JOURNAL OF INTERNATIONAL ACADEMIC RESEARCH FOR MULTIDISCIPLINARY Impact Factor 2.417, ISSN: , Volume 3, Issue 11, December 2015

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1 DROUGHT RISK ASSESSMENT USING REMOTE SENSING DATA CASE STUDY: THE EASTERN NORTH REGION IN SYRIA DR. AYMEN A. ALRUBAYE* ANG. OLA MERHEG** *Researcher in Marine Science Center University of Basra-Basra-Iraq **Assist Lecturer in Remote sensing office in Lattakia-Syria ABSTRACT In this study, drought as a result of global climate change in the eastern north of Syria has been investigated using a set of data containing precipitation data for period from 1975 to 2010, and MODIS time series images for period from 2000 to MODIS data have been used to calculate NDVI (Normalized Difference Vegetation Index). The NDVI maps give us an idea about the vegetation status, and clearly show that 2008 described as drought period in the study area. According to SPI values, there was serve drought in 2008 over the study area, and (2003, 2004, 2005) were wet years. The NDVI and SPI was found to be positive linear correlated in all stations. Risk map obtained by SPI indicates the areas facing drought. Results of this study verify needing to use this tools (NDVI), along with their correlation with SPI to identify areas with problem to formulate practical management decisions. KEYWORDS: monitoring, Remote Sensing, NDVI and SPI correlation. INTRODUCTION Although drought is a complex phenomenon, it has been defined specifically by the remote sensing community as a period of abnormally dry weather, which results in a change in vegetation cover (Heim, 2002; Tucker & Choudhury, 1987). is a recurrent climate process occurs with uneven temporal and spatial characteristics over a broad area and over an extended period of time. Therefore, detecting drought onsets and ends and assessing its severity using satellite-derived information are becoming popular in disaster, desertification, and climate change studies. In the last decades, observations show that the frequency and intensity of droughts have increased in some parts of the world (Hulme & Kelly, 1993; McCarthy et al., 2001). Iraq, Syria, Turkey and Iran, have been dealing with decreased rainfall affected negatively the agricultural sector, livelihood system, employment and water allowable quantity and quality (UNDP, 2010). may be treated as a meteorological, hydrological, or agricultural phenomenon. In each one the variable representing water availability are different. Meteorological drought is 41

2 a situation of rainfall shortage from normal precipitation over an area. Agricultural drought occurs when soil moisture and rainfall are inadequate during the growing season. Hydrological drought represents the long-term meteorological drought that cause a decline in reservoirs, lakes, streams, rivers, and groundwater level (Rathore, 2004). is characterized as a multi-dimensional phenomenon (severity, duration, magnitude). Attention has been paid so far to simplify these dimensions to reach a practical way to assess the severity of drought. The mitigation of drought impact requires rapid and continuous real time data. Remote sensing technology represents an excellent tool to collect data in digital form rapidly and repetitively at various levels (global and regional levels). The space technology has outstanding possibilities to provide baseline data of natural resources, soil degradation, climate change, and other important area of concern. In recent years, the development in space technology to address drought issues (drought detection, monitoring, and assessment) have been dealt with the current, before, during, and after-situation of drought event. According to Kogan (1990) drought can be detected 4-6 weeks earlier than before, and its impact can be diagnosed far in advance of the most critical stage of plant growth (harvest stage). Vegetation is the first feature can be affected by drought; as a result remote sensing indices have been developed for the quantification of drought based on brightness values of the land cover types. Many of vegetation indices are introduced using ratios of visible, nearinfrared, and mid-infrared portions of the electromagnetic spectrum (Tucker, 1979; Goward, et al., 1991 and Yang, et al., 1998). The Normalized Difference Vegetation Index (NDVI) is suggested as an indicator of vegetation health and density by Tucker (1979).Since climate is a key factor affecting vegetation conditions, NDVI has been widely used at regional and global scales to identify weather impacts on crop growth conditions and yields (Li, et al., 2004; Vicente-Serrano, et al., 2006 and Jain, et al., 2009).Based on the positive and significant correlation between NDVI and SPI, several studies concluded that the NDVI was the most common form of vegetation index can be used effectively in drought early warning system (Anyamba and Tucker, 2005; Martiny, et al., 2006; Murthy et al., 2009 and Quiring and Ganesh, 2010). Medium resolution sensors such as MODIS provide daily coverage of the earth, and so weather events are much less of an obstacle. MODIS have been extensive used in drought studies, as it monitors earth surface continuously, freely accessible and furthermore it's broadly recognized around the world (Persendt, 2009). 42

