Agricultural Drought Severity assessment using land Surface temperature and NDVI in Nakuru, Kenya

8/12/2019 Agricultural Drought Severity assessment using land Surface temperature and NDVI in Nakuru, Kenya

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Agricultural Drought Severity Assessment Using Land Surface Temperature and NDVI In Na-

kuru region, Kenya.

*Kipterer John Kapoi,1 Omowumi Alabi,

2

1Regional Centre for mapping of resources for development (RCMRD); Kenya:E-Mails:

[email protected]*2African Regional Centre for Space Science and Technology Education in English, Obafemi

Awolowo university Campus, Ile Ife, Osun State ; Nigeria: E-Mail: [email protected]

Abstract

This study was focused on Nakuru, a tropical region in the Rift Valley of Kenya, bounded between latitude

0.28N and 1.16S, and longitude 36.27 E and 36.55E. The main aim of this research is to assess the agricul-

tural drought in the high potential region of Kenya with an objective of mapping the agricultural drought severity

levels, assessing the precipitation and normalized difference vegetation index deviation over its long term mean

average in the region and to generate land surface temperature and emissivity maps to compare the surface tem-

perature proportion during the drought and normal period.

The data were obtained from NOAA-AVHRR, LANDSAT TM and ETM+ and was processed with ERDASImagine and GIS software of the Environmental Systems Research Institute (ESRI). The land surface tempera-

ture was derived using Plancks radioactive principles. The thermal band of Landsat TM was utilized to extract

the radiance and brightness temperature. The brightness temperature was combined with surface emissivity to

derive the land surface temperature (LST) while NDVI was derived from bands 3 and 4 and its result was di-

vided by the LST to determine the moisture levels. The products were classified into five main classes to reflect

the moisture levels. Rainfall and NDVI performance were also processed from NOAA AVHRR and long termmean established and compared with the specific year of student performance.

The result of the study revealed that NOAA-AVHRR data offers very useful information in drought monitoring

and early warning, LST and NDVI are useful in moisture level mapping that can be used to detect drought and

the drought in Nakuru is characterized by both low and high temperatures that exacerbates the crop failure.

Key words: Drought, Land Surface temperature (LST), land use classes, emissivity, Vegetation

Introduction

The frequent drought experienced in the greater horn of Africa has negatively impacted on the natural

habitats, livelihoods and health, and the food production levels in the region. According to the Food

and Agricultural Organization (FAO, 2011), the decline in food production associated with the

drought conditions in this part of the world is one of the major factors responsible for the increased

cost of food and malnutrition experienced by the inhabitants. Other affiliated consequences of the

drought situation which include land degradation and soil erosion are most pronounced in the agricul-

turally potential areas.

An example of an agricultural region in the greater horn of Africa that has been subjected to the devas-

tating impact of drought is Nakuru County in Kenya. According to Mubea et al. (2009), Lake Nakuru,

the major tourist attraction in this agro-ecological zone has been decreasing in size, with a depth re-duction from 2.6m to 1.4m (Morgan, 2009). Furthermore, some of the perennial rivers in the region

have become seasonal (Roncoli et al., 2010 and Morgan, 2009). The declined water conditions experi-

enced in the region have also resulted in mass death and migration of birds, notably flamingoes and

marabou stocks (Thome, 2009 and Mwenya, 2009). Furthermore, this fertile zone, once accredited

with good crop yield, has over time experienced a decline in wheat production (Hezron et al., 2007).


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According to Roncoli et al. (2010), the changing weather has resulted in adaptation strategies from

wheat production to maize, sweet potatoes, and the cultivation of vegetables.

Some severe examples of weather changes associated with the drought situations in Nakuru include

adverse changes in temperatures, with extreme prolonged cold weather destroying crops and reducing

livestock production. The harsh, foggy, windy and frost moments have also had a negative impact on

pollination (Roncoli et al., 2010).

These changes which have resulted in decrease in household food production levels have also gener-

ated secondary social problems such as conflicts between the crop farmers and livestock keepers. Fur-

thermore, the continual diminution of rainfall in the area resulted in declining forests as a result of

pressure from population to meet their energy demands. The region has also experienced reduced in-

vestment in farming as farmers have reduced their cultivation to sizeable land in order to minimize

massive losses in case of crop failure as a result of drought (Mubea et al., 2009 and Walubengo, 2007).

In Kenya, little attention is given to drought assessment in agricultural potential areas such as Nakuru.

The impacts of drought, as observed in the previous studies (Hezron et al., 2007; Mubea et al., 2009;

Morgan, 2009; Thome, 2009; Mwenya, 2009; Roncoli et al., 2010) cannot be underestimated as it af-

fects the overall food production levels and the government grain reserves. This research is designedto assess the drought levels in the agricultural potential zone of Nakuru using remotely sensed data to

determine the drought severity levels based on temperature and vegetation health.

