My research is focused on advancing methods using geospatial technologies to monitor wildlife and analyse movement in relation to anthropogenic risk, this is being carried out using several methods:
Automating detection of African Elephants in very-high-resolution satellite imagery (Worldview 3 & 4) using a convolutional neural network (CNN). I am working on this in collaboration with the Machine Learning Research Group. We are testing two Tensorflow models measured against human performance.
I conducted a wildlife census in southern Zambia using a long-range fixed-wing drone – WingtraOne and I am now collating audiograms for different species comparing them to audiograms from different drones to understand at what altitude drones should be flown to minimise disturbance.
Using high-resolution satellite imagery, I am looking at elephant movement in relation to mobile livestock enclosures (bomas) analysing changes between 2011 & 2019 in relation to the abandonment and establishment of new bomas.
Using the Environmental Evidence Protocol I have conducted a Systematic Evidence Map to gain a better understanding of illegal hunting hotspots and correlations with several covariates, including, proximity to roads, water bodies, human settlements and different land ownership areas.
I was previously based in Brussels working with the German Development Agency on a European Commission project. I have a MSc in Remote Sensing and Environmental Science from Uppsala University, Sweden and the University of Life Sciences in Vienna, Austria. While working on the MSc I was a Visiting Research Scholar at the United Nations Office of Drugs and Crime, researching the use of geospatial technologies to combat wildlife crime. I established the University of Oxford Drone Society in 2018 and have been president for several years. We are grateful to have received sponsorship from the Oxford Foundry Impact Fund and Thales Group. We run a selection of workshops for the Said Business School and are working to attach a UHF radio receiver to a drone to help in tracking wildlife, in particular, the Scottish Wildlife Cat.
The spatial distribution of illegal hunting of terrestrial mammals in Sub-Saharan Africa: a systematic map
What spatially explicit quantitative evidence exists that shows the effect of land tenure on illegal hunting of endangered terrestrial mammals in sub-Saharan Africa? A systematic map protocol
© 2018 The Author(s). Background: Over the last two decades there has been an increase in the demand for land in Sub Saharan Africa, particularly from foreign agribusiness investment to provide food for an increasing human population. The majority of land outside of protected areas in sub-Saharan Africa is under customary tenure. Due to poor land administration in the region, communities living in undocumented land areas tend to be at greater risk of eviction from increasing liberalisation of land markets. To prevent local displacement and disturbance to investment caused by land disputes tenure clarification is growing in importance on national and international agendas. Land conversion can fragment wildlife habitat while reducing the suitable range areas of terrestrial mammal populations on the continent. Simultaneously illegal hunting is on the rise for a wide variety of taxa driven by a demand for food and income from the sale of animal products. To enable a better understanding of how land tenure arrangements impact upon spatial variations in illegal hunting, this protocol sets out the parameters for an evidence map which will collate and analyse the spatially explicit quantitative evidence that exists showing the effect of land tenure on illegal hunting of endangered terrestrial mammals in sub-Saharan Africa. Sub-Saharan Africa is the region of focus as it contains the highest number of terrestrial mammals listed as vulnerable, endangered or critically endangered by the International Union for Conservation of Nature. Taking stock of what methods have been used to gather data and where evidence exists can guide future research in this area while informing conservation interventions. Methods: This evidence map will compare: (1) data availability on the spatial distribution of illicit hunting of endangered terrestrial mammals across different land tenure regimes in sub-Saharan Africa; (2) research methodologies that have primarily been used to collect quantitative data on illegal hunting and comparability of existing data; (3) preferences in the research body toward particular taxa and geographical areas, (4) the evidence map will provide an analysis on the influence other environmental and anthropogenic determinants that influence the spatial distribution of illicit hunting incidences, e.g., proximity to roads, water bodies, range patrol bases etc. Eight academic databases and numerous organisation repositories will be searched for relevant studies by three authors. Double screening will be carried out on all articles to locate studies that meet the specified inclusion criteria, for inclusion studies must contain spatially explicit quantitative data on illegal hunting of endangered terrestrial mammals as defined by the International Union for the Conservation of Nature. Relevant information from studies will be extracted to a custom-made extraction form. The resulting map will consist of a narrative synthesis, descriptive statistics and a heat map in the form of a matrix. By providing an overview of the evidence base the resulting map can inform future meta-analyses by showing where there is sufficient comparable data while guiding conservation interventions by indicating geographical areas where species are most at risk.