Our former M.Sc. student Henrike published her work “Protection status and national socio-economic context shape land conversion in and around a key transboundary protected area complex in West Africa” where she outlined the capabilities of remotely sensed land cover information and its change over time to inform conservation activities. From the abstract: “ransboundary cooperation is being promoted as an effective way to conserve biodiversity that straddles national borders. However, monitoring the ecological outcomes of these large-scale endeavours is challenging, and as a result, the factors and processes likely to shape their effectiveness remain poorly identified and understood. To address this knowledge gap, we tested three hypotheses pertaining to natural vegetation loss across the W-Arly-Pendjari protected area complex, a key biodiversity hotspot in West Africa. Using a new methodology to compare land cover change across large remote areas where independent validation data is unevenly distributed across time, we demonstrate widespread agricultural expansion outside protected areas over the past 13 years.”
read more here:
Schulte to Bühne, H., Wegmann, M., Durant, S. M., Ransom, C., de Ornellas, P., Grange, S., Beatty, H., Pettorelli, N. (2017), Protection status and national socio-economic context shape land conversion in and around a key transboundary protected area complex in West Africa. Remote Sensing in Ecology and Conservation. doi: 10.1002/rse2.47
Another publication from the Phd by Yvonne Walz just got published. Read the abstract and check out the online availability: Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health.
Walz, Y., Wegmann, M., Dech, S.W., Vounatsou, P., Poda, J-N., N’Goran, E.K., Utzinger, J., Raso, G. (2015) Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing. PLoS Negl Trop Dis 9(11): e0004217. doi:10.1371/journal.pntd.0004217
The review article lead by Yvonne Walz is published online first. Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions.
We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised.
We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is – in principle – far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from.
Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.
Yvonne Walz, Martin Wegmann, Stefan Dech, Giovanna Raso and Jürg Utzinger (2015) Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook. Parasites & Vector http://www.parasitesandvectors.com/content/8/1/163
Yvonne Walz with her supervisor Prof. Stefan Dech
On January 14 2015 Yvonne Walz defended her PhD on remote sensing analysis for Schistosomiasis in West Africa successfully – congratulations!
She is currently working at the United Nation University in Bonn in a related topic of disease mapping and remote sensing.
the abstract of her PhD:
Global environmental change leads to the emergence of new human health risks. As a consequence, transmission opportunities of environment-related diseases are transformed and human infection with new emerging pathogens increase. The main motivation for this study is the considerable demand for disease surveillance and monitoring in relation to dynamic environmental drivers. Remote sensing (RS) data belong to the key data sources for environmental modelling due to their capabilities to deliver spatially continuous information repeatedly for large areas with an ecologically adequate spatial resolution. A major research gap as identified by this study is the disregard of the spatial mismatch inherent in current modelling approaches of profiling disease risk using remote sensing data. Typically, epidemiological data are aggregated at school or village level. However, these point data do neither represent the spatial distribution of habitats, where disease-related species find their suitable environmental conditions, nor the place, where infection has occurred. As a consequence, the prevalence data and remotely sensed environmental variables, which aim to characterise the habitat of disease-related species, are spatially disjunct. The main objective of this study is to improve RS-based disease risk models by incorporating the ecological and spatial context of disease transmission. Exemplified by the analysis of the human schistosomiasis disease in West Africa, this objective includes the quantification of the impact of scales and ecological regions on model performance. In this study, the conditions that modify the transmission of schistosomiasis are reviewed in detail. A conceptual underpinning of the linkages between geographical RS measures, disease transmission ecology, and epidemiological survey data is developed. During a field-based analysis, environmental suitability for schistosomiasis transmission was assessed on the ground, which is then quantified by a habitat suitability index (HSI) and applied to RS data. This conceptual model of environmental suitability is refined by the development of a hierarchical model approach that statistically links school-based disease prevalence with the ecologically relevant measurements of RS data. The statistical models of schistosomiasis risk are derived from two different algorithms; the Random Forest and the partial least squares regression (PLSR). Scale impact is analysed based on different spatial resolutions of RS data. Furthermore, varying buffer extents are analysed around school-based measurements. Three distinctive sites of Burkina Faso and Côte d’Ivoire are specifically modelled to represent a gradient of ecozones from dry savannah to tropical rainforest including flat and mountainous regions. The model results reveal the applicability of RS data to spatially delineate and quantitatively evaluate environmental suitability for the transmission of schistosomiasis. In specific, the multi-temporal derivation of water bodies and the assessment of their riparian vegetation coverage based on high-resolution RapidEye and Landsat data proofed relevant. In contrast, elevation data and water surface temperature are constraint in their ability to characterise habitat conditions for disease-related parasites and freshwater snail species. With increasing buffer extent observed around the school location, the performance of statistical models increases, improving the prediction of transmission risk. The most important RS variables identified to model schistosomiasis risk are the measure of distance to water bodies, topographic variables, and land surface temperature (LST). However, each ecological region requires a different set of RS variables to optimise the modelling of schistosomiasis risk. A key result of the hierarchical model approach is its superior performance to explain the spatial risk of schistosomiasis. Overall, this study stresses the key importance of considering the ecological and spatial context for disease risk profiling and demonstrates the potential of RS data. The methodological approach of this study contributes substantially to provide more accurate and relevant geoinformation, which supports an efficient planning and decision-making within the public health sector.
1. supervisor: Prof. Dech, 2. supervisor. Prof. Utzinger, mentored by Dr. M. Wegmann
Gerald Forkuor with his supervisor Prof. Christopher Conrad
On January 14 2015 Gerald Forkuor defended his PhD on remote sensing for crop mapping in West Africa successfully – congratulations!
He is currently working at the International Water Management Institute in Ghana in a related topic of remote sensing.
The PhD dissertation is available under https://opus.uni-wuerzburg.de/files/10868/Thesis_Gerald_Forkuor_2014.pdf