University of East Anglia Featured PhD Programmes
Xi’an Jiaotong-Liverpool University Featured PhD Programmes
Lancaster University Featured PhD Programmes

Using satellite data to improve mapping of stem density and forest carbon for sustainable forest management

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  • Full or part time
    Dr N Augustin
    Dr E Mitchard
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Forests are important due to the many ecosystem services they provide. The most important one being climate protection because they act as a carbon sink: Sustainable forest management and use of the natural resource timber helps in reducing carbon dioxide. As forests remove carbon dioxide from the air they contribute to reducing green house gas in the atmosphere. Timber stores carbon dioxide and by replacing other materials with timber this effect is enhanced. In addition forests provide a source of livelihood, recreation, wildlife habitat, keep the climate in balance, filter pollutants and fine particles, help maintain the hydrological balance, protect from soil erosion and avalanches and secure drinking water.

This project is an exciting opportunity to get involved in interdisciplinary research in statistics, data and forest science, to improve mapping of forest carbon for monitoring forest properties, and to help with mitigation of climate change effects. It will equip you with important skills in remote sensing, statistical modelling and machine learning, as well as data science skills, with hands on opportunities to learn about forest science. You will collect field data in Scotland in partnership with forest managers, and spend 3 months on a placement within your CASE Partner (Forest Research).

Concentrating initially on European temperate forests we will explore the following questions:
1. Review and validation of current state of the art methods for mapping forest carbon and stand density. Model validation: Validation of how well the models predict forest variables, using the field data as ground truth.
2. Statistical Design: What is the optimal combination of field and remotely sensed data, i.e. optical allocation of resources for mapping forest carbon. Does this optimum depend on the forest type being mapped (i.e. even-age monoculture stands and mixed species/age woodlands).
3. Model development and validation: Improving statistical models for mapping; state of the art method use of the shelf models (multiple regression, k-nearest neighbours, support vector regression, random forest algorithms) . The developed model will likely involve fusion of data obtained at point (field plot tree level data) and areal resolutions (pixels from satellite and lidar data). Possible avenues for this are machine learning methods for merging different data sources (see e.g. Baez-Villanueva et al, 2020) or generalized spatial fusion models implemented using a Baysian hierarchical framework (see e.g. Wang et al, 2017, Cameletti et al, 2019). Model validation using the field data as ground truth.
4. How to solve the signal saturation problem. Does incorporating LiDAR and field plot data help with this?
5. How well do the methods translate to other types of forest (temperate or tropical)?

The tools and data to be used include:
1. Software: R, INLA, Python, Stan
2. Datasets:
o Scotland level data from a combination of managed single-age stands (mostly of Sitka Spruce trees) and mixed native woodlands, based on field plots from the CASE partner and open access aircraft-derived LiDAR data.
o Forest inventory and LiDAR datasets for the Rioja region of Spain and the whole of Denmark, from their National Forest Inventories, including hundreds of thousands of tree measurements from about 1700 plots used in Joshi (2017).
o Using the expertise of Tim Baker, open access forest plots from unmanaged forests across the tropics
o In all cases we also will be using the corresponding data from L-band synthetic aperture radar (SAR) from the ALOS-2 satellite, C-band SAR data from the Sentinel-1 satellite, and optical data from the Sentinel-2 satellite.

Required skills and qualifications prior to application

You will need a good undergraduate/master’s degree in a numerate science discipline such as statistics, maths, physics, or engineering. Experience with statistical modelling is desirable. You will receive training in research methods in general, as well as the specific tools to be used. An enquiring mind, good personal organisation and self-motivation are essential!

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,409 for 2020/21), tax-free stipend at the UK research council rate (£15,285 for 2020/21), and a research training and support grant to support national and international conference travel.


Baez-Villanueva OM, Zambrano-Bigiarini M, Beck HE, McNamara I, Ribbe L, Nauditt A, Birkel C, Verbist K, Giraldo-Osorio JD, Thinh NX. RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements. Remote Sensing of Environment. 2020 Mar 15;239:111-606.

Cameletti M, Gómez-Rubio V, Blangiardo M. Bayesian modelling for spatially misaligned health and air pollution data through the INLA-SPDE approach. Spatial Statistics. 2019 Jun 1;31:100353.
Joshi N, Mitchard ET, Brolly M, Schumacher J, Fernández-Landa A, Johannsen VK, Marchamalo M, Fensholt R. Understanding ‘saturation’of radar signals over forests. Scientific reports. 2017 Jun 14;7(1):1-1.

Poorazimy M, Shataee S, McRoberts RE, Mohammadi J. Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran. Remote Sensing of Environment. 2020 Apr 1;240:111669.

Thapa RB, Watanabe M, Motohka T, Shimada M. Potential of high-resolution ALOS–PALSAR mosaic texture for aboveground forest carbon tracking in tropical region. Remote Sensing of Environment. 2015 Apr 1;160:122-33.

Vashum KT, Jayakumar S. Methods to estimate above-ground biomass and carbon stock in natural forests-a review. Journal of Ecosystem & Ecography. 2012;2(4):1-7.

Wang C, Puhan MA, Furrer R, SNC Study Group. Generalized spatial fusion model framework for joint analysis of point and areal data. Spatial Statistics. 2018 Mar 1;23:72-90.

FindAPhD. Copyright 2005-2020
All rights reserved.