Can remotely sensed meteorological data significantly contribute to reduce costs of tsetse surveys?

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomiasis animal production, agriculturerkina> and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.     

A Landscape and Climate Data Logistic Model of Tsetse Distribution in Kenya

Background

Trypanosoma spp, biologically transmitted by the tsetse fly in Africa, are a major cause of illness resulting in both high morbidity and mortality among humans, cattle, wild ungulates, and other species. However, tsetse fly distributions change rapidly due to environmental changes, and fine-scale distribution maps are few. Due to data scarcity, most presence/absence estimates in Kenya prior to 2000 are a combination of local reports, entomological knowledge, and topographic information. The availability of tsetse fly abundance data are limited, or at least have not been collected into aggregate, publicly available national datasets. Despite this limitation, other avenues exist for estimating tsetse distributions including remotely sensed data, climate information, and statistical tools.

Methodology/Principal Findings

Here we present a logistic regression model of tsetse abundance. The goal of this model is to estimate the distribution of tsetse fly in Kenya in the year 2000, and to provide a method by which to anticipate their future distribution. Multiple predictor variables were tested for significance and for predictive power; ultimately, a parsimonious subset of variables was identified and used to construct the regression model with the 1973 tsetse map. These data were validated against year 2000 Food and Agriculture Organization (FAO) estimates. Mapcurves Goodness-Of-Fit scores were used to evaluate the modeled fly distribution against FAO estimates and against 1973 presence/absence data, each driven by appropriate climate data.

Conclusions/Significance

Logistic regression can be effectively used to produce a model that projects fly abundance under elevated greenhouse gas scenarios. This model identifies potential areas for tsetse abandonment and expansion.     

Tsetse Fly (G.f. fuscipes) Distribution in the Lake Victoria Basin of Uganda

Tsetse flies transmit trypanosomes, the causative agent of human and animal African trypanosomiasis. The tsetse vector is extensively distributed across sub-Saharan Africa. Trypanosomiasis maintenance is determined by the interrelationship of three elements: vertebrate host, parasite and the vector responsible for transmission. Mapping the distribution and abundance of tsetse flies assists in predicting trypanosomiasis distributions and developing rational strategies for disease and vector control. Given scarce resources to carry out regular full scale field tsetse surveys to up-date existing tsetse maps, there is a need to devise inexpensive means for regularly obtaining dependable area-wide tsetse data to guide control activities. In this study we used spatial epidemiological modelling techniques (logistic regression) involving 5000 field-based tsetse-data (G. f. fuscipes) points over an area of 40,000 km², with satellite-derived environmental surrogates composed of precipitation, temperature, land cover, normalised difference vegetation index (NDVI) and elevation at the sub-national level. We used these extensive tsetse data to analyse the relationships between presence of tsetse (G. f. fuscipes) and environmental variables. The strength of the results was enhanced through the application of a spatial autologistic regression model (SARM). Using the SARM we showed that the probability of tsetse presence increased with proportion of forest cover and riverine vegetation. The key outputs are a predictive tsetse distribution map for the Lake Victoria basin of Uganda and an improved understanding of the association between tsetse presence and environmental variables. The predicted spatial distribution of tsetse in the Lake Victoria basin of Uganda will provide significant new information to assist with the spatial targeting of tsetse and trypanosomiasis control.     

Using MODIS satellite imagery to predict hantavirus risk

Aims Sin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (> 50%). The primary virus host is the deer mouse, and greater abundance of deer mice has been shown to increase the human risk of HPS. Our aim is to identify and compare vegetation indices and associated time lags for predicting hantavirus risk using remotely sensed imagery. Location Utah, USA. Methods A 5‐year time‐series of moderate‐resolution imaging spectroradiometer (MODIS) satellite imagery and corresponding field data was utilized to compare various vegetation indices that measure productivity with the goal of indirectly estimating mouse abundance and SNV prevalence. Relationships between the vegetation indices and deer mouse density, SNV prevalence and the number of infected deer mice at various time lags were examined to assess which indices and associated time lags might be valuable in predicting SNV outbreaks. Results The results reveal varying levels of positive correlation between the vegetation indices and deer mouse density as well as the number of infected deer mice. Among the vegetation indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) produced the highest correlations with deer mouse density and the number of infected deer mice using a time lag of 1.0 to 1.3 years for May and June imagery. Main conclusions This study demonstrates the potential for using MODIS time‐series satellite imagery in estimating deer mouse abundance and predicting hantavirus risk. The 1‐year time lag provides a great opportunity to apply satellite imagery to predict upcoming SNV outbreaks, allowing preventive strategies to be adopted. Analysis of different predictive indices and lags could also be valuable in identifying the time windows for data collection for practical uses in monitoring rodent abundance and subsequent disease risk to humans.     

