Influence of Relative Soil Moisture
derived by Remote Sensing
on Flea Abundance
for monitoring Plague in Kazakhstan
Thesis for Msc Research Earth Sciences, Hydrology (33,5 ECTS)
15th august, 2011
Agnes Vader
studentnumber 0263265
agnesvader@hotmail.com
Supervisor: Professor Steven de Jong, Faculty of Geosciences
Assistant reader: Msc Liesbeth Wilschut, Phd Candidate Physical Geography
1
Contents
1 Introduction……………………………………………………………………………………………………………………………………1
1.1 Plague……………………………………………………………………………………………………………………………………………1
1.2 Flea abundance…………………………………………………………………………………………………………………………….2
1.3 Soil moisture…………………………………………………………………………………………………………………………………3
1.4 Research questions……………………………………………………………………………………………………………………….3
2 Background……………………………………………………………………………………………………………………………………..5
2.1 Plague……………………………………………………………………………………………………………………………………………5
2.2 Gerbils…………………………………………………………………………………………………………………………………………..6
2.3 Fleas…………………………………………………………………………………………………………………………………………..…6
2.4 Study area……………………………………………………………………………………………………………………………………..7
2.5 Remote sensing for soil moisture…………………………………………………………………………………………………..8
2.6 Previous research for plague monitoring…………………………………………………………………………………….10
3 Data & Programs…………………………………………………………………………………………………………………………..12
3.1 Flea data…………………………………………………………………………………………………………………………………..…12
3.2 Satellite images………………………………………………………………………………………………………………………..…13
3.3 Programs……………………………………………………………………………………………………………………………….……16
4 Methods………………………………………………………………………………………………………………………………………..17
4.1 Flea data statistics…………………………………………………………………………………………………………………….…17
4.2 Soil moisture estimation…………………………………………………………………………………………………………..…17
4.3 Combine flea index and soil moisture……………………………………………………………………………………….…23
4.4 Combine flea index and visual landscape patterns (SPOT)……………………………………………………………24
5 Results……………………………………………………………………………………………………………………………………….….25
5.1 Flea index value, statistics ………………………………………………………………………………………………………….25
5.2
Flea index value, spatial distribution………………………………………………………………………………………..…25
5.3
Linking flea index to soil moisture indicators…………………………………………………………………………….…26
6 Discussion…………………………………………………………………………………………………………………………………..………29
6.1 Reliability field data …………………………………………………………………………………………………………………..……29
6.2 Landsat data limitations………………………………………………………………………………………………………………..…29
6.3 Value ranges of soil moisture indicators………………………………………………………………………………………..…30
2
6.4 Flea abundance response to soil moisture conditions………………………………………………………………………30
7. Conclusions…………………………………………………………………………………………………………………………………….…31
8. Recommendations for further research……………………………………………………………………………………………32
References
Appendices
Appendix A: Basic statistics of flea index
Appendices B: Autumn 2011 plots
Appendix B1: Scatterplot of flea indices, autumn 2011
Appendix B2: Histograms of flea indices, autumn 2011
Appendix B3: Spatial distribution of burrows with or without flea index, autumn 2011
Appendix B4: Local distribution of flea index, within squares, autumn 2011
Appendix B5: Averaged temperature/flea index plots, autumn 2011
Appendix B6: Averaged TC Brightness/flea index plots, autumn 2011
Appendix B7: Averaged TC Wetness/flea index plots, autumn 2011
Appendix B8: Averaged NDVI/flea index plots, autumn 2011
Appendix B9: Temperature/flea index plots, autumn 2011
Appendix B10: TC Brightness/flea index plots, autumn 2011
Appendix B11: TC Wetness/flea index plots, autumn 2011
Appendix B12: NDVI/flea index plots, autumn 2011
Appendix B13: TVX plots, square 13, 2011
Appendix B14: TVX plots, square 21, 2011
Appendix B15: TVX plots, square 22, 2011
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Appendix B16: TVX plots, square 23, 2011
Appendix B17: TVX plots with flea index values, square 13, 2011
Appendix B18: TVX plots with flea index values, square 21, 2011
Appendix B19: TVX plots with flea index values, square 22, 2011
Appendix B20: TVX plots with flea index values, square 23, 2011
Appendices C: Summer 2012 plots
Appendix C1: Scatterplot of flea indices, summer 2012
Appendix C2: Plots of flea indices per square, summer 2012
Appendix C3: Local distribution of flea index, within squares, summer 2012
Appendices D: Autumn 2012 plots
Appendix D1: Scatterplot of flea indices, autumn 2012
Appendix D2: Boxplots of flea indices, autumn 2012, all and per square
Appendix D3: Local distribution of flea index, within squares, autumn 2012
Appendix D4: Temperature/flea index plots, square 53& 60, autumn 2012
Appendix D5: Temperature/flea index plots, square 41, 65 & 71, autumn 2012
Appendix D6: NDVI/flea index plots, square 53& 60, autumn 2012
Appendix D7: NDVI/flea index plots, square 41, 65 & 71, autumn 2012
Appendix D8: TVX plots with flea index values, square 41, autumn 2012
Appendix D9: TVX plots with flea index values, square 53, autumn 2012
Appendix D10: TVX plots with flea index values, square 60, autumn 2012
Appendix D11: TVX plots with flea index values, square 65, autumn 2012
Appendix D12: TVX plots with flea index values, square 71, autumn 2012
Appendix E: Flea index on SPOT image, square 13, 2011
4
1. Introduction
1.1 Plague
Plague is especially famous from the Black Death pandemic killing nearly half the population in Europe in
the 14th century. It was called the Black Death, because of the characteristic black spots on the victims.
Worldwide it is estimated to have been fatal for 75 million people (www.oddee.com).
If not treated, plague is a for humans lethal infectious disease caused by the bacterium Yersinia pestis.
Apart from humans Y.pestis can infect more than 200 animals, among them great gerbils. Most of these
gerbils are resistant to the plague bacterium, making them very suitable as host reservoirs (Gage &
Kosoy, 2004).
Plague is a vector borne disease meaning it is transferred
among hosts by vectors. Over 30 species of fleas are
suitable for transmitting Y. pestis, in particular the
oriental rat flea (Xenopsylla Cheopis). Fleas that live on
infected hosts can get the bacillus when they bite their
host and ingest contaminated blood (figure 1). This thesis
focuses on a research area in Kazakhstan, central Asia.
The main host in this region is the great gerbil. The
dominant vectors in the Central Asia are fleas although
Figure 1 Plague life cycle, source: www.columbia.edu
ticks, another vector, have been observed (Fedorov,
1960).
