Faculty of Geodesy & Geomatics Engineering,
K. N. Toosi University of Technology,
P O Box 15875-4416, Tehran, Iran
Satellite Technology is increasingly being used in monitoring environmental parameters. Some of these parameters are indicative of disease outbreak such as malaria. By boosting the level of accuracy in determination of environmental parameters using satellite imageries, we may be able to assess the potential of disease outbreak more precisely and consequently decrease the expenses of mitigation efforts. In our work we introduce the most suitable techniques for the environmental parameters using 7ETM+ images applicable in the south of Iran around cities of Kahnooj and Minab in Kerman and Hormozgan provinces respectively. In this regards some ground truth data has been collected and the work has so far shown promising progress.
Malaria disease can be found in the vast area of the world where a great portion of population are in the risk of entanglement each year. Unfortunately numbers of people lose their lives because of this disease. This disease can be spread out by variety of Anopheles insects in a particular natural condition. Those who are bitten by this insect (host) will experience body disorder. Despite of many researches in malaria, this disease is still one of the main problems in global health organization and still there are many unsolved problems in this regards where the most important of which is that what is best way in controlling this disease and securing against it? In this research we try to find an answer to this fundamental question by deploying remote sensing science as well as environmental data (weather parameters).
With the development of the technology and the human domination to his environment, the human instrumentations for the study in the different fields of the science have been improved. One of these reliable instrumentations is Remote Sensing (RS) techniques as well as Geographical Information System (GIS) in the study and reconnaissance of the environmental conditions. In fact it is tried to review the application of RS and GIS techniques in identification of the regions with the potential outbreak of malaria. The RS data can help in identifying the relation between the environmental condition (climate) and the outbreak of malaria. Using this knowledge can lead us to build a controlling system that is able to forecast the probability of the outbreak of the disease as a result of suitable environmental condition in favor of outbreak. In this research 7ETM+ images will be used for the southeast regions of Iran including the cities of Kahnooj and Minab. Also some ground truth in the year 2003 would be used for model evaluation as ground truths.
Malaria is an infected disease which is being transferred by Anopheles. The life cycle of an adult Anopheles, depending on the temperature, is from 7 to 21 days . By raising in environment temperature, the transfer of the malaria increases rapidly. The regions suitable for the outbreak of malaria and the statistics of the disease can be described as the following:
Globally: Malaria is found in tropical and subtropical regions around the globe and its outbreak happens in more than 100 countries of the world. More than 40% of the earth's population live in zones where malaria exists . The world health organization (WHO) estimates that each year 300-500 million cases of malaria occur and 1.5 to 2.7 million people die of malaria.
In Iran: Malaria was endemic in most parts of Iran around 100 years ago. Based on the periodical reports from Iran to WHO/EMRO1, as the result of extensive malaria control programs in the last 5 decades, the malaria incidence rate has dropped dramatically. However, malaria is still one of the most common parasitic diseases in Iran (50 to 60 thousand cases per year) and one of the main public health concerns in the southeast of the country in Sistan and Baloochestan, Hormozgan and South parts of kerman provinces .
3. The direct effects of climate on the incidence of Malaria
Climatic conditions directly influence mosquito and parasite development and the duration of the incidence of the disease . In this section, the suitable conditions for the outbreaks are being considered and the limits of each environmental parameter will be discussed.
Temperature is important because it governs the rate at which mosquitoes develop into adults (Figure 1), how frequency they blood feed (and, therefore, acquire parasites) (Figure 2) and the incubation time of parasites in the mosquito (Figure 3) .
Also Temperature has an effect on the survival rate of adult mosquitoes. No mosquitoes can survive in the temperature below 5 and above 40 degree centigrade.
Considering this, the suitable temperature limit for incidence of Malaria is between 20-35 degree centigrade.
3.2. Relative humidity
Humidity is one of the factors that have a direct effect on the survival of the mosquitoes . In other words, suitable humidity is one of the important factors for developing anopheles. Different species need different humilities. If the average relative humidity per month is below 55% and above 80%, the life duration of mosquitoes will be decreased and thus the amount of malaria incidence reduces. The amount of optimized humidity is between 60-65%.
Figure 1: Development of eggs and pupae at different temperatures 
Figure 2: The duration of the first gonotrophic cycle of three Anopheles species 
Figure 3: Development of malaria parasite at difference temperatures 
Breeding and early prevalence of anopheles is done in water basins which are demonstrated as larva . Since the fly range of mosquitoes is limited and breeding should be done in water, then the abundance of mosquitoes can be found around the places where there are patches of stilled waters. The dams built by humans, watering plans and developing agricultural projects can produce patches of stilled water and as a result changing ecosystem which in turn can cause the increase in abundance of mosquitoes.
Rain provides the breeding sites for malaria mosquitoes and helps creating humid environment, which prolongs the life of anopheles . The rain duration is far more important than its intensity. On the other hand, heavy rains and floods may have a flushing effect, cleaning breeding sites of mosquitoes. In seasons with the possibility of malaria and in times of raining, usually epidemics will be there within 20 to 30 days past raining events.
Vegetation cover has an important but indirect role on the abundance of malaria . Various vegetation covers and the density and species regarding the kind of anopheles cab be a good resting place for transfer of the disease. It almost can be said that all vegetation cover are suitable for anopheles.
There is also a relationship between the increase in the altitude and decrease in abundance of mosquitoes . With increasing altitude as a result of decreasing the temperature, the mosquitoes' abundance will decrease. Around the equator, in heights more than 2500 meters and in other places on the earth with heights more than 1500 meters, no anopheles cab be found.