Existing Work in the Field and Gap in the State of Knowledge

National and local organizations have devised mitigation measures against the severe impacts of

drought in Nakuru. For example, the Kenya Wildlife Service (KWS), an authority for wildlife service

in Kenya has intervened by drilling boreholes, constructing water troughs and pumping water to the

troughs for wildlife in the National parks. Furthermore, the authority, as a result of receding pasture

has relocated the large herbivores (e.g. elephants, rhinoceros and buffaloes) to other parks (Mulanda

and Macharia, 2009). The urban dwellers in the region have also devised strategic methods of adapta-

tion. In order to cope with the declining purchasing power resulting from increasing cost of food and

dwindling food supply to the urban dwellers, urban households have been forced to diversify theirlivelihoods by introducing urban farming in compounds, along streets, river banks, under power line

and any other open spaces in the urban areas (Africa Studies Centre, 2006).

A major setback to the present drought monitoring scheme in Nakuru County and other high potential

agricultural regions in Kenya is the lack of early drought warning programmes or strategies from the

government. Nakuru is unlike the arid and semi arid regions which have adequate early warning pro-

jects such as the National Drought Management Authority (NDMA) formerly Arid Land Resource

Management Project (ALRMP).

ALRMP, a project funded by the World Bank and the Government of Kenya, developed a remote

sensing methodology for drought monitoring that relies on NOAA-AVHRR rainfall estimate (RFE)

and normalized vegetation index (NDVI) input data. This remote sensing drought monitoring methodis supplemented by semi structured questionnaires that are administered by sentinel monitors in the

interior rural areas. These questionnaires are designed to capture personal household details of the in-

habitants, such as income level, malnutrition data, conditions of livestock, water and pasture accessi-

bility and commodity prices in the markets (ALRMP, 2011).

The limitations associated with the remotely sensed drought monitoring scheme of ALRMP is avail-

ability of NDVI data after the sensor failed in 2010. Other associated problems include reliable time


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series precipitation analysis to quantify pasture through normalized difference vegetation index as well

as unclear relationship on the primary and secondary data analysis thresholds. Although this method

still provides very effective means of drought monitoring in low agricultural potential districts (i.e.

arid and semi arid land of Kenya), the results from these analysis are not reliable in high agricultural

potential zones of Kenya such as Nakuru.

In the neighboring war torn country Somalia, the existing drought monitoring methods such as Com-bined Drought Index (CDI), developed by United Nations Food and Agricultural Organization under

Somalia Water and Land Information Management System (FAO-SWALIM), based in Nairobi with

monitoring field stations in Somalia, has successfully managed to cover a portion of the country, using

the few existing rainfall stations managed by the humanitarian organizations and local authorities.

This assessment method has had its adequate applications in data scarce environments in Somalia and

parts of Kenya (Balint and Mutua, 2011). The major limitation of this method is the spatial coverage

considering the fact that the scheme heavily relies on in-situ meteorological data which is poorly dis-

tributed and susceptible to gross measurement errors.

The main strength of the method applied in this research is its capability to integrate both temperature

and vegetation vigor values for detection of moisture levels which is a reflection of moisture contentsin the leaf. The method however, is not limited to one form of data, as a variety of remote sensing data

with thermal bands and vegetation detection capability exists making it one of the most Versatile op-

tion. An example of such data is MODIS, SPOT, NOAA-AVHRR and LANDSAT data among others.

Drought

There is no clear definition of drought; it only depends on the context and regions. Scientist over time

tried to define this phenomenon, but still there is no clear definition. Previous observations observed

that there was no clear universal definition of drought because its perception is dependent on water

deficit, Nguyen, (2006). Other observations as of Palmer, (1965) defined it as a prolonged and abnor-

mal moisture deficiency, whereas Byun. H, (2010) defines drought as a relative term to normal condi-

tions while water shortage is absolute term for water demand.

In an attempt to contextualize to tropical region, SDMC a disaster management Centre in New Delhi,

defines it as; a climatic anomaly characterized by deficient supply of moisture resulting either from

subnormal rainfall, erratic rainfall distribution, higher water needs or combination of all these factors,

SDMC,(2012). According to SDMC, (2012), droughts are however resultant of acute water shortage

due to lack of rains over extended period of time affecting various human activities leading to crop

failure, un replenished ground water resources, depletion of water in lakes, reservoirs, shortage of

drinking water and reduced fodder availability, SDMC,(2012).

Drought Categories

Wilhite and Glantz (1985) analyzed more than 150 drought definitions and broadly categorized them

into four main categories; meteorological, Agricultural, Hydrological and Socio economic droughts.

The droughts further are classified in the context of permanent, seasonal, contingent and invisible, J.O

Ayoade, (2004).

Meteorological Drought: The meteorological drought gas been defines as the degree of dryness spec-

ified by deficiencies of precipitation and the duration of the dry spell. (WMO, No.1006, 2006: A.

Schuman, 2006). Meteorologist makes distinction between absolute and partial drought. Absolute

drought I is said to be a period of 15 consecutive days to none of which is credited with 0.2mm or

more of rainfall, whereas as partial is a period of at least 29 consecutive days of which its mean rain-


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fall does not exceed 0.2mm, J.O.Ayoade, (2004).