Nowadays, plague is mostly found in Africa and especially
Madagascar, accounting for 95% of reported Plague cases
(Stenseth, 2008), (see figure2). Other plague foci still persist
in Asia and the Americas. Globally, over 2000 fatalities each
year are reported caused by plague (see figure3). In the
1990s plague outbreaks have happened in locations where
the disease had been absent for a long time (Davis et al.,
2004; Neerinckx et al., 2010), therefore research in this field
Figure 2 Reported plague cases, source:
www.sciencedaily.com
still is highly significant to understand conditions for reemergence.
1
Figure 3 Reported plague cases by country, source: www.cdc.gov
1.2 Flea abundance
A lot of factors in the disease spreading of plague remain unestablished. Outbreaks occur in a wide
range of environmental conditions (Neerincks et al., 2008).
A study by Reijniers et al. (2012), showed that there is a flea abundance threshold: below a certain flea
abundance plague is not able to invade successfully. Statistically, a positive dependency of human
plague on flea density was shown by Samia et al., 2011. Their research states key interest in summer
precipitation, positively affecting flea population size in fall. Other studies (Gage & Kosoy, 2004;
Stenseth et al., 2006; Enscore et al., 2002, Krasnov et al., 2001) as well, relate that flea survival depends
on local microclimatic factors, such as soil moisture and temperature. This raises the question whether
soil moisture can be directly related to flea abundance, and consequently to chances of plague invasion.
In autumn 2011 and summer and autumn 2012 extensive research on the abundance of fleas inside
burrows was performed. In 15 squares (areas of 500 by 500 meters) the fleas present in the burrows
were counted. These data can be used to relate relative soil moisture derived by remote sensing to flea
abundance.
2
1.3 Soil moisture
Soil moisture is defined as the water content in the upmost layer of soil and accounts for an important
hydrological factor in fluxes between surface and atmospheric and ecological processes (Wang & Qu,
2009). The outcome is vital in amongst others climate change models. However due to lack of data this
factor is usually unaccounted for. In situ measuring of soil water contents is time consuming, expensive
and difficult to conduct, especially in the remote study area in Kazakhstan. The most efficient way to
enhance in situ observations for large scale is to connect them to pixel characteristics from satellite
images that are linked to moisture properties. The combination of in point measurements with satellite
data, which have better spatial coverage, can give a good indication of soil water contents in large,
remote areas. Several indicators have proven to be good indicators for soil moisture content;
Temperature, NDVI, Tasseled Cap transformations and Temperature/vegetation index (Wang et al.,
2009; Crist & Cicone, 1984; Goward et al., 2002).
For this research, only satellite images were available for soil moisture estimation, therefore merely
relative soil moisture content estimations could be derived for further investigation.
1.4 Research questions
The goal of this thesis is to link hydrological observations as supplementary information to the
knowledge acquired by previous studies to improve predictions of plague occurrences and to better
understand those. The critical elements for plague outbreak predictions are both host and vector
abundances. Gerbils have the ability to use food or their fat to generate metabolic water, so they are
rather insensitive for aridity. Fleas, though, show sensitivity for climatic changes and are more likely to
respond to local hydrology.
The hydrological focus will be on soil moisture content. This parameter will be attempted to derive
through remote sensing data, as the study area is vast. The moisture contents and their corresponding
flea indexes, collected in field studies in 2011 and 2012, will be evaluated.
The main objective in this paper is to find how relative soil moisture can be used as an indicator for flea
abundance, and therefore define its role in human plague monitoring.
3
To find answers to sustain and fulfill this objective, the following research questions need to be
examined:
1) Can remote sensed indicators provide information about spatial distribution of relative soil
moisture content in Kazakhstan?
2) Can the remote sensed parameters provide information about interseasonal variation?
3) Does relative soil moisture content spatially relate to flea index?
4) Does relative soil moisture content temporally relate to flea index?
The anticipated results for these questions are stated in the following hypotheses:
1) It is assumed that the selected parameters temperature, tasseled cap transformation, NDVI and
TVX provide enough details for adequate estimations of soil moisture content and its spatial
variation the study area to create maps indicating the wettest areas.
2) The satellite information data of spring, summer and fall will be studied. As the parameters do
not provide absolute information, it may be hard to pick up seasonal trends as the relative
variation probably remains more or less stable. Therefore, TVX plots may be essential for this
objective, because these plots include two parameters, so their shape is more likely to represent
moisture variation, through the months.
3) Hypothetically, these relative soil moisture estimations will have a positive relationship with
their location bound flea indexes and a statistical analysis will determine a linear regression.
4) Climate influences, suggest that higher moist contents in summer will increase the flea
population in fall. Relative high indications for soil water through summer should be
accompanied by high flea indices in autumn. A time series of Landsat summer data compared
with Autumn flea indices should show a trend stating this expectation.
4
2. Background
2.1 Plague
There are three forms of plague; bubonic, septicemic and pneumonic. The difference between these
three types is the location of the infection.
If not treated, all forms can progress to death. All three forms can be treated with antibiotics. The earlier
diagnosis and antibiotic therapy are reduce in mortality depends on how early treatment is started
(table 1).
Table 1 Plague case fatalities
plague form
bubonic
% of worldwide cases
case fatality without treatment
case fatality with treatment
80-95
40-70%
5-15%
septicemic
?
100%
40%
pneumatic
?
100%
> 50%
Bubonic plague, the most common form, is an infection attacking the lymphatic system
(www.columbia.edu) and refers to one of its symptoms on infected. It causes swollen lymph nodes,
which are called buboes. This usually results from an infected flea bite, after which the bacteria travel to
and multiply in the most nearby lymph node.
Septicemic plague is the rarest form of plague, it is a deadly blood infection, nearly always fatal without
treatment. It might be caused by flea as well as rodent bites.
Pneumonic plague is one of the most deadly infectious diseases, the location of this infection is the
respiratory system. This form of plague is not exclusively spread by vectors, but can also be
contaminated from person to person. And it is highly infectious, so it can spread rapidly.
The most common ways to spread the disease are described in the introduction. Also direct
transmission between animals is possible through broken skin for instance. Humans can become
infected by bites of the vectors or by handling with infected hosts (see table). Domesticated animals can
become involved in de pest cycle, they may become infected or act as temporary vessels for fleas. In
5
Asia and Africa cases are known of transmission of Y. pestis by eating contaminated camel meat
(Fedorov, 1960).
