Forest structure, process and dynamics in temperate and tropical environments:
using unmanned aerial vehicles (UAVs) and near-earth observation to
characterise global forests
Supervisors
Prof. Mathew Williams ([email protected], www.geos.ed.ac.uk/homes/mwilliam),
(University of Edinburgh), Dr. Casey Ryan (University of Edinburgh) and Dr Mike Perks
(Forest Research).
Case Partnership: Forest Research
Project Background
Forests are complex ecological and environmental systems and so present major challenges
for sustainable management. Across the spectrum from planted stands to natural forests,
information on tree growth rates, species composition, mortality, disturbance, carbon uptake
and losses are difficult to gather due to innate variability across landscapes and the large areas
involved. However, this information is critical for managers and policy makers, as structural
forest information informs and underpins decision of management regarding tree productivity,
in supporting and assessing reforestation efforts, and in the development of understanding of
biodiversity and other ecosystem services, including habitat connectivity mapping. Satellites
offer opportunities to monitor forests in increasingly effective ways, but are limited by clouds,
atmospheric conditions and resolution, and signal interpretation. The availability of unmanned
aerial vehicles and microsensors provides an opportunity to map forest stands at high
resolution, repeatedly, with close links to field data, and upscaling with satellite or spatial
mapping data. This project will build on
ongoing efforts using UAVs in Edinburgh, a
long term research effort in the tropical
woodlands of Southern Africa, strong
research links with Forest Research, the
research
agency
of
the
Forestry
Commission (FC), and with the UK National
Centre for Earth Observation (NCEO).
Figure 1. Miombo woodlands in Africa have
open canopies and varied mixes of trees
and grass. These systems are disturbed by
fire and have very varied diversity and tree
sizes.
Key Research Questions
1. How effectively can near earth observation (NEO) characterise forest biomass in managed
(UK) and natural (Tanzania) situations?
2. How accurate are NEO at quantifying the size structure of stems, biodiversity differences
and the distribution of foliage over varying stands?
3. Can NEO be used to calibrate and add value to satellite observations of forest states?
4. Can NEO improve estimates of management (clearance, thinning) effects on green house
gas emissions?
Methodology
The project will involve the student in data gathering, analysis and interpretation to determine
how UAV and sensors / data can be most effectively used to determine forest structure,
including biomass and leaf area index, and forest process, including rates of carbon
sequestration and canopy processes. Further, the UAV data will be assessed for determining
the effects of forest management, such as clear cutting or afforestation / regeneration on forest
status, soil conditions and provide linkage to greenhouse gas emissions assessments
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After training the project will focus on:
1) Gathering UAV and supporting data on structural aspects of forest stands in a variety of
biomes and management types. This will occur over forest sites with on-going research
efforts and historical data, including spruce stands in Northumberland, native reforestation
schemes in Scotland, and miombo woodlands of Tanzania. Initial training will be at the UK
site to develop skills, working with the local UAV team for support. A trip to existing field
sites in Africa will test the systems over very different forest structures. The student will
also collate supporting data for the field sites from partners on a range of linked projects,
and prepare relevant satellite and spatial mapping data.
2) Data processing. The student will spend time in Prague with collaborators to gather skills
about image and data processing, at Forest Research and also with colleagues in NCEO
in the UK. The student will develop products describing forest states and link to
independent ground observations for validation and analysis.
3) Synthesis and investigation. Here the data will be used to address the questions set out
above, and complementary questions developed by the student and the project team. The
student will have access to and training in forest process modelling, so that information on
forest states (for instance tree canopy cover) can be used to inform estimates of forest
processes (e.g. photosynthesis). Working with FR, the student will investigate the effect of
forest management on the state of the forest, and examine how changes in topography
and hydrology affect greenhouse gas emissions.
Figure 2. Harwood Forest in Northumberland is a well-studied
forest with ongoing research measurements (including a flux
tower shown here), and a range of management interventions,
including recent clear cuts.
Training and Facilities
A comprehensive training programme will be provided
comprising both specialist scientific training and generic
transferable and professional skills. Training in UAV operations
will be provided by links to existing funded work around
sustainable agriculture using UAVs. Further support is provided
by UK and international partners with skills in image processing.
Support for data interpretation and modelling will be provided by
the supervisory team.
Requirements
A background in ecological and/or environmental science, or a related biological discipline is
favoured, but transfers from physical sciences are possible. Strong quantitative skills are
essential, and experience or interest in UAVs would be desirable.
Project summary
This project will combine field data with near earth observations from drones to map and model
forest structure in the UK and Africa, to inform better understanding of forest dynamics in
response to management and disturbance.
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