Agricultural Drought:The Agriculturalist perspective of drought is when moisture storage available

through rainfall or soil is insufficient to ensure optimal crop growth as in, J.O Ayoade (2004). Agricul-

tural drought has a centre focus in the precipitation deficits and its impacts. It has great relationship

with the meteorological and hydrological characteristics, as observed (A. Ellis, 2010) in the difference

between the precipitation and potential evapotranspiration balance. The difference between the actualevapotranspiration and reduced water content in soil and reservoirs levels, as affects plant water de-

mand, is evident in both plant physical and biological growth properties, Parul Chopra, (2006).

Hydrological Drought: To hydrologist, drought is as a result of low flow in rivers below a critical

threshold discharge. Hydrological drought has been defined in various interrelated versions by many

researchers as significant decrease in availability of water in all its forms, i.e. Surface water, stream

flow, Lake Reservoir levels, ground water and ground water levels appearing in the land phase of hy-

drological cycle as in the case of Khana,(2009). Hydrological drought may be the result of long term

meteorological droughts that results in drying up of rivers and decline in ground water levels, Rathore,

(2004). Descriptions such as those reflected in Tallaksen and Van Lanen, (2004) that refers hydrologi-

cal drought as sustained and regionally extensive occurrence of below average natural water availabil-

ity. This definition seems to agree with the fact that the drought phenomenon is closely associatedwith long term absence of precipitation, and prolonged or increased evapotranspiration.

Socio Economic Drought: The socio economic drought as observed in the case of Wilhite and Glantz,

(1985), is said to occur as a result of physical water shortage that ends up affecting people at individ-

ual scale. The greater demand on commodities than the supply of economic good can best describe

this situation.

AridLandsResourceManagement Project(ALRMP), Baringo, 2009 describes this impacts of drought

situation through the observation on the livestock body conditions, where the livestock body condi-

tions were found to be deteriorating, with decline in milk supply affecting the market prices, whereas

in the agro pastoral areas, crop failure cases reported, affecting the market cereal availability and

farmers purchasing power. This scenario clearly reflects the creeping drought in the socio economic

context.

The socio economic droughts reflects the elements meteorological, agricultural and hydrological is

drought as it results from the absence or low precipitation, vegetation content reduction for forage,

stream flow reduction and declining levels of water as reflected in drying up and diminishing recharge

capacities of aquifers.

Impacts of Droughts

Economic impacts

The massive loss of livestock in Baringo and significant loss of rangeland and pasture marked a record

observation on stock population changes in Baringo (1983-1985) from onset of the end of severe

drought, Homewood et al, (1987). Drought events in many cases impose negative impacts on envi-ronment and causes widespread structural damages as observed in, Akhtari et al, (2011). Increased

insect infestation, wind erosion, plant and animal and diseases as well as forest and range fires in India

are commonly observed impacts in India, Chopra, (2006). These events are common to drought prone

areas in the greater horn of Africa, and in Baringo County.

The effects of drought are clearly manifested by reduced crop production, loss of agriculture, land deg-

radation, livestock population deaths unemployment and health problems, Murad et al, (2011). The


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most common economic impacts in Baringo county (ALRMP,2009) is associated with wasted animal

body conditions, reduced milk production, direct loss of browse and pasture, predation of small rumi-

nants by the Hyena and baboons as well as crop failures is common.

Environmental Impacts

The environmental impacts in Baringo county as per (ALRMP, 2009-2011) is realized through the

hydrological effects where the water sources e.g. water pans, rivers dry up and the reporting of re-

duced levels of water by flowing springs and drying up of the available boreholes. The loss of biodi-

versity, natural habitat, degradation of landscape, increased soil erosion leading to permanent loss of

land productivity and the loss of wetlands impacts negatively on plant and animal species and the eco-

system. These key elements induce migration of host communities as of the case of Baringo County

(ALRMP, 2009) where the pastoral and agro-pastoral communities migrate to search for better and

quality pasture and water.

Social Impacts

The typical social impact as a result of drought stress includes the conflicts and public safety, health

and nutrition affecting quality of life, population migration and increased poverty. For the case of the

study area, the malnutrition levels has been reported to be declining as stated in ALRMP, 2009 bulle-

tins, that resulted from unavailability of essential commodities in markets and declining milk produc-

tion by livestock. The loss of human lives through protracted drought impacts occasioned by increased

heat stress and declining purchasing power in Kenyas arid and semi arid counties has been a key

cause of water and management conflicts among the pastoral and agro-pastoral livelihood zones.

Agricultural Drought Indices

Normalized Difference Vegetation Index (NDVI)

The NDVI is a dimensionless variable. The index can be used to provide information for agriculture

and vegetation health situation. This information is useful in determining water stress levels in vegeta-

tion and estimation of crop yield (Penuelas et al, 1993 and is useful in drought assessment (Tucker,

1980).It provides information of vegetation health that can be used as a means of monitoring changes

in vegetation over time. The healthy vegetation absorbs most of the visible light that it receives and

reflects a large proportion of the near infra red light. Unhealthy or sparse vegetation reflects more visi-

ble light and less near infra red, Frantzova (2010).