3 In recent years, Geographic Information Science (GIS) and Remote Sensing (RS) have played a key role in studying different types of hazards either natural or man-made (Chopra, 2006). This study stresses upon the use of RS in the field of drought risk evaluation. In the present work an effort has been made to derive drought risk areas facing agricultural as well as meteorological drought by use of temporal images from MODIS based Normalized Difference Vegetation Index (NDVI) ( ) and meteorological based Standardized Precipitation Index (SPI). Correlation and regression analysis was performed between NDVI and SPI. SPI values were interpolated to get the spatial pattern of meteorological based drought. Resultant risk map obtained by integrating agriculture and meteorological drought risk map indicates the areas facing a combined hazard. Problem statement is one of the major environmental disasters, which have been occurring in almost all climate zones and damage to environment and economies of several countries has been extensive and death toll of livestock unprecedented. damages are more pronounced in areas where there is a direct threat to livelihoods. The eastern north region of Syria with a population of 3.9 million is an arid and semi-arid climatic conditions, characterized by erratic rainfall and successive drought years together with high rate of land use changing and herb declining has adversely affected in vegetation conditions thereby increasing drought risk. Evaluation of probable risk arising out of drought in the region would help in developing better management plans for mitigating drought impacts. i. Data and Methods 1- Study Area Hasake, DerAzzor and Rakka are located on longitude 38 to 43 and from latitude 34 to 38. The region shares its borders with Iraq in the east, Turkey in the north, Aleppo in the west, and Homs in the south (figure 1). The governorate's area is almost Km 2 (41% of Syria). Agriculture is a key component of the economy, particularly cereal production. 43

4 Figure1. study area The climate of study area is classified as semi-arid, arid and very arid climate. Most of rain falls in winter and spring (October through May). There is no rainfall during summer (the period from June to September). The climatic data for period from 2000 to 2010 of these governorates are taken from 5 meteorological stations: Hasake, Kameshly, DerAzzor, Bokmal and Rakka (table 1). Station Elevation Category Precipitation E(X) N(Y) Kameshli Simi- arid Hasake Arid Rakka Arid DerAzzor Very arid Bokmal Very arid Table 1. Climatic Station in Study area with their characteristics. 2- indices indices have been developed as a means to measure drought. A drought index assimilates thousands of data on rainfall, snow pack and other water-supply indicators into a comprehensible picture. There are several indices that measure how much precipitation for a given period of time has deviated from historically established norms. One of the widely used drought indices is Standardized Precipitation Index SPI. indicators assimilate information on rainfall, stored soil moisture or water supply but do not express much local spatial detail. Also, drought indices calculated at one location is only valid for single location. Thus, a major drawback of climate based drought indicators is their lack of spatial detail as well as they are dependent on data collected at weather stations which sometimes are sparsely distributed affecting the reliability of drought indices (Brown et al., 2002). Satellite derived drought indicators calculated from satellite- derived 44