2.2 Gerbils
Great gerbils (Rhombomys opimus) are the largest of gerbils, up to 20 cm in length. The habitat of these
diurnal rodents is arid or semi-arid and mainly consists of sandy deserts in Central Asia. They live in
isolated colonies in permanent burrows; tunnel networks with multiple holes for entrance and separate
chambers for nests and food storage. Their burrows go 1,5 to 2 m deep depending on the subsurface
material (Wilschut et al., 2013) , their size varies depending on landscape and substrate. The relation
between the shape and size of a burrow and its environments are dependent on landscape
characteristics (Wilschut et al., 2013). The number and properties of the burrows hardly change over
time, though their occupancy is highly variable (Davis et al., 2004).
A gerbil colony typically consists of a family formed by a breeding male and one to seven females and
juveniles (http://www.apjtm.net/admin/picture/UploadFile/20111231154252498.pdf). The gerbils are
active all year , they do not hibernate, but do spend most of winter in an inactive state underground,
where temperature is stable between 20-25C. They spend 90% of their activities within their own
territory (Moshkin et al., 2003). Because they are hardly socially active with other colonies and relatively
insensitive for stress and water shortages, flea distribution between family groups is kept minimal and
risk of plague outbreak is generally low. Research from Davis et al.(2006) states that flea transmission
occurs rather within populations than between them. Burrows near infected burrows tend not to have
higher risk to become contaminated. However, a large population suggests instability, changing the rate
of contact between the gerbils increasing the probability of flea transmissions (Davis et al., 2004).
Even though the gerbils are rather insensitive for their arid circumstances, research (Kausrud et al., 2007
and 2010) predicted climate change in this area, providing moister and warmer conditions, will lead to
higher gerbil density.
2.3 Fleas
A very important factor in plague spreading, besides Y. pestis itself and the movements of the hosts, is
the flea. Not all species are an effective vector for Y. pestis. The oriental rat flea, however, is particularly
suitable for transmitting and carrying the bacterium, because the bacterium causes an ingestion block in
6
the flea, essential for effective transmission. As a result the hungry flea will bite more to survive,
spreading the Y. pestis. Other types of fleas may also spread the disease, because they carry bacteria in
parts of their mouth. The plague bacteria survive a few days in blood of rodents and longer in fleas. In
Kazakhstan, most the common vector is Xenopsylla gerbillli minax. This species fertility character is so
called wavy (i.e. peaks are followed by falls). In winter, the production of eggs is somewhat less intensive
than through the rest of the year (Zolotova et al., 1979). Increase of humidity, would increase host
populations as well as flea populations. The latter both by the humidity and the growth of host numbers
(Kausrud et al., 2010).
2.4 Study area
The Republic of Kazakhstan is a vast landlocked country in central Asia. It is the ninth largest country of
the world and with 2727300 sq km larger
than Western Europe. The study area for the
data from this thesis is located in the Almaty
Province in southeastern Kazakhstan,
between Lake Balkhash and the Ili river and
Karatal river, both major rivers draining into
this basin (figure 4). Especially the Ili river,
contributing 73 % of the hydrological inflow
of Lake Balkhash basin, which is one of the
Figure 5 Pre Balkhash Focus Study area, Kazakhstan
biggest drainage areas in (semi) arid central
Asia (Kezer & Matsayuma, 2006).
Figure 4 Study area (map from Kezer & Matsayuma, 2006)
This region (74-78E and 448-478N) contains the PreBalkhash plague focus. The area is in a continental
arid, desert location and the sparse vegetation consists of scrubland mostly. The climate is continental
with high annual variation in average temperature, ranging from 30 ⁰C in July to -14 ⁰C in January, and
average annual precipitation is 131mm, providing severe conditions, the great gerbils are well adapted
to.
7
2.5 Remote sensing for soil moisture estimation
Soil moisture content can be measured with 3 methods: in situ measurements, hydrological modeling
using meteorological data and remote sensing (Tansey et al., 1999).
Several methods can be used for point-based measurements but they are either expensive, unreliable or
time consuming. Some of these methods retrieve most accurate soil moisture data, however these data
are locally limited and difficult to extrapolate, so they can not represent the spatial distribution of soil
moisture content, which is rather variable.
The second way requires the use of meteorological data, from which precipitation and relative
evaporation can be derived. These data are applied in soil water models, calculating soil water content
by the principal of conservation of mass balance. The meteorological data in the study area are rather
scarce as there is only one weather station (in Bakanas) which cannot represent the whole pre Balkhash
focus. Also the reliability of the available meteorological data is doubtful, as often precipitation has the
same amount for few days in a row, which might suggest measurements are averaged over these days.
The third approach, remote sensing, will be further explored in this thesis.
Remote sensing
Different types of remote sensing can be
distinguished by subdividing the spectral domain of
the wavelengths observed or by the method of
measurement, active or passive. The different
techniques have their own relationships with
response of soil moisture content (Lingli & Wu,
2009). A huge advantage of remote sensing for soil
moisture content measurements is that this
method can provide large scale data and insight
into the spatial and temporal variation.
Figure 5 Wavelengths in the electromagnetic spectrum,
source: growblu.com
8
2.5.1 Optical remote sensing
Optical remote sensing uses the wavelengths between 0.4 and 2.5µm (figure 5). This is reflectance, the
radiation of the sun reflected from land surface. In nearly all types of soil, increase of soil moisture
content causes decrease of reflectance of the soil. However, there are many other factors playing a role
in reflectance and optical remote sensing has only shallow penetration of earth surface. Therefore the
reliability of soil reflection for measuring soil moisture is rather limited.
2.5.2 Thermal infrared remote sensing
The wavelengths of thermal emission of the earth’s surface range between 3.5 and 14 µm (figure 5).
This type of remote sensing estimates surface soil moisture based on its relation with soil surface
temperature. Also the infrared wavelengths can be used to calculate a vegetation index, which
combined with the temperature can be used for surface soil moisture estimation. The temperature,
singly or in combination with vegetation parameter is a strong indicator for soil moist, but like the
optical remote sensing method, it is limited by its shallow soil penetration.
2.5.3 Microwave remote sensing
Microwave sensors measure wavelengths in the radiation window of 0.5 to 100 cm (figure 5). It is useful
for soil moisture estimation, because there is a large difference in dielectric characteristics of water
compared to soil. As the moisture content increases, the dielectric constant will also increase (Tansey et
al., 1999) and this change can be detected by microwave remote sensing. This type is the most effective
technique for soil moisture estimation (Wang et al., 2009).
Passive microwave sensors observe microwave emission from the earth’s surface. Its major
disadvantage is low spatial distribution. The Soil Moisture and Ocean Salinity satellite (SMOS) is
equipped with an instrument specialized in measuring wetness change of the land.
