M.Sc thesis: Agent-based modeling to understand Mediterranean wetland dynamic based on multiple remote sensing data

M.Sc thesis: Agent-based modeling to understand Mediterranean wetland dynamic based on multiple remote sensing data

M.Sc thesis (+ a two-month internship):

Agent-based modeling to understand Mediterranean wetland (former saltworks) dynamic based on multiple remote sensing data

 

UAV imagery over a portion of the study site. Image courtesy Cyril Fleurant (Uni Angers)

The Camargue’s former saltworks is a 6500-ha site located at the Mediterranean coast in southern France. The site has been recently purchased by the Conservatoire du Littoral, a public organization created in 1975 to ensure the protection of outstanding natural areas along the coast. The ongoing management of the area has been entrusted to the natural regional parc (PNR camargue), the national reserve of Camargue and the Tour du Valat. The site comprises a wide range of habitats. It has traditionally been home to the single colony of Flamingos nesting in France and is used by thousands of shorebirds during breeding and migration. Various construction works such as embankments (to control circulation of pumped sea-water through lagoons) and sea-front dike (to prevent uncontrolled flooding by the sea) together with salt exploitation and sea-level rise led to profound changes in the landscape that in turn call for the restoration of natural processes of coastal lagoon ecosystems. However, the conservation and management measures are restricted to be timely done as a result of difficult access for ground survey. Very high resolution remote sensing can introduce alternatives to this by providing continuous and objective surface coverage.

In this context, this M.Sc project aims at developing predictive tools on the basis of remote sensing data to follow habitat dynamics in order to help adaptive ecosystem management. The objective is to develop a method to understand the fast changes of the habitats using very high resolution remote sensing data. To this aim, LiDAR and very high resolution optical data (WorldView 2) and other GIS layers will be analyzed to produce spatially-continuous input for a state-of-the-art agent-based model. Few studies have applied this modeling approach to image analysis but the first results are promising .

Agent-based modeling will allow considering multiple non parametric factors that characterize the landscape dynamics. This approach will allow taking complex spatial and temporal processes as well as changing factors into account. The GAMA agent-based simulation platform (Taillandier et al. 2014, http://gama-platform.org/)  was initially developed to integrate GIS data in the  simulation. Within the envisaged M.Sc work this platform will be used for prediction based on the layers created from remote sensing data.

The M.Sc thesis is planned to be ideally started with a preliminary phase of two-month internship at the LETG, University of Angers . During the internship the M.Sc student will encompass a NetLogo and GAMA learning phase and gets to know the area and data.  A site visit at Tour du Valat research centre may help to understand the management objective of the area. The second phase would be the M.Sc thesis, during which the candidate will spend time at both Universities of Würzburg (4 months) and Angers (2 months). The stay in Angers is supported by an existing ERASMUS agreement between the two universities.

Interested candidates are wellcome to send an Email to Dr. Hooman Latifi.

Supervisors:

Dr. Aurélie Davranche (University of Angers, France)

Dr. Hooman Latifi (University of Würzburg)

Dr. Brigitte Poulin (Tour du Valat, France)

new publication: Mapping Bushmeat Hunting Pressure in Central Africa

new publication: Mapping Bushmeat Hunting Pressure in Central Africa

Biotropica_Ziegler_Fa_Wegmann_bushmeat_hunting_pressure_2016

Hunting pressure modelled for Central Africa (Biotropica link)

Our analysis on mapping bushmeat hunting pressure in Africa based on various co-variates, such as land cover, is now available online. Is is related to our article in NATURE Scientific Reports.

Hunting and trade of wild animals for their meat (bushmeat), especially mammals, is commonplace in tropical forests worldwide. In West and Central Africa, bushmeat extraction has increased substantially during recent decades. Currently, such levels of hunting pose a major threat to native wildlife. In this paper, we compiled published data on hunting offtake of mammals, from a number of studies conducted between 1990 and 2007 in Cameroon, Central African Republic, Democratic Republic of Congo, Equatorial Guinea, Gabon, and Republic of Congo. From these data sources, we estimated annual extraction rates of all hunted species and analyzed the relationship between environmental and anthropogenic variables surrounding each hunting rate and levels of bushmeat extraction. We defined hunting pressure as a function of bushmeat offtake and number of hunted species and confirm that hunting pressure is significantly correlated with road density, distance to protected areas and population density. These correlations are then used to map hunting pressure across the Congo Basin. We show that predicted risk areas show a patchy distribution throughout the study region and that many protected areas are located in high-risk areas. We suggest that such a map can be used to identify areas of greatest impact of hunting to guide large-scale conservation planning initiatives for central Africa.

 

Stefan Ziegler, John E. Fa, Christian Wohlfart, Bruno Streit,Stefanie Jacob and Martin Wegmann (2016) Mapping Bushmeat Hunting Pressure in Central Africa. Biotropica. http://onlinelibrary.wiley.com/doi/10.1111/btp.12286/abstract

new publication: Modeling and Validation of Environmental Suitability of diseases

new publication: Modeling and Validation of Environmental Suitability of diseases

YvonneWalz_remote-sensing_eu_article_publication_PLOSNeglTropDis_2015Another 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

new article: ecological modelling to improve remote sensing disease risk analysis

new article: ecological modelling to improve remote sensing disease risk analysis

A new article by our former PhD student Dr. Yvonne Walz just got published. The article “Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling” aims at the advantages of using spatial modelling approaches for disease risk analysis using remote sensing. Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

http://geospatialhealth.net/index.php/gh/article/view/398