Research by Thomas, et al (2004) observed that, NDVI has limited capability for estimating vegeta-

tion water condition as it is affected by other variables. Ceccato et al(2002b) in Thomas, T.J et al,

(2004), summarized the limitations of using NDVI as ; different plant species has their own relation-

ship of chlorophyll content and vegetation water condition, and a decrease in chlorophyll content doesnot imply a decrease in vegetation water condition, whereas decrease in vegetation water condition

does not imply a decrease in chlorophyll content.

Another limitation observed by Tsegaye Tadesse (1998) is that, in cases of extended periods of cloud

coverage, the NDVI values tend to be depressed giving a false impression of water stress or droughtcondition. To remove this effect, the temperature condition (TCI) is used. The TCI is derived from

brightness temperature (BT) and its algorithm is similar to that of NDVI vegetation response. The

combination of VCI/TCI is also used to estimate vegetation stress.

Normalized Difference Water index (NDWI)

The NDWI is a dimensionless product whose value ranges between -1 to +1 depending on the leaf wa-


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ter content, vegetation type and cover. High values of NDWI correspond to high vegetation water con-

tent and high vegetation fraction cover and vice versa, whereas its anomalies are in standard deviation

units commonly ranging from -4 to +4, Joint Research Council (JRC, 2011).

The NDWI anomalies are more dependent of time series available to calculate the mean values and the

standard deviations. To achieve better results, the period should be long enough to characterize the

area study area. The indices are calculated as normalized difference vegetation index but the red bandis replaced by the short wave infra red band, (SWIR) canal of (1580- 1750um) of SPOT VGT.

NDWI is very efficient in the domain of stress because it is sensitive to soil moisture content, vegeta-

tion cover and leaf moisture content, Tychon et al(2007). Some of the noted weaknesses of NDWI are

in its susceptibility to soil background effects on partial vegetation cover. Drought and water stress are

not the only factors that can cause a decrease in the NDWI values or anomalies. The Change in land

cover or pest and diseases can also be responsible for such variations of the signal, Gao et al(1996).

To enhance the information obtained from the index, the indicator must be used jointly with other in-

dicator that gives more information on precipitation and soil moisture to determine the vegetation re-

sponse in case of drought investigations Gao, et al,(1996), JRC (2011). MODIS NDWI has been used

to detect and monitor the moisture conditions of vegetation canopies, Xiao et al (2002), and has been

tested as drought indicator (Gu, et al, (2007) and found that the values exhibited quicker response to

drought condition than NDVI.

Water Supplying Vegetation Index (WSVI)

Water supplying vegetation index is an indicator based on the relationship between normalized differ-

ence Vegetation Index and surface temperature. This method provides effective method of estimating

surface moisture condition Luke, et al,(2001).WSVI is an index that combine both aspects of vegeta-

tion and temperature as observed byXiao, et al,(1995).

In the classifications of WSVI, Higher values are indicative of greater moisture amounts and in this

situation canopy temperature are lower and NDVI values are higher, whereas the lower values is and

indicative of drought. WSVI values ranges from a value of -4 for extreme drought to +4 for highlymoist condition, Luke, et al,(2001).

Jiyuan, et al, () observed in his applications that, WSVI takes into account the effects of vegetation

reflection in red, near infra red and thermal band. It was found that, the method is effective where

NDVI is greater than 0.3. Jain et al (2010), observed that, in drought condition, NDVI values derived

from satellite data will fall below normal while the crop canopy temperature will rise above normal

both of these effects are related to available water supply, and combining both would yield a sensitive

measure of drought condition. The visual output of WSVI was compared with PDSI in Luke et al

(2001), showed similarities in estimates of general moisture conditions.

Normalized Difference Drought Index (NDDI)

The Normalized Difference Drought Index (NDDI) is another strong drought index. The NDDI com-bines information from both the NDWI and NDVI data derived from the satellite data Gu, et al,

(2007). NDDI is found to be more responsive and have wider dynamic range values than a simple

NDVI and NDWI differencing through drought periods, Charat, et al, (2006), Liu Cheng-lin , et al,

2008).


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Charat, et al,(2006) observed that, the NDVI and NDWI values decreases with decreasing slope gra-

dient of cumulative rainfall while rapid increase of NDDI values during dry months of the years, thus

more sensitive to water content than NDVI and making it better index for drought identification.

In studies undertaken using MODIS NDVI and NDWI over great plains of United States, it was found

that NDDI had stronger response to summer drought conditions than a simple difference between

NDVI and NDWI and is therefore more sensitive indicator of drought in grass land than NDVI alone.It was also observed that, NDDI values increased during summer condition s which demonstrated an

additional indicator for large grassland drought monitoring, Gu, Y. et al,(2007).

Land surface temperature (LST)

Land Surface temperature is how the surface of the earth would feel to touch in a particular location.

Monitoring of land surface temperature enables critical assessment of the influence and how the sur-

face is influenced by weather and climate Patters. It is sometimes referred to as the surface skin tem-

perature of the earth.

