Satellite Based Analysis for Drought Monitoring and Risk

International Journal of Scientific Research and Engineering Development-– Volume 4 Issue 1, Jan-Feb 2021

Available at www.ijsred.com

ISSN : 2581-7175 ©IJSRED:All Rights are Reserved Page 986

Satellite Based Analysis for Drought Monitoring and Risk

Assessment- Sudan

Elbasri Abulgasim1 and Solafa Babiker²

1Associate prof., Faculty of agricultural technology and fish science, AlNeelain University, Sudan.

²Assistance prof., Remote sensing and seismology authority. National center for research, Sudan.

Po -box 2024

Abstract:

Over the years , increasing of drought events more frequent triggered the agricultural sector and food

security in dry regions, therefore, timely high detailed information are essential for drought preparedness,

mitigation and response that formed straightforward importance of monitoring process for large regions

where availability of relevant ground observation data are much challenging. This study investigated

monitoring, assessment and frequency of drought hazard in Sudan using geospatial data by combined

satellite-based drought indices namely standardized precipitation index (SPI) and vegetation health index

(VHI), generated for three years of 2009, 2011and 2015, these indices are integrated to derive drought hazard

maps and drought frequency occurrence .The regression analysis was generated for SPI index using available

rainfall stations data and for the indices against each other .The results indicated that most of the country

experienced severe drought especially Northern states, parts of Eastern and Western country, while the

moderate drought was found in the western region. Regression analysis resulted high significant r², therefore,

the study concluded validity of two indices for drought hazard assessment and VHI is good indicator for

drought assessment in different vegetation types. High spatial variation of two indices inside the country

evidenced the effect of climate change on Sudan and high frequent occurrence indicated high vulnerability

for drought event, this result will assist decision making for drought planning programs and adaptation

practices for agricultural sustainability plans.

Keywords: Drought, Enhanced vegetation index, Standardized precipitation index, Hazards,

Assessment, Agriculture.

RESEARCH ARTICLE OPEN ACCESS




Introduction:

Drought is acyclic phenomena usually occur over large areas, more than half of the terrestrial earth is

susceptible to drought each year as reported by Kogan,(1997). Tucker and Chaudhury (1987) are defined

drought as a period of reduced plant growth in relation to the historical average caused by reduced

precipitation. Four main types of drought are defined in the literature (WMO, 1975; Wilhite and Glantz,

1985; White and O’Meagher, 1995; McVicar and Jupp, 1998) that are meteorological, agricultural,

hydrologic and socioeconomic drought but the most two types affected each other in shorter time scale are

meteorological and agricultural. As global aspect, the United Nations Framework Convention on Climate

Change agenda quantifying loss and damage from extreme climate events such as droughts has become

important for policy implementation (IPCC 2012), improved drought monitoring and management will be

fundamental to implementing the Sendai Framework for Disaster Risk Reduction 2015–2030 and the

Sustainable Development Goals in respect to the magnitude of associated disaster losses.

Remote sensing of crop canopies use spectral characteristics of incoming radiation to enable crop spectral

signature to be obtained and related to crop health indicators, healthy vegetation contains large quantities of

chlorophyll; its reflectance in the blue and red parts of the spectrum is low since chlorophyll absorbs this

energy, in contrast, reflectance in the green and near-infrared spectral regions is high as Campbell (2002).

Stressed or damaged crops experience a decrease in chlorophyll content and changes to the internal leaf

structure results reflectance decrease in the green region and internal leaf damage results near-infrared

reflectance decrease that provide early detection of crop stress, therefore, using ratio of reflected infrared to

red wavelengths offer potentials of measure the vegetation health (Shaver, et al. 2010).

In order to achieve quantitative analysis for crop drought status and growth development, remote-sensing-

based drought indices were widespread used and considered as unique source of information that identify

crops conditions and types at different regions (OK. et.al, 2012 and Villa, et al 2015. Normalized Difference

Vegetation Index (NDVI), Temperature Condition Index (TCI) identifies vegetation stress caused by high

temperature (Ghaleb, et al 2015 ), and Vegetation Condition Index (VCI) is commonly used to identify the

changes of vegetation from bad to optimum condition ( Singh, et al 2013) , Vegetation health Index (VHI) is

consisted of TCI and VCI is classified into vegetation index which describe the condition of vegetation in

particular area and categorized it into drought classes, however, they are often used as indices for drought

monitoring , crop development and provide rapid estimate of leaf area index, nitrogen content and grain yield

(Bosquet, et al 2011 ).




