The Relationship between Vegetation and Rainfall in Central Sudan

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International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 doi: 10.14355/ijrsa.2016.06.004

The Relationship between Vegetation and Rainfall in Central Sudan N. A. Hameed, A. Bannari Department of Geoinformatics, College of Graduate Studies, Arabian Gulf University, Manama, Kingdom of Bahrain anadir@agu.edu.bh Abstract Daily dynamic vegetation cover mapping at the global scale is the most important parameter to retrieve from coarse-spatial resolution global land surface optical satellite remote sensing to understand the climate change impact on the rainfall cycle and its variability in time. The objective of this research was the investigation of the change in vegetation cover dynamic in time and its relationship with rainfall in Central regions of Sudan for a decade (2000- 2010). To achieve our objective, the Normalized Difference Vegetation Index (NDVI) time series obtained from SPOT-VGT sensor, precipitation data measured over the study area by different weather stations, GIS and statistical analysis were used. The obtained results show significant level of agreement between NDVI and rainfall values during the study period (0.6 ≤ R2 ≤ 0.8). Certainly, such derived results could be useful as imputing in the carbon cycle models and/or climate impact modeling, as well the development of new policy for climate change adaptation.

Keywords Rainfall; Vegetation; NDVI; SPOT-VGT Sensor; Statistical Analysis

Introduction The findings of the Intergovernmental Panel on Climate Change (IPCC) have shown that climate change is already having strong impacts on human societies and the natural world, and is expected to do so for decades to come [1].

Sudan is a least developed country in Africa and one of the most vulnerable areas to climate change and climate variability. This situation is aggravated by the interaction of multiple stresses occurring at various levels, such as endemic poverty; institutional weaknesses; limited access to capital including markets, infrastructure and technology; ecosystem degradation; complex disasters and conflicts. These in turn have weakened people’s adaptive capacity, increasing their vulnerability to projected climate change [2].

Sudan is a large country with a dynamic ecosystem that responds to fluctuations in climate and anthropogenic land use patterns. The rainfall regime is characterized by wide variations from year to year. Since the mid-1960s, these regions has experienced a systematic decrease in rainfall and wide spread droughts affecting a larger area of Sub-Saharan zones and have undermined food security and are strongly linked to human displacement and ethnical conflicts [3-7]. In fact, in Sudan, the most vulnerable groups to climate risks are traditional rain-fed farmers and pastoralists. They are least able to cope with climate-related shocks, especially those in western, central, and eastern Sudan, where severity of drought depends on the variability of rainfall both in amount, distribution and frequency. The most heavily affected are the northern Kordofan and Darfur States [8]. Sector wise, Sudan’s National Adaptation Programme of Action 2007, and its First National Communication to the UNFCCC 2003, have the identified agriculture, water resources and health as the three sectors most vulnerable to climate change.

Furthermore, long term observation of space-born remote sensing data provides a means to explore temporal variation on the earth’s surface. This improves the understanding of variability required by numerous global change studies to explain annual and inter-annual trends and to separate those from individual events [9].

Vegetation cover assessment and mapping was one of the first uses of satellite remote sensing imagery and has been one of the most common ever since [10-11]. Indeed, the monitoring of Earth vegetation cover involves the utilization of vegetation indices as a radiometric measurement of spatial and temporal patterns of vegetation photosynthetic activity. These play an important role in the derivation of biophysical parameters, such as percentage of vegetation cover, leaf area index (LAI), absorbed photosynthetically active radiation (APAR), rate of the biomass production, etc. Interest lies in the detection of changes in land use and the monitoring of the seasonal dynamics of vegetation on local, regional and/or global scales. In the literature, over fifty vegetation indices were 30

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developed to measure the vegetation cover in different applications and under quite particular conditions, mainly in forestry and agriculture [12]. However, the Normalized Difference Vegetation Index (NDVI), which is spectroradiometric reflection measurements in the red (R) and near-infrared (NIR) wavelengths [13], is the most popular and the most used index especially for vegetation cover investigation at the global scale [14].