LST is a very important variable required for a wide variety of applications for instance climatologi-

cal, hydrological, agricultural, biochemical and change detection studies, Prasanjit Dash (2005). The

LST as a climatic variable is related to surface energy balance and integrated thermal state of atmos-phere, Jin, (1999).it acts as an indicator of climate change due to its upward terrestrial radiation influ-

encing sensible latent heat flux exchange of air. Yin, (2007)

Land surface temperature therefore can provide information about surface physical properties and cli-

mate which plays a role in environmental processes, Javed et al, (2008). The LST research shows that

land surface temperature varies with surface soil water content and vegetation cover, Weng et al

(2003) that the higher latent heat exchange is found with vegetated areas while the sensible heat ex-

change found in sparsely vegetated and urban areas. The land surface temperature is sensitive to vege-

tation and soil moisture and it can therefore be used to detect land use, land cover changes, Javed et al,

(2008).

LST validation is difficult because derived quantity is representative of the whole pixel, while pointtemperature measurement covers a short distance; hence a field validation is possible for homogene-

ous areas e.g. dense vegetation, desert and others, Dash, (2005).Dash, (2005) observed problems that

are associated with LST and summarized them as follows;

i. The surface emitted radiance is altered by atmosphere before reaching the top of Atmos-phere (TOA) sensors.

ii. Radiance measurement by sensor are made in one direction which is not necessarily rep-resentative for upper hemisphere, hence angular characterization of emissivity is difficult

depending on anisotropy and

iii. Separation of temperature from surface radiance is unfeasible because of under determi-nation for sensor with spectral channels.

Land surface temperature estimation methods

There are three main methods of estimating LST.

i. Single channel method. In this method, Top of Atmosphere (TOA) radiance is directly com-pared with radiative transfer calculations of known land surface emissivitys and land sur-

face temperature are derived. This method is accurate but it needs exact atmospheric infor-

mation.


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ii. Split Window Technique (SWT)/Multi channel method. This is based on differential absorp-

tion in two spectral channels within 10 12!m atmospheric window and land surface tem-

perature is related to this measurements.

iii. Multi angle Method. This method is similar to SWT, but the differential absorption is due to

different atmospheric slant path-lengths when the same target is observed under different

viewing angles in the same spectral range.

Land surface emissivity and its retrieval methods

Emissivity is defined as the radiation efficiency of a real world surface as compared to a blackbody

radiator. A black body is the hypothetical object that absorbs all radiation that falls on it. It is also de-

fined as the ratio of emittance from a body to that of blackbody (a perfect emitter) at the same tem-

perature. Sobrino et al (2004) developed and proposed an algorithm to compute emissivity (") of

mixed pixels composed of bares soil and vegetation.

Data and Methods

The data type to be used is Landsat 5 and 7 satellite image from USGS for 2000, 2010, 2011 with var-

ied spatial resolutions of 15m at panchromatic, 30m at Visible and near and mid Infra red and 120m

for TM and 60m for ETM+ at Thermal infra red and NOAA-RFE(Rainfall) data of 8km spatial resolu-tion.

A stepwise processing chain was established based on the inputs and flow involved in the extraction of

quantities in the satellite image. The processing tools that will be utilized in this research will be the

use of ESRI ArcGIS products, Leica Geosystems products; ERDAS IMAGINE and Microsoft suite.

The output results for the NDVI data generated in 30m spatial resolution of Landsat TM bands 3 and 4

will be resampled to 120m cell resolution to overlay with the Land surface temperature output gener-

ated from band 6 of the same image with 120m spatial resolution to enable cell statistics analysis in

the assessment. Water bodies urban, artificial surfaces and bare rocks layer will be developed from

the globe cover land use classes and will be used as a mask in the final NDVI and LST layers for

analysis, so that results will not be biased as this land use classes will affect the result. The process-

ing flow below outlines the major processing steps to be undertaken and the final output of the project

as agricultural drought map.

Processing flow

Fig

1:


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Processing chain

Normalized Differnce Vegetation Index extraction

The NDVI is a dimensionless variable. The index can be used to provide information for agriculture

that can be used to determine water stress levels in vegetation and estimation of crop yield. Penuelas et

al,(1993) and is useful in drought assessment (Tucker, 1980). NDVI is expressed as follows:

; (1)

This is similarly Landsat TM, TM 3 (0.66 !m, red band and TM4, (0.83 !m near infra red band)

respectively, are used to compute vegetation indices and NDVI, therefore based on these concept,

the same can be expressed in LANDSAT TM and ETM+ data as;

; (2)

Atmospheric correction based on image data

In order to obtain accurate NDVI values that are more representative, the top of the atmosphere values

(TOA) has to be corrected and this can be computed by use of the algorithm developed by Chavez

(1996) as provided below in, Quinqin et al,(2010);

:Chavez, (1996) (3)

Where; Lsat= radiance at sensor, d =Earth-Sun distance, Eo = Spectral solar irradiance on top of the

atmosphere, #z =Solar Zenith Angle, Tz=Atmospheric transmissivity between sun and surface Lp=

irradiance resulted from interactions of the electromagnetic radiance with the atmospheric components

(molecules and aerosols) that can be obtained as;

Lp = (Lmin - L1%) (4)

Where; Lmin is irradiance that corresponds to digital count value for the sum of all pixels with digital

counts lower or equal to this value of 0.01% of all the pixels from the image and is expressed as;

; (5)

