About This Course
Remote sensing has become an indispensable tool in the modern environmental professional's toolkit. As satellite constellations multiply, sensor technologies improve, and open-access imagery platforms proliferate, the ability to extract meaningful environmental intelligence from remotely sensed data is no longer a luxury — it is a core competency for anyone working in conservation, natural resource management, climate adaptation, or environmental impact assessment.
This ten-day programme offered by TrainingComplex provides a rigorous, practice-oriented journey through the full remote sensing workflow — from understanding electromagnetic radiation and sensor physics, through image acquisition and pre-processing, to advanced analysis, interpretation, and reporting for real-world environmental monitoring applications. Participants will work with industry-standard platforms including Google Earth Engine, QGIS, and SNAP, processing real datasets drawn from Landsat, Sentinel, MODIS, and drone-based imagery.
The course covers a broad spectrum of environmental applications: land cover and land use change detection, vegetation and biodiversity assessment, surface water and wetland monitoring, soil degradation mapping, atmospheric monitoring, and disaster risk and post-disaster assessment. Each application domain is treated with the depth needed to deliver operational results, not merely introductory awareness.
Designed for mid-career professionals who already work in environmental fields but want to harness satellite and aerial data more effectively, the programme strikes a balance between conceptual rigour and practical application. By the final day, every participant will have completed a capstone environmental monitoring project using real imagery relevant to their region or sector, equipping them to immediately apply these skills on return to their organisation.
Who Should Attend
- Environmental scientists and researchers working in monitoring, assessment, or conservation roles
- GIS analysts and spatial data professionals seeking to expand into remote sensing applications
- Natural resource managers responsible for forests, rangelands, watersheds, or protected areas
- Climate change adaptation and mitigation specialists requiring landscape-scale monitoring tools
- Environmental impact assessment practitioners who incorporate spatial data in their reports
- Government officers in environment, lands, water, or agriculture ministries
- NGO and development programme staff managing environmental monitoring and evaluation components
- Academics and postgraduate students working on environment-related research projects
Learning Objectives
- Explain the physical principles of remote sensing, including electromagnetic radiation, spectral reflectance, and sensor characteristics
- Distinguish between and appropriately select optical, radar, thermal, and multispectral sensors for specific environmental monitoring tasks
- Acquire, pre-process, and atmospherically correct satellite imagery from Landsat, Sentinel-2, MODIS, and other open-access sources
- Apply image classification techniques — supervised, unsupervised, and object-based — to produce accurate land cover and land use maps
- Conduct change detection analyses to quantify and map deforestation, land degradation, urban expansion, and vegetation dynamics over time
- Derive and interpret key environmental indices including NDVI, EVI, NDWI, NBR, and LST from multi-spectral imagery
- Monitor surface water extent, wetland boundaries, and flood inundation using optical and SAR data
- Assess vegetation condition, biomass proxies, and biodiversity indicators from satellite time-series datasets
- Process Synthetic Aperture Radar (SAR) imagery for applications where cloud cover limits optical data availability
- Utilise Google Earth Engine for cloud-based large-area environmental monitoring and time-series analysis
- Integrate remote sensing outputs with field-collected ground truth data to validate maps and quantify accuracy
- Design and implement a complete remote sensing-based environmental monitoring workflow appropriate to a specific organisational or project context
- Communicate remote sensing findings effectively through maps, dashboards, and technical reports for diverse audiences
Course Modules
Module 1: Foundations of Remote Sensing: Principles and Sensor Systems
- Electromagnetic spectrum, spectral signatures, and reflectance properties of key environmental surfaces
- Active vs. passive sensing systems: optical, thermal, microwave, and LiDAR
- Spatial, spectral, temporal, and radiometric resolution trade-offs in sensor selection
