The mapping of tsetse distribution is one the most consolidated fields of application of RS and GIS in the context of Tsetse and Trypanosomosis (T&T) intervention. A number of studies have tackled the issue on all scales ranging from continental to local. It is possible to distinguish two classes of mapping, one based on the so-called biological approach, the other using statistical techniques.

The biological approach is also called predictive or process-based approach. This type of models studies the population dynamics of a species in terms of the biotic and abiotic mortalities. Some mortalities are driven by environmental conditions that may be detected from satellite. If potential fecundity exceeds mortality, presence is predicted. The number and detail of input data that are required by this type of models make them suitable only for small scale mapping.

The statistical technique is also known as descriptive approach. It is based on direct correlation between species presence or absence and a set of predictor variables such as rainfall, temperature and relative humidity. A significant number of parameters influencing tsetse distribution can be derived from satellite measurements, thus it is possible to estimate their presence over large geographical areas and different environmental conditions. This method provides estimates of absence/presence in a statistical way and allows the mapping of tsetse distribution with a high accuracy over the entire Africa.

Unfortunately, tsetse presence/absence maps are not sufficient to assess the disease challenge and to map the risk. In this regard, a very important parameter is also the vector density. A review of the available literature shows that the application of RS techniques to map tsetse abundance is far less widespread than the mapping of distribution. This is because the abundance is determined by density dependent processes that do not necessarily have satellite variable proxies. Nevertheless, studies to map tsetse abundance do exist: for example the national map in Togo at a grid resolution of 0.125° for all tsetse species present in the country.

Some satellites provide observations on a daily basis; such data can be analysed to depict time-dependent seasonal fluctuations and even longer-term trends. The latter can possibly be put into relation with the impact of climate change.

The historical classification of tsetse (genus Glossina), based on morphological criteria, divides the species into three groups, which have different habitat requirements that are thought to reflect their evolutionary history. The fusca group flies (subgenus Austenina) with supposedly the most primitive male genitalia, occur mainly in the low-land rainforests of West and Central Africa; the palpalis group species (subgenus Nemorhina) occupy similar forest habitats throughout sub-Saharan Africa and also extend into riverine and lakeside forests or the moist areas between such forests; and finally the morsitans group of flies (subgenus Glossina s.s.) occurs in a variety of savannah habitats lying between the forest edges and deserts.
Tsetse live in habitats that provide shade for developing puparia (pupae) and resting sites for adults. Their development is temperature and humidity limited, like that of many invertebrates. These factors together enabled the early map-makers to estimate the distributional limits of many species of tsetse based on vegetation type, meteorological records and altitude. Before the remotely sensed satellite data came into practice, all large scale distribution maps were affected by severe information gaps, inconsistencies and inaccuracies due to the different times and methods of survey. Remote sensing is now able to depict many features of climate, vegetation and altitude, making it possible to produce more precise maps of habitats suitable for flies. Satellite images offer several advantages over field surveys: the data are free from any human bias, make remote places accessible, can be continuously produced and refined, and show real-time information.
All satellite sensors show trade-offs between spectral, temporal and spatial resolution. Data provided by the sensors need to be processed, integrated and interpreted in the epidemiological context under study. Most biological applications to date have used passive satellite sensors, which detect reflections or emissions ultimately arising from the sun’s activity. Active sensors (radar) are gathering increasing interest, mainly due to or also because of their ability to produce images even under cloudy conditions.
Meteorological satellites, despite their poor spatial resolution, provide images at a frequency that is sufficient to capture habitat seasonality at national, regional and continental level. Among these, NOAA-AVHRR (National Oceanic & Atmospheric Administration - Advanced Very High Resolution Radiometer) and Meteosat proved to be particularly useful for the study of tsetse distributions. The quality of all imagery from passive sensors is adversely affected by atmospheric contamination, the most obvious of which is cloud (although other aerosols are also important). The very high revisit frequency of meteorological satellites permits to combine them by selecting for each pixel the highest value from all of those recorded for that pixel in each period of time. This is believed to be the most cloud or contaminant-free value. The resulting images are called maximum value composites (MVCs). Data from each of the sensor channels can be used directly or processed to produce various indices that are related more directly to important variable such as soil surface temperature.
Commonly used products include: land surface temperature of the earth’s surface (LST), derived from AVHRR channels 4 and 5; middle infrared band (MIR) from AVHRR channel 3, also related to the temperature of the earth surface; normalized difference vegetation index (NDVI) derived from AVHRR channels 1 and 2 and related to plant photosynthetic activity; vapour pressure deficit (VPD), also derived from AVHRR channels 1 and 2 and effectively a measure of atmospheric drying power; near surface air temperature (TvX) derived from LST and vegetation index measurements; cold cloud duration (CCD) from Meteosat that is correlated with rainfall in convective precipitation systems.
Monthly composite imagery usually shows strong serial correlation and therefore data redundancy, which may be eliminated in two different ways. Either the data are subjected to principal component analysis (PCA) and the resultant significant principal components are used in the analyses, or the data are subjected to temporal Fourier analysis that describes the cycles of temperature, vegetation growth etc., in terms of annual, biannual, triannual and other cycles with longer or shorter periods. This method removes data redundancy and produces a set of orthogonal (i.e. uncorrelated) outputs whilst retaining a description of seasonality (lost in PCA) that is of vital interest in vector and disease mapping.
The mapping of tsetse distribution takes advantage of other data, either directly derived from satellite (Digital Elevetion Models, Land Cover Maps, etc.) or from a combination of satellite and ground-based meteorological data [e.g. the Length of the Growing Period (LGP), derived from temperature, rainfall and evapotraspiration meteorological records].
AVHRR data were used to analyse tsetse distribution in many studies. Most of them relied on discriminant analysis while some others used optimal threshold distribution . The section of this site that displays maps shows an application of RS and multivariate statistical analysis to produce tsetse species distribution maps.
A review of major studies dealing with tsetse mapping by means of remotely sensed data show that the number and type of variables that better describe fly distribution varies among different geographical regions and eco-climatic zones. For example, in West Africa, thermal variables tend to be more important in describing distributions than are vegetation index (NDVI) or rainfall (CCD) variables . In the same region, rainfall (CCD) data were relatively more important in describing the abundance of flies (i.e. flies per trap per day), than they were for describing their distribution.
One criticism of the environmental envelope approach to mapping fly distributions is that it ignores the influence of biotic factors that are important in determining the abundance of flies within their geographical limits . Nevertheless, the high mapping accuracy achieved without modelling biotic factors supports the idea that they are less important than the abiotic ones (though it is also possible that some satellite data are acting as surrogates for un-quantified biotic factors ).
In conclusion, satellites appear able to describe with remarkable statistical accuracy the distribution of virtually all fly species. The main problem remains the ultimate degree of accuracy that such an approach can provide. It is not possible to achieve the 100 percent reliability tsetse control personnel would like, but this level is also failed by ground surveys and it is really essential only for vector elimination purposes. In the more common context of tsetse control, the identification of “hotspots” and ecological corridors of reinvasion can be described with accuracy by remote sensing techniques. In this line of work, an effort is needed in the validation of the models and in the utilization of new generation satellites, which promise higher accuracy as a consequence of more data channels and better spatial and spectral resolution.

