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Analysing forest aboveground carbon dynamics using dense time-series of satellite data and artificial intelligence

Department of Geography

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Prof Heiko Balzter No more applications being accepted Competition Funded PhD Project (European/UK Students Only)

About the Project

Recent advances in computing technology, cloud computing and high-performance computing are paralleled with advanced artificial intelligence (AI) algorithms and significant investment in the European Copernicus Earth Observation programme and its Sentinel satellite missions. AI enables automatic detection of spatial and temporal patterns in environmental data such as satellite images based on training data. The paradigm of looking for spatial and temporal patterns instead of the historic focus on spectral information in satellite imagery allows the identification of the different types of forest dynamics (disturbance and succession). AI can also be used to accurately estimate from space forest biophysical parameters that are difficult to measure in the ground and are considered a large uncertainty in the global carbon cycle such as aboveground biomass (Rodriguez-Veiga et al, 2017) (Figure 1).

Machine learning / AI (Le Cun et al. 2015) have previously been applied to hyperspectral image classification (Hu et al. 2015), CORINE land cover mapping from Sentinel-1 SAR images (Balzter et al. 2015), forest biomass mapping using a combination or SAR and optical images (Rodriguez-Veiga et al, 2016), or even providing evidence of slavery from WorldView satellite images (Boyd et al. 2018).

This interdisciplinary studentship (National Centre for Earth Observation) aims to explore the application of AI for analysing spatial and temporal trends of aboveground biomass carbon in dense time-series satellite data. Time-series stacks of multispectral optical and synthetic Aperture Radar (SAR) sensors will be input into the AI. The AI will be trained based on measurements collected from in-situ forest inventories and visual interpretation of very high resolution images

Academic entry requirements
UK Bachelor Degree with at least 2:1 in a relevant subject or overseas equivalent.

Informal enquiries
Project Enquiries: Prof. Heiko Balzter, [Email Address Removed]

Funding Enquiries: [Email Address Removed]

How to apply
Please follow refer to the How to Apply section at and use the GEOGRAPHY Apply button to submit your PhD application. Upload your CENTA Studentship Form in the proposal section of the application form.

In the funding section of the application please indicate you wish to be considered for NERC CENTA Studentship
Under the proposal section please provide the name of the supervisor and project title/project code you want to apply for.

Available for UK and EU applicants only

Applicants must meet requirements for both academic qualifications and residential eligibility:

Funding Notes

This project is one of a number of fully funded studentships available to the best UK and EU candidates available as part of the NERC DTP CENTA consortium.

For more details of the CENTA consortium please see the CENTA website:

Applicants must meet requirements for both academic qualifications and residential eligibility:

The studentship includes a 3.5 year tuition fee waiver at UK/EU rates

An annual tax free stipend (£15,285 for 2020/1)

Research Training Support Grant (RTSG) of £8,000


Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. and Kudlur, M. (2016): Tensorflow: a system for large-scale machine learning. OSDI 16, 265-283,

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.

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.
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