Global Landscapes & Climate Change

We address complex and challenging problems related to how the Earth’s surface evolves spatially and temporally and particularly how it interacts with the atmosphere and hydrosphere to influence processes that sustain life. Our multidisciplinary expertise allows us to address scientific and societal challenges including: a) How resilient are landscapes to major disturbances (e.g. landslides) and what are the implications for land use (e.g. flood risk)? b) How will landscapes adjust to future environmental change and what are the implications for biodiversity and food security? c) How can we best implement adaptation to climate change? d) Improving our capacity to understand and predict landscape responses to flood and storm pressures. We examine how landscapes respond to environmental change, how we can overcome the hazards and minimise societal risks that arise from these responses. Our quantitative approach draws on our strengths in coastal and fluvial geomorphology, Earth observation, geomatics, numerical analysis and advanced analytical techniques through our links with the Life & its Interactions with Chnaging Environments and Dynamic Earth & Planetary Evolution Themes, as well as, our links with SUERC and the School for Interdisciplinary Studies.

Keywords: geomorphology, coasts, rivers, sustainability, tipping points, ecosystem services, Earth observation, remote sensing, geomatics, geospatial, carbon cycle, sustainable development goals, computational modelling


Theme members

Prof Trevor Hoey, Dr Jim Hansom, Dr Cristina Persano, Dr Larissa Naylor, Dr Rhian Thomas, Dr Thorsten Balke, Dr Elizabeth Petrie, Dr Martin Hurst,  Dr Brian Barrett, Dr Richard Williams, Dr Amanda Owen


Current MSc by Research Opportunities (non-funded)

Ground motion measurements for Earth Science using precise Global Navigation Satellite System (GNSS) techniques

Supervisor/s: Dr Elizabeth Petrie and collaborators


GNSS has many applications in Earth science, from monitoring tectonic motion and deformation around volcanoes, to measuring glacial isostatic adjustment. A variety of projects would be possible and I would be happy to discuss possibilities. However, below is an example of one potential project:

The number of GNSS sites being operated in Antarctica has increased sharply in the last few years, and their record lengths are becoming potentially viable as robust indicators of vertical motion. However, the available Antarctic dataset is highly variable in terms of operator, equipment, collection purpose, and data quality. The project will assess the effects of snow cover on the antenna on the Antarctic GNSS time series. This will be done using Signal to Noise ratio data (SNR). SNR data consist of measurements of GNSS signal power relative to a receiver-calculated noise floor and are commonly reported by geodetic quality GNSS receivers and output to GNSS format RINEX files (Larson, 2013). Once the effects have been assessed, a set of improved timeseries will be generated.

Specific and transferrable skills:

The student will develop skills in using one of the major scientific GPS software packages to process GNSS data to high precision. The student will also develop skills in Linux, data analysis and scientific writing, and gain experience of GNSS data collection.

Required background:

The ideal situation would be for the student to have a background in the relevant area of Earth science to which they would like to apply the GNSS monitoring, as well as experience in using Linux, basic programming skills, maths, statistics, and an understanding of GNSS. In practice, a student who has experience in some of the areas should be able to learn the remaining ones (potential degree backgrounds - Earth Science, Physics, Mathematics, Computing Science, Geospatial & Mapping Sciences).


If interested, please contact Dr. Elizabeth Petrie at:

Quantifying tropical river morphological change using satellite remote sensing

Supervisors: Dr. Richard Williams, Dr Brian Barrett


The aim of this project is to quantify rates of morphological change for a tropical river in an island archipelago.


Rivers in South-East Asia are under considerable pressure from climate change, land use change and rapid urbanisation. Rivers in this part of the world also pose considerable risks to people, property and infrastructure, due to both flooding and morphological change from channel scour and bank erosion. A recent workshop that was held between the University of Glasgow, and universities and consultancies from Indonesia and the Philippines, has revealed that there have been no studies to quantify rates of morphological change along tropical rivers in either of these archipelago nations. This represents a considerable gap in the global understanding of river morphodynamics across a representative set of rivers with different controls and styles. Moreover, this dearth of knowledge poses a considerable challenge for river managers in these countries since land use planning and natural hazard management decision making is not based upon knowledge of the natural active width of these river systems. A first step in addressing this knowledge gap is to quantify morphological change by assessing river change using multi-temporal remote sensing imagery for a river that exemplifies tropical archipelago fluvial systems.


The project will focus upon the Cagayan River, which drains the northern island of Luzon in the Philippines. The Cagayan River is 520 km long with an estimated annual discharge of 53,943 million m3. The objectives are: 1) to collate annual time-series satellite imagery for the Cagayan River and its major tributaries, 2) to develop a volumetric sediment budget for the Cagayan River, using field estimates of river bank height and bar thickness, together with the map of river erosion and deposition, and 3) to assess the Cagayan River’s rates and style of morphodynamic change as an exemplar of a tropical archipelago river, in the context of a continuum of global rivers with different rates of morphological change. Techniques for automatic extraction of the water areas will be investigated and evaluated against manually digitised boundaries. Maps of planimetric erosion and deposition will be produced, taking into account the uncertainty in the delineation of features through digitisation. Field measurements of representative bank erosion and bar thickness heights will be used to produce estimates of morphological change volumes, for each zone of erosion and deposition. These will be used to calculate a sediment budget and analyse longitudinal trends in sediment transfer or accumulation.

