- Satellite imagery will be used to map England’s forest biomass stocks
- Using remotely sensed data and models to monitor the status of ecosystem services such habitat quality, recreation, timber production, biodiversity, scenic beauty, water supply and carbon storage
The UK Government has committed to achieving a net zero carbon balance by 2050 to mitigate climate change. Afforestation on an unprecedented scale is very likely to play a major role in achieving this goal. However, Defra’s 25-year plan has a number of other environmental targets it wants to achieve, for example no net biodiversity loss from any land use change intervention. To ensure that we fully understand the current state of forests in England and the range of ecosystem services that they provide to people, this studentship aims to map the amount of forest biomass in England from satellite imagery and to estimate a selection of ecosystem services. These services can be split into provisioning services which provide goods and materials such as food and clean water, regulating services that provide a healthy microclimate, remove carbon from the atmosphere and filter the air that we breathe, and cultural ecosystem services, e.g. the value of landscapes for recreation and education.
Monitoring biodiversity and the status of ecosystem services over time is often limited by data availability. However, Earth Observation (EO), in combination with in-situ data, has the potential to fill this gap due to its capability to generate a variety of data at different spatial and temporal scales. EO has also been recognized as a fundamental approach to estimate essential climate variables (ECV) and essential biodiversity variables (EBV) such as land cover, biomass, habitat structure, etc. Information on the temporal dynamics of ES is of key importance to understand time lags, abrupt changes, threshold effects, and feedbacks in ecosystem service management (Rieb et al., 2017).
Pioneering research at the University of Leicester has experimented with ecosystem services models from the InVEST model suite and how they can be coupled to maps from Earth observation. Our research group has also developed an algorithm for mapping forest biomass from a combination of optical and radar satellite images. This studentship will use these existing tools and combine them in a novel way to provide a focused assessment of selected ecosystem services on forests in England, which is the geographic focus of this PhD. The main supervisor, Prof. Balzter, coordinates the Landscape Decisions programme funded by UK Research and Innovation and maintains close links with Defra and a range of other stakeholders.
- How accurately can the forest carbon stocks in England be estimated using satellite imagery and forest inventory data?
- How can remotely sensed forest biomass maps be used to estimate selected ecosystem services, e.g. timber production, carbon storage, biodiversity, habitat quality, and water supply?
HostNational Centre for Earth Observation
- Climate and Environmental Sustainability
- Prof. Heiko Balzter, National Centre for Earth Observation (NCEO), University of Leicester
- Dr. Pedro Rodriguez-Veiga, National Centre for Earth Observation (NCEO), University of Leicester
- Dr. Fernando Espirito-Santo, School of Geography, Geology and the Environment, University of Leicester
The methods will include satellite remote sensing and ecosystem services models. Optical and radar images will be used to estimate forest aboveground biomass at different points in time following the approach described in Rodriguez-Veiga et al. (2020) (Figure 2). Unlike optical sensors, radar can take images through clouds and therefore provides weather-independent observations. Satellite images from the Sentinel-1 (C-band radar), Sentinel-2 (multispectral optical) and ALOS-2 (L-band radar) satellites will be used for the forest biomass mapping algorithm. Information on the forest canopy height from Spaceborne LiDAR footprints from the GEDI and ICESAT-2 missions will be used to train the model that relates the satellite images to forest biomass.
The satellite-derived forest biomass maps will be used to assess the status of ecosystem services in England at different times. Ancillary datasets will be used as input to the standard Integrated valuation of ecosystem services and trade-off (InVEST) models to map the supply of ecosystem services by different forest areas.
Training and skills
The National Centre for Earth Observation will provide access to its Researcher Forum, staff conferences/workshops and national-level training.
The student will be trained in satellite image analysis and ecosystem services modelling. The student will have access to the MSc modules “Satellite Data Analysis in Python” and “Earth Observation and Remote Sensing”, as well as other suitable modules that satisfy their training needs. Complementary individual training in using Google earth engine and machine learning and will be available from NCEO and the School of Geography, Geology and the Environment. Further training will take place ‘on-the-job’ as part of the research team in NCEO.
Partners and collaboration
Potential partners include (but not limited to) Natural England, Forest Research, and JNCC
Please visit the University of Leicester website for application guidance:
Literature review, refinement of research questions and work plan, liaison and consultation with project partners, installation of algorithms, training in satellite data processing in Python and InVEST.