Active microwave sensors measure the difference between pulses they sent and their backscatter from
the surface. A limitation for this method is its sensitivity for surface roughness and vegetation density.
To improve the technique, optical data to filter vegetation could be implemented (Moran et al., 2000).
In this research Landsat 7 data were used for calculations, this sensor merely provides optical and
thermal infrared data, excluding microwave remote sensing for further investigation in this thesis.
9
2.6 Previous research for plague monitoring
A large volume of data since 1949 had been collected by Russian scientists. In this period the plague was
intensely monitored, providing great resource for analyzing gerbil populations and pest spreading. These
data were reviewed to find proof for the abundance threshold theory, suggesting outbreaks of an
infectious disease are to be preceded by an abundance of hosts.
Information about the gerbil abundance of the recent and previous year can be used as predictors for
plague risk in the following year, so outbreaks are preceded by two years of abundance exceeding a
threshold value (Davis et al., 2004).The reason for this delay remains unclear. Although plague outbreak
requires this abundance threshold value exceeding, it is not to be said, the exceeding is always followed
by an epizootic (Davis et al., 2007).
It is expected that due to global climate change, the temperature and humidity in Kazakhstan will
increase. Analysis of historical data in Kazakhstan shows a positive response in gerbil population, plague
transmission, and flea abundance in particular (Stenseth et al., 2006).
Other models as well suggest increase of precipitation, and therefore water content, increases flea
populations and plague risks (Gage & Kosoy, 2004). These relations and predictions are statistically
derived, the physical know how of gerbil and flea population dynamics and human plague related to
climatic change remains largely unknown.
As ground research is expensive and time consuming, research has been done to study which amount of
data can be retrieved with remote sensing. Vegetation on and around the burrow systems is affected,
and can easily be observed by remote sensing with object based classification (Addink et al., 2004).
Satellite images were used for burrow identification, measuring burrow density and collecting samples
of activity. Burrow density information provides insight in new or expanding plague foci developments.
Also the influence of landscape types and vegetation on the spatial pattern of these burrows has been
examined (Wilschut et al., 2013). The distribution of burrows may influence the invasion and
persistence of plague.
Besides burrow occupancy, as an indicator for host abundance, flea population monitoring is a key
element in human plague dynamics. For plague in Kazakhstan, the transportation rate of fleas is
dependent on the occupancy of gerbil burrows, because contact increases with host density as well as
10
vector density. On that ground, Reijniers et al., 2012 suggests the plague invasion threshold is a product
of host and vector abundances. The ‘flea burden’ is the number of fleas caught from trapped gerbils,
divided by the number of gerbils researched in that particular season. These joint threshold values for
host and flea abundance are displayed in figure 6, and provide improvement for insight and prediction in
disease occurrence.
Figure 6 source: Reijniers et al., 2012
11
3 Data & Programs
3.1 Flea data
Within the pre Balkhash focus data was collected from so called squares, sample areas sized 250 by 250
meters. These squares lay within sectors, 10 by 10 km regions, spread throughout the study area . The
squares were subdivided for research into 4 even squares (figure)
Figure 7 Visualization of data in one of the examined squares, source: Laudisoit & Begon, 2011
In the selected squares, all gerbil burrow systems were mapped in a field work. At the entrances of
occupied burrows, the rodents were trapped for intensive examination by a team of biologists. The fleas
and ticks on their bodies at entrances of the holes were counted and collected.
The flea data sets comprise information about observed burrows; their location, sector and altitude.
Information about gerbils and fleas in da occupied burrows, the numbers if gerbils found, their sexe and
the numbers of fleas and ticks found. These data were used to calculate the flea index, which is the
number of fleas found, divided by the observed number of gerbils in the burrow. Also there is some
geographical information about the measurement sites; it is remarked whether they are alluvial, sandy
and the fact whether or not the burrow is located on a dune.
12
The used data were collected in September 2011, and in the summer and autumn of 2012.
Table 2 numbers of sampled burrows in the datasets
burrows with flea
dataset
square
sector
autumn 2011
burrows index
541
234
4
11742
128
15
13
9123
100
44
21, 22, 23
7934
313
175*
549
337
summer 2012
54
9123
106
57
72
10512
97
65
48
10531
71
40
66
10544
81
61
42
11742
87
54
451
92
autumn 2012
60
7934
93
26
53
9123
117
42
71
10512
89
9
65
10544
71
8
41
11742
81
7
3.2 Satellite images
3.2.1 Landsat
The Landsat 7 satellite, launched in 1999 is an earth observation satellite, critically for land surface and
global change monitoring. On board is the Enhanced Thematic Mapper Plus (ETM +), designed to collect
panchromatic data. The swath width of the satellite is 185 km and it covers the earth’s surface each 16
days, transmitting up to 532 images per day. Band widths are shown in table 2.
13
Table 3 ranges and resolutions of Landsat 7 bands, (Lilesand &Kiefer, 2004)
Wavelength
Name spectral
resolution
(µm)
range
(m)
1
0.45-0.52
blue
30
2
0.52 - 0.60
green
30
3
0.63-0.69
red
30
4
0.76 - 0.90
near-infrared
30
5
1.55 - 1.75
mid-infrared
30
6
10.40 - 12.50
thermal infrared
60
7
2.08 - 2.35
mid-infrared
30
Band #
Landsat 7 is a very accurate observing device, however the satellite suffered from a hardware
component failure in May 2003. Images since then lack stripes of data on either side, leaving only a belt
of unspoiled band reflections in the middle (see figure).
Figure 6 RGB image (band
combination 7,4,2) of Landsat 7 ,
P151, r028, July 22nd 2011. The
black dots clearly mark the two
sides missing data values.
The raw Landsat scenes data
were acquired from
glovis.usgs.gov. The selected
images from 2011 are from the
overlap region of Path 151, Row
28 and Path 150, Row 29 of the
worldwide Reference System.
The 5 sectors from which flea
data from 2011 was used fall within the undisturbed paths at the center of the imagery. 4 sectors are
14
covered in the first region (see figure 10), of which cloud free images
were used from May 3rd, July 6th and 22nd, august 7th, September 8th,
October 10th and November 11th. The remaining sector in the second
region has supporting imagery from May 12th and 28th, July 31st,
September 1st and November 4th. The sectors of 2012 are overlapped by
these satellite regions as well. The dates from the first region used for
research are from June 22nd, July 24th, august 9th and 25th and October
28th. The dates from the second region are May 14th and 30th, July 1st,
august 18th, September 19th, October 5th and November 22nd. All data
Figure 7 Square with examined
burrows, narrowly missing the
satellite lines with no data.
were visually selected, images with cloud contamination at data sectors
were eliminated.