4. Ways of acquiring parameters
In the previous section we explained about the necessary parameters for incidence of malaria, which can be evaluated by satellite data. In this section, the ways of access to these parameters from the satellite images will be explained.
With the advancement in technology of thermal remote sensing, LST2 can be acquired easily for a wide area. Of course the outcome of LST involves errors due to the undefined surface emissivity . Regarding the aforementioned topics, since we are concerned with about 15 degrees temperature difference (suitable temperature thresholds for mosquitoes life), the accuracy of remote sensing measurements is sufficient. For acquiring temperature from the image of landsat 7ETM+ we are forced to use the mono window method. First the amount of received spectral radiation at sensor (Lsensor3) is calculated using pixels' DN values in thermal band. Then the at sensor brightness temperature (Tsensor) is calculated by using at sensor radiance.
Other parameters necessary for LST calculation are surface emissivity (e) in thermal band, transmissivity (t) of the atmosphere in thermal band and the average effective temperature of the atmosphere (Ta). LST will be calculated through the following equation .
Where a, b are constants and Ci and Cj are acquired by the functions based on surface emissivity and the transmission of the atmosphere in thermal band.
4.2. Relative humidity
The following formula is used for acquiring relative humidity :
Where and are water vapor and saturated water vapor densities respectively. To calculate these densities values we need to know partial vapor pressure, where these pressure values is provided by synoptic weather stations. Also the saturated water vapor pressure can be calculated through traditional equations using surface temperature which this in turn can be retrieved from satellite thermal channels.
4.3. Water Basin Detection
In order to localize the water basins and detecting wet surfaces, we can use Tacceldcap transform. The structure of this transformation matrix for landsat images is as follows:
Where CH1 to CH7 are band 1 to band 7 DNs. Also b1 to b7 are the indices for Tacceledcap transform.
Also NDWI4 and NDVI5 can be used for recognition of lakes and basins. The structures of these two indices are as follow :
Where B stands for the reflectance in blue band, RED is reflectance in red and NIR is for reflectance in near infrared band. The limit between -0.60 to -0.85 for NDWI displays the surfaces of lakes and basins. Also the negative amounts of NDVI stands for water surfaces.
Malaria incidence depends on environmental parameters. Some of these parameters are Temperature, Relative Humidity, Precipitation and Vegetation density. Whereas with usage of satellite data, these parameters can be assessed. Thus we can create a model that can produce an output recognizing the high risk regions of malaria incidence. Because we deal with a 15 degree centigrade temperature difference and a 25 percent relative humidity difference, then the remote sensing uncertainties are acceptable.
- A.G.Nel, Malaria, Ministry of Health, Tehran, First Edition.
- Malaria (Plasmodium) and Other Haemospororina (Sporoza). In: Medical and Veterinary Entomology, editor. Kettle DS. Oxon, UK: CAB International, 1995. pp. 558-90.
- Edrissian GH, Nateghpoor M, Afshar A, Sayedzadeh A, Mohsseni G, Satvat M, Emadi A. Monitoring the Response of Plasmodium falciparum and P. vivax to Antimalarial Drugs in the Malarious Areas in South-East Iran. Arch Iranian Med 1999;2 (2):2-7.
- Martens WJ, Niessen LW, Rotmans J, Jetten TH, McMichael AJ. Potential impact of global climate change on malaria risk. Environ Health Perspect 1995;103 (5):458-64.
- Muirhead-Thomson RC. Mosquito behaviour in relation to malaria transmission and control in tropics. London: Edward Arnold, 1951.
- Patz JA, Lindsay SW. New challenges, new tools: the impact of climate change on infectious diseases. Curr Opin Microbiol 1999;2 (4):445-51.
- Haghdoost, A.A. , 2004. Assessment of seasonal and climatic effects on the incidence and species composition of malaria by using GIS methods. London School of Hygiene and Tropical Medicine,Faculity of Medicine, University of London.
- Jonathan, A. , Patz, and Sarah H. Olson , 2006. Malaria risk and temperature: Influences from global climate change and local land use practices. PNAS 2006;103;5635-5636; originally published online Apr 4, 2006.
- Sumana , B. , Sharma , C. , Dhiman , R.C. , Mitra , A.P. , 2006. Climate change and malaria in India. CURRENT SCIENCE, VOL. 90, NO. 3, 10 FEBRUARY 2006.
- M.A.Faghih, Malariology and Malaria Eradication, Tehran University, Iran, First Edition, 1996.
- Gemperli, A. , Vounatsou, P. , Anderegg, D. , and Pluschke, G. , 2004. EPIDEMIO: EARTH OBSERVATION IN EPIDEMIOLOGY. Proc. of the 2004 Envisat & ERS Symposium, Salzburg, Austria , 6-10 September 2004 (ESA SP-572, April 2005).
- Running, S.W., Justice, C., Salomonson, V., et al., 1994. Terrestrial remote sensing science and algorithms planned for EOS/MODIS. International Journal of Remote Sensing 15 (17), 2620-3587.
- Qin, Z., Karnieli, A., Berliner, P., 2001. A mono-algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing 22 (18), 583-594.
- Baise, Haris, Robert, General Meteorology.
- Huggel, C. 1998. Periglaziale Seen im Luft- und Satellitenbild. Diploma thesis, Department of Geography, University of Zurich.