Module 1: Foundations of Environmental Science and Geospatial Technologies

• Key environmental challenges: Climate change, pollution, biodiversity loss, resource depletion.
• Principles of environmental monitoring and assessment.
• Overview of GIS concepts: Spatial data models, coordinate systems, projections, spatial analysis.
• Overview of Remote Sensing concepts: Electromagnetic spectrum, sensor types, image resolution, spectral signatures.
• The power of integrating GIS and Remote Sensing for holistic environmental understanding.
• Practical session: Exploring diverse environmental datasets (vector and raster) in a GIS environment and discussing their relevance to environmental issues.

Module 2: Remote Sensing Data Acquisition and Pre-processing for Environmental Applications

• Satellite missions crucial for environmental monitoring (e.g., Landsat, Sentinel, MODIS, high-resolution commercial imagery).
• Active vs. Passive sensors for environmental data collection (e.g., optical, thermal, radar, LiDAR).
• Data acquisition strategies and access platforms for environmental remote sensing data.
• Image pre-processing: Radiometric calibration, atmospheric correction, geometric correction, mosaicking.
• Cloud masking and gap filling for environmental time series.
• Practical session: Acquiring satellite imagery suitable for an environmental application (e.g., forest health) and performing essential pre-processing steps.

Module 3: GIS Data Models and Management for Environmental Data

• Designing geodatabases for environmental data (e.g., pollution sources, protected areas, sampling sites).
• Data capture techniques: Digitization, GPS data integration, existing data import.
• Building and managing attribute tables for environmental features.
• Topology rules and network datasets for environmental analysis (e.g., pollution dispersion).
• Data sharing and interoperability standards for environmental data.
• Practical session: Designing and creating a geodatabase schema for an environmental project (e.g., water quality monitoring sites) and populating it with sample data.

Module 4: Land Cover and Land Use Mapping and Change Detection

• Definition of land cover and land use, and their importance in environmental change.
• Remote sensing image classification techniques for environmental mapping (supervised, unsupervised, object-based).
• Change detection analysis: Image differencing, post-classification comparison, change vector analysis.
• Monitoring deforestation, land degradation, and urban sprawl.
• Assessing impacts of land use change on ecosystems and services.
• Practical session: Performing a land cover classification and a change detection analysis to quantify environmental changes over time.

Module 5: Water Resources Monitoring and Management

• Mapping surface water bodies: Rivers, lakes, wetlands, floodplains using optical and radar data.
• Remote sensing for water quality parameters (e.g., chlorophyll-a, suspended sediments, turbidity, temperature).
• Watershed delineation and hydrological modeling (e.g., runoff, flow accumulation) using DEMs.
• GIS for water allocation, irrigation planning, and groundwater vulnerability mapping.
• Drought monitoring and flood extent mapping.
• Practical session: Delineating watersheds, mapping water bodies, and assessing water quality parameters from satellite imagery.

Module 6: Biodiversity and Ecosystem Health Assessment

• Mapping habitats and ecosystems using remote sensing and GIS.
• Assessing vegetation health and stress using spectral indices (e.g., NDVI, EVI) and hyperspectral data.
• Identifying biodiversity hotspots and conservation priority areas.
• Monitoring ecosystem services (e.g., carbon sequestration, pollination).
• Using GIS for wildlife corridor analysis and protected area management.
• Practical session: Mapping and assessing habitat fragmentation using land cover data and calculating vegetation indices for ecosystem health.

Module 7: Environmental Pollution and Hazard Mapping

• Mapping sources of pollution (e.g., industrial sites, waste dumps, agricultural runoff).
• Remote sensing for detecting air and water pollution (e.g., atmospheric aerosols, oil spills, algal blooms).
• Modeling pollution dispersion and exposure pathways using GIS.
• Mapping environmental hazards: Landslides, erosion, contaminated sites.
• Assessing population vulnerability to environmental hazards.
• Practical session: Mapping potential pollution sources and modeling their impact on surrounding areas or water bodies.

Module 8: Climate Change Impact Assessment and Adaptation

• Mapping climate change indicators: Temperature anomalies, precipitation changes, sea-level rise.
• Assessing climate vulnerability and risk for ecosystems and human settlements.
• Modeling future climate scenarios and their environmental implications (e.g., species range shifts).
• Identifying areas for climate change adaptation strategies (e.g., resilient agriculture, coastal protection).
• Using GIS to support climate change mitigation efforts (e.g., carbon mapping).
• Practical session: Visualizing climate change projections and identifying areas vulnerable to specific climate impacts (e.g., sea-level rise inundation).