Some sectors of the 2012 data fell into the striped, spoilt data areas, however in some images they
were locates between the stripes (figure 9), so it was possible to use these as input.
Figure 10 Landsat image, 11-11-11, displaying locations of four researched squares
15
3.2.2 Spot image
For visual observation, an image with higher resolution to optically identify landscape features is
required. For this, Spot 5 satellite sensor was used providing imagery with a 2,5 meter resolution,
providing detailed landscape information. The SPOT 5 satellite was launched in 2002, the name stands
for Satellite pour l’Observation de la Terre. The already orthorectified image used for this report was
available from previous research by lies and was acquired from July 17th, 2011. Its swath width is about
60 by 60 km and comprises 4 out of 5 researched sectors from the used flea data locations at that year.
3.3 Programs
3.3.1 ENVI 4.7
ENVI (Environment for Visualizing Images) is a software application designed for processing and
analyzing geospatial imagery. Tools provide scientific algorithms for automatically processing raw
satellite images. Coefficients and equations are integrated for editing images for classification or further
transformation of the imagery data to perform calculations for scientific remote sensing research. Its
output results are map products, designed to easily integrate with ArcGIS products (www.exelisvis.com).
3.3.2 R
R is free software, specially designed for statistical computations, data analyzing and graphical display.
Its modeling language, also called R, is an implementation of S programming language. The system can
be extended with different libraries or packages, making it suitable for handling satellite imagery and
geo-spatial coordinates (www.R-project.org).
3.3.3 ArcMap 10
ArcMap is an application of ArcGIS, a geographic information system (GIS), created for compiling,
analyzing and managing geographic information and maps. In ArcMap files can be produced that
combine (elements of) maps with geographic data in different layers. Also, relationships between the
data can be examined. The outputs are cartographic (www.esri.com).
16
4 Methods
4.1 Flea data statistics
Statistic and spatial distribution flea index
To examine the abundance of fleas in the different areas, the flea index was used. To investigate the
patterns in the flea index, first the basic statistics were calculated. This was done for the 1541 mapped
burrows. The basic statistics of these data were calculated and plotted to check whether there is an
initial pattern to be found in these measurements. Boxplots, dot charts and histograms were plotted
using the R software, showing the distribution of the flea index. Also, the mean, median, first and third
quartile, skewness, kurtosis and variance of the flea index were calculated. Maps of flea indices were as
well created in order to investigate whether there is a spatial pattern in the data.
4.2 Soil moisture estimation
To examine the relation between the flea abundance and soil moisture, a proxy for soil moisture has to
be derived and mapped. Several variables were selected as indicators for soil moisture content and
some were combined to provide most optimal information. The variables selected are tasseled cap
Brightness, tasseled cap Wetness, Earth Surface Temperature, NDVI and TVX. These proxies are all
derived from remote sensed data and not scaled with in situ measurements, so only relative water
contents can be estimated. No absolute value can be assigned to these outcomes without calibration
and correction. This is not a problem, since the aim of this study is a first attempt to relate flea
abundance to relative soil moisture content.
4.2.1 Calibration satellite images
In order to be able to compare the values of the satellite images from different seasons, the satellite
images first had to be calibrated to reflectance values. This was done as follows; to transform the raw
Landsat 7 data the programs R and ENVI were used.
Convert DN values to radiance
First the radiance was calculated from the digital numbers (DN). For the calculations fixed formulas for
Landsat 7 were used.
17
Lλ = ((LMAXλ - LMINλ)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMINλ
Lλ
= Spectral Radiance (W/m2*ster*µm)
LMAXλ
= spectral Radiance scaled to QCALMAX (W/m2*ster*µm)
LMINλ
= spectral Radiance scaled to QCALMIN (W/m2*ster*µm)
QCAL
= quantized calibrated pixel value in DN
QCALMIN
= minimum quantized calibrated pixel value in DN = 1
QCALMAX
= maximum quantized calibrated pixel value in DN = 255
Lmax en Lmin are fixed numbers per spectral band per gain (table..)
Table 4 source: Chander et.al., 2009)
Low Gain
Band
High Gain
Number
LMINλ
LMAXλ
LMINλ
LMAXλ
1
-6.2
293.7
-6.2
191.6
2
-6.4
300.9
-6.4
196.5
3
-5
234.4
-5
152.9
4
-5.1
241.1
-5.1
157.4
5
-1
47.57
-1
31.06
6
0
17.04
3.2
12.65
7
-0.35
16.54
-0.35
10.8
8
-4.7
243.1
-4.7
158.3
Low gain range is about 1,5 times the high-dynamic range and used for images with high brightness, as
their dynamics are high, but their sensitivity is low (Chander et al., 2009).
18
Convert radiance to reflectance
The radiance was used to calculate de reflectance
𝛒𝐩 =
𝛑 ∗ 𝐋𝛌 ∗ 𝐝𝟐
𝐄𝐒𝐔𝐍𝛌 ∗ 𝐜𝐨𝐬𝛉𝐬
𝛒𝐩
= planetary reflectance
𝐋𝛌
= Spectral Radiance at the sensor’s aperture
𝐝
= distance between the earth and the sun in astronomical units (retrieved from a
worksheet from landsathandbook.gsfc.nasa.gov)
𝐄𝐒𝐔𝐍𝛌 = mean solar exoatmospheric irradiances (see table 5)
𝛉𝐬
= solar zenith angle
Table 5 Solar spectral irradiances (Chander et al., 2009)
Band
watts/(m2 * μm)
1
1997
2
1812
3
1533
4
1039
5
230.8
7
84.9
8
1362
4.2.2 Calculate soil moisture indicators
The edited satellite data were used to conduct and calculate the here above mentioned indicators for
relative soil moisture estimations.
19
Temperature
Earth surface temperature is conversed from sixth band (thermal infrared) radiance from the Landsat 7
ETM+ images. Band 61 (the low gain temperature band was used for further calculations).
𝐓=
𝐊𝟐
𝐊𝟏
𝐥𝐧 (
+ 𝟏)
𝐋𝛌
T
= temperature (Kelvin)
K2
= 1282.71 Kelvin (fixed number for Landsat 7)
K1
= 666.09 watts/(m2*sr*µm)
Lλ
= spectral radiance watts/(m2*sr*µm)
LST (Land Surface Temperature) is negatively related to soil moisture (Goward et al. 2002). It is sensitive
for evaporative cooling (the water contents evaporate, using energy and thus reducing the temperature)
and an important factor in examination of water balance. It is a simple, strong and straightforward
parameter for calculating soil water content (Lingli & Wu, 2009).