NDVI = (NIR – R) / (NIR + R) NIR is the reflectance in the near infrared band (760-900 nm), which is strongly reflected by leaf cellular structures; while, R is the reflectance in the red band (630-690 nm) which is characterized by chlorophyll content absorption at the canopy.

The integration of satellite remote sensing in combination with field ground measurements is particularly appropriate for monitoring the vegetation of such large areas like Sudan [15,16]. They found good correlations between NDVI and vegetation productivity, and concluded that rainfall estimates could be used to forecast biomass production [15-20]. Moreover, annual maximum NDVI in the Sahel was found to correlate best with accumulated rainfall and in semi-arid environments to be more strongly related to soil moisture, a function of rainfall accumulated over a period of time [19]. Indeed, the relationship between vegetation cover and rainfall in semiarid environments of African Sahel countries has received a great deal of interest [21-24]. Most previous regional-scale studies were based on time series of satellite indicators of vegetation greenness and rainfall measurements from weather ground stations [25]. The objective of this study was the investigation of the vegetation cover dynamics as function of rainfall variations in Central Sudan for a decade (2000 – 2010). To achieve our objective, NDVI time series acquired by SPOT-VGT sensor, measured rainfall over the study area at different weather ground stations, GIS and statistical analysis were used.

Materials and Methods Study Area The study areas cover central, eastern, and western States of the Sudan, located between 10º to 18º North and 22ºto 38º East (Figure 1). The population is around 31 million living in approximately 1,263,400 Km2 covering the flowing States: Red Sea, North Darfur, West Darfur, South Darfur, North Kordofan, South Kordofan, River Nile, Northern, Kassala, Khartoum, AI Gedarif, Gezira, White Nile, Blue Nile, and Sennar [26].

The rainfall seasons is classified into 4 periods: monsoon season (June to September), advancing monsoon season (March to May), post monsoon season (October to November) and winter season (December to February) [6,27].

Rainfall usually occurs in isolated showers, which vary considerably in duration, location, and from year to year.

Obviously, the number of consecutive months with adequate rainfall determines the growth season for natural vegetation as well as crops [6,28]. The rainfall increases from the dry north to the humid South. The annual amount is very low (near to zero millimeters) in the extreme north, rising to 500 mm in central regions, and to more than 900 mm in the extreme southwest of the country [6, 28-30]. However, there are major contrasts in rainfall (P) and potential evapotranspiration (PET), thus producing distinct climatic types ranging within hyper-arid, arid, semiarid and dry sub-humid, as classified using P/PET ratio [31,32]. Rainfall is very variable and becoming increasingly unpredictable, with a coefficient of variation decreasing significantly from north to south, from 190% to less than 15% [2,33].

Soils of the study area are products of Basement Complex, as parent materials, plus the alluvial deposits of Nile River and its tributaries [27,34,35]. Soils are broadly divided into six main categories according to their locations and manner of formation: i) desert; ii) semi-desert; iii) sand (Qoz); iv) alkaline catena; v) alluvial; and vi) iron stone plateau. Within these soil categories, there are many local variations with respect to drainage conditions. The topography is generally flat, with low slopes from south to the north, featureless plain reflecting proximity to the surface of the ancient and little-disturbed Basement rocks of the African continent. The vegetation ranges from extremely scanty in the far north to short grass and sparse thorn scrub to open grassland plains dominated by “Acacia Senegal”. The more humid part of the country contains a variety of vegetation types from savannah to broken woodland and tropical forests [36]. Based on the rainfall variation and the soil properties, Harrison and Jackson (1958) have described the vegetation of the Sudan in five ecological zones; namely: Desert, Semi-desert, Woodland Savannah, Flood region, and Mountain vegetation [37-42].

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FIGURE 1. LOCATION OF THE STUDY AREA.