Using AVHRR-derived products, various researchers have developed algorithms for time-series analysis

relating a specific period of interest to a long-term statistic NDVI Anomaly Index (Liu and Negron-Juarez

2001), Kogan (1995) has suggested the Vegetation Condition index(VCI) which it relates the NDVI of the

composite period of interest to the long-term minimum NDVI (NDVI min), normalized by the range of

NDVI values calculated from the long-term record .Also Kogan (1995) developed the Temperature

Condition Index (TCI) based on brightness temperature derived from the AVHRR band 4. Several authors

have used products of the NOAA-AVHRR to provide a more ecological and physical interpretation of

remotely sensed data for examining vegetation conditions (e.g. Gutman 1990, McVicar and Jupp 1998,

Karnieli & Dall’olmo 03). Vegetation health Index (VHI) has been applied for different applications, such as

drought detection, drought severity and duration, early drought warning (Seiler et al. 1998). McVicar and

Bierwirth (2001) found a strong correlation (r2 = 0.81) between accumulated rainfall and integrated NDVI

and surface temperature in their investigation of AVHRR data for drought assessment. Rainfall based

drought indices offer direct and simple methods to monitor the intensity of drought situations, the

Standardized Precipitation Index (SPI) developed by McKee et al. (1993) is a valuable tool for the estimation

of the intensity and duration of drought events. SPI quantifies degree of wetness/dryness by comparing

accumulated rainfall over different time periods with the historical rainfall period, SPI was used to detect and

characterize drought episodes worldwide (Patel et al 2007) .Using rainfall satellite Bayissa et al (2017) and

Hassaballah et al (2017) reported the efficiency of CHRIPS satellite application for drought estimation. Tian

et al. (2018) evaluated six drought indices included precipitation percentiles, SPI, and SPEI in monitoring

agricultural drought in south central USA and found no single index was able to capture all aspects of

drought in the region. SPI values that are in table 2 showed interpretation of SPI maps for drought conditions

as SPI values greater than 0 indicate conditions wetter than the median, whereas negative SPI values indicate

drier than median conditions. . Hamid and Bannari (2016) reported that Rainfall is very variable and is

becoming increasingly unpredictable, with a coefficient of variation decreasing significantly from north to

south, from 190% to less than 15%, annually the rainfall increases from the dry north to the humid South and

the annual amount is less than 100 mm in the north, averaging 200 mm around Khartoum, rising to 400 mm

in central regions, and to more than 800 mm in the extreme southwest of the country as study of Elagib

(1996).

According to the crop calendar of main rainfed crops, the growing stages is started from August to October

(FAO, GIEWS 2018) and the rainfed cereals production mean rate was decreased for years 2008 -2013 by




3.304% ,from 993 thousands metric ton compared to 3281of the country production . Central burro of Sudan

estimated population as 39 million growing at rate of 2.7 % with more than 30 million living in rural areas,

the majority of the populations are farmers and pastoralist and over 80% of the employment takes place in

agriculture but rainfed areas have generally been perceived as drought prone high risk and to date they have

received limited attention therefore, the climate situation placing vulnerable people at rain-fed areas and their

food security at high risk

The overall objectives of the study is concerned of drought hazard assessment, monitor its intensity and local

variability in extended country like Sudan using satellite based techniques of drought-related indices and test

their accuracy and correlation, frequency was assessed to characterize probability of drought and its impact

on vegetation health when lacking crops ground observations data and sufficient rainfall stations.

Table (1): Threshold values of SPI and VHI in terms of drought condition.