Module 9: Geospatial Modeling for Environmental Prediction

• Introduction to spatial modeling concepts for environmental applications.
• Predictive mapping of environmental phenomena (e.g., species distribution, pollution spread).
• Suitability analysis for conservation, restoration, or land use planning.
• Hydrological and ecological modeling using raster and vector data.
• Scenario building and forecasting environmental changes.
• Practical session: Building a simple environmental suitability model (e.g., for a specific habitat or a restoration site) based on multiple environmental criteria.

Module 10: Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA)

• Role of integrated GIS and remote sensing in streamlining EIA/SEA processes.
• Mapping baseline environmental conditions and sensitive receptors.
• Analyzing potential environmental impacts of development projects or policies.
• Spatial analysis for cumulative impact assessment.
• Visualizing mitigation measures and alternative scenarios for decision-makers.
• Practical session: Conducting a multi-criteria analysis to assess environmental sensitivity for a proposed development area within an EIA framework.

Module 11: Geospatial Analysis for Conservation Planning

• Identifying conservation priorities and designing protected area networks.
• Gap analysis: Assessing coverage of protected areas relative to biodiversity targets.
• Connectivity analysis for ecological corridors and landscape linkages.
• Mapping and valuing ecosystem services and their spatial distribution.
• Using GIS for land acquisition, restoration planning, and protected area expansion strategies.
• Practical session: Performing a gap analysis to identify under-protected areas for a specific ecosystem or species.

Module 12: Soil and Land Degradation Monitoring

• Mapping soil types and properties using remote sensing and ancillary data.
• Assessing soil erosion risk and mapping degraded lands.
• Monitoring desertification and land degradation processes over time.
• Using remote sensing for soil moisture content estimation.
• GIS for sustainable land management planning and restoration efforts.
• Practical session: Mapping areas prone to soil erosion using slope, land cover, and soil type data.

Module 13: Urban Environmental Analysis

• Mapping urban land cover, impervious surfaces, and green infrastructure.
• Assessing urban heat islands (UHI) using thermal remote sensing.
• Analyzing urban air and water quality issues.
• Identifying environmental justice concerns in urban areas.
• GIS for urban greening initiatives and sustainable urban development.
• Practical session: Mapping urban heat island hotspots and analyzing their relationship with impervious surfaces and vegetation.

Module 14: Integrating Field Data and Citizen Science

• Planning and executing GPS-based field data collection for environmental monitoring.
• Integrating mobile GIS data with remote sensing products for ground-truthing and validation.
• Utilizing citizen science platforms and data for environmental monitoring (e.g., biodiversity observations, pollution reports).
• Data harmonization and quality control for integrated datasets.
• The role of participatory GIS (PGIS) in environmental management.
• Practical session: Importing and integrating field-collected GPS data points with remote sensing imagery for validation or further environmental analysis.

Module 15: Environmental Policy, Governance, and Reporting

• Overview of international and national environmental policies and agreements (e.g., SDGs, multilateral environmental agreements).
• The role of geospatial information in policy formulation, implementation, and enforcement.
• Using GIS for environmental compliance monitoring and reporting.
• Developing geospatial indicators for environmental sustainability.
• Communicating environmental data to policymakers and stakeholders.
• Practical session: Developing a simple geospatial indicator (e.g., forest cover change rate) and preparing a map for a policy brief.

Module 16: Communication and Visualization of Environmental Data

• Principles of effective cartography for environmental maps.
• Designing clear, informative, and compelling maps for diverse audiences.
• Creating interactive web maps and dashboards for environmental data dissemination.
• Storytelling with maps: Communicating complex environmental issues and solutions.
• Utilizing GIS for public awareness campaigns and stakeholder engagement.
• Practical session: Designing a professional-quality map visualizing a complex environmental issue (e.g., biodiversity loss, water scarcity) and preparing it for presentation and web publication.

Module 17: Advanced Topics and Emerging Trends in Environmental Geospatial Science

• Cloud computing platforms for environmental monitoring (e.g., Google Earth Engine, Microsoft Planetary Computer).
• Big Data analytics for large-scale environmental datasets.
• Artificial Intelligence and Machine Learning in environmental remote sensing and modeling.
• UAVs (Drones) for high-resolution local environmental data collection.
• Real-time monitoring and sensor networks in environmental applications.
• Practical session: Exploring basic functionalities of a cloud-based platform for environmental data access and analysis, or a simple AI/ML application.

Module 18: Capstone Project: Integrated Environmental Application

• Comprehensive review of all integrated GIS and remote sensing techniques for environmental applications.
• Guided individual or group project work on a selected, complex environmental challenge.
• Designing and executing an end-to-end environmental geospatial analysis workflow.
• Presenting project findings, recommendations, and policy implications.
• Discussion of future directions and career paths in environmental geospatial science.
• Practical session: Participants work on a capstone project, applying learned techniques to address a specific environmental issue (e.g., assessing the impact of mining on water quality, mapping ecosystem vulnerability to climate change, monitoring protected area effectiveness).

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588