تحلیل سنجش از دوری دینامیک فضایی پوشش گیاهی محصول سنجنده MODIS در ارتباط با شرایط اقلیمی (مورد: استان چهارمحال و بختیاری)
محورهای موضوعی :
اکوسیستم ها
بهزاد امرایی
1
(استادیارگروه زیست شناسی، دانشگاه پیام نور، تهران، ایران. *(مسوول مکاتبات))
منصور حلیمی
2
(دکتری آب و هواشناسی، دانشگاه تربیت مدرس، تهران، ایران.)
تاریخ دریافت : 1398/04/08
تاریخ پذیرش : 1399/06/16
تاریخ انتشار : 1402/02/01
کلید واژه:
تحلیل همبستگی فضایی,
شاخص سبزینگی,
عناصر اقلیمی,
چکیده مقاله :
زمینه و هدف: در هر منطقه ای کلونی های گیاهی حاصل تعامل بلندمدت بین پوشش گیاهی و شرایط اقلیم محلی است. تغییرات اقلیمی می تواند به صورت بارزی در تغییرات پوشش گیاهی آن منطقه منعکس شود. هدف اساسی این تحقیق تحلیل روابط زمانی مکانی بین تعیین گرهای آب و هوایی و شاخص های پوشش گیاهی سنجنده MODIS در استان چهار محال و بختیاری است.روش بررسی: در این راستا محصول پوشش گیاهی سنجنده MODIS طی 10 سال (2008-2018) به صورت ماهانه، اخذ گردید. دو شاخص اقلیمی دمای لایه رویه ای خاک (LST) MODIS، و بارش های ایستگاه های باران سنجی و سینوپتیک، به عنوان تعیین گرهای عمده اقلیمی مورد استفاده قرار گرفت. از آنالیز همبستگی فضایی پیکسل به پیکسل Pearson در سطح اطمینان 95/0 (P_value =0.05)، برای استخراج همبستگی فضایی و پوشش گیاهی MODSI با تعیین گرهای اقلیمی استفاده شده است.یافته ها: نتایج گویای آن بود که در این منطقه به دلیل نبود تنش رطوبتی شدید، پوشش گیاهی، دینامیک فضایی و درون سالی پوشش گیاهی منطقه توسط دمای لایه رویه ای خاک کنترل می شود. به طوری که در مقیاس ماهانه، پوشش گیاهی MODSI به صورت همزمان، با دمای لایه روی خاک به ویژه در ماه های اواخر زمستان و اول بهار همبستگی داشت. در ماه های فصل تابستان تنش حرارتی شدید باعث افت سطح سبزینگی و به تبع آن افت NDVI سنجنده MODIS شده است و ارتباط دمای خاک و پوشش گیاهی در همه جای استان تضعیف شده است.بحث و نتیجه گیری: در استان کهکیلویه و بویراحمد، عامل دمای رویه ای خاک با کنترل فازهای فنولوژیک پوشش گیاهی منطقه، ارتباط مستقیم و معنی داری دارد و محرک اصلی دینامیک زمانی و مکانی پوشش گیاهی می باشد که محصول سنجش از دوری سنجنده MODIS این وابستگی را به وضوح نشان داد.
چکیده انگلیسی:
Background and Objective: the climate factors are main determinant of vegetation spatiotemporal dynamics. A visual comparison of climate and vegetation on a global scale immediately reveals a strong association between climate and vegetation. The main object of this study is to reveal the spatiotemporal association between climatic factors and Modis derived NDVI in Charmahal & Bakhtiary province of Iran.Material and Methodology: In this study, we use MOD13A3 of MODIS product as NDVI layer for study area. MOD11A2 as land surface temperature and mean monthly accumulative rainfall of synoptic station for study area during 2008 to 2018. We used the correlation analysis in 0.95 confident level (P_value =0.5) to reveal the spatiotemporal association between the NDVI and climatic factors.Findings: The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months, the pixels which have the high value of NDVI are spatially associated with the pixels which have highest value of LST (6 to 12C°). In winter the spatial correlation between NDVI and LST is so high which is statistical significant in 0.95 confident level. In transient months such as May, October and November, the spatial correlation between NDVI and LST is falling to 0.30 to 0.35, which is not statistical, significant in 0.95 confident level. Finally, in summer season or warm months including Jun to September, we found the minimum spatial association between the NDVI and LST.Disscosion & Conclusion: we found that the maximum correlation between NDVI and LST simultaneously appears and no lag time has been observed. The spatial correlation of NDVI and monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1-month lag time but in other months we do not find any significant correlation between NDVI and rainfall.
منابع و مأخذ:
Barbosa, H. A., Huete, A. R., Baethgen, W. E., 2006. A 20-year study of NDVI variability over the Northeast Region of Brazil. Journal of Arid Environments,Vol. 67, pp 288–307.