Preparation of training and validation database (GEDI, in-situ, and HR imagery). Training the algorithms, completing test runs and evaluating outcomes, iteratively refining data flow and accuracy, running ‘experiments’, submitting one paper for publication on the forest biomass in England.
Ecosystem Services model outputs, evaluating and comparing results, submitting 2 papers for publication on the use of forest biomass maps for ecosystem services quantification and on the trade-offs and synergies between multiple ecosystem services provided by England’s forests.
Balzter, H., Cole, B., Thiel, C. and Schmullius, C. (2015): Mapping CORINE Land Cover from Sentinel-1 SAR and SRTM Digital Elevation Model using Random Forests. Remote Sensing 7, 14876-14898. https://doi.org/10.3390/rs71114876
Boyd, D.S., Jackson, B., Wardlaw, J., Foody, G.M., Marsh, S. and Bales, K. (2018): Slavery from space: Demonstrating the role for satellite remote sensing to inform evidence-based action related to UN SDG number 8. ISPRS Journal of Photogrammetry and Remote Sensing 142, 380-388. https://doi.org/10.1016/j.isprsjprs.2018.02.012
Cole, B., Smith, G. and Balzter, H. (2018): Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006-2012 detected by Copernicus IMAGE2012 satellite data. International Journal of Applied Earth Observation and Geoinformation 73, 107–122. https://doi.org/10.1016/j.jag.2018.06.003
Comber, A., Balzter, H., Cole, B., Fisher, P., Johnson, S. and Ogutu, B. (2016): Methods for quantifying regional differences in land cover change. Special Issue on Validation and Inter-Comparison of Land Cover and Land Use Data, Remote Sensing 8, 176-195. https://doi.org/10.3390/rs8030176
IPBES (2019). ‘Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services’. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. https://doi.org/10.5281/zenodo.3553579
Le Toan, T., S. Quegan, M. W. J. Davidson, H. Balzter, P. Paillou, K. Papathanassiou, S. Plummer, F. Rocca, S. Saatchi, H. Shugart and L. Ulander (2011): The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sensing of Environment 115(11): 2850-2860. https://doi.org/10.1016/j.rse.2011.03.020
Rieb, J. T., Chaplin-Kramer, R., Daily, G. C., Armsworth, P. R., Böhning-Gaese, K., Bonn, A., Cumming, G. S., Eigenbrod, F., Grimm, V. & Jackson, B. M. (2017). ‘When, where, and how nature matters for ecosystem services: challenges for the next generation of ecosystem service models’. BioScience, 67, 820-833, ISSN: 0006-3568.
Rodríguez-Veiga, P., Carreiras, J., Smallman, T. L., Exbrayat, J.-F., Ndambiri, J., Mutwiri, F., Nyasaka, D., Quegan, S., Williams, M. & Balzter, H. (2020). ‘Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion’. Remote Sensing, 12, 2380.
Rodríguez-Veiga, P., S. Quegan, J. Carreiras, H. J. Persson, J. E. S. Fransson, A. Hoscilo, D. Ziółkowski, K. Stereńczak, S. Lohberger, M. Stängel, A. Berninger, F. Siegert, V. Avitabile, M. Herold, S. Mermoz, A. Bouvet, T. Le Toan, N. Carvalhais, M. Santoro, O. Cartus, Y. Rauste, R. Mathieu, G. P. Asner, C. Thiel, C. Pathe, C. Schmullius, F. M. Seifert, K. Tansey and H. Balzter (2019): Forest biomass retrieval approaches from earth observation in different biomes. International Journal of Applied Earth Observation and Geoinformation 77: 53-68. https://doi.org/10.1016/j.jag.2018.12.008
Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K. & Balzter, H. (2017). ‘Quantifying Forest Biomass Carbon Stocks From Space’. Current Forestry Reports, 1-18.
Current COVID-19 pandemic might affect project delivery due to the following:
- Travel disruptions.
- This problem can be mitigated by video-conference software to hold meetings and training events.
- The project is not reliant on fieldwork as it uses primarily digital data.
- Slow internet causing disruptions in video-conference meetings:
- This may affect partners and can be mitigated by holding virtual meetings at times when internet traffic is at minimum
- Prevention of face to face meetings due to national/local lockdowns
- The project can be run entirely remotely if needed. Regular check-ins online are scheduled. Project design to remain entirely digital, no physical experiments.
- PhD training cannot take place in person
- Teaching and learning is now taking place following the blended learning approach under IGNITE, which can be switched to completely online teaching under conditions of restrictions and includes asynchronous and synchronous delivery.