- Overview of major satellite programmes: Landsat, Sentinel, MODIS, PlanetScope, and UAV platforms
- Solar illumination geometry, atmospheric path effects, and their influence on at-sensor radiance
- Platform orbits and revisit cycles: sun-synchronous, geostationary, and inclined orbits and their monitoring implications
Module 2: Satellite Data Acquisition, Access, and Pre-Processing
- Navigating open-access data portals: USGS EarthExplorer, Copernicus Open Access Hub, and NASA Earthdata
- Geometric correction, orthorectification, and image co-registration techniques
- Radiometric calibration and atmospheric correction methods (DOS, FLAASH, Sen2Cor)
- Cloud masking strategies and compositing approaches for cloud-prone tropical environments
- Understanding metadata and image quality flags: interpreting acquisition parameters, solar angles, and data tier levels
- Mosaicking and tile management for large-area coverage: seamline blending, projection harmonisation, and file format optimisation
Module 3: Image Interpretation and Visual Analysis
- Principles of visual image interpretation: tone, texture, pattern, shape, and context
- Creating and interpreting false-colour composites for vegetation, water, and bare soil discrimination
- Building photo-interpretation keys for land cover mapping in diverse biomes
- Field verification planning: linking visual interpretation findings to ground truth collection
- Multi-date visual comparison for identifying seasonal change, disturbance events, and land use transitions
- Scale-dependent interpretation: adapting visual analysis criteria when moving between coarse, medium, and very-high-resolution imagery
Module 4: Land Cover Classification: Supervised and Unsupervised Approaches
- Unsupervised classification using K-means and ISODATA clustering for exploratory land cover mapping
- Supervised classification workflows: training sample collection, Maximum Likelihood, Random Forest, and SVM classifiers
- Object-based image analysis (OBIA) for high-resolution imagery and heterogeneous landscapes
- Accuracy assessment: confusion matrices, overall accuracy, Kappa coefficient, and producer/user accuracy
- Training sample design: stratified random sampling, class balance, and spectral separability assessment using transformed divergence
- Post-classification refinement: sieving, majority filtering, and integrating ancillary GIS layers to resolve classification confusion
Module 5: Spectral Indices for Environmental Monitoring
- Vegetation indices: NDVI, EVI, SAVI, and LAI derivation and ecological interpretation
- Water and moisture indices: NDWI, MNDWI, and soil moisture proxies
- Burn severity and post-fire recovery: NBR and delta-NBR from Sentinel-2 and Landsat
- Land Surface Temperature (LST) retrieval and urban heat island analysis from thermal bands
- Geological and soil mapping indices: Clay Mineral Ratio, Ferrous Iron Index, and bare soil composite indicators
- Index saturation and sensor-specific limitations: understanding signal saturation in dense canopies and selecting appropriate indices by ecosystem type
Module 6: Land Use and Land Cover Change Detection
- Post-classification comparison and image differencing techniques for bi-temporal change detection
- Multi-temporal vegetation index analysis and phenology-based change signals
- Deforestation and forest degradation mapping using the CODED and LandTrendr algorithms
- Quantifying and attributing change: area statistics, transition matrices, and driver analysis
- Time-series breakpoint detection using BFAST and CCDC for identifying abrupt and gradual land cover transitions
- Error propagation in change detection: understanding how classification uncertainties compound and strategies for uncertainty reporting
Module 7: Vegetation, Biomass, and Biodiversity Monitoring
- Time-series NDVI and EVI analysis for rangeland condition and crop phenology monitoring
- Above-ground biomass estimation using optical indices and SAR backscatter proxies
- Habitat mapping and fragmentation analysis for biodiversity assessment
- Monitoring invasive species spread and bush encroachment using multi-date imagery
- Canopy height and forest structural diversity estimation using spaceborne LiDAR data from GEDI
- Landscape connectivity metrics: applying patch-level remote sensing outputs to corridor analysis and species movement modelling
Module 8: Water Resources and Wetland Monitoring
- Mapping surface water extent and seasonal dynamics using the JRC Global Surface Water dataset
- Flood inundation mapping with optical (Sentinel-2) and SAR (Sentinel-1) data