Tasseled cap transformation
The tasseled cap transformation is a data transformation developed in 1976 by Kauth and Thomas (Crist
& Cicone, 1984), using satellite bands with optical wavelengths. In the tasseled cap transformation the
original bands of satellite imagery are converted into new bands. It does not create new data, but
optimizes information retrieval from the existing data set. The original image bands are linearly
combined (similar to principal component analysis) to reduce the data volume.
Originally, it was designed to describe temporal soil properties for crops, but the transformations were
also found suitable for mapping vegetation health and environmental and atmospheric conditions.
By interaction of the spectral bands in different ratios information can be obtained that can be
associated with physical characteristics of a study area. The coefficients for the data are derived
empirically and differ for each image sensor.
20
W
e
t
n
e
s
s
Brightness
Clear water
Wet soil
Dry soil
concrete
Figure 81 based on Crist & Cicone, 1984: direction of moisture variation.
Figure..
The transformation results in a set of 6 multispectral features. Most information is to be found in the
first three bands; Brightness, Greenness (variation in vegetation health) and Wetness. Band 4 called
Haze is usually a noise band containing very little information. Band 5 has useful contents and band 6 is
another noise band. The first feature is called Brightness and refers to the contrast between darker and
brighter soils. It is a measure for soil background reflectance and is useful to determine spatial surface
patterns based on soil moisture contents in (semi)arid. Barren, dry land has more reflection and
therefore a high brightness index. The Tasseled Cap transformation provides information for relative soil
moisture estimation because it separates bare (bright) soils from vegetated and wet soils.
The third band, wetness, is sensitive to soil and canopy moisture, the soil moisture status being the most
distinctly represented (Crist & Cicone, 1984). It represents the contrast between the sums of visible and
near-infrared bands. The latter change more significantly than the visible ranges.
The formulas for TCB (Brightness index) and TCW (Wetness index) with specific Landsat 7 constants for
bands 1-5 and 7 are:
TCB
=
0.3561(#1) + 0.3972(#2) + 0.3904(#3) + 0.6966(#4) + 0.2286(#5) + 0.1596(#7)
TCW
=
0.2626(#1) + 0.2124(#2) + 0.0926(#3) + 0.0656(#4) - 0.7629(#5) - 0.5388(#7)
In theory, TC Brightness is negatively related to soil moisture content, whereas TC Wetness is expected
to have a positive link.
21
NDVI
Normalized Difference Vegetation Index (NDVI) first described by Rouse et al, 1973 is a ratio of
reflectance bands and an indicator for vegetated areas and their health. In short, chlorophyll, the
pigment of plant leaves absorbs visible red for photosynthesis and the turgid structure of the cells in
those leaves reflects most infrared light. Vigor vegetation has more leaves, emphasizing the difference
of reflection in these wavelengths. Healthy vegetation absorbs almost all visible wavelengths and
reflects a large portion of near infrareds;
𝐍𝐃𝐕𝐈 =
(𝐍𝐈𝐑 − 𝐕𝐈𝐒)
(𝐍𝐈𝐑 + 𝐕𝐈𝐒)
NIR
=
spectral reflectance band 4 in Landsat 7 (near-infrared)
VIS
=
spectral reflectance band 3 in Landsat 7 (visible red)
The NDVI values range between -1 and 1; higher values suggest dense vegetation , 0 means total
absence of vegetation and bodies of water might fall in the negative range. So low NDVI values point to
barren soil, clouds or water bodies. Soil moisture is one of the factors that interrelate with variation in
NDVI. For application in this study high NDVI values are seen as an indicator for relative moist
conditions in the arid Balkhash area.
TVX
NDVI is a greenness index representing the presence of vegetation and its chlorophyll. It is however less
useful for the indication of moisture. The most significant limitation of the NDVI is its time lagged
indication for water availability, because leaves remain green for a while in droughts. Surface
temperature (LST) responds much quicker to water resources and combining these two variables create
a more sensitive approximation for soil water contents. Temperature vegetation index (TVX) combines
surface temperatures with spectral vegetation indices, in this case NDVI, to examine local moist
conditions. The TVX method is a 2d plot with X axis NDVI and y axis Temperature. Typically the
distribution in the plot simulates a triangle as locations with vegetation and there for a relative large
NDVI are relative cool, however the cool places lacking vegetation will be rather moist. As an extra
22
interest the plot for the different dates can be arranged in a time series to observe seasonal changes in
TVX.
Ts
Bare soil
Vegetation
No Evaporation
No Transpiration
Max Evaporation
Relative
moist bare
soil
Max Transpiration
NDVI
Figure.. Figure 92 Indicative plot based on Goward et al., 2002, illustrating the pixel location for moist conditions
In theory the pixels in a TVX plot would resemble a triangle or trapezoid (Goward et al., 2002). The more
to the right in in the graph (figure..), the higher is NDVI, indicating vegetated areas. So the bare soil
pixels on the left are indicating relative moist, based on temperature measurements. Bare soil with high
temperature suggest dry conditions as moist would cool the surface because evaporation as the water
contents would proceed heating the soil. Conclusively, low NDVI and low LST combined indicate
relatively moist bare soil. Mapping these locations creates relative soil moisture patterns, providing
hydrological insight in the study area. TVX methods have been successfully used for detecting water
stress in (semi) arid regions (Moran et al., 1994; Bajgiran et al., 2012). In ENVI, it is also possible to select
random parts of the TVX plot and link these to the locations of their observation points on the map, to
create images with spatial moisture patterns.
4.3 Combine flea index and soil moisture
To find a relation between the soil moisture and the flea index, the flea data need to be linked to the
relative soil moisture according to the indicators at their location. In order to this the gps coordinates of
23
the flea data were conversed to UTM projection. This was done in R by adding package rgdal and
converting the coordinates to UTM zone 43 (eastern Kazakhstan).
Then the flea index from the flea data can be compared to the soil moisture proxies above. Plotting
these values will show whether the flea population distribution seems to be affected by hydrological
influences.
If there is a clear relation visible in plots, statistical calculations and linear regressions may be performed
to empirically link relative soil moisture to flea index numbers.
4.4 Combine flea index and visual landscape patterns (SPOT)
Recently, current research suggests distribution of flea populations might by related to geographical
structures, visible on high resolution satellite images. In spot images dune patterns and vegetation
structures are clearly visible and plotting flea indices on these images might show relation with these
features.
