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


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. Commercial forestry plantations are also very important in the fight against deforestation and climate change, through providing sustainable and local supplies of timber to be used for low-carbon construction materials, and to reduce the reliance on imported timber.

The increasing anthropogenic threat to forests has led to a surge in research aimed at accurately mapping forest carbon, largely in support of global efforts on reducing emissions from deforestation and forest degradation and achieving sustainable forestry. Because most of the forest carbon is contained above ground, mapping of above ground biomass (AGB) is an important part of quantifying forest carbon as it is estimated to constitute 80% of the aboveground terrestrial carbon stocks. In addition, AGB is an important measure for quantifying forest yield for sustainable management.

Traditionally field plot measurements on the ground are used to estimate individual tree biomass and forest stand properties. Since this data source is very expensive and also not feasible for mapping AGB of large forest areas, more recently radar data from airborne and space-borne remote sensing has been used, with the radar enabling penetration of the canopy providing measurements for volume (Poorazimy, 2020; Joshi et al, 2017; Thapa et al, 2015; Vashum et al, 2012). The airborne based radar data (LiDAR) is more precise but also a lot more expensive, whereas the satellite image data based on synthetic aperture radar (SAR) is a lot cheaper. However, using SAR for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest above ground volume (AGV) above a certain ‘saturation’ point (Joshi et al, 2017). This limits the SAR capability for estimation of high density forest biomass. Supplementing SAR based remote sensing data with optical remote sensing data (Poorazimy et al, 2020) has recently produced promising results.

The Project

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).

Here we propose to improve maps of forest properties by coming up with a statistical model which optimally combines different data sources. The different data sources are at different levels and resolutions (tree level, pixel level, areas). This research will combine ideas regarding optimal statistical design and complex statistical modelling for optimising resources spent on forest carbon monitoring.

The research would be directly relevant to the forest industry because the produced maps could be used for forest management, improving the availability and quality of data on stand monitoring. They would also be useful for governments, providing information on the growth of semi-natural and natural forests in addition to even-aged forestry stands, and perhaps the models could be applicable beyond their initial test data in Europe to contribute to global forest monitoring efforts for conservation and forestry.

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:
    • 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.
    • 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).
    • Using the expertise of Tim Baker, open access forest plots from unmanaged forests across the tropics
    • 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.

The Student

The studentship will be based at the School of Mathematics, University of Edinburgh. You may also spend time in GeoSciences working with Ed Mitchard, and the CASE partner for extended periods, while working on the practical aspects including data collection. You will interact with other PhD candidates within the SENSE Centre for Doctoral Training within Edinburgh and Leeds University. 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!

Informal enquiries: Please contact


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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.