Where Tz (Atmospheric transmissivity) of TM 3 and TM 4 is 0.85 and 0.91 respectively

The spectral solar irradiance for the Landsat TM and ETM+ quantities to be applied was sourced from

in, Quinqin et al, (2010) as shown in the table below;

TM and ETM+ Solar Irradiance (Eo) (Wm-2

x!m

Band 1 2 3 4 5 7


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!+

TM4

(Markham

&Barker,

1986) 195.8 182.8 155.9 104.5 21.91 7.457

TM

5(Neckel &Labs

(1984) 195.7 182.9 155.7 104.7 21.93 7.452

ETM+ (Ig-

bal (1983) 1969 1840 1551 1044 225.7 82.07

Table 1 Solar Irradiance (Quinqin et al, (2010)

Radiance and Temperature brightness retrieval

The radiance is computed using the algorithm; L$= Gain* DN +Bias (Landsat7 Science user data

Handbook Chapter 11, (2002); (6)

Where: L$is Radiance, DN is digital Numbers values recorded, Gain is (L!max L!min)/255 (slope

of the response function). Bias is the Lmin(intercept response function). L$maxis the highest and L$min

is the lowest radiance measured at detector, (saturation in MWcm-2

r-1

). In the Landsat 5 metadata,

L$maxand L$minwere obtained from metadata to be 15.303 and 1.238 respectively.

The Sensor calibrations constants for the Landsat TM and ETM+ is expressed in the table below

TM and ETM+ Thermal Band Calibration Constants

Constant 1-K1 (Wm-2

sr-

1!m)

Constant 2-K2

(Kelvin)

ETM+ (Markham

&Barker 1986)

666.09 1282.71

TM (Irish, 2000) 607.76 1260.56

Table 2 Thermal Band Calibration; NASA, Quinqin et al, (2010)

The radiance brightness temperature was thereafter extracted based on the sensor algorithm available

in the Landsat Handbook in the equation 7;

: (7)

Where, TBis the sensor brightness temperature in Kelvin (K), K1 is calibration constant 1 equal to

666.09 Wm-2

sr-1!m, K2 calibration Constant 2 equal to 1282.71 watts/m

2sr !m, L$is spectral radi-

ance expressed in Watts/m2sr !m. Refer Table 3: Thermal Band Calibrations.


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Land surface Emissivity Estimation

Sobrino et al (2004) developed and proposed an algorithm to compute emissivity (") of mixed pixels

composed of bares soil and vegetation as expressed in the equation;

"= "v PV+ "s (1- PV) + %E; Sobrino et al,(2004); (8)

Where "v and "s is emissivities of vegetation (0.99) and soil (0.97) respectively, %E is the effect ofgeometric distribution of natural surfaces and internal reflection with plain surface assumed to be of

negligible unit and heterogeneous and rough surfaces e.g. forest among others takes a value of 0.55.

Sobriono et al, (2004).The term PVis vegetation proportion obtained according to Carlson and

Ripleys (1997) as expressed in the equation below;

;Carlson and Ripleys (1997); (9)

Where; NDVI max = 0.5 and NDVI min = 0.2

Land Surface Temperature retrieval

The brightness values obtained was therefore converted to land surface temperature. The algorithm for

conversion applied is as shown below;

; (10)

Where: LST= Land surface temperature,!= Wavelength of emitted radiance for which the peak re-

sponse and the average of limiting wavelength is spectral radiance. (!=11.5!m) Markham and Barker,

(1985) will be used, &= hc/', where 'is Stefan Boltzmann constant, h=Planck constant and c=speed

of light in a vacuum, TB = Sensor Brightness temperature and lnis the natural logarithm to base10

(alog) "= surface emissivity.

Drought Assessment using WSVI Index

The Water supplying vegetation index (WSVI) is one of the indices that were developed to combine

the NDVI and the temperature (Land surface temperature) to detect the moisture condition, Luke et al

(2001). The Expression for this index was developed by Xiao et al,(1995) and is expressed as shown

below

; Xiao et al,(1995), (11)

Where, LST is Land surface temperature.

NOAA-AVHRR RFE Performance in the period of study

The NOAA-AVHRR rainfall estimates will be downloaded and the long term mean average of ten

years will be established for the area of study. The precipitation performance for the years of study

will be compared with the long term mean average performance to assess deviations from normal

trends. Graphical and image trends will be derived to establish the rainfall changes from the long term

mean.


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The Study Area

This research is focused on Nakuru County, a region in Kenya bounded between latitude 0.28N and

1.16S, and longitude 36.27 E and 36.55E. Nakuru has a land mass of 7,495 km2(CRA, 2012), with

a population of 1,603325 and growth rate of 3.4% per annum (CBS, 2009).