24
5. Results
5.1 Flea index value, statistics
In R the basic statistics of the flea index in flea data sets were analyzed, to do so statistical parameters
were calculated (appendix A). On first sight on this table, it is quite remarkable a flea index of 37 is
prominent in the summer data of 2012. This seems rather striking since the other datasets have more
variation in flea index. This high index is logically accompanied by large negative skewnesses.
The autumn datasets of 2011 and 2012 show lower mean flea index, and a more evenly distribution. The
skewness of these data is overall positive, and for most squares around 0,5.
Compared with the year before the values of the 2012 data are lower over the whole range. Also the
number of examined burrows is lower.
The data are plotted in various ways to graphically display the distribution of the flea index. To do so
scatterplots, histograms and boxplots were created (appendix A1, A2, B1, B2, C1, C2). The scatterplots
consist of dots whose colors correspond to specific squares within that dataset. The matrixes confirm
the data from the table with statistics. The autumn sets are rather evenly distributed, though the left
side of the plot, with lower values, is a bit more densely dotted. Also the 2012 values tend to be lower
than those of 2011. And the summer data clearly shows a large number of burrows with the same flea
index value of 37. To further explore this rather striking ‘coincidence’, the squares of this data were
plotted separately for each square (app. C2). All the squares were dominated by a flea index of 37,
invaliding these measurements for this research. The histograms of the data’s flea indices state that by
far the most occurring flea index is 1. The boxplots confirm the rather even variation in index, as was
visible in the scatterplots.
5.2 Flea index value, spatial distribution
Finally, to check for spatial patterns of the flea index within the squares, the indices were plotted at
their geographical location. For the dataset of 2011 maps were created with all examined burrows,
displaying whether or not they were found to have a flea index (app. B3). Visually, there was not a
pattern to be found in these images, except for a curving line of researched burrows in square 13,
implicating a road or geographical object along which measurements were done.
25
To optimize the chance to find a spatial pattern, the flea indices were mapped at their location, showing
their value (app. B4, C3 & D3). Again, an immense difference is to be found between the summer and
autumn sets, providing more arguments for elimination of the summer data. The other images show a
visibly random geographical distribution.
5.3 Linking flea index to soil moisture indicators
A warm and humid summer precedes an increase in flea population size. So hypothetically, relative high
soil water content could provide conditions for a relative high flea index. To find a relation between the
flea index and soil moisture, several indicators for soil water content were derived from the satellite
data, as stated in chapter 4. Temperature and TC Brightness are negatively related to soil moisture
content, TC Wetness has a positive relation and NDVI should be low. The TVX plots should be triangular
and their left lower corner should contain relative moist bare soil. Theoretically, the flea index data
should give an approximately similar response to these parameters.
To quickly find the most interesting parameter response for further examination, plots were made for
averaged values of temperature, TC Brightness, TC wetness and NDVI (app. B5-9). Each square is
subdivided in four evenly sized squares. The mean values of the quarters of each square were calculated
and plotted against the ‘flea number’ of that quarter square. The flea number is the product of the
mean flea index and the number of burrows with flea index for that particular square. So each square is
represented by 4 dots and has its own color in the graphs. These points should more or less form a
tangent to represent the relationship with flea index and might show particular differences between the
parameter values per square.
Appendix B5 shows the 4 average temperatures per square. The different colors are fairly aligned,
showing the mean temperatures of the squares show less variation within squares than between
squares. The plots from July tend to have a promising downward trend. The blue represents square 13,
square 21 is green and square 22 and 23 are pink and peach.
The TC Brightness plots in the next appendix hardly change through the year. The values slightly dip in
October, but the shape of the graph does not change until November, where there is a significant
difference between square 13’s blue dots, staying at the same value as the month before, and the
remaining points whose brightness values decrease even more. This discrepancy, shall receive further
notice in the more extensive plots.
26
The averaged TC wetness graphs (app. B8) shows merely little variation in values, within and between
the squares. Up until October the values approximate -0.25 and in November they are a little higher and
show a little more variation, instead of forming the nearly horizontal line as in the previous months.
Temperature
The plots with temperature and flea index values of 2011 (app. B10), do not show the expected negative
trend. The clouds of dots form essentially horizontal bands, not clearly showing response in the flea
indexes. Another remarkable observation in these data is the temperature of square 13 falling beneath
the range of the other temperature calculations from the other squares on the 11th of November. Closer
investigation of the satellite data shows this squares remote sensing data are contaminated, because of
haze over that region on the data retrieval date.
The plots for the 2012 data (app. D4&5) is rather similar except that the temperatures of the squares
seem to be somewhat less homogeneous. Especially in the fall, the difference in temperature ranges of
the square rise up to a couple of degrees.
TC Brightness
The brightness plots (app. B10), are basically horizontal and do not state any correlation between TC
Brightness and flea index. At the 11th November the Brightness values of Square 13 can be observed in
the highest range of the plot. This should be due to haziness.
TC Wetness
The points in the brightness graphs (app. B11) form rather straight horizontal lines, showing no sign of
correlation with flea index values. Also, can be noted that the range of TC Wetness is quite narrow;
seasonally, as well as locally. All data fall into a 0.1 window and the difference in values between May
and November is 0.1 maximum.
NDVI
The NDVI plots (app. B12) were crossed with dots forming a horizontal belt. No trend of relation is
visible. The hypotheses suggest low NDVI values for high flea index. In practice all flea indexes were
assigns relatively low NDVI values. Through the year (winter excluded, as there were no winter data
observed) the whole cloud of NDVI values in the examined sectors ranges from 0.05 to 0.15.
27
TVX
For the 2011 data a time series of TVX plots was created, to observe seasonal variation of the matrix
shape. The results of square 13, 21, 22 and 23 can be found in appendices B13-16. The point clouds
hardly seem to resemble the theoretically predicted triangles. The shapes cannot have slopes assigned
to them. In addition to that, no seasonal trend in shape could be observed. The 2012 data comprise
fewer measurements, also some of the data was eliminated by falling into striped satellite areas, so for
these set no time series was constructed.
The next step is to check whether the high flea indexes are concentrated more or less in the lower left
sides of the TVX plots. To visualize this concept the dots in the matrixes represent their flea index by
color intensity and size (app. B17-20 & D8-12). Again particular interest is focused on the plots from
summer satellite data, which should relate to flea abundance. Taking a look at these plots, the flea
indexes seem to be randomly spread over the hardly triangular data clouds.
Linking flea index variation to landscape attributes
Appendix E shows an attempt to find spatial correlation between landscape and flea index. On the left is
a SPOT image of square 13, in the middle is this image mapped with burrows locations projected on it
and on the right is a plot show the indexes that represented by those locations. Although the high
resolution of the spot image provides a much more detailed view, it is still rather hard to visually
distinguish landscape features, not having ground truth data or personal knowledge of the study area.
