Biodiversity monitoring
Institute of Zoology
Zoological Society of London
Regent's Park
NW1 4RY
United Kingdom
Capitalising on the Big Data era: establishing a multi-source monitoring framework for England's natural capital assets and flows.
From satellite remote sensing to observations crowd-sourced from amateur naturalists, recent technological advances have revolutionised our ability to monitor biodiversity. In addition, developments in open source platforms and automated data processes are allowing vast amount of data to be analysed in relatively short time frames.
Theoretical work on the valuation of nature has led to a broad adoption of the natural capital approach by businesses and policy makers. These developments make it possible to report on natural capital assets and flows on an annual basis to national bodies, providing that the various sources of information available can be brought together efficiently.
So far, however, this idea hasn't been tested at large spatial scales. To fill this gap in knowledge, Merry's PhD project aims to demonstrate how large and diverse biodiversity datasets could be effectively combined to regularly assess the distribution and quality of natural capital. This project is funded by UKRI.
2018-Present: PhD Researcher, NERC QMEE DTP, Institute of Zoology and University of Reading
2017-2018: Remote Sensing Technician, Institute of Zoology
2014-2017: MSc in Geography, Freie Universität Berlin
2005-2008: BSc in Geography, Durham University
Warren-Thomas, E., Agus, F., Akbar, P. G., Crowson, M., Hamer, K. C., Hariyadi, B., … Hill, J. K. (2022). No evidence for trade-offs between bird diversity, yield and water table depth on oil palm smallholdings: Implications for tropical peatland landscape restoration. Journal of Applied Ecology, 59(5), 1231–1247. https://doi.org/https://doi.org/10.1111/1365-2664.14135
Ward, C., Stringer, L. C., Warren-Thomas, E., Agus, F., Crowson, M., Hamer, K., … Hill, J. K. (2021). Smallholder perceptions of land restoration activities: rewetting tropical peatland oil palm areas in Sumatra, Indonesia. Regional Environmental Change, 21(1). https://doi.org/10.1007/s10113-020-01737-z
Lopes, M., Frison, P.-L., Crowson, M., Warren-Thomas, E., Hariyadi, B., Kartika, W. D., … Pettorelli, N. (2020). Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series. Methods in Ecology and Evolution, 11(4), 532–541. https://doi.org/https://doi.org/10.1111/2041-210X.13359
Crowson, M., Warren-Thomas, E., Hill, J. K., Hariyadi, B., Agus, F., Saad, A., … Pettorelli, N. (2019). A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia. Remote Sensing in Ecology and Conservation, 5(3), 247–258. https://doi.org/https://doi.org/10.1002/rse2.102
Crowson, M., Hagensieker, R., & Waske, B. (2019). Mapping land cover change in northern Brazil with limited training data. International Journal of Applied Earth Observation and Geoinformation, 78, 202–214. https://doi.org/https://doi.org/10.1016/j.jag.2018.10.004