investigating climate instability effects on water supplies. (Case study: Darab city). National conference on water crisis management. Islamic Azad University of Marvdasht. 1388. [Persian].
14. Hosseini SZ, Kappas M, Propastin P. Estimating Relationship between Vegetation Dynamic and Precipitation in Central Iran. Toledo. Spain. 2011.
15. Fang J, Piao S, Tang Z, Peng C, Ji W. Interannual Variability in Net Primary Production and Precipitation. Science. 2001; 293(5536): 1723a-1724a.
16. Wellens J. Monitoring and modeling rangeland vegetation in Tunisia using satellite and meteorological data. PhD thesis, University of Reading. 1993.
17. Crlppen RE, Blom RG. Unveiling the lithology of vegetated terrains in remotely sensed imagery. Photogrammetric Engineering & Remote Sensing. 2001; 67(8): 935-943.
18. Wu C, Niu Z, Tang Q, Huang W. Estimating chlorophyll content from hyper spectral vegetation indices: modeling and validation. Agricultural and Forest Meteorology. 2008; 148: 1230-1241.
19. Buyantuyev A, Wu J, Gries C. Estimating vegetation cover in an urban environment based on Landsat ETM+ imagery: A case study in Phoenix. USA. International Journal of Remote Sensing. 2007; 28(2):269-291.
20. Song X. Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data. International Journal of Remote Sensing. 2004; 25(16): 3105-3111.