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Matt Aitkenhead

Matt has a background in a wide range of soil and environmental topics, and in the modelling of complex environmental system. He has qualifications in a range of subjects, and have worked for several years in Aberdeen at the University and the James Hutton Institute. He has over 50 publications including refereed papers, conference proceedings, technical reports and popular articles. He also has expertise in soil modelling, land cover mapping, remote sensing interpretation, environmental modelling, soil formation, land use and land use change, artificial intelligence and computer programming. A key aspect of Matt's work is the development of novel modelling and classification methods and software packages that utilise spatial remote sensing and environmental data in a wide range of environmental applications. This includes the development, with other staff members, or mobile phone apps for environmental monitoring in real-time.

Under the Scottish Government research theme on Ecosystem Services, Matt currently leads on an appraisal and mapping of ecosystem services in Scotland using existing information.  He is responsible for the development of approaches to relate specific indicators and ecosystem services to existing spatial datasets and in developing novel mapping approaches for ecosystem services and indicators using process-based modelling. A key component is the identification of soil processes influencing changes in ecosystem services provision.

These skills and expertise will be applied within the ALTER project to help in the development and implement of spatial mapping and functional modelling from the field to landscape scale, with particular emphasis on working with SARI, IWMI and Aberdeen colleagues at the Ethiopia Alaba case study and supporting CAFEA and Aberdeen with the Ugandan site.   

Soil process and function model development – this work involves the implementation of a wide range of plant/soil processes and functions into the soil profile model MOSES (Modelling Of Soil Ecosystem Services), with the aim of allowing us to explore the relationships between underlying soil processes and the ecosystem services provided by soil.
Development of methods for analysing hyperspectral proximal sensing data (e.g. NIR, XRF, visible-range hyperspectral sensors) – recent technological advances allow us to acquire much more information from ‘proximal sensing’, the use of hand-held field equipment that obtains hyperspectral reflectance data across a range of wavelengths including visible and infrared.  The interpretation of this data to allow predictions and measurements of soil characteristics has the potential to allow rapid and cost-effective assessments of soil nutrient status and soil fertility.

Development of decision support tools and data integration/data mining software – the integration of novel interpretative approaches with decision support frameworks that allow land managers to make more informed decisions, using sensor and GPS-derived data that can be generated automatically or with novel equipment and analysis approaches.