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Sunshine, rainfall and humidity, temperature, atmospheric pressure and wind make up the climate of a particular area. Temperature and humidity are the most important of these for tsetse, and light can also be important.
Glossina lives well at 25-26° but if temperature goes much higher or much lower there may be some damage to the fly. In general a temperature above 38° is damaging to the adults, while a temperature below about 17° will not allow the adult to live a normal active life.
Rainfall probably does not have any direct effect on tsetse, but does so indirectly by affecting the humidity and maintaining different vegetation zones. A humid atmosphere allows tsetse to spread away from sheltered habitats, so long as the temperature is not too high.

Remote Sensing is used at a wide range of geographical scales to monitor meteorological and climatic conditions. Several services exist that provide up-to date information on precipitation, moisture, temperature and other parameters that can be relevant in tsetse and Trypanosomosis interventions. While remotely sensed datasets have often been used to predict areas of suitability for tsetse species in Africa, it is yet to be fully explored to what extent they could be useful in the implementation phase of interventions. Anomalies in the rainfall or temperature could be used to forecast potential movements of flies’ populations and thus support control/eradication campaigns. Similar applications are currently being experimented to monitor zones at epidemic risk (ex. malaria).

Many meteo-climatic datasets can be either viewed on the web or downloaded as georeferenced datasets to be used within a GIS system. In this field, the most relevant space instruments are “Meteosat”, operated by Eumetsat (Europe’s meteorological satellite agency) and the “Advanced Very High Resolution Radiometer” (AVHRR) of NOAA (USA National Oceanic and Atmospheric Administration). Due to the size of the areas mapped, most of the datasets regularly produced have a relatively low resolution (usually 1 kilometre or lower) which makes them suitable for national or regional studies. Nevertheless, higher resolution raw datasets can be acquired over specific areas to obtain more detailed information. In this regard, the most interesting sensors are MODIS, mounted on the NASA satellites Terra and Aqua, and MERIS, on ENVISAT satellite operated by the European Space Agency.

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