Knowledge background of student

Enthusiastic/ motivated individuals with a background in Geography, Earth Science, Environmental Science/Geosciences, Statistics or a related discipline with a minimum of 2:1 (or equivalent) in their bachelor degree and a willingness to learn new techniques are encouraged to apply. Some experience in image processing and/or scripting (e.g. Python, R) is desirable.

Career prospects

The successful applicant will be equipped with a broad range of skills (e.g. programming, image interpretation and analysis, GIS, statistics, effective communication, report writing), knowledge (e.g. fluvial geomorphology, remote sensing) and also benefit from the disciplinary expertise of the supervisory team. The aforementioned skills are highly sought after by employers in environmental, energy, conservation and information science sectors. Graduates would also be well prepared to pursue further studies, e.g. PhD.


If interested, please contact Dr. Richard Williams at:

Advancing sustainable forage-based livestock production systems in Colombia using multi-source remote sensing

Dr Brian Barrett (, Dr Juan Andres Cardoso (International Centre for Tropical Agriculture (CIAT) – Colombia)

 Aim of the project

This project aims to investigate the potential of multispectral and Synthetic Aperture Radar (SAR) data for grassland use inventory and biomass estimation at the field-scale. The proposed research objectives are to (i) improve the ability of differentiating between grassland types (intensively vs extensively managed) on a large-scale across heterogeneous landscapes using remote sensing data and state-of-the-art machine learning classifiers, (ii) investigate the sensitivity of multispectral and radar derived parameters to grass growth, grazing and cutting/mowing activities, (iii) analyse the ability of remote sensing measurements to estimate pasture biomass and within-field biomass variability and develop a robust pasture biomass model, calibrated with in-situ data, to deliver near real-time biomass estimates over large areas.

 Rationale of the project

Grasslands are valuable ecosystems that support a multitude of roles, most importantly food security, biodiversity conservation and greenhouse gas mitigation. The global importance of grasslands is indicated by their extent, covering ~37% of the earth`s terrestrial area and 80% of agriculturally productive land. Forage-based livestock production systems are integral to ensuring reliable and sustainable food provision. In Colombia, the country faces a challenge in helping its small and medium-scale farmers shift to sustainable lower-carbon cattle production systems that use less land, conserve more forests and provide higher incomes. It is difficult to accurately predict grass growth both within and between years, due to varying management and meteorological conditions. Remote sensing data shows great potential for reliably forecasting feed supply and for monitoring management practices in these agricultural areas. Given the difficulty of acquiring multiple acquisitions throughout a growing season with optical sensors limited by frequent cloud cover, SAR data are almost independent of weather and illumination conditions and therefore can be an important alternative or complementary data source to provide the best opportunity for generating a multi-temporal data set. Consequently, the ability to predict grassland biomass and inform land management using remote sensing could be a valuable resource for the Colombian smallholder farmers and the wider agri-industry.



The project will investigate the use of airborne and satellite multispectral and radar remote sensing to quantify rates of forage productivity in Colombia. To achieve this, representative farms from across Colombia (Antioquia, Cauca, Valle de Cauca, and the Orinoquía) have been selected. These areas represent a range of livestock production systems (intensive, extensive, silvopastoral) and have contrasting soil, climate and cultural characteristics. For example, Antioquia is one of the main dairy and beef producing regions in Colombia, while the Orinoquía region, a vast area representing 22% of continental Colombia with c. 21% of the national cattle herd is dominated by natural savannahs and introduced pastures used primarily for extensive cattle ranching. The project will use data that will be collected at these sites and also a dataset collected in 2018 at Palmira (Cali) and Patía for analysis. Fieldwork data primarily acquired by collaborators at the International Centre for Tropical Agriculture (CIAT) and UAV acquisitions will be acquired simultaneous to the satellite acquisitions to facilitate an intercomparison and robust calibration and validation of the approaches. Fresh and dry weight biomass will be calculated and forage quality analyses (crude protein (CP), neutral detergent fiber (NDF)) will be carried out in the forage nutritional quality laboratory of CIAT. Machine-learning approaches such as Support Vector Machines (SVM), Random Forest (RF) and Extra Trees will be explored for accurately distinguishing between forage types. By analysing the spectral reflectance (from multispectral observations) and the backscatter signature (from radar observations) of forages under different grazing intensities, a system for reliably quantifying forage biomass and quality will be developed. A series of simulations will be run during the field trials investigating the impact of overgrazing and drought and the skill of the developed approaches in dry and wet season conditions.

Please note that there is a £1000 programme cost due from the student. This cost partially covers the student’s expenses to visit Colombia with the project team for sampling and subsequent conference/workshop presentations.


Knowledge background of the student

Student with a minimum 2:1 in a relevant degree (e.g. Geography, Ecology, Geoscience, Environmental Science, Computer Science, Environmental Statistics). They need to be able to work independently, effectively managing their project, but also be part of a larger research team and work alongside others from different disciplines. The work from this project will contribute to a larger BBSRC funded project awarded to the project supervisors and other collaborators.


Career prospects

The student will receive training in the use of remote sensing and GIS software packages, including the use of Python and/or R for processing and analysis, statistics, multispectral (UAV and satellite) and radar data processing and analysis and field data collection. Research based skills including scientific writing, presentation (poster and oral) and outreach skills will be gained as part of this project. Such skill sets are relevant for a future career in both industry (geospatial, environmental) and academia (PhD programmes).


The student will be eligible to attend a range of study- and career-enhancing workshops as part of their postgraduate training at the University of Glasgow.