Environmental Observation by Remote Sensing

• Credit value: 30 credits at Level 7
• Convenor: Dr Roberto Murcio
• Assessment: practical coursework of up to 4000 words (100%)

In this module you will gain knowledge and practical skills in earth observation techniques and remote sensing data processing. Subjects include physical principles of remote sensing, key earth observation missions, and data depositories. Practical work will focus on both the use of bespoke image processing software and big data analysis using cloud-based platforms. We also address the utilisation of remote sensing data to inform modelling environmental dynamics using advanced geospatial analysis.

Indicative syllabus

• Environmental applications of Earth observation by remote sensing
• Physical principles of active and passive remote sensing: electromagnetic radiation
• Earth observation missions: platforms, sensors and the ground segment; image sources
• Image management and visualisation: analysis ready data, software tools
• Image processing in the spectral, spatial, and temporal domains: GeoAI algorithms
• Machine learning and image classification: land cover mapping and monitoring, accuracy assessment
• Environmental intelligence as the product of environmental systems, GeoAI, image processing and Earth observation data

By the end of this module, you will be able to:

• explain the relationships between environmental systems, GeoAI, image processing and Earth observation data
• explain the physical principles of remote sensing and the technological constraints on Earth observation missions
• source Earth observation data and work with diverse data formats and standards
• visualise single-band, multiband and multitemporal EO data
• apply GeoAI methods to explore and analyse spectral, spatial and temporal patterns in EO data
• design and implement digital image processing workflows to detect features and characterise properties of Earth surface phenomena
• critically evaluate concepts and methods to integrate remote sensing imagery and other geodata
• develop methods to handle and report error and uncertainty
• solve environmental problems and develop, monitor and regulate data-driven policy using environmental intelligence generated from EO data, GeoAI and image processing.