SPI Value Drought Condition

<- 2.0 to Extremely dry

-2.0 to - 1.5 Severely dry

-1.5 to -1.0 Moderately dry

-0.5 to 0.5 Near normal

1.0 to 1.5 Moderately wet

1.5 to 2.0 Very wet

>2.0 Extremely wet

VHI Value

<40 Severe

40 – 50 Moderate

50 – 65 Low

>65 No drought

International Journal of Scientific Research and Engineering

ISSN : 2581-7175

Methodology:

Studied area:

Sudan is one of the least developed country in Africa

areas to climate change and climate variability.

vary from hyper arid in the northern parts (16 N), to areas dominated by arid and semi arid climate in central

parts (16 N to 13N), dry sub-humid is dominated southern parts (13N to 10N) and middle sub

south (9N-8.4N) ,therefore, average ann

in isohyets rainfall map (figure2) .

Figure (1): Sudan Location.

International Journal of Scientific Research and Engineering Development-– Volume 4 Issue 1, Jan


7175 ©IJSRED:All Rights are Reserved

Sudan is one of the least developed country in Africa (figure1) and one of the most fragile and vulnerable

areas to climate change and climate variability. Due to the wide latitudinal range, the climatic zones in Sudan


humid is dominated southern parts (13N to 10N) and middle sub

average annual rainfall represented wide variation across the country as showed

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the most fragile and vulnerable

Due to the wide latitudinal range, the climatic zones in Sudan


humid is dominated southern parts (13N to 10N) and middle sub-humid in the

wide variation across the country as showed




Figure 2: isohyets rainfall map of Sudan.

Historically the rainfall was severely reduced and the cycles of droughts affected permanently this region

during the last decades as reported by USGS (2011). Four seasons are characterized the rainfall patterns in

Sudan that are monsoon season from June to September, advancing monsoon season from March to May,

post monsoon season from October to November and winter season from December to February (ElTahir

1992).

Data Sources:

CHIRP rainfall satellite data of rainfall peak months (June –October) are downloaded (ftp://chg-

ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/africa_dekad/) and used to derived standardized

precipitation index maps ( SPI) based on threshold values showed in table (1) and used to quantify

precipitation deficits as anomaly percentile on multiple timescales depends on commonly available

precipitation data. The SPI value is positive if the precipitation over a particular time period is greater than

the historical mean precipitation and is negative if the precipitation is less than the historical mean

precipitation.

Monthly data of AVHRR was downloaded (ftp://ftp.star. nesdis.noaa.gov/pub/ corp/scsb

/wguo/data/Blended_VH_4km/geo_TIFF/) and used to derive the annual vegetation health index (VHI) map

for three periods based on threshold values showed in table (1). VHI was calculated by an additive




combination of vegetation condition index (VCI) and temperature condition index (TCI) as equation showed

below:

VHI = α VCI + (1- α) TCI. “α” is the relative contribution of VCI and TCI , it has been assigned a value of

0.5.

The drought indices maps for three periods were integrated to produce accumulated hazard maps and the

frequency of drought occurrence was computed. Correlation of SPI data was generated using recorded

rainfall amount in different stations that showed in table 2 and the correlation of SPI and VHI was generated.

Table 2: Annual precipitations of metrological ground stations.

Rain_2015 Rain_2011 Rain_2009 Station

13.0 17.0 14.6 Dongla

34.6 132.4 52.4 Kerma

89.8 174.6 116.0 Abu Hamad

89.6 34.0 36.5 Suakin

69.1 134.9 162.5 Ed Debba

43.0 57.0 74.0 Karima

88.5 26.5 30.8 Marawi

168.7 132.2 127.7 Atbara

172.9 280.3 159.3 Shendi

73.1 30.0 82.3 El Matamma

129.5 132.3 112.2 Omdurman

10.4 13.8 101.0 Khartoum North

74.4 136.1 105.4 New Halfa

103.7 69.7 83.4 Hamrat El Sheikh

88.0 47.0 71.5 Sodari

126.2 238.7 250.6 Jebrat El Sheikh

18.7 5.2 32.0 El Qutainah

115.5 142.6 159.7 Bara




Results and Discussion:

SPI hazard maps for year 2009, 2011 and 2015 showed clear drought events that affected the country, it is

spatial distribution showed sever to moderate classes as clear localized drought intensities affected the

country. Negative SPI values indicated rainfall deficit occurred during the rainy season for three periods,

considering the spatial distribution of the event, the moderate wet conditions of the SPI found at a borders of

desert northern state might be caused by large intra-seasonal variation in rainfall patterns and low mean

seasonal precipitation, such problems often arise when applying the SPI at short time scales (1, 2 or 3

months) to a region of low seasonal precipitation that agreed with finding of Loukas et al.(2003).More details

about drought index maps and its variability showed in figure (3) .Vegetation health index maps (figure 4)

indicated that most of the country susceptible to medium or severe drought conditions , however, season

2011 showed relative large areas of the country at normal conditions especially in southern to western parts,

this is due to the fact that most of vegetation cover in this areas are dominated by trees and shrubs which is

tolerate drought conditions. In other areas for the same season, sever to moderate drought are showed where

crop rainfed and grasslands are found , this is attributed to agricultural practices depend on rainfall that are

dominated. Comparison of SPI and VHI accumulated hazard maps (figure 5) represented that where near

normal SPI dominated the country, sever dry VHI was associated , nevertheless, VHI is consist of additive of

TCI and VCI, this result indicated the great effect of temperature factor on vegetation which impacted the

heat-related stress processes and increase drought effects, as reported by Agbor et al (2018) .

Drought Frequency

Finally, comparison of SPI and VHI frequency for the most drought years observed in the country indicated

that, SPI frequency represented low drought frequency in the center of the country as showed in figure (6),

which could be explained by higher distribution of rainfall in these parts. High frequency areas indicated

dominance of low rainfall amount and drought conditions as agreed by the country rainfall isohyets

distribution showed in figure (2). VHI low frequency are found in the high land in the west of Sudan that

characterized by its optimum climate and dense trees as reported by Leder and Leder ( 2018) also the

topographic condition like mountainous area on the East was not going to influence significantly to surface

temperature, on the other hand, frequency map represented low probability of drought events with low VHI

values in the north extended to the east region that attributed to degraded areas in desert and semi desert

ecological zone covered this parts of the country as findings of Mohamed et al (2016). Generally the study




found that magnitude of low frequency was controlled by decreased rainfall amount and vegetation

degradation events and type ; agricultural and grass lands represented high frequency comparedto the wood

land forests in the south, around water courses, mountains forests in the east and high altitude hills in the

west that showed low VHI frequency.

Figure (3): Standardized precipitation index maps.




Figure (4): Vegetation health index maps.




Figure (5): Drought Hazard maps.

. .

Figure (5): Frequency maps.




Correlation of SPI index by recorded rainfall data was showed in table (3), for available 19 stations in Sudan

for three years revealed high correlation indicated that the index could be used and it could predict the

rainfall in un-gauge places in high accuracy, the negative relation indicated association of rainfall and SPI

decrease during dry periods.

VHI and SPI Linear regression analysis of the entire dataset reveals significant (F<0.001) high relationship

between VHI and SPI as showed in table (3), This agreed with findings of Karnieli et al (2006) indicated that

VHI can be successfully applied in the low latitudes, mainly in arid, semi-arid, and sub-humid climatic

regions, where water is the main limiting factor for vegetation growth.

Table 3: Regression analysis of SPI with metrological rainfall data and VHI.

Conclusions:

In this research drought indices were used to provide quantitative assessment of drought severity, spatial

variation and frequency during dry periods using satellite data and rainfall records, it indicated the capability

of indices used to distinguish drought events across the country. Spatio-temporal distribution and monitoring

approach identify vulnerable regions located in remote areas where metrological observation points are

lacked. Frequency maps showed droughts probabilities and hot spots across the country although other

factors like the vegetation type and density affected the frequency. Significant relationship of SPI and VHI

was obtained by regression analysis indicated that each index could characterize drought effectively and VHI

was more efficient for vegetation changes. Adoption of these approaches in natural resources sectors for

drought monitoring provide cost effective method for drought pattern recognition especially for dry regions

and assist decision making in drought plan, associated mitigation and emergency management responses.

Index R2

SPI –rainfall 2009 -1.616



VHI-SPI 2009 0.77

VHI-SPI 2011 0.75

VHI-SPI 2015 0.84




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Documents

Satellite Based Analysis for Drought Monitoring and Risk