The fertility of Nakuru can be traced to the geology of the area. Nakuru lies within the Great Rift Val-

ley which is characterized by volcanic activities and volcanic land formations. According to studies byWegulo et al. (2010), the soils in this study area are molic andosols that developed from volcanic

ashes and pyroclastic rocks from recent volcanoes. These soils are well drained, deep to moderately

deep firm clay loam with humic top soil of high fertility.The study area has two agro-ecological zones

that are classified as Upper Highlands (UH2) with altitude ranging from 2580m to 2800m above the

mean sea level. Nakuru has an average mean temperature of 12C to 13.7C with an annual average

rainfall of 1100 mm to 1400 mm per annum. The altitude of the Lower Highlands (LH3) range be-

tween 1890m and 2190m above the sea level while the mean average temperature varies from 15C to

17.5C with an annual rainfall average of 810 mm to 1100 mm. The map of the study area is shown in

Figure 1.Results and Analysis

Agricultural drought severity levels.

The data used to generate the water supplying vegetation Index to determine the moisture levels, was

the land surface temperature and normalized difference vegetation Index from Landsat Image. The


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moisture levels were derived from the NDVI and land surface analysis. The index values were re-

classified into five (5) major vegetation moisture levels classes and applied to all analysis.

Class Index Severity Levels

1 (-0.00157) - (-0.00133) Very Low Moisture

2 (-0.00133) - (+0.0084) Low Moisture

3 (+0.0084) - (+0.0155) Moderate

4 (+0.0155) - (+0.0243) High Moisture

5 > (+0.0243) Very High Moisture

Table: Moisture Classification

The results indicates that about 39.71% of the study area in the year 2000 experienced moisture defi-

cit, the vegetation moisture levels ranged from low to very low, which is an indication of poor rainfall

performance well below the long term mean average, with poor vegetation health implying severe

drought. The forested areas and areas with higher vegetation density around lakes had high to very

high moisture levels, with areas that are normally of high moisture having moderate moisture levels, a

clear indication of vegetation stress.

The year 2010 indicated very adequate moisture levels in the vegetated area. Low to very low moisture

levels was 7.03% with high o very high moisture having 67.36% coverage and moderate levels of

25.61%.This indicates that, the years rainfall and vegetation performance adequate enough and better

harvest would be realized. The rainfall and vegetation performance is confirmed by its performance in

the year as shown in figure 4.2b (Graph of Rainfall and Vegetation 2010). The rainfall and vegetationhealth was well and above its normal long term mean average.

In the year 2011, he results indicates that, regions of low to very low moisture levels covered 12.24%,

with high to very high moisture levels taking 59.99% of the area with moderate moisture levels of

37.77% Coverage. This indicated that the years rainfall and vegetation performance was very stable

and pasture and browse for livestock as well as crops were adequate and doing well.

Precipitation and NDVI deviation over the long term average

The data utilized to achieve this result was derived from NOAA-AVHRR. The satellite images has a

resolution of 8km by 8km.A long term mean average for the study area (1981 to 2008) was derived

and used for comparison for rainfall and vegetation performance in the specific interest years of 2000,


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2010 and 2011.The graphical analysis was derived and analyzed in simple Microsoft excel as shown

below.

Figure 4.2.1: Graph of Rainfall and Vegetation 2000

The rainfall performance for the first agronomic season in the county normally referred to as long

rains season in Kenya, (March to May) recorded rainfall that was below long term mean average, and

the vegetation health was poor as it fell below the long term average mean and failed to recover over

time. The vegetation health improved gradually with improved rains that were close to the long term

normal average especially between second dekad of June through to first dekad of august. Short rains

season is usually expected to start on August through to November realized improved rainfall per-

formance that was above long term mean average in the third dekad of October and the rest of No-

vember. December also recorded above normal rainfall on its first and second dekad. The vegetation

health in the second season was well above normal in some instances and close to normal indicating

that, the pasture, browse and crops performed well in the season.

The region experienced low rainfall in its first season, implying that most rain fed Agricultural crop

production failed as a result of prolonged deficits of rainfall. This is evident by precipitation that wasbelow the normal long term average. This is evident by the vegetation health situation that fell to a low

of 0.29 against its long term normal average of 0.41.

Rainfall and vegetation, 2010

The graph below shows the rainfall performance in the year 2010.The senor failed to provide vegeta-

tion data from the second dekad of March, however, SPOT vegetation data for the year was used to

provide the remaining average data.

Figure 4.2.2: Graph of Rainfall and Vegetation 2010

The rainfall season started very early this year, as January received rainfall high above normal long

term average. The performance for the first agronomic season (March to May) was very good. The

first dekad of March received the higher rainfall of 127.22 mm against its long term mean average of


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Class

Temperature

Range (oC) Class Level

1 0.0 - 12.82 Very Low

2 12.82 - 25.64 Low

2 25.64 - 32.06 Medium

4 32.06 - 37.82 High

5 >37.82 Very High

Table Temperature Classification

The results obtained after applying the classification range was generated as shown in the map below,

and the chart alongside was generated to show class spatial coverage in percentage 2000. The land

mass under high temperature (32.06oC - 37.82

oC) and very high temperature (>37.82

oC), is 27.59%

and 21.69 %, which makes up to 49.28% which is very large area. This result indicates that most of

the crops and other vegetation (pasture) were water stressed, or under drought situation. About 21.98%of the vegetation in farms that are near or close to forested areas, were within the medium temperature

range of category of 25.64oC - 32.06

oC, implying that, some crop produce could have chances of sur-

vival, though they may not adequately reach their maximum output. The very low temperature class

with a range of between; 0.0oC - 12.82

oC was identified in pixels contaminated by clouds and areas of

no data.

