Satellite Monitoring Skills for Environmental Scientists Training Course

Course overview

Environmental scientists are increasingly expected to provide defensible evidence of land-cover change, vegetation condition, water extent, wildfire impact and habitat disturbance across areas that cannot be surveyed frequently on the ground. Satellite data can supply this evidence, but unreliable results often arise from poorly chosen imagery, unmanaged cloud contamination, inconsistent coordinate systems, unvalidated classifications or maps that do not answer the regulatory or management question. This course equips scientists to turn Earth observation data into repeatable monitoring outputs that can withstand technical review.

Participants work through the full satellite-monitoring workflow using Sentinel-2 and Landsat imagery: defining an environmental monitoring question, selecting spatial and temporal resolution, accessing imagery, applying atmospheric and cloud masking steps, calculating spectral indices, classifying land cover, detecting change and assessing accuracy against reference data. They use QGIS, Google Earth Engine, ESA SNAP and GDAL to build analyses for practical scenarios including wetland change, vegetation stress, post-fire recovery and surface-water monitoring. Particular emphasis is placed on uncertainty, validation and documenting analytical decisions.

Instructor demonstrations are followed by guided labs using realistic environmental datasets and structured peer review of maps and methods. Participants build a documented satellite-monitoring workflow for a selected environmental issue, including data sources, processing steps, quality checks, maps, charts and a short technical interpretation for decision-makers. The final deliverable is a reusable project template that can be adapted for organisational monitoring programmes, impact assessments, restoration projects or compliance reporting.

The course is designed for environmental professionals who already work with spatial data and need stronger remote-sensing capability without becoming specialist image-processing programmers. It is equally valuable for managers responsible for commissioning, reviewing or operationalising satellite-derived environmental evidence.

Course objectives

By the end of this course, participants will be able to:

• Define a satellite-monitoring specification linking an environmental question to spatial resolution, revisit frequency, spectral bands and success measures
• Acquire and organise Sentinel-2 and Landsat imagery with appropriate metadata, coordinate reference systems and area-of-interest boundaries
• Pre-process optical imagery by applying atmospheric correction checks, cloud and shadow masking, clipping and band compositing
• Calculate and interpret NDVI, NDWI, NBR and bare-soil spectral indices for environmental condition monitoring
• Build a supervised land-cover classification using training samples, spectral predictors and class definitions
• Detect environmental change through image comparison, index differencing, threshold selection and change-mask creation
• Assess map reliability using reference samples, confusion matrices, producer's accuracy, user's accuracy and overall accuracy
• Produce a documented monitoring map pack with methods notes, uncertainty statements, charts and management recommendations

Benefits of attending

Target competencies

Who should attend

• Environmental Scientists — who need defensible satellite evidence for monitoring, assessment and reporting
• Ecologists — who monitor habitat extent, vegetation condition and restoration outcomes across large areas
• GIS Analysts — who support environmental teams and need to add image processing and change detection to their workflow
• Environmental Consultants — who prepare baseline studies, impact assessments and compliance evidence for clients
• Conservation Officers — who track land-use pressure, protected-area condition and disturbance indicators
• Natural Resource Managers — who need repeatable spatial intelligence for land, water, forest or catchment decisions

Requirements and prerequisites

Participants should be comfortable working with vector layers, attribute tables, map projections and basic raster display in a GIS, ideally QGIS or ArcGIS Pro. They should understand environmental variables such as land cover, vegetation condition, hydrology or disturbance, and be able to interpret maps and simple charts. Familiarity with Sentinel or Landsat imagery is helpful but not essential. Participants do not need programming experience, advanced statistics, Python, machine-learning expertise or prior use of Google Earth Engine. A laptop capable of running QGIS and ESA SNAP is required for practical work.

Training methodology

The five days combine short instructor-led explanations with step-by-step software demonstrations and extended practical labs. Participants process Sentinel-2 and Landsat scenes in QGIS, ESA SNAP and Google Earth Engine, then compare results against field or reference data. Environmental case studies frame each exercise, including wetland extent, post-fire recovery and vegetation stress. Small-group reviews focus on selecting defensible methods and communicating uncertainty. On the final day, each participant adapts the workflow to a workplace monitoring question and receives instructor feedback on their project plan and map pack.

Course outline

Day 1: Satellite data for environmental monitoring design

• Environmental monitoring questions and measurable Earth observation indicators
• Electromagnetic spectrum, reflectance and environmental feature signatures
• Sentinel-2 and Landsat mission characteristics, bands and revisit cycles
• Spatial, spectral, temporal and radiometric resolution trade-offs
• Level-1 and Level-2 imagery products and metadata interpretation
• Coordinate reference systems, scene footprints and area-of-interest preparation
• Data discovery through Copernicus Data Space and USGS EarthExplorer

Workshop: Participants create a monitoring specification for a wetland, catchment or habitat site and select suitable Sentinel-2 and Landsat scenes with documented selection criteria.

Day 2: Image preparation and spectral indicators

• Raster inspection and multiband visualisation in QGIS
• Surface reflectance, atmospheric correction and processing-level checks
• Cloud, cirrus and cloud-shadow masking using quality assessment bands
• Band stacking, clipping and raster alignment with GDAL tools
• True-colour, false-colour and shortwave infrared composite design
• NDVI, NDWI, NBR and bare-soil index calculation
• Raster symbology, threshold exploration and index interpretation

Workshop: Participants prepare a cloud-screened Sentinel-2 composite and produce an indexed vegetation and water-condition map for a defined study area.

Day 3: Land-cover mapping and change detection

• Land-cover class schemas aligned to environmental reporting needs
• Training-sample design and representative polygon collection
• Supervised classification concepts and random forest implementation
• Classification workflows in Google Earth Engine
• Post-classification filtering and minimum mapping unit decisions
• Image differencing and index-based change detection
• Threshold calibration for vegetation loss, burn severity and water expansion

Workshop: Participants create a land-cover classification and change mask for a before-and-after disturbance scenario, recording class and threshold decisions.

Day 4: Validation, time series and uncertainty

• Reference data sources, field observations and interpretation samples
• Stratified random sampling for map validation
• Confusion matrices and overall, producer's and user's accuracy
• Omission and commission error interpretation
• Sentinel-2 time-series charting in Google Earth Engine
• Seasonality, phenology and anomalous-condition interpretation
• Uncertainty statements, limitations and reproducibility records

Workshop: Participants validate their classification with reference points, calculate accuracy measures and prepare a time-series interpretation with stated limitations.

Day 5: Operational monitoring and decision-ready reporting

• Designing repeatable monitoring workflows and processing checklists
• Batch raster processing and command-line GDAL workflow concepts
• Project folder structures, naming conventions and metadata logs
• Map layouts for technical reviewers and non-technical decision-makers
• Change statistics, area summaries and environmental indicator charts
• Quality assurance checkpoints before publishing satellite outputs
• Monitoring plans for field verification, update cycles and escalation triggers

Workshop: Participants assemble a final satellite-monitoring map pack and implementation plan for their own environmental use case, then present it for instructor and peer review.

Tools & standards covered

QGIS, Google Earth Engine, ESA SNAP, GDAL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions