Using The Normalized Difference Vegetation Index (NDVI) in Drought Assessing in The Eastern North Region in Syria

Climate change is the major global challenge facing water resources managers because of its impacts on many life fields beginning with agriculture activities to economic - social regions. In this study, drought in the eastern north of Syria have been investigated (Hasake, Rakka, DerAzzor, Bokmal, Kameshli) using a set of data containing precipitation data for period from 2000 to 2010, and MODIS time series images for the same period. This study assure that 2008/2009 described as drought period in the study area, and the NDVI maps ,which we have, give us an idea about the vegetation status and patterns in the study area. The study clearly show that NDVI and rainfall was found to be highly correlated in Rakka with P- Value= 0.003; and medium correlated in the other stations with P- Value > 0.05. Results of this study verify needing to use this index (NDVI), along with precipitation data, in drought monitoring in the eastern north region of Syria. So that,. It would help managers in making decisions to face drought in this area.

Monitoring the response of natural stands of Cedrus libani A. Richard in Syria to climatic variables by MODIS NDVI

This study was analyzed the temporal variation of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) of natural stands of cedrus libani in the northern humid part and eastern exposure of the Syrian coastal mountains (Slenfeh, Jawbat Burghal), and its correlation with climatic variables (temperature and precipitation) during the period of 2004-2014. We examined the interannual and seasonal variation in NDVI values of Cedrus stands, and accumulative effects of climatic variables (temperature and precipitation) on stands using simple linear regression and correlation (Pearson). The NDVI values of Cedrus libani stands showed significant increase in Slenfeh and Jawbat Burghal (0.006, 0.004 /year) respectively. We found that the annual mean NDVI was significantly correlated with annual mean precipitation in Jawbat Burghal (R = 0.689).The significant increase trend of seasonal mean NDVI was in Slenfeh summer and Jawbat Burghal winter (R = 0.638, R = 0.724) respectively. The results showed, there were accumulative effects of temperature on Cedrus libani in Slenfeh and Jawbat Burghal in autumn and winter, while the accumulative effects of precipitation in autumn and summer were noted.

Estimation of cotton productivity depending on early growth stage using image space in pilot area (Al-Kaltta village)

This research aimed to use space image at certain growth stage for predicting cotton yield in its direct physio-spectral relationship with productivity. Timely, at the beginning of August better spectral growth stage is coincidence with a maximum leaf area index of cotton plants at Al-Kaltta fields. Normalized difference vegetation index (NDVI) showed superiority on each of red and near infrared channels in relation with productivity. Exponential model was used to predict cotton productivity depending on NDVI values during stage that maximum LAI.

Preprocessing of Landsat Imageries Used to Mapping NDVI in North Lattakia Forests

The aim of the research was to clarify the pre-processing steps required for satellite images before starting to analyze and extract data from them using the ENVI program. Radiometric and topographic correction applied to the Landsat image 2017, and then we calculated the NDVI index for this image before and after applying pre-processing. The results showed a difference in the spectral values of the image before and after the radiometric correction, especially in near infrared band. The reflection values were recorded in the original image between (40-50) and (300-3500) in the corrected image. The difference in the reflection values after the topographical correction was also visible on the near- infrared and infrared bands, especially in the points where shadows of the terrain. Differences in the values of NDVI for 2017 were observed before and after the application of pre-processing on the image, especially in points of good and very good vegetation coverage with high values of the index. The study concluded that it is important to follow the minimum number of steps required for preprocessing steps in order to avoid unnecessary steps and recommend well tested, readily available, and adequately documented data approaches and data products.

Using Remote Sensing in Drought Monitoring Case Study: The Eastern Region in Syria

In this study, drought in the eastern region of Syria (Hasake, Rakka, DerAzzor, Bokmal & Kameshli) has been investigated using SPI, NDVI indices. We used a set of data containing precipitation data for period from 1975 to 2010 to calculate Standardized Precipitation Index SPI, and MODIS time series images in April for period from 2000 to 2010 to calculate the Normalized Difference Vegetation Index NDVI.