Data 1) Spot-Vegetation The VEGETATION (VGT) sensor is a multispectral instrument flown onboard SPOT-4 and SPOT-5 satellite platforms [43]. This instrument uses the linear-array technology and thus produces high-quality imagery at coarse spatial resolution with greatly reduced radiometric and geometric distortions [44]. It was designed for vegetation and land surface characterization [45], recording the electromagnetic radiation reflectivity in 4 spectral bands: blue (0.43-0.47 mm), red (0.61-0.68 mm), NIR (0.78-0.89 mm), and SWIR (1.58-1.75 mm). The blue band was designed for atmospheric scattering corrections. The R and NIR are particularly well adopted to describe the vegetation photosynthesis activity, while the SWIR is sensitive to soil moisture, vegetation cover and leaf moisture content, and can improve the discrimination between vegetation and other land covers. The VGT instrument provides global coverage with a daily temporal resolution since 1998, enabling the observation of long-term environmental and climate changes on a regional and worldwide scale [46-48]. It covers a swath width of 2250 km with 1 km pixel size, following a sun-synchronous circular orbit at 830 km altitude under an inclination of 98.7ยบ.

2) NDVI Time Series Data SPOT-VGT provides three different standard products which are VGT-P (physical product), VGT-S1 (daily synthesis product) and VGT-S10 (10-day synthesis product). The VGT-S10 data are generated by selecting the VGT-S1 pixels that have the maximum NDVI. This 10-day composites product is generated from the atmospherically corrected surface reflectances in the R and NIR. Indeed, these spectral bands are corrected from molecular and aerosol scattering, as well as water vapor, ozone and absorption correction through maximum value compositing [49]. The surface reflectances were corrected for band-to-band miss-registration effects and linearity of radiometric response [49]. Moreover, the SPOT-VGT cloud clearing, and bi-directional correction procedures were substantially applied to reduce the noise in reflectances and, consequently, on the NDVI values [50]. The final products have a high geometrical quality using WGRS-84 as reference, suitable for multitemporal analyses in GIS environment with other mapping documents. In this research, 396 NDVI products were acquired over our study area for the period 2000- 2010.

3) Meteorological Data To extract the rainfall data during the considered period (2000 to 2010), 20 weathear stations were obtained based on their spatial distribution uniformity to cover the entire region of interest. Stations are geographically located in the following regions: 2 stations (Atbara and Shendi) at far north parts in desert zone, 8 stations 32

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(Halfa Gadida, Kassala, Khartoum, Wad Madani, Ed Dueim, Kosti, Al Obayid, Al Fashir and En Nahud) in the central and eastern parts in the semi-deserts zone, and the rest in the humid zone of the study area (Sennar, Gedarif, Babanosa, Abu Naama, Ed Dueim, Renk, Ed Damazin, Kadugli, Nyala and Rashad).

Software’s and Data processing Four software packages were used in this research: VGT-Extract Software, ERDAS Imagine processing software, ArcGIS, and JMP. The VGT-Extract was used to facilitate the integration of SPOT-VGT products into commonly used remote sensing software for further analysis, post-processing, and visualization. As well for automatically unpack (un-compress) the product and mosaic the data layers, extract the geographic bounding box of the study site and convert the data to different formats. The ERDAS system was used to import, convert, preprocess and process SPOT-VGT NDVI products. The decadal NDVI (396 products) were integrated to produce a monthly maximum NDVI maps (total of 132 maps). The use of maximum value composites reduces some of the extraneous variability inherent in the NDVI caused by differences in atmospheric conditions [50]. The Annual Maximum Image (AMI) during 11 years was added to produce long term maximum map product. The process was carried out on a pixel-by-pixel basis and the result of the division was normally rounded to the nearest integer [14]. Then, these products were analyzed against the annual mean rainfall data for the same period. Thus, it was necessary to have all annually maximum NDVI products combined together in one map using Layer Stacking. This procedure takes individual information layers and stacks them on the top of each other to produce multi-layers products [51].