The Land surface emissivity used to obtain the land surface temperatures was generated, and the emis-

sivity variation over land indicated low of 0.8389 and high of 0.9944. The emissivity variation over

dense vegetation was at 0.9711, Water Bodies, 0.9894, while mixed Vegetation (Agricultural land)

had 0.9811.The table below (table 4.3b) shows more details on the features standard deviations,

minimum and maximum recorded values, while and the Map (figure 4.3b) shows detailed spatial

variations.

In the year 2010, the results reveal that about 0.8% and 5.78% of the land was within low and very

low temperatures, and 64.63% (25.64oC to 32.06

oC) was in medium, while areas with high and very

high temperatures had 24.86% and 3.93% respectively. Compared to the year of 2000, this year sig-

nificantly improved, as the land mass that was within low and very low temperatures in 2000 was

about 49.28% compared to this year which was 28.79%, a difference of about 20.49%. The areas un-

der low (12.82oC - 25.64

oC) and very low (0.0

oC - 12.82

oC) temperature are low

The land surface emissivities in this year showed variations with water remaining highly emissive fol-

lowed by agricultural and forested areas. The table (table 4.3.3) and map below shows the detailed

variations and the standard deviation over the feature class characteristics.The emissivity variations in

this year showed clear distinction between water bodies, agricultural land, and dense forested areas.

The temperature variation in this year reveals that, low, medium and high temperatures dominated

most of the parts of the study area, with medium temperatures, (25.64oC to 32.06

oC) realized in cov-

ering about 39.2% of the area. Low temperature between (12.82oC - 25.64

oC), covered significant

portion of the area with about 30.92% coverage.


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!(

From the rainfall performance data, the first season of this year started early and very well above the

normal average and performed steadily throughout to the next season, this year, appears to be normal

as the rainfall fluctuated within normal and above the normal in most cases.

The land surface emissivity revealed very strong relationship supporting the vegetation performance.

The average emissivity of this year was at 0.9765 for agricultural land same as the emissivity realized

in the previous year where the rainfall and vegetation performed well above the normal, whereas thedense vegetation was at 0.9702 compared to 0.9704 the previous year (2010) .The map below shows

the surface emissivity variations over space, whereas the table shows average emissivitys over land

use classes.

CONCLUSIONS AND RECOMMENDATIONS

The Combination of NOAA AVHRR rainfall estimates and normalized difference vegetation index,

provides very useful information for a drought monitoring and early warning system. This is indicated

by the graphical analysis that indicated by the continued decline in vegetation index precipitation over

its long term mean average.

The use of temperature and vegetation index provides adequate means for mapping drought extend

over the agricultural fields, though the method of deriving the emissivities and land surface tempera-ture is highly technical for quick results though scripts can be developed to derive the data easily. The

existing methods take into consideration a number of scientific factors that may not be professionally

friendly. Use of time series data offers potentials to establishing long term mean average for vegeta-

tion moisture levels that will provide adequate monitoring for effective early warning system to the

farmers.

The land surface temperature provides adequate indication of moisture levels on agricultural land near

dense vegetation cover such as forests which is an indicative of adequate moisture in plants, neverthe-

less, the produce is uncertain due to extreme high and low temperatures experienced by the vegetation.

The drought period indicates that the extreme low and high and very high temperatures dominates the

land (study area) causing more destructions to the crops and livestock production. The values obtainedin the (Figure 4.3.2) for the year 2000 reveals this result, which was converges with other observed

researches in the same study area such as that of Roncoli et al,( 2010) where she observed that during

drought in Nakuru, prolonged cold weather destroying crops and reducing livestock production, harsh

foggy, windy and frost moments affecting pollinators is experienced.

Time series analysis for vegetation moisture monitoring is recommended for these studies in order to

establish long term mean average that can be used for monitoring and provision of timely warnings to

the farmers soon the moisture level performance constantly remains below average.

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Appendices

Moisture Levels, 2000

The Map and chart below represents the spatial outcome of the analysis.

Moisture levels, 2000

Figure Map

of Nakuru showing Moisture Levels, 2000


Chart 4.1.4: Moisture levels, 2010


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Figure 4.1.3: Map of Nakuru showing Moisture Levels, 2010


Figure 4.1.6: Moisture levels, 2011

Figure 4.1.5: Map of Nakuru showing Moisture Levels, 2011

Temperature spatial variation in the year 2000

Figure 4.3.2: Graph of LST Proportion, 2000


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Figure 4.3.1: Map of Nakuru Showing LST variations,

Temperature spatial variation in the year 2010

Figure 4.3.5: Graph of LST Proportion, 2010

Figure 4.3.4: Map of Nakuru Showing LST

variations 2010

Temperature spatial variation in the year,

2011


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Figure 4.3.8: Graph of LST variation 2010

Figure 4.3.7: Map of Nakuru Showing LST variations 2010

Agricultural Drought Severity assessment using land Surface temperature and NDVI in Nakuru, Kenya

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