5 surface parameters have been widely used to study droughts such as Normalized Difference Vegetation Index (NDVI) Standardized Precipitation Index (SPI) Tom Mckee, Nolan Doesken and John Kleist of Colorado Climate Centre formulated the SPI in The purpose is to assign a single numeric value to the precipitation that can be compared across regions with markedly different climates. Technically, the SPI is the number of standard deviations that the observed value would deviate from the long-term mean, for a normally distributed random variable. Since precipitation is not normally distributed, a transformation is first applied so that the transformed precipitation values follow a normal distribution (Guttman, 1998, 1999). The SPI was designed to quantify the precipitation deficit for multiple time scales. These time scales reflect the impact of drought on the availability of different water resources (Richard & Heim, 2002). Soil moisture conditions respond to precipitation anomalies on a relatively short scale while groundwater, stream flow, and reservoir storage reflect the longerterm precipitation anomalies. Thus, McKee et al. (1993) originally calculated the SPI for 3, 6, 12, 24 and 48 month time scales. A drought event occurs any time the SPI is continuously negative and reaches intensity of -1.0 or less. The event ends when the SPI becomes positive. Each drought event, therefore, has a duration defined by its beginning and end, and intensity for each month that the event continues. The positive sum of the SPI for all the months within a drought event can be termed the drought's "magnitude" (table 2). Table 2. Standardized Precipitation Index. (Source: 45

6 SPI used to monitor the 1996 drought in the United States of America (Hayes et al., 1999), in Turkey (Komuscu, 1999), Argentina (Seiler et al., 2002), Brazil (Wu et al., 2006), Spain (Vincente-Serrano et al., 2004), Korea (Byun & Kim, 2010) for real time monitoring or retrospective analysis of droughts. 1-month SPI reflects short-term conditions and its application can be related closely to soil moisture; the 3-month SPI provides a seasonal estimation of precipitation; 6- and 9- month SPI indicates medium term trends in precipitation patterns (Ji & Peters, 2003), therefore 6- month was calculated for the 5 stations using monthly rainfall data for the period of only for April. The threshold for indicating severity of meteorological drought has been adopted from U.S. Mitigation Centre ( The category column in drought severity classification table (table 2) has been modified to suit the reclassification of the SPI maps (table 3). Initially SPI values had been interpolated using Ordinary Kriging from ArcGIS 9.3. The interpolated maps are thus been reclassified into different drought severity classes. Interpolated maps of April month were chosen to be reclassified according to table 3. Tow model years for drought (2008) and wet year (2004) has been chosen to present the differences between drought and wet years. SPI and above -0.8 to to to and less Description No drought Abnormally dry Moderately dry Severely dry Extremely dry Table 3. Meteorological drought classes based on SPI. (Source: Chopra, 2006). 2-2-Normalized Difference Vegetation Index (NDVI) Tucker first suggested NDVI in 1979 as an index of vegetation health and density (Tucker, 1979). NDVI is defined as: Where, NIR, RED are the reflectance in the near infrared and red bands. NDVI is a good indicator of green biomass, leaf area index, and patterns of production (Thenkabail et al., 2004). (Wang et al., 2010). It is the most commonly used vegetation index. It varies from +1 46

7 to -1. Since climate is one of the most important factors affecting vegetation condition, MODIS-NDVI data have been used to evaluate climatic and environmental changes at regional and global scales (Ji & Peters, 2003; Singh et al., 2003) It can be used not only for accurate description of continental land cover, vegetation classification and vegetation vigor but is also effective for monitoring rainfall and drought, estimating net primary production of vegetation, detecting weather impacts and other events important for agriculture, ecology and economics (Singh et al., 2003; Kogan, 1990, 1995). NDVI has been used successfully to identify stressed and damaged crops (Vogt et al., 1998). Many studies in the Sahel Zone (Anyamba et al., 2005), India (SINGH et al., 2003), Mediterranean (Vogt et al., 1998), Senegal (Li et al., 2004) and India (Chopra, 2006) indicate meaningful direct relationships between NDVI derived from MODIS data, rainfall and vegetation cover. Ji and Peters (2003) undertook a study relating to assessing vegetation response in the northern Great Plains using vegetation and drought indices. The study aimed to determine the response of vegetation to moisture availability through analysis of monthly MODIS-NDVI and SPI in the northern U.S. Great Plains. The study focused on three major areas namely relationship between NDVI and SPI at different time scales, response of NDVI to SPI during different time periods within a growing season and regional characteristics of the NDVI- SPI relationship. An analysis was conducted on time series of monthly NDVI ( ) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI SPI relationship occurred in areas with low soil water-holding capacity (Ji & Peters, 2003). The satellite data that was used is derived from the MODIS sensor. MODIS is the primary sensor for monitoring the global ecosystems for the NASA Earth Observing System (EOS) 47