Variation of tsetse fly abundance in relation to habitat and host presence in the Maasai Steppe,Tanzania

Human activities modify ecosystem structure and function and can also alter the vital rates of vectors and thus the risk of infection with vector‐borne diseases. In the Maasai Steppe ecosystem of northern Tanzania, local communities depend on livestock and suitable pasture that is shared with wildlife, which can increase tsetse abundance and the risk of trypanosomiasis. We monitored the monthly tsetse fly abundance adjacent to Tarangire National Park in 2014–2015 using geo‐referenced, baited epsilon traps. We examined the effect of habitat types and vegetation greenness (NDVI) on the relative abundance of tsetse fly species. Host availability (livestock and wildlife) was also recorded within 100×100 m of each trap site. The highest tsetse abundance was found in the ecotone between Acacia‐Commiphora woodland and grassland, and the lowest in riverine woodland. Glossina swynnertoni was the most abundant species (68%) trapped throughout the entire study, while G. pallidipes was the least common (4%). Relative species abundance was negatively associated with NDVI, with greatest abundance observed in the dry season. The relationship with the abundance of wildlife and livestock was more complex, as we found positive and negative associations depending on the host and fly species. While habitat is important for tsetse distribution, hosts also play a critical role in affecting fly abundance and, potentially, trypanosomiasis risk.     

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km² study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (r_s = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery.     

Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models

BackgroundTsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health. This stresses effective agricultural production and productivity in Africa. Delimiting the extent and magnitude of tsetse coverage has been a challenge over decades due to limited resources and unsatisfactory technology. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables.MethodologyEntomological data for the period 2008–2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. Data harmonisation for the purposes of responsiveness and matching was carried out. The key tool for tsetse trapping was itemized as pyramidal trap in many instances and biconical trap in others. Based on the spatially explicit assembled data, we ran two regression models; standard Poisson and Zero-Inflated Poisson (ZIP), to explore the associations between tsetse abundance in Uganda and several environmental and climatic covariates. The covariate data were constituted largely by satellite sensor data in form of meteorological and vegetation surrogates in association with elevation and land cover data. We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic.ResultsA total of 1,187 tsetse sampling points were identified and considered as representative for the country. The model results indicated the significance and level of responsiveness of each covariate in influencing tsetse abundance across the study area. Woodland vegetation, elevation, temperature, rainfall, and dry season normalised difference vegetation index (NDVI) were important in determining tsetse abundance and spatial distribution at varied scales. The resultant prediction map shows scaled tsetse abundance with estimated fitted numbers ranging from 0 to 59 flies per trap per day (FTD). Tsetse abundance was found to be largest at low elevations, in areas of high vegetative activity, in game parks, forests and shrubs during the dry season. There was very limited responsiveness of selected predictors to tsetse abundance during the wet season, matching the known fact that tsetse disperse most significantly during wet season.ConclusionsA methodology was advanced to enable compilation of entomological data for 10 years, which supported the generation of tsetse abundance maps for Uganda through modelling. Our findings indicate the spatial distribution of the G. f. fuscipes as; low 0–5 FTD (48%), medium 5.1–35 FTD (18%) and high 35.1–60 FTD (34%) grounded on seasonality. This approach, amidst entomological data shortages due to limited resources and absence of expertise, can be adopted to enable mapping of the vector to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies.     

Potential impacts of climate change on geographical distribution of three primary vectors of African Trypanosomiasis in Tanzania’s Maasai Steppe: G. m. morsitans,G. pallidipes and G. swynnertoni

In the Maasai Steppe, public health and economy are threatened by African Trypanosomiasis, a debilitating and fatal disease to livestock (African Animal Trypanosomiasis -AAT) and humans (Human African Trypanosomiasis—HAT), if not treated. The tsetse fly is the primary vector for both HAT and AAT and climate is an important predictor of their occurrence and the parasites they carry. While understanding tsetse fly distribution is essential for informing vector and disease control strategies, existing distribution maps are old and were based on coarse spatial resolution data, consequently, inaccurately representing vector and disease dynamics necessary to design and implement fit-for-purpose mitigation strategies. Also, the assertion that climate change is altering tsetse fly distribution in Tanzania lacks empirical evidence. Despite tsetse flies posing public health risks and economic hardship, no study has modelled their distributions at a scale needed for local planning. This study used MaxEnt species distribution modelling (SDM) and ecological niche modeling tools to predict potential distribution of three tsetse fly species in Tanzania’s Maasai Steppe from current climate information, and project their distributions to midcentury climatic conditions under representative concentration pathways (RCP) 4.5 scenarios. Current climate results predicted that G. m. morsitans, G. pallidipes and G swynnertoni cover 19,225 km², 7,113 km² and 32,335 km² and future prediction indicated that by the year 2050, the habitable area may decrease by up to 23.13%, 12.9% and 22.8% of current habitable area, respectively. This information can serve as a useful predictor of potential HAT and AAT hotspots and inform surveillance strategies. Distribution maps generated by this study can be useful in guiding tsetse fly control managers, and health, livestock and wildlife officers when setting surveys and surveillance programs. The maps can also inform protected area managers of potential encroachment into the protected areas (PAs) due to shrinkage of tsetse fly habitats outside PAs.     