Chen, X. Q., Xu, C. X., & Tan, Z. J., 2001. An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China. International Journal of Biometeorology, Vol 45,pp170–177.
Goward, S. N., and Prince, S. D., 1995. Transient Effects of Climate on Vegetation Dynamics: Satellite Observations. Journal of Biogeography, Vol 22, pp. 549–563.
Maselli, F., & M. Chiesi., 2006. Integration of Multi-source NDVI Data for the Estimation of Mediterranean Forest Productivity. International Journal of Remote Sensing,Vol 27, pp. 55-72.
Farajzadeh, M.,Fathnia, A.,Alijani, B.,Zeaiean., P., 2011.Assessment of the Effect of Climatic Factors on the Growth of Dense Pastures of Iran, Using AVHRR Images. Physical Geographical Research,Vol 43(75), pp1-15. (In Persian)
Ehsani, A., Arzani, H., Farahpour, M., Ahmadi, H., Jafari, M., Jalili, A., Mirdavoudi, H. R., Abasi H. R., Azimi, M. S., 2007. The effect of climatic conditions on range forage production in steppe rang lands, Akhtarabad of Saveh. Iranian journal of Range and Desert Research, Vol 14 (2), pp 249-261. (In Persian)
Potter, C.S., & Brooks, V., 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing, Vol 15, pp 2921–2948.
Schultz, P. A., and Halpert, M. S., 1995. Global Analysis of the Relationships Among a Vegetation Index, Precipitation, and Land Surface Temperature. International Journal of Remote Sensing, Vol 16, pp. 2755- 2777.
Myneni, R. B., Tucker, C. J., Asrar, G., Keeling, C. D., 1998. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991.Journal of Geophysical Research, Vol 103, pp 6145–6160.
Tucker, C. J., Slyback, D. A., Pinzon, J. E., Los, S. O., Myneni, R. B., & Taylor, M. G., 2001.Higher northern latitude NDVI and growing season trends from 1982–1999. International Journal of Biometeorology, Vol 45, pp 184–190
Zhou, L. M., Tucker, C.J., Kaufmann, R.K., Slayback, D., Shabanov, N.V., &Myneni, R.B., 2001.Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research, Vol 106 (17), pp 20069–20083.
Prasad, A. K., Sarkar, S., Singh, R. P., &Kafatos, M., 2007. Inter-annual variability of vegetation covers and rainfall over India. Advances in Space Research,Vol 39, pp79–87.
Anyamba, A., and Tucker, C. J., 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments,Vol 63,pp 596-614.
Yin G, Hu Z, Chen X, Tiyip T.,2016. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land. Vol 8(3), pp 375-388.
Chen Z, Jiang WG, Tang ZH, Jia K.,2016. Spatial-Temporal Pattern of Vegetation Index Change and the Relationship to Land Surface Temperature in Zoige. ISPRSInternational Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences,pp 849-852.
Khosravi H., Haydari E., Shekoohizadegan S., Zareie , 2017. Assessment the Effect of Drought on Vegetation in Desert Area using Landsat Data. The Egyptian Journal of Remote Sensing and Space Science, Vol 20, pp S3-S12. (In Persian)
Jiang, Y., Wang, R., Peng, Q., Wu, X., Ning, H., Li, C., 2018. The relationship between drought activity and vegetation cover in Northwest China from 1982 to Natural Hazards, Vol 92(1), pp 145-163
Hadian, F., Jafari, R., Bashari, H., Soltani, S., 2014. Monitoring the Effects of Precipitation on Vegetation Cover Changes Using Remote Sensing Techniques in 12 Years Period (Case study: Semirom Isfahan). journal of range and watershade management,Vol 66(4), pp 477-646. (In Persian)
Jabbari, S., Khajeddin, S J., Jafari, R., Soltani, S.,2015. Investigation of the Pasture Vegetation Changes Using Satellite Data in Semirom, Isfahan. ijae. Vol 3 (10), pp 27-39.
Mohammadyari, F., Pourkhabaz, HR., Tavakoli, M., Aghdar, Hossein., 2015. MappingVegetation and monitoring its Changes using Remote Sensing and GIS Techniques (Case study: Behbahancity), Scientfc - Research Quarterly of Geographical Data (SEPEHR), Vol 23( 92), PP 23-34. (In Persian)
Kazeminia, A., 2018. Application of Remote Sensing and GIS in the Investigating Vegetation Coverage GEJ. Vol 9 (1) , pp 75-85. (In Persian)
Kabiri, K., 2004. Ghe impact of drought on Irans vegetation cover in 90 decades using NOAA Imaginary, M.Sc dissertation, Khaje Nasiradin Tosiunoversity. (In Persian)
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Barbosa, H. A., Huete, A. R., Baethgen, W. E., 2006. A 20-year study of NDVI variability over the Northeast Region of Brazil. Journal of Arid Environments,Vol. 67, pp 288–307.