- Wetland delineation and health assessment using multi-spectral and radar composites
- Reservoir storage estimation and shoreline change detection from time-series imagery
- Evapotranspiration estimation over irrigated landscapes and wetlands using energy balance models (SEBS, SSEBop)
- Water quality parameter retrieval: mapping turbidity, chlorophyll-a, and cyanobacterial bloom extent from multispectral imagery
Module 9: Synthetic Aperture Radar (SAR) for Environmental Applications
- SAR fundamentals: backscatter, polarisation, and coherence principles for environmental interpretation
- Sentinel-1 pre-processing in ESA SNAP: orbit correction, speckle filtering, terrain correction
- SAR-based flood and soil moisture mapping in cloud-covered and data-scarce regions
- InSAR applications for land subsidence, landslide monitoring, and wetland water level change
- Dual- and full-polarimetry analysis: interpreting VV, VH, and polarimetric decompositions for vegetation structure and surface roughness
- Multi-frequency SAR comparisons: contrasting C-band, L-band (ALOS-2), and P-band sensitivities for biomass and soil penetration depth
Module 10: Atmospheric and Climate-Related Monitoring
- Monitoring aerosol optical depth, dust events, and wildfire smoke plumes using MODIS and VIIRS
- Vegetation drought stress detection through combined thermal and spectral index analysis
- Snow cover and glacier extent monitoring using MODIS and Sentinel-2 imagery
- Sea surface temperature and coastal upwelling mapping for marine environmental monitoring
- Precipitation anomaly monitoring using CHIRPS and GPM datasets for drought early warning and hydrological assessment
- Long-term climate trend analysis: extracting decadal signals from remote sensing time series and cross-validating with reanalysis products
Module 11: Google Earth Engine for Large-Area Environmental Monitoring
- Introduction to the GEE JavaScript API: image collections, reducers, and cloud masking at scale
- Building time-series charts and trend analyses for vegetation, water, and fire monitoring
- Running supervised classification and change detection workflows on GEE for national-scale datasets
- Exporting analysis-ready maps, statistics, and time-series data for reporting and further GIS processing
- Optimising GEE scripts for computational efficiency: tiling strategies, scale parameters, and avoiding memory limit errors
- Integrating community datasets and user-uploaded assets: combining GEE's public catalogue with custom shapefiles, field data, and third-party rasters
Module 12: Drone and Very-High-Resolution Imagery in Environmental Assessment
- UAV mission planning, flight parameters, and imagery acquisition protocols for environmental surveys
- Photogrammetric processing: structure-from-motion, point clouds, and orthomosaic generation
- Very-high-resolution land cover mapping and canopy structure analysis from drone data
- Integrating UAV outputs with satellite imagery for multi-scale environmental monitoring workflows
- Regulatory and safety considerations for UAV operations in environmental contexts: airspace permissions, no-fly zones, and data sovereignty
- Multispectral and thermal sensor payloads for drones: calibration panel use, band alignment, and index derivation from UAV multispectral imagery
Module 13: Capstone Project: Designing and Implementing an Environmental Monitoring System
- Problem scoping and monitoring design: defining objectives, spatial extent, temporal frequency, and sensor selection
- End-to-end project execution: data acquisition, processing, classification, change detection, and validation
- Developing a monitoring protocol document and data management plan for operational use
- Presenting monitoring results and maps to a simulated stakeholder audience and incorporating feedback
- Cost-benefit and feasibility assessment: evaluating open versus commercial data options, cloud computing costs, and local technical capacity requirements
- Transition planning for operational sustainability: defining update cycles, quality control procedures, and capacity building needs for long-term monitoring continuity
Requirements & Prerequisites
Participants should have a working background in environmental science, natural resource management, GIS, or a related field. Basic familiarity with GIS concepts and software such as QGIS or ArcGIS is recommended, though not mandatory for highly motivated candidates from environmental disciplines. Prior exposure to remote sensing is helpful but not required, as the programme builds foundational concepts before advancing to applied analysis.
