Detection The Spatiotemporal Association between Climatic Factors and Vegetation Cover (NDVI) Incorporate MODIS and TRMM Product (Case study: CharmahalvaBakhtiary Province of Iran) Behzad Amraei

Abstract

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 Methods: 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.Results: 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.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.