8 program. 16-day composites images at 250m resolution are directly downloadable from the USGS data center ( For each governorate 16-day composites (April) were downloaded for period from All of images were re-projected into GIS friendly format (IMG) using MODIS reprojection tool from USGS. Most of the image pre-processing was already on the downloaded MODIS; hence images were only geo-referencing to calculate: NDVI= (NIR RED)/ (NIR+RED). (Eq.1) Average NDVIy=(NDVI1+ NDVI2+..+ NDVI10 )/ 10. (Eq.2) Where NDVIy is NDVI across study period, NDVI1 (April 2000), NDVI2 (April 2001), NDVI10 (April 2010). NDVI Anomaly i = (NDVIi Mean NDVI)/ Mean NDVI 100.(Eq.3) Where NDVI i= NDVI in the year and Mean NDVI =long term mean NDVI in the period study. The resulting NDVI anomaly percentage assigned to respective grid cell was reclassified into five drought severity classes in table 4 (Chopra, 2006). Percent of NDVI Anomalies 0 to to to -30 Less than-30 Agricultural drought class Slight drought Moderately drought Severe drought Very severe drought Table 4. Agricultural drought classes based on NDVI Anomaly. (Source: Chopra, 2006). Finally, correlation analysis was performed between the NDVI and SPI values. Software such as ESRI ArcGIS 9.3, and IRDAS Imagine 9.2 are used for image processing and analyzing software, Minitab 16 to analyze the relationship, and Microsoft excel for arrangement data Mapping meteorological drought with SPI SPI has been used to quantify the precipitation deficit in the monsoon and the nonmonsoon periods from 2000 to Monthly rainfall data have been collected from. Since drought is a regional phenomenon, to demarcate its spatial extent, SPI values of the 12 raingauge stations in and around the eastern north region of Syria (Figure 2) have been 48

9 interpolated using kriging interpolation technique in ArcView 9.3 GIS package. Classification of SPI maps has been carried out using the method proposed by McKee et al. Figure2. Station in & around the study area used in mapping based on SPI. ii. Results and Discussion 1- Seasonal pattern of rainfall and NDVI Figure 3 shows the temporal pattern of NDVI and rainfall from It is evident from the graph that during the low rainfall years NDVI values were also low and two major dips in 2008, 2009 shows low rainfall and NDVI which clearly marks that these were the drought years. Figure3. Temporal trends of NDVI and Rainfall ( ). 49

10 2- Meteorological drought SPI had been mainly computed to derive meteorological drought. To do so, the images had been converted to binary images, and April 6-month SPI was chosen for computing the meteorological drought. Then, drought reclassified into severity classes as presented in (table 3), and the result were showed in table 5. It can be observed from the table that Severe to extreme drought occurred during 2008 the whole study region suffered from drought. In other years, growth season was mostly drought-free, moderate drought appeared in some parts of the study region during the growth season of 2000 in DerAzzor and 2006 in Kameshli. Severe drought was observed in the year 2000 over all the study area. On the other hand, 2003, 2004, 2005 were wet years in all stations. Table 5. Meteorological drought based on 6- month SPI (April) in study area for the period ( ). station Bokmal DerAzzor Hasake Kameshli Rakka Year SPI SPI SPI SPI SPI Severe -1.4 Moderate -1.9 Severe Severe Severe Slight Moderate Slight Slight Very severe Very severe -2.4 Very severe Very severe Very severe -1.1 Slight -1.0 Slight Slight Slight Slight 1- Spatial and seasonal pattern of NDVI Figure 4 shows NDVI maps in April for period (2000 to 2010) for Bokmal, DerAzzor, Hasake, Kameshli and Rakka. Figures give you an idea about the amount and distribution of vegetations in studied governorate maps which reflect the vegetation situation and greenness. The highest average NDVI values observed were (0.19, 0.23, 0.39, 0.71, 0.3) for Bokmal, DerAzzor, Hasake, Kameshli and Rakka respectively in