Chen, X. Q., Xu, C. X., & Tan, Z. J., 2001. An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China. International Journal of Biometeorology, Vol 45,pp170–177.
Goward, S. N., and Prince, S. D., 1995. Transient Effects of Climate on Vegetation Dynamics: Satellite Observations. Journal of Biogeography, Vol 22, pp. 549–563.
Maselli, F., & M. Chiesi., 2006. Integration of Multi-source NDVI Data for the Estimation of Mediterranean Forest Productivity. International Journal of Remote Sensing,Vol 27, pp. 55-72.
Farajzadeh, M.,Fathnia, A.,Alijani, B.,Zeaiean., P., 2011.Assessment of the Effect of Climatic Factors on the Growth of Dense Pastures of Iran, Using AVHRR Images. Physical Geographical Research,Vol 43(75), pp1-15. (In Persian)
Ehsani, A., Arzani, H., Farahpour, M., Ahmadi, H., Jafari, M., Jalili, A., Mirdavoudi, H. R., Abasi H. R., Azimi, M. S., 2007. The effect of climatic conditions on range forage production in steppe rang lands, Akhtarabad of Saveh. Iranian journal of Range and Desert Research, Vol 14 (2), pp 249-261. (In Persian)
Potter, C.S., & Brooks, V., 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing, Vol 15, pp 2921–2948.
Schultz, P. A., and Halpert, M. S., 1995. Global Analysis of the Relationships Among a Vegetation Index, Precipitation, and Land Surface Temperature. International Journal of Remote Sensing, Vol 16, pp. 2755- 2777.
Myneni, R. B., Tucker, C. J., Asrar, G., Keeling, C. D., 1998. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991.Journal of Geophysical Research, Vol 103, pp 6145–6160.
Tucker, C. J., Slyback, D. A., Pinzon, J. E., Los, S. O., Myneni, R. B., & Taylor, M. G., 2001.Higher northern latitude NDVI and growing season trends from 1982–1999. International Journal of Biometeorology, Vol 45, pp 184–190
Zhou, L. M., Tucker, C.J., Kaufmann, R.K., Slayback, D., Shabanov, N.V., &Myneni, R.B., 2001.Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research, Vol 106 (17), pp 20069–20083.
Prasad, A. K., Sarkar, S., Singh, R. P., &Kafatos, M., 2007. Inter-annual variability of vegetation covers and rainfall over India. Advances in Space Research,Vol 39, pp79–87.
Anyamba, A., and Tucker, C. J., 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments,Vol 63,pp 596-614.
Yin G, Hu Z, Chen X, Tiyip T.,2016. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land. Vol 8(3), pp 375-388.
Chen Z, Jiang WG, Tang ZH, Jia K.,2016. Spatial-Temporal Pattern of Vegetation Index Change and the Relationship to Land Surface Temperature in Zoige. ISPRSInternational Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences,pp 849-852.
Khosravi H., Haydari E., Shekoohizadegan S., Zareie , 2017. Assessment the Effect of Drought on Vegetation in Desert Area using Landsat Data. The Egyptian Journal of Remote Sensing and Space Science, Vol 20, pp S3-S12. (In Persian)
Jiang, Y., Wang, R., Peng, Q., Wu, X., Ning, H., Li, C., 2018. The relationship between drought activity and vegetation cover in Northwest China from 1982 to Natural Hazards, Vol 92(1), pp 145-163
Hadian, F., Jafari, R., Bashari, H., Soltani, S., 2014. Monitoring the Effects of Precipitation on Vegetation Cover Changes Using Remote Sensing Techniques in 12 Years Period (Case study: Semirom Isfahan). journal of range and watershade management,Vol 66(4), pp 477-646. (In Persian)
Jabbari, S., Khajeddin, S J., Jafari, R., Soltani, S.,2015. Investigation of the Pasture Vegetation Changes Using Satellite Data in Semirom, Isfahan. ijae. Vol 3 (10), pp 27-39.
Mohammadyari, F., Pourkhabaz, HR., Tavakoli, M., Aghdar, Hossein., 2015. MappingVegetation and monitoring its Changes using Remote Sensing and GIS Techniques (Case study: Behbahancity), Scientfc - Research Quarterly of Geographical Data (SEPEHR), Vol 23( 92), PP 23-34. (In Persian)
Kazeminia, A., 2018. Application of Remote Sensing and GIS in the Investigating Vegetation Coverage GEJ. Vol 9 (1) , pp 75-85. (In Persian)
Kabiri, K., 2004. Ghe impact of drought on Irans vegetation cover in 90 decades using NOAA Imaginary, M.Sc dissertation, Khaje Nasiradin Tosiunoversity. (In Persian)