The output was 11 maps for the all annually maximum NDVI. Furthermore, since the geographical location of the weathear stations and the SPOT-VGT products were obtained from different sources and have different references projections, ArcGIS was used to re-project them in the same geographic coordinate system (Latitude and Longitude). For this purposes, the weathear station locations in Excel file were converted to point geometry feature class using COGO method for calculating coordinate points from X and Y values. Moreover, additional mean rainfall attributes were added to the wheatear layer. This feature class was interpolated using inverse distance weight (IDW) method to create the mean annual rainfall map in GIS environment. Then, the output cell size was selected similarly to the NDVI spatial resolution (1 km by 1 Km). The final output was annual mean rainfall map for the study period 2000 - 2010. Furthermore, mean annual multiNDVI product series and mean annual rainfall map series were overlaid for statistical analysis. Finally, the JMP statistical software was used to examine the relationships between the two considered variables (Rainfall and NDVI), year by year, using least squares regression fit.

Results Analysis The combined maps of annually mean NDVI for the period 2000-2010 resulting from the layer stacking (Figure 2) have been analyzed against the mean annual rainfall for the same period (Figure 3). It is important to state that no attempt has been made to investigate the vegetation actually present in each pixel. The visual analysis of the obtained maps (Figure 2) shows clearly the variability of vegetation cover density in the middle of the study site was rainfall which plays an important role in the growth of vegetation cover. However, in the south and north-east as well some patterns in the west, the vegetation cover density change looks less drastic as a function of rainfall, since these regions are irrigated from many secondary rivers originating from the Ethiopians-mountains, and feed the Nile River (as shown in the Figure 1). For instance, in the el-jazira region which is a triangle receiving different secondary water sources (Figure 1), it looks like an irrigated agricultural fields. Indeed, these regions illustrate a constant vegetation patterns.

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FIGURE 2. NDVI ANNUALLY MEAN MAPS FROM 2000 TO 2010

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FIGURE 3. RAINFALL ANNUAL MEAN MAP FOR THE PERIOD 2000 - 2010.

Furthermore, the rainfall regime in the study area is characterized by wide variations in duration and location from year to year. Figure 3 shows that over the study period (2000 to 2010) the lowest recorded annual rainfall was about zero millimeters in the far north (Atbara) in 2000, and the highest record almost 920 millimeters in the southeast part of the area (Ed Damazin) in 2008. In general, we observe that the rainfall increases from the dry North to the humid South, and the average is between 0 and 100 millimeters in the extreme north of the area rising to about 200 mm around Khartoum, in central Sudan the average is about 300 millimeters, and more than 800 mm in the extreme south and southwest parts of the Area. Nevertheless, Jebel Marra, Nuba Mountains and the Ethiopian foot-hills receive more rain than the surrounding plains. The coefficient of rainfall variability decreases from north to south (120 to less than 20%). The average distribution of decadal rainfall is between 353 and 503 millimeters, as summarized in Table 2. These results corroborate the climatic trend over the same study area between 1970 and 1996 as demonstrated by other researchers [2,8,52-54]. Moreover, as we discuss above, the visual analysis of the regions where the annual mean of precipitations is between 400 and 800 mm (last four classes in the Figure 3), illustrate a good spatial correlation with vegetation cover density in the same regions as derived from NDVI from 2000 to 2010 (Figure 2).

Nevertheless, although this visual analysis shows certain good tendency regarding some specific geographic locations, the global results demonstrate that the NDVI and the rainfall values are not well uniform and strongly homogeneous during the study period as summarized in Table 2 and illustrated in Figure 4. Even if these two variables are changing from year to year, they follow the same trend, except in 2001 and especially in 2009. In fact, during 2009 the NDVI values increase while those of rainfall decrease. In this case, it is probable that the biomass density increases based on the irrigation of agricultural fields. However, in general Figure 4 illustrates positive trends between NDVI and rainfall during the study period. These findings corroborate the results obtained by Abduel-Hameid et al., [55].

TABLE 2. NDVI ANNUAL MEAN VS. ANNUAL MEAN RAINFALL (IN MM) DURING THE STUDY PERIOD.

Year

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Rainfall Mean

363.50

353.20

362.05

417.35

333.85

387.30

416.35

503.20

399.30

325.60

390.90

NDVI Mean

115.75

101.95

114.15

113.35

108.10

109.85

105.05

116.85

105.35

106.65

106.60

FIGURE 4. NDVI MEANS DYNAMIC VS. RAINFALL MEANS (IN MM) FROM 2000 TO 2010.