11 The lowest NDVI values observed were (0.12, 0.14) in 2000 and 2008 for Bokmal and Rakka, and (0.12, 0.12, 0.17) in 2009 for DerAzzor, Hasake and Kameshli respectively. NDVI has been found to be lowest due to the extremely unfavorable weather. The year 2008 was a year of drought with precipitation levels much below the normal. Maximum vegetation is developed in years with optimal weather; since such weather encourages efficient use of ecosystem resources (like an increase in the rate of soil nutrition uptake). In contrast, lake of water in drought years reduces the amount of soil nutrition uptake which suppresses vegetation growth through a reduction in ecosystem resources. The pattern of change of NDVI are generally representing the seasonal fluctuation between the early rainy season (October, November, and December) and the main rainy season (January, February, March, and April). Season 2008 started and ends with very unfavorable conditions making planting of crops difficult and reducing harvest. Figure 4 reflect the fluctuation of NDVI values in relative to the changes in local weather conditions, while clearly show a little stable NDVI patterns in Kameshli and around Furat river. This can be attributed to irrigation farming throughout the year and are not influenced much by variability in rainfall. The results of the NDVI analysis show the sensitivity of NDVI to detect drought events and seasonal vegetation dynamics across all seasons. These results are in good agreements with many studies of NDVI time series to exam the response of vegetation vigor to climatic variations of variables like rainfall to understand causes of observed changes in vegetation greenness (Fensholt & Proud, 2012; Fensholt & Rasmussen, 2011; Eastman, et al., 2009). The results obtained in their study reflect the possibility of using satellite images index (like NDVI) to monitor drought under crop development and measure the degree of stress of crop cover due to water stress conditions. 51

12 Figure4. NDVI maps in (April) for period from ( ). 2- Agricultural drought risk based on NDVI anomaly Monthly NDVI images were generated for all growing season (October through July). The month with maximum NDVI value (April) was selected to assess vegetation anomalies during the specific growing season (year). Time series of NDVI anomaly used to detect agricultural drought (Murad & Saiful-Islam, 2011; Chopra, 2006). 52

13 The threshold values used in this study to classify agricultural drought risk using NDVI anomalies that presented in table (4). Table (6) shows the NDVI anomalies for the study area (Bokmal, DerAzzor, Hasake, Kameshli and Rakka). It is evident from figure that during the low rainfall years NDVI values were also low and two major years 2008 and 2009 are classified as very severe \severe drought for all stations. It can be observed that during 2008 Bokmal had moderate drought and Very severe drought in other stations; while 2009, Kameshli and Hasake had severe drought and moderate drought in DerAzzor and Rakka table (6). Vegetation shows a good response and NDVI values with rainfall amount, which confirmed that rainfall has a great impact on vegetation conditions. Further, same figure shows that all stations had slight drought during , and severe drought in 2000, where the years 2003, 2004, and 2005 were near normal years. Thus this study shows that the NDVI value is highly depend on average rainfall condition in a region. These results are in agreement with those reported by Li et al. (2002) in China; Chopra (2006) in India and Shahabfar & Eitzinger (2011) in Iran. They found that NDVI has positive relation to rainfall and NDVI is good indicator vegetation vigor. Further NDVI is excellent pointer to assess agricultural drought from the point of agricultural production and its linkages, so that the drought risk can be marked out taking into consideration the crop yield and describing an area at risk. Table 6. Agricultural drought based on NDVI Anomaly in study area for the period ( ). station Bokmal DerAzzor Hasake Kameshli Rakka Year NDVI Anomaly NDVI Anomaly NDVI Anomaly NDVI Anomaly NDVI Anomaly Slight -21 Severe -22 Severe -19 Moderate -20 Severe Slight Slight Slight -1 Slight 0 Slight Slight Slight -7 Slight -6 Slight -4 Slight -10 Slight Moderate -21 Severe -30 Severe -37 Very severe -25 Severe Slight -17 Moderate -27 Severe -32 Very severe -18 Moderate Slight -5 Slight 0 Slight Slight 53