28
6. Discussion
6.1 Reliability field data
The three data sets retrieved from field work observations by a biological research team, raise a few
questions.
The first dataset, from the autumn of 2011, is the most usable of all three sets. Although it is impossible
to verify these observations, the values and variation in the data seem plausible and the sample sizes
are large enough for descriptive statistics.
The summer data from 2012 shows an extensive repetition of flea index 37. It has been considered to
remove the burrows with this particular flea index, but that would invalidate approximately half of the
burrows with measured flea index. The remaining burrows would not only give doubtful representation
of the dataset, but also leave very little data to work with, hardly sustainable to perform reliable
statistical research.
The fall data of 2012 seem to be a more reliable data set, comprising a more normal distribution of flea
index. However some squares in this data set contain but few index observations. If the sample size is
that small, the data can only be used if normally distributed, which is impossible to test for such small
observation numbers.
6.2 Landsat data limitations
In remote sensing the pixel value is a mean value of the measured landscape surface. This surface may
contain a lot of variety, including shadow, rocks, water, different soil types and vegetation. The Landsat
Thematic Mapper has a spatial resolution of 30 m, providing a large area for variation. However, the
landscape in the study area, however is rather monotone, so the variety of reflectance within pixel size
should not be tremendous. In this case the large pixel and image size of Landsat are practical for
covering a large scale region such as the pre Balkhash focus study area.
Landsat data are not the obvious choice for soil moisture estimations, as optical and thermal satellite
measurements can only measure soil moisture to a few mm. Deeper observations (requiring wavelength
measurements in the microwave spectral domain) would provide more insight in soil water contents
and may increase variation of soil moisture estimation values.
29
6.3 Value ranges of soil moisture indicators
Surprisingly, none of the soil moisture indicators functioned as predicted. Previous research has shown
their ability for soil moisture estimations, though mostly performed with active microwave sensors.
Although those studies mostly extend remote sensing data with in situ measurements, for relative soil
moisture content the same relationships are expected.
The range of the calculated moisture indicators were extremely small, this might be because variation
was smoothed out by averaging the 30 by 30 m pixels and even 60 by 60 m in the case of the thermal
infrared band. A higher resolution sensor will enlarge the data value range and might enhance a
relationship with flea index.
Another striking element in this thesis is the shapes of its TVX plots. Previous studies creating these plots
for (semi)arid areas did result in triangular shaped matrices. Probably the lack in variation of
temperature and NDVI values in the study area contributes to the odd shaped graphs. Possibly the input
of microwave sensor data and/or incorporating a DEM will improve accuracy of the data, shaping the
plots into the desired triangles.
6.4 Flea abundance response to soil moisture conditions
The flea data set does not state the species of found fleas. It was assumed most of the observed fleas
are Xenopsylla gerbilli minax, the most common species in this region. Some of the other local varieties
have different life cycles and reproduction peaks and are likely to have various responses to relative and
soil moisture content.
Also, about the response of fleas’ life cycles to hydrological conditions is relatively little known. A north
American study observed time lagged response of the human plague dynamics of 2 years for winter
precipitation and time lagged effects for summer precipitation of 1 year (Enscore et al., 2002). It is
suggested that time lagged response of the variable flea population dynamics plays an important role in
this delay.
30
7. Conclusions
1) Can remote sensed indicators provide information about spatial distribution of relative soil
moisture content in Kazakhstan? As the ranges of values of soil moisture indicators were
particularly small, it is hard to use those for spatial moist distribution mapping. More details in local
variation and ground truth control data are necessities for providing accurate spatial soil moisture
patterns. The landsat data can merely be used as a tool to ‘extrapolate’ stronger data.
2) Can the remote sensed parameters provide information about interseasonal variation of soil
moisture contents? The pattern within the plots of the moisture indicators from the various dates of
image retrieval show very little variation through the year. At the end of the year, in November,
Temperature drops significantly and the TC Wetness values are a little higher, though TC Brightness
and NDVI remain stable. Shapes of the TVX plots do not alter significantly. The observations cannot
provide insight in seasonal variation of the soil moisture contents.
3) Does relative soil moisture content spatially relate to flea index? Plotting the flea index values
against soil moisture indicator value of their observation location has resulted in virtual horizontally
lines within the graph. The lack of slope confirms this study has not been able to find any evidence
of a relationship between flea index and soil moisture content.
4) Does relative soil moisture content temporally relate to flea index? Comparison of relative high
summer moist data with autumn flea index observations did not show a correlation.
Looking at the objective of this thesis, how can relative soil moisture be used as an indicator for flea
abundance, and therefore define its role in human plague monitoring?, it is disappointing to report
merely suggestions based on previous research have come forth, rather than based on the physical data
examined.
The lack of evidence supporting the implied hypotheses in chapter 1, does not automatically reject
those. The expectations were derived from previous studies and seem very likely, despite the absence of
proof this thesis was aiming for.
31
8 Recommendations for Further research
In the future, more detailed research could provide the desired bases for a soil moisture correlation with
flea population size and distribution. Various points of interest can be thought of for further
examination.
Distinguishing flea species
For more thorough research regarding the flea populations’ reaction to hydrology, it is necessary to
determine the various fleas found on field work data base. Then ‘specific flea index’ could be derived,
i.e. number of fleas of species A, collected from the host species Y , divided by the number of individuals
of host species , divided by the number of individuals of host species Y examined. Plots per species could
be created with this specific flea index to survey the fleas’ life cycles and their particular climatic
responses. Then a more accurate prognosis could be made for flea populations’ fluctuations.
Microwave remote sensing
As mentioned before, remote sensing data in the microwave spectral region provide most accurate soil
moisture information, as their penetration into the ground is deeper, than optical or thermal
wavelengths. To retrieve more complete data, it is recommendable to use microwave sensors.
Preferably these remote sensed data should be supplemented with in situ measurements for references
and calibration.
Historical data
To further evaluate the response of flea population to hydrological parameters, it might be interesting
to take another look at the historical data, collected from 1949-1995. To examine how the flea burden
responded to large hydrological events in the past. For example, in 1970 construction of the Kaptchagay
Dam was completed, causing a reservoir to be filled up in the following years (Propastin, 2008). The
filling was stopped in 1991. Both these years comprise drastically changes in hydrological and ecological
conditions. If flea population and hydrology are related, there should be (traces of) evidence in data of
these years and their following years. Advisably, the flea burdens in the situation before and after these
impacts should be closely observed and compared.
32
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