Remote Sensing for Environmental Monitoring
A structured programme covering field methodology, sensor calibration, and data interpretation for environmental monitoring professionals.
Satellite data is now accessible enough that environmental professionals without a remote sensing background can use it productively — provided they understand what the numbers actually represent.
What this course addresses
Many practitioners download NDVI images or Sentinel-2 composites without fully understanding atmospheric correction, temporal resolution trade-offs, or how cloud cover affects time-series analysis. This course fills those gaps methodically.
Tools used throughout
Sessions use Google Earth Engine and QGIS, both free to access. You do not need prior GIS experience, though basic familiarity with spatial data concepts will help you move faster in week one.
- Optical and multispectral imagery interpretation
- Vegetation indices and their ecological meaning
- Land cover classification methods
- Change detection for habitat monitoring
- Drone survey planning and data processing basics
The course includes four applied projects using real datasets from Irish peatlands, coastal zones, and agricultural areas. Each project is reviewed individually with written feedback.
Who should consider this
Ecologists, land managers, and environmental consultants who need to monitor large areas efficiently. Also useful for local authority staff involved in habitat mapping or flood risk assessment.
The programme runs eight weeks, with self-paced video content supplemented by weekly live Q&A sessions every Thursday evening.
Questions about this programme? Contact us at [email protected] or call +353 45 487 200. Oakpartners has been delivering specialist environmental education since 2017.
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Week 1 — Fundamentals of remote sensing
Electromagnetic spectrum basics, sensor types, and resolution trade-offs. Introduction to Google Earth Engine interface. -
Week 2 — Optical imagery and preprocessing
Atmospheric correction, cloud masking, and compositing techniques using Sentinel-2 data. -
Week 3 — Vegetation and land cover indices
NDVI, EVI, NDWI, and their ecological applications. Interpreting index values in Irish landscape contexts. -
Week 4 — Land cover classification
Supervised and unsupervised classification methods. Accuracy assessment and confusion matrices. -
Week 5 — Change detection methods
Time-series analysis, post-classification comparison, and detecting habitat degradation. -
Week 6 — Drone survey planning
Flight planning, sensor selection, and processing drone imagery for environmental assessment. -
Week 7 — Applied project work
Participants complete an independent monitoring project using a provided dataset. Peer review session. -
Week 8 — Reporting and presentation
Structuring remote sensing outputs for non-specialist audiences. Final project submission and feedback.