14 3- Mapping Severity Using SPI risk has been identified using SPI in the Eastern North Region in Syria by interpolating SPI values over 10 years. SPI during selected drought year of 2008 and normal year of 2004 have been presented to show the pattern of SPI during these years. After the interpolation of SPI, selected years were reclassified into severity classes as presented in (table 3). In (figure 5, 6), 6-month SPI for the month of April is presented to quantify severity of drought for selected drought year 2008 and wet year Figure5. Meteorological Map Based on 6- month SPI map in (April) Figure6. Meteorological Map Based on 6- month SPI map in (April) Evaluation of Relationship of SPI with NDVI anomaly NDVI anomaly and SPI have been computed for the state as whole and it shows that when SPI is positive NDVI anomaly is also positive, which states that NDVI anomaly and SPI shares a liner correlation (figure 7). 54

15 Figure7. SPI-NDVI correlation. Since SPI represent the water deficit or excess, positive SPI represents that water has been available to plants in just the right amount so that the NDVI anomaly was nearly -20% when SPI was These low values pertain to year 2008, which was severe drought year. So it can be said that a strong relationship exist between SPI and NDVI anomaly, according to which a drought can be declared when SPI values fall below the threshold of District wise correlation between SPI and NDVI anomaly showed that NDVI anomaly and SPI had a significant correlation in almost of the districts of the state. Based on the relationships between NDVI anomaly and 6-month SPI of April, SPI threshold of -1.5 corresponds to 20% of negative anomaly in NDVI. 55

16 Table 7 shows this correlation between SPI, NDVI. It is clear from the table that They are highly correlated in all stations especially in Rakka and Hasaka (0.9), and P-Value (<5%) in all stations. P- Value Relation co-efficient Table 7. NDVI- SPI correlation Station Bokmal DerAzzor Hasake Kameshli Rakka iii. Discussion and Conclusion Agricultural drought was categorized under this study using vegetation indices (NDVI). The highest NDVI values observed were (0.19, 0.23, 0.39, 0.71, 0.3) for Bokmal, DerAzzor, Hasake, Kameshli and Rakka respectively in The lowest NDVI values observed were (0.12, 0.14) in 2000 and 2008 for Bokmal and Rakka, and (0.12, 0.12, 0.17) in 2009 for DerAzzor, Hasake and Kameshli respectively Whole of the study area had negative NDVI anomalies corresponding negative SPI values. Also, the fluctuation of NDVI values were relative to the changes in local weather conditions in the study area, The findings of NDVI analysis confirmed the sensitivity of this index to detect drought events and seasonal vegetation dynamics across all seasons. The statistical relationship between NDVI and SPI reflected by significance correlation coefficient values (0.6 to 0.9). The study evaluated the effective of NDVI as an indicator of vegetation-moisture conditions In addition to classifying meteorological drought based on SPI values, risk areas have been identified using SPI maps which help in preparing management plans. iv. References 1. Anyamba, A.; Tucker, C. J Analysis Of Sahelian Vegetation Dynamic Using NOAA-AHVRR NDVI Data From Journal Of Arid Environments, 63: Anyamba, A.; Tucker, C. J.; Huete, A. R.; Boken, V. K Monitoring Using Coarse- Resolution Polar-Orbiting Satellite Data. IN: Monitoring And Predicting Agricultural : A Global Study. Oxford University Press, Inc. 3. Brown, F. J.; Reed et al A Prototype Monitoring System Integrating Climate And Satellite Data. Percoa 15/Land Satellite Information IV/ASPRS Commission I/FIEOS Byun, H. R.; Kim, D.W. Comparing The Effective Index And The Standardized Precipitation Index. Options Méditerranéennes, A No. 95, Economics Of And Preparedness In A Climate Change Context: Chopra, P Risk Assessment Using Remote Sensing And GIS, A Case Study In Gujarat, M. Sc. Thesis, Dept. Of Geo-Information Science And Earth Observation, ITC, Netherlands. 6. Eastman, J. R.; Sangermano, F.; Ghimire, B.; Zhu, H. L.; Chen, H.; Neeti, N Seasonal Trend Analysis Of Image Time Series. International Journal Of Remote Sensing, 30:

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