Assessing penguin colony size and distribution using digital mapping and satellite remote sensing

Changes in penguin abundance and distribution can be used to understand the response of species to climate change and fisheries pressures, and as a gauge of ecosystem health. Traditionally, population estimates have involved direct counts, but remote sensing and digital mapping methodologies can provide us with alternative techniques for assessing the size and distribution of penguin populations. Here, we demonstrate the use of a field-based digital mapping system (DMS), combining a handheld geographic information system with integrated geographical positioning system as a method for: (a) assessing penguin colony area and (b) ground-truthing colony area as derived from satellite imagery. Work took place at Signy Island, South Orkneys, where colonies of the three congeneric pygoscelid penguins: Adélie Pygoscelis adeliae, chinstrap P. antarctica and gentoo P. papua were surveyed. Colony areas were derived by mapping colony boundaries using the DMS with visual counts of the number of nesting birds made concurrently. Area was found to be a good predictor for number of nests for all three species of penguin. Using a maximum likelihood multivariate classification of remotely sensed satellite imagery (QuickBird2, 18 January 2010; Digital Globe ID: 01001000B90AD00), we were able to identify penguin colonies from the spectral signature of guano and differentiate between colonies of Adélie and chinstrap penguins. The area classified (all species combined) from satellite imagery versus area from DMS data was closely related (R ² = 0.88). Combining these techniques gives a simple and transferrable methodology for examining penguin distribution and abundance at local and regional scales.     

Temporal transferability of wildlife habitat models: implications for habitat monitoring

Aim Temporal transferability is an important issue when habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of habitat monitoring. While the combination of remote sensing technology and habitat modelling provides a useful tool for habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite‐derived variables affect temporal transferability of habitat models and their usefulness for habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single‐year or multi‐year (i.e. 3‐year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda habitat within and across time periods by using threshold‐independent and threshold‐dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda habitat. However, the models developed using multi‐year data exhibited significantly higher temporal transferability than those developed using single‐year data. In addition, models developed with phenology metrics, especially when using multi‐year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with habitat modelling constitutes a suitable tool for characterizing wildlife habitat and monitoring its temporal dynamics. Using multi‐year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter‐annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for habitat monitoring through the integration of remote sensing technology and habitat modelling, which may be useful for the conservation of the giant panda and many other species.     

Land cover and tsetse fly distributions in sub-Saharan Africa

Abstract.  This study aims to provide trypanosomiasis-affected countries with standardized datasets and methodologies for mapping the habitat of the tsetse fly ( Glossina spp., the disease vector) by customizing and integrating state-of-the-art land cover maps on different spatial scales. Using a combination of inductive and deductive approaches, land cover and fly distribution maps are analysed in a geographic information system (GIS) to estimate the suitability of different land cover units for the three groups (subgenera) of Glossina. All land cover datasets used for and produced by the study comply with the Land Cover Classification System (LCCS). At the continental scale, a strong correlation between land cover and tsetse habitat is found for both the fusca and palpalis groups, whereas a weaker correlation found for the morsitans group may be indicative of less restrictive ecological requirements. At the regional and national levels, thematic aggregation of the multi-purpose Africover datasets yielded high-resolution, standardized land cover maps tailored for tsetse habitat for eight East African countries. The national maps provide remarkable spatial resolution, thematic detail and geographical coverage. They may be applied in subsequent phases of tsetse and trypanosomiasis control projects, including the planning of entomological surveys, actual tsetse control operations and planning for land use in reclaimed areas. The methodology and datasets discussed in the paper may have applications beyond the tsetse and trypanosomiasis issue and may be used with reference to other arthropod vectors, vector-borne and parasitic diseases.     

A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda

Tsetse flies (genus Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub‐Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co‐occurrence of both forms of human‐infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field‐survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field‐survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well‐connected habitat in northwestern Uganda, and twenty‐four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model‐predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well‐connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods.     

