Mohammad Hossein Nasserzadeh, Zahra Hejazizadeh, Zahra Gholampour, Bohloul Alijani,
Volume 20, Issue 57 (6-2020)
Abstract
The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental. The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1 of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). 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 spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among 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 among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.
Hadi Zare Khormizie, Hamid Reza Ghafarian Malamiri,
Volume 23, Issue 69 (7-2023)
Abstract
Knowledge of rangeland vegetation characteristics as well as factors affecting it in environmental planning, land management and sustainable development is very important. However, regional and up-to-date maps of pasture vegetation cover are not always available. In this study, in order to plot the vegetation cover percentage of the rangelands and monitor its changes in drought and wet periods, NDVI products of MODIS sensor during the years from 2000 to 2017 with a spatial resolution of 250 m and a 16-day time resolution, and The SPI drought index were used. The study area is the part of the rangelands located in the Southern province of Yazd. In 2013, in order to provide ground truth data, a field work was done to take the sampling rate of vegetation from the rangeland level in the study area. According to the results, the NDVI index has a good ability to map vegetation cover, so the coefficient of determination (R2) between this index and the sample points was 0.71. Based on the results, the average vegetation cover of the studied area was 11.3% during the years 2000 to 2017. The highest and lowest amount of vegetation cover in the study area was in 2000 and 2002, with moderate mild conditions and very severe drought, respectively (14.6% and 9.2% respectively). The most important factors influencing the vegetation cover in the study area are rainfall and drought periods, so that the coefficient of determination (R2) between the SPI drought index and the average vegetation percentage was 0.85. In general, based on the results there is a high potential for assessing and monitoring rangeland vegetation changes using satellite data and remote sensing technique.
