MSc thesis started by Jakob Schwalb-Willmann

MSc thesis started by Jakob Schwalb-Willmann

Jakob Schwalb-Willmann just started his M.Sc. thesis titled “A deep learning movement prediction model using environmental data to identify movement anomalies”. He will combine animal movement and remote sensing data in order to develop a generic, data-driven DL-based model that predicts movements from movement history alongside environmental covariates in order to detect movement anomalies. He will establish simulated, controlled environments that allow precise adjustments of the model inputs to test the model’s feedbacks and its variability. It can be considered as a precursor study for the model’s deployment on real data and to only experimentally apply it on such due to the given constraints (time and content) of his M.Sc. thesis. The first supervisor is Dr. Martin Wegmann.

M.Sc. handed in on animal movement and remote sensing

M.Sc. handed in on animal movement and remote sensing

The M.Sc. thesis “Can animal movement and remote sensing data help to improve conservation efforts?” by Matthias Biber M.Sc. student within the Global Change Ecology program handed in his thesis. He explored the potential of remote sensing data to explain animal movement patterns and if these linkages can help to improve conservation efforts. He used Zebra as study animal in Southern and Eastern Africa. The first supervisor was Martin Wegmann, the second supervisor of his M.Sc. was Thomas Müller from BIK-F.

abstract:
Climate and land-use change have a growing influence on the world’s ecosystems, in particular in Africa, and increasingly threaten wildlife. The resulting habitat loss and fragmentation can impede animal movement, which is especially true for migratory species. Ungulate migration has declined in recent years, but its drivers are still unclear. Animal movement and remote sensing data was combined to analyse the influence of  various vegetation and water indices on the habitat selection of migratory plains zebras in Botswana’s Ngamiland. The study area experienced a more or less steady state in normalised difference vegetation index (NDVI) over the last 33 years. More than half of the study area was covered by PAs. NDVI increased stronger in PAs compared to areas that were not protected. NDVI was always higher in the Okavango Delta  compared to the Makgadikgadi Pans. Although zebras are thought to select for areas with high NDVI, they experienced a lower NDVI in the Makgadikgadi grasslands during wet season. Step selection functions (SSFs) showed that NDVI derived from Landsat as well as NDVI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) were significant drivers of habitat selection across all individuals. Migration  seems to be driven by the high nutritional value of the Makgadikgadi grasslands and not seasonal resource limitation. Landsat imagery was shown to provide different environmental information compared to MODIS data. This highlights not only the importance of NDVI for explaining animal movement, but also the importance of Landsat imagery for monitoring habitat extent and fragmentation. Incorporating the animal’s  behavioural state and memory into SSFs will help to improve our ecological understanding of animal movement in the future.

MSc topic on wetland modeling

MSc topic on wetland modeling

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 center 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 welcome to send an Email to Dr. Hooman Latifi (hooman.latifi@uni-wuerzburg.de).

Supervisors:

Dr. Aurélie Davranche (University of Angers, France)

Dr. Hooman Latifi (University of Würzburg)

Dr. Brigitte Poulin (Tour du Valat, France)

M.Sc. started on monitoring protected areas

M.Sc. started on monitoring protected areas

rp_EO-MOVE_sentinel_wuerzburg_birds_movement-286x300.jpgHenrike Schulte to Bühne started her M.Sc. „Quantifying landcover change using remote sensing data in a transboundary protected area“ in cooperation with the Zoological Society of London, Dr. Nathalie Pettorelli within the Global Change Ecology study program. Her M.Sc. is dealing with evaluating the status and change of nationalparks especially transboundary ones. Countries increasingly cooperate across boundaries for conservation purposes and these need to be evaluated and monitored constantly. Henrike is using remote sensing data to analyze environmental changes and is discussing the results with respect to conservation planning and political implications.

M.Sc. thesis on animal movement interactions and the environment

M.Sc. thesis on animal movement interactions and the environment

joe_premeir_movement_graph_2016Joe Premier submitted his M.Sc. thesis on “The Lynx Effect: Behaviour of Roe Deer in the Presence of Lynx in a European Forest Ecosystem” within the Global Change Ecology M.Sc. program. He was co-supervised by Marco Heurich from the Bavarian Forest Nationalpark. Predation risk is one of the main drivers of prey behaviour. In this study the behavioural responses of roe deer under the predation risk of lynx were investigated using a combination of spatial analysis and statistical analyses. Evidence for the hypothesis that roe deer exhibit avoidance behaviour to lynx locations both spatially and temporally could not be found, however the upper limit of avoidance behaviour was constrained to within 4 hours. It was hypothesised that the activity level of roe deer was driven by proximity to lynx, with activity levels increasing with decreasing separation distance. The activity level of roe deer was in general found not to be strongly driven by the variable distance to lynx. As hypothesised, the activity level of roe was associated with habitat, such that lower activity levels occurred in areas of highest visibility (low cover) and higher activity in lowest visibility (high cover). It was found in general that a LiDAR habitat index was the most important explanatory variable of roe deer activity level. In the specific case of the closest encounters (within 24 hours and 1Km) during the night, lynx’s most active time, activity level of roe deer was found to be elevated compared to less proximate individuals. There is also a suggestion that these roe deer move further than those more distant to lynx. The hypothesis that roe deer select habitats of lower predation risk when close to lynx was partially supported; it was found that roe deer selected lower predation risk areas when closest to lynx (within 24 hours and 1Km) during winter nights and consistently inhabited lower predation risk habitats during summer when compared to winter. Furthermore, it was shown that activity level was lower in high risk habitats as hypothesised. Under the predation risk of an ambush hunter, in this case lynx, it is suggested that roe deer adopt a “business as usual” behaviour, with energy diverted for anti-predator behaviour limited to scenarios of heightened risk. It is believed a near continuous GPS tracking schedule would be required to resolve lethal and non-lethal encounter events and illuminate avoidance behaviour and perception distance further.

MSc opportunities: remote sensing in ecology and conservation

MSc opportunities: remote sensing in ecology and conservation

species_richness_HyMapBiodiversity analysis and conservation decision relies on adequate and meaningful data that are available on a long-term and global basis.  Such environmental information need adequate spatial and temporal resolution and remote sensing data does provide a wide range of potentially suitable data sets. Various approaches concerning remote sensing data use for biodiversity and conservation exist to improve the application and integration. This is especially important since the use of remote sensing has increased widely over the past decades, and therefore interdisciplinary approaches foster the understanding of needed parameters for biodiversity monitoring, which provide guidance to observation systems as to what and how to measure key aspects of biodiversity. Multi-scale, multi-sensor and multi-temporal remote sensing data analysis do provide needed and crucial information for ecological analysis, especially within conservation applications, species distribution analysis or animal movement analysis. A variety of MSc topics is available aiming to improve remote sensing usage in ecology conservation, as well as its application. It is aimed at, that all MSc are in close collaboration with ecology or conservation organizations to support a truly interdisciplinary thesis. Martin Wegmann is dedicated to use his scientific network to organizations such as SCBI, ZSL, IUCN, UNEP-WCMC, WWF etc. for embedding EAGLE MSc thesis.

For more details, please see the webpage of Martin Wegmann and browse through the list of past MSc topics. Moreover, please visit www.remote-sensing-biodiversity.org for updates on data, events and publications related to remote sensing data analysis in ecology and conservation. Additionally, AniMove.org and EcoSens.org offer further details on potential MSc topics.