Development of a satellite-based hazard rating system for Dendroctonus frontallis (Coleoptera: Scolytidae) in the Ouachita Mountains of Arkansas

The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae), is the most damaging forest insect pest of pines (Pinus spp.) throughout the southeastern United States. Hazard rating schemes have been developed for D. frontalis, but for these schemes to be accurate and effective, they require extensive on-site measurements of stand attributes such as host density, age, and basal area. We developed a stand hazard-rating scheme for several watersheds in the Ouachita Highlands of Arkansas based upon remotely sensed data and a geographic information system. A hazard model was developed using stand attributes (tree species, stand age and density, pine basal area, and landform information) and was used to establish baseline hazard maps for the watersheds. Landsat 7 ETM+ data were used for developing new hazard maps. Two dates of Landsat imagery were used in the analyses (August 1999 and October 1999). The highest correlations between hazard rating scores and remotely sensed variables from either of the dates included individual Landsat 7 ETM+ bands in the near- and mid-infrared regions as well as variables derived from various bands (i.e., Tasseled cap parameters, principal component parameters, and vegetation indices such as the calculated simple ratio and normalized difference vegetation index). Best subset regression analyses produced models to predict stand hazard to southern pine beetle that consisted of similar variables that resembled but were more detailed than maps produced using inverse distance weighted techniques. Although the models are specific for the study area, with modifications, they should be transferable to geographically similar areas.     

Seasonal variation of tsetse fly species abundance and prevalence of trypanosomes in the Maasai Steppe,Tanzania

Tsetse flies, the vectors of trypanosomiasis, represent a threat to public health and economy in sub‐Saharan Africa. Despite these concerns, information on temporal and spatial dynamics of tsetse and trypanosomes remain limited and may be a reason that control strategies are less effective. The current study assessed the temporal variation of the relative abundance of tsetse fly species and trypanosome prevalence in relation to climate in the Maasai Steppe of Tanzania in 2014–2015. Tsetse flies were captured using odor‐baited Epsilon traps deployed in ten sites selected through random subsampling of the major vegetation types in the area. Fly species were identified morphologically and trypanosome species classified using PCR. The climate dataset was acquired from the African Flood and Drought Monitor repository. Three species of tsetse flies were identified: G. swynnertoni (70.8%), G. m. morsitans (23.4%), and G.pallidipes (5.8%). All species showed monthly changes in abundance with most of the flies collected in July. The relative abundance of G. m. morsitans and G. swynnertoni was negatively correlated with maximum and minimum temperature, respectively. Three trypanosome species were recorded: T. vivax (82.1%), T. brucei (8.93%), and T. congolense (3.57%). The peak of trypanosome infections in the flies was found in October and was three months after the tsetse abundance peak; prevalence was negatively correlated with tsetse abundance. A strong positive relationship was found between trypanosome prevalence and temperature. In conclusion, we find that trypanosome prevalence is dependent on fly availability, and temperature drives both tsetse fly relative abundance and trypanosome prevalence.     

Predicting krill swarm characteristics important for marine predators foraging off East Antarctica

Open ocean predator‐prey interactions are often difficult to interpret because of a lack of information on prey fields at scales relevant to predator behaviour. Hence, there is strong interest in identifying the biological and physical factors influencing the distribution and abundance of prey species, which may be of broad predictive use for conservation planning and evaluating effects of environmental change. This study focuses on a key Southern Ocean prey species, Antarctic krill Euphausia superba, using acoustic observations of individual swarms (aggregations) from a large‐scale survey off East Antarctica. We developed two sets of statistical models describing swarm characteristics, one set using underway survey data for the explanatory variables, and the other using their satellite remotely sensed analogues. While survey data are in situ and contemporaneous with the swarm data, remotely sensed data are all that is available for prediction and inference about prey distribution in other areas or at other times. The fitted models showed that the primary biophysical influences on krill swarm characteristics included daylight (solar elevation/radiation) and proximity to the Antarctic continental slope, but there were also complex relationships with current velocities and gradients. Overall model performance was similar regardless of whether underway or remotely sensed predictors were used. We applied the latter models to generate regional‐scale spatial predictions using a 10‐yr remotely‐sensed time series. This retrospective modelling identified areas off east Antarctica where relatively dense krill swarms were consistently predicted during austral mid‐summers, which may underpin key foraging areas for marine predators. Spatiotemporal predictions along Antarctic predator satellite tracks, from independent studies, illustrate the potential for uptake into further quantitative modelling of predator movements and foraging. The approach is widely applicable to other krill‐dependent ecosystems, and our findings are relevant to similar efforts examining biophysical linkages elsewhere in the Southern Ocean and beyond.     