Training Methodology
- Conceptual lectures with real-world environmental case studies drawn from Africa, the Middle East, and global hotspots to ground each remote sensing principle in authentic context
- Hands-on computer lab sessions using QGIS, Google Earth Engine JavaScript API, and ESA SNAP, with step-by-step guided workflows on actual satellite datasets
- Guided image interpretation exercises where participants visually analyse and annotate multi-band composites before applying automated classification
- Change detection practicals using multi-temporal Landsat and Sentinel-2 imagery over deforestation, wetland loss, and urban sprawl sites
- Small-group problem-solving workshops in which teams diagnose image artefacts, classification errors, or sensor selection challenges and present their reasoning
- Field data integration exercises linking GPS-collected ground truth points to classified imagery for accuracy assessment
- Google Earth Engine coding labs with scaffolded scripts that participants adapt to their own area of interest
- Peer review sessions where participants critique each other's classification outputs and change detection maps using structured accuracy metrics
- Capstone project work across the final two days, in which each participant designs and executes an end-to-end monitoring workflow for a real environmental problem relevant to their organisation
- Briefing and map presentation exercise simulating a stakeholder report, building skills in communicating spatial findings to non-technical decision-makers
Organisational Benefits
- Organisations gain staff capable of generating timely, cost-effective environmental intelligence without full dependence on outsourced geospatial consultants
- Enhanced capacity to monitor project areas, protected zones, or concessions at landscape scale using freely available satellite data
- Improved quality and credibility of environmental impact assessments, management plans, and donor reports through rigorous spatial evidence
- Reduced field monitoring costs by prioritising ground surveys based on remote sensing-derived change alerts
- Strengthened compliance and reporting capability for environmental regulations, biodiversity commitments, and carbon accounting schemes
- Better integration of remote sensing data into monitoring and evaluation frameworks, improving project accountability and adaptive management
- Increased organisational competitiveness when bidding for contracts or grants that require demonstrated geospatial and environmental monitoring expertise
- A workforce equipped to leverage emerging technologies — including drone imagery, radar, and AI-assisted classification — as they become standard in the sector
Where Is Remote Sensing Applications in Environmental Monitoring Training Offered?
This course runs in-person across: Nairobi, Abuja, Kigali, Dubai, Arusha, Johannesburg, Pretoria, Kisumu, Naivasha, Cape Town, Dar es Salaam, Addis Ababa, Kampala, Nakuru, Accra, Zanzibar, Mombasa, Riyadh, Lagos, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
28 Sept 2026 – 9 Oct 2026 |
KES 160,000 |
| Nairobi, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Abuja, Nigeria |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 5,600 |
| Kigali, Rwanda |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 3,800 |
| Dubai, United Arab Emirates |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 8,200 |
| Arusha, Tanzania |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 4,200 |
| Johannesburg, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,000 |
| Pretoria, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 6,600 |
| Kisumu, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
KES 230,000 |
| Cape Town, South Africa |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 7,600 |
| Live Virtual |
Online |
19 Oct 2026 – 28 Oct 2026 |
KES 160,000 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,600 |
| Addis Ababa, Ethiopia |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,800 |
| Kampala, Uganda |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
+235 more dates — view live schedule →
Frequently Asked Questions
Do I need prior remote sensing experience to attend this course?
No prior remote sensing experience is required. The programme begins with foundational principles before progressing to applied analysis, making it accessible to environmental professionals who are new to the field. A basic familiarity with GIS concepts will help you get the most from the practical sessions.
What software will we use, and do I need to bring my own laptop?
The course uses QGIS, ESA SNAP, and Google Earth Engine — all of which are free and open-source or browser-based. Participants are required to bring a laptop with at least 8 GB RAM running Windows 10/11 or macOS. Installation guides and GEE account setup instructions will be sent in advance.
Will I receive a certificate upon completion?
Yes. Participants who attend all sessions and complete the capstone project will receive a TrainingComplex Certificate of Completion in Remote Sensing Applications in Environmental Monitoring. The certificate details the core competencies covered and is suitable for inclusion in professional development portfolios.
Is the course suitable for government officers with limited programming experience?
Yes. The Google Earth Engine sessions use scaffolded, pre-written scripts that participants adapt rather than write from scratch, making them accessible to those with little or no coding background. The course prioritises practical environmental interpretation over software development skills.
What datasets will we work with during the practicals?
Participants work with real, freely available satellite datasets including Landsat 8/9, Sentinel-1 and Sentinel-2, MODIS time-series, and sample drone orthomosaics. Where possible, datasets are selected from regions relevant to participants' home countries to make the exercises directly applicable to their professional contexts.