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With the aim of establishing a causal relationship between the annual mean rainfall and NDVI, a linear regression analysis was carried out for each year during the study period (2000-2010), considering each metrological station and its surrounding NDVI value (Figure 5). Table 3 summarizes the correlation coefficient (R2) at significance level p<0.0001 and linear regression equations for the different years. Similarly, the statistical investigation showed significant correlations between the time series of NDVI and the inter-annual variability of rainfall. The R2 values varies between 0.60 and 0.80, respectively, for 2003 and 2004. However, the obtained correlations between these two variables in this research are sufficiently significant to allow the use of NDVI as an indicator for rainfall variations mapping. These results corroborate the finding of Tucker and Nicholson [56], and other studies that demonstrated similar correlation between NDVI trends and rainfall in time [23,24].

Undoubtedly, these correlation variations could be explained by many factors, especially the NDVI sensitivity to soil background artifacts [57,58], and its problems of saturation and linearity [59]. Certainly, the soil background impact on a 1 km pixel size is significant especially if the biomass density changes from year to another in the arid environment as Sudan, where soil colors are clear and bright. These factors increase or decrease reflectanceâ&#x20AC;&#x2122;s in the red and NIR spectral bands and, consequently, limit the detection of vegetation cover changes using NDVI, which causes probably errors in the modeling process, and in the results interpretation and analysis. Otherwise, when the biomass is not so dense and scattered, the NDVI capability to estimate accurately the biomass became relatively limited, then the correlation coefficient becomes sensitive to this soil artifacts. Furthermore, if the biomass covers completely the pixels (100% cover), the values of NDVI do not exceed 75% to 80%. In this case, NDVI underestimates the biomass density by 20 to 25% errors [59]. Consequently, the correlation between the rainfall and NDVI must be subject to some variability. Nevertheless, despite these weaknesses, this method remains useful as imput in the carbon cycle models and/or climate impact modeling.

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FIGURE 5. CORRELATIONS BETWEEN NDVI AND RAINFALL FROM 2000 TO 2010 AT SIGNIFICANCE LEVEL p<0.0001.

TABLE 3. LINEAR REGRESSION EQUATIONS AND (R2) FOR THE STUDY PERIOD OVER THE STUDY AREA AT SIGNIFICANCE LEVEL p<0.0001.

Year

Linear regression equation

R2

2000

NDVI = 0.1374304 * X + 65.79

0.73

2001

NDVI = 0.1279850 * X + 58.75

0.68

2002

NDVI = 0.1357480 * X + 65.00

0.66

2003

NDVI = 0.1513466 * X + 52.63

2004

NDVI = 0.15544109 * X + 56.22

0.80

2005

NDVI = 0.1554541 * X + 49.64

0.71

2006

NDVI = 0.1441723 * X + 45.02

0.71

2007

NDVI = 0.1317842 * X + 46.52

0.77

2008

NDVI = 0.1084049 * X + 64.73

0.62

2009

NDVI = 0.1795321 * X + 48.19

0.76

2010

NDVI = 0.1158770 * X + 61.41

0.62

0.60

Note: X in the rainfall value.

Conclusions Rainfall is the most important constraint to the vegetation growth in semi-arid zone of Sudan, which justifies the attempt to predict vegetation greenness from rainfall estimates through linear regression. In this study, we 37

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demonstrated significant correlations between time series of NDVI SPOT-VGT and the inter-annual variability of rainfall values during the considered study period (0.6 ≤ R2 ≤ 0.8). Although the NDVI follows in general the characteristics of rainfall trend, these correlations are not constant and uniform during the study period (2000 2010). Indeed, they change from year to year since the NDVI values are sensitive to the soil background artifacts, especially when the vegetation cover is scattered in arid land as Sudan environment. However, the obtained correlations are sufficiently significant to allow the use of NDVI as an indicator of rainfall variations mapping.

These results corroborate the finding of several studies in the literature, and such derived results could be useful as imputing in the carbon cycle models and/or climate impact modeling, as well as the development of new policy for climate change adaptation.

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