Distribution and Density of Tsetse Flies (Glossinidae: Diptera) at the Game/People/Livestock Interface of the Nkhotakota Game Reserve Human Sleeping Sickness Focus in Malawi

In large parts sub-Saharan Africa, tsetse flies, the vectors of African human or animal trypanosomiasis, are, or will in the foreseeable future, be confined to protected areas such as game or national parks. Challenge of people and livestock is likely to occur at the game/livestock/people interface of such infested areas. Since tsetse control in protected areas is difficult, management of trypanosomiasis in people and/or livestock requires a good understanding of tsetse population dynamics along such interfaces. The Nkhotakota Game Reserve, an important focus of human trypanosomiasis in Malawi, is a tsetse-infested protected area surrounded by a virtually tsetse-free zone. The abundance of tsetse (Glossina morsitans morsitans) along the interface, within and outside the game reserve, was monitored over 15 months using epsilon traps. A land cover map described the vegetation surrounding the traps. Few flies were captured outside the reserve. Inside, the abundance of tsetse at the interface was low but increased away from the boundary. This uneven distribution of tsetse inside the reserve is attributed to the uneven distribution of wildlife, the main host of tsetse, being concentrated deeper inside the reserve. Challenge of people and livestock at the interface is thus expected to be low, and cases of trypanosomiasis are likely due to people and/or livestock entering the reserve. Effective control of trypanosomiasis in people and livestock could be achieved by increasing the awareness among people of dangers associated with entering the reserve.     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

The relationship between the spectral diversity of satellite imagery,habitat heterogeneity,and plant species richness

Assessment of habitat heterogeneity and plant species richness at the landscape scale is often based on intensive and extensive fieldwork at great cost of time and money. We evaluated the use of satellite imagery as a quantitative measure of the relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness. A 16 km² portion of a military training area in Germany was systematically sampled by plant taxonomic experts on a grid of one hundred 1-ha plots. The diversity of disturbance types, resulting habitat heterogeneity, and plant species richness were determined for each plot. Using an IKONOS multispectral satellite image, we examined 168 metrics of spectral diversity as potential indicators of those independent variables. Across all potential relationships, a simple count of values per spectral band per plot, after compressing the data from the original 11-bit format with 2048 potential values per band into a maximum of 100 values per band, resulted in the most consistent predictor for various metrics of habitat heterogeneity and plant species richness. The count of values in the green band generally out-performed the other bands. The relationship between spectral diversity and plant species richness was stronger than for measures of habitat heterogeneity. Based on the results, we conclude that remotely sensed assessment of spectral diversity, when coupled with limited ground-truthing, can provide reasonable estimates of habitat heterogeneity and plant species richness across broad areas.     

Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices

The assessment of landscape spatial patterns is a key issue in landscape management. Landscape pattern indices (LPIs) are tools appropriate for analyzing landscape spatial patterns. LPIs are often derived from raster land cover maps that are extracted from remotely sensed data through hard classification. However, pixel-based hard classification methods suffer from the mixed pixel problem (in which pixels contain more than one land cover class), making for inaccurate classification maps and LPIs. In addition, LPIs generated by hard classification methods are characterized by grain sizes (the sampling unit sizes) that limit the derived landscape pattern to a certain scale. Sub-pixel mapping (SPM) models can enable fine-scale estimation of the spatial patterns of land cover classes without requiring additional data; hence, this is an appropriate downscaling method for land cover mapping. The fraction images generated by soft classification estimate the area proportion of each land cover class within each pixel, and using these images as input enables SPM models to alleviate the mixed pixel problem. At the same time, by transforming fraction images into a finer-scaled hard classification map, SPM models can minimize the influence of grain size on LPIs calculation. In this research, simulated landscape thematic patterns that can provide different landscape spatial patterns, eight commonly used LPIs and a SPM model that maximizes the spatial dependence between neighbouring sub-pixels were applied to assess the efficiency of deriving LPIs from sub-pixel model maps. Results showed that the SPM model can more precisely characterize landscape patterns than hard classification methods can. Landscape fragmentation, class abundance, the uncertainty in SPM, and the spatial resolution of the remotely sensed data influenced LPIs derived from sub-pixel maps. The largest patch index, landscape division, and patch cohesion derived from remotely sensed data with different spatial resolutions through the SPM model were suitable for inter-comparison, whereas the patch density, mean patch area, edge density, landscape shape index, and area-weighted mean shape index derived from the sub-pixel maps were sensitive to the spatial resolution of the remotely sensed data.