Introduction

The escalating global imperative for sustainable forest management and climate change mitigation hinges on accurate, detailed, and up-to-date information about forest structure, health, and carbon stocks. Traditional forest inventory methods are often labor-intensive, costly, and provide limited spatial resolution, making it challenging to monitor vast and often inaccessible forest landscapes effectively. Light Detection and Ranging (LiDAR) technology has revolutionized this domain by offering an unparalleled ability to penetrate dense forest canopies and generate highly precise 3D point clouds. This data enables the accurate measurement of individual tree heights, canopy density, biomass, and ultimately, carbon sequestration potential, providing critical insights for sustainable forestry, carbon accounting, biodiversity conservation, and informed policy-making. By leveraging LiDAR, forest managers and environmental scientists can unlock a new era of data-driven decision-making, optimizing resource utilization and maximizing forests' role as vital carbon sinks. This essential training course focuses on LiDAR for Forest Management & Carbon Sequestration, equipping professionals with the expertise to utilize this cutting-edge technology for enhanced environmental stewardship.

This intensive training course provides a comprehensive and practical guide to utilizing LiDAR technology for advanced forest management and precise carbon sequestration assessment. Participants will gain in-depth knowledge of LiDAR data acquisition methods (airborne, terrestrial, drone-based) tailored for forestry applications, and master the techniques for processing raw LiDAR point clouds to generate high-resolution Digital Terrain Models (DTMs) and Canopy Height Models (CHMs). We will delve into applying these models for individual tree detection, biomass estimation, forest inventory, and accurate carbon stock quantification. By mastering the technical skills and methodological approaches for LiDAR-based forest analysis, you will be equipped to conduct efficient forest assessments, contribute to carbon accounting initiatives, and implement data-driven strategies for sustainable forest ecosystems.

Target Audience

• Foresters & Forest Managers
• Environmental Scientists & Researchers
• Climate Change Analysts
• Land Use Planners & Conservationists
• GIS & Remote Sensing Specialists
• Ecologists & Biologists
• Carbon Project Developers
• Government & NGO Environmental Agencies

Course Objectives

• Understand the fundamental principles of LiDAR technology and its unique advantages for forest management.
• Differentiate between various LiDAR acquisition platforms (aerial, terrestrial, drone) for forestry applications.
• Learn to plan and execute LiDAR data collection missions specifically for forest environments.
• Master the process of pre-processing raw LiDAR point clouds, including noise reduction and classification.
• Develop proficiency in generating Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) from LiDAR data.
• Understand techniques for individual tree detection and crown delineation using LiDAR.
• Gain expertise in estimating forest biomass and timber volume from LiDAR-derived metrics.
• Learn methods for calculating forest carbon stock and carbon sequestration potential using LiDAR data.
• Explore the use of LiDAR for forest health monitoring, change detection, and habitat assessment.
• Integrate LiDAR data with Geographic Information Systems (GIS) for comprehensive forest spatial analysis.
• Design a complete LiDAR-based project for forest inventory or carbon accounting.

DURATION

10 Days

COURSE CONTENT

Module 1: Introduction to LiDAR Technology & Forestry

• Fundamentals of LiDAR: how it works, active vs. passive remote sensing.
• The unique benefits of LiDAR for penetrating forest canopy and capturing 3D structure.
• Evolution of LiDAR in forestry: from airborne to drone-based and terrestrial.
• Key applications of LiDAR in modern forest management.
• Overview of carbon sequestration and the role of forests.

Module 2: LiDAR Data Acquisition Platforms & Sensors

• Airborne LiDAR: fixed-wing and helicopter systems for large-area coverage.
• Drone-based LiDAR (UAV-LiDAR): flexibility, high density, rapid deployment for smaller areas.
• Terrestrial LiDAR Scanning (TLS): ground-based for detailed individual tree measurements.
• Sensor specifications: pulse rate, accuracy, laser wavelength.
• Planning LiDAR flights: altitude, speed, overlap for optimal forest data.

Module 3: LiDAR Data Pre-processing Essentials

• Understanding raw LiDAR data formats (LAS, LAZ).
• Filtering and noise reduction techniques for clean point clouds.
• Point cloud classification: distinguishing ground points from vegetation, buildings, etc.
• Georeferencing and projection systems for accurate spatial positioning.
• Quality control and initial assessment of LiDAR data.

Module 4: Digital Terrain Model (DTM) Generation

• The importance of a bare-earth DTM for forest analysis.
• Algorithms for ground point extraction and terrain interpolation.
• Creating high-resolution DTMs from classified ground points.
• Identifying hydrological features and micro-topography from DTMs.
• Applications of DTMs in forest road planning and hydrological modeling.

Module 5: Canopy Height Model (CHM) Creation & Analysis

• Generating a Canopy Height Model (CHM) from raw LiDAR and DTM.
• Understanding CHM interpretation: direct measurement of tree heights.
• Deriving canopy density and cover metrics from the CHM.
• Analyzing vertical forest structure using CHM profiles.
• Applications of CHMs in forest inventory and health assessment.

Module 6: Individual Tree Detection & Characterization

• Algorithms for identifying individual tree crowns from LiDAR point clouds/CHM.
• Extracting individual tree attributes: height, crown diameter, tree position.
• Automated vs. semi-automated tree segmentation methods.
• Challenges in dense canopy areas and mitigation strategies.
• Applications in precision silviculture and selective harvesting.

Module 7: Forest Biomass Estimation with LiDAR

• The relationship between forest structure (from LiDAR) and biomass.
• Allometric equations for converting LiDAR metrics to biomass.
• Direct estimation of Above-Ground Biomass (AGB) using LiDAR point clouds.
• Developing predictive models for biomass estimation across landscapes.
• Uncertainty and error assessment in LiDAR-based biomass estimates.

Module 8: Carbon Stock & Sequestration Assessment

• Converting biomass estimates to carbon stock using carbon content factors.
• Calculating carbon sequestration potential over time.
• Monitoring carbon dynamics in forests (growth, disturbance, harvest).
• Using LiDAR for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) monitoring.
• Applications in carbon credit projects and reporting.

Module 9: Forest Health, Change Detection & Disturbance Mapping

• Detecting changes in forest structure due to growth, disease, or disturbance.
• Using multi-temporal LiDAR for mapping deforestation, degradation, and regeneration.
• Identifying areas affected by pests, diseases, or wildfires.
• Assessing storm damage and windthrow with LiDAR.
• Mapping forest gaps and understory characteristics.

Module 10: Integration with Geographic Information Systems (GIS)

• Importing and managing LiDAR-derived products in GIS software.
• Performing spatial analysis: overlay, buffering, network analysis with forest data.
• Creating professional maps and visualizations for forest management.
• Integrating LiDAR data with other geospatial datasets (e.g., optical imagery, field plots).
• Customizing GIS workflows for specific forestry tasks.

Module 11: Field Validation & Accuracy Assessment

• Collecting ground truth data for calibrating and validating LiDAR outputs.
• Designing field plots for forest inventory.
• Comparing LiDAR measurements with traditional field measurements.
• Statistical methods for assessing accuracy and precision.
• Importance of iterative refinement between field and remote sensing data.

Module 12: Best Practices & Operational Considerations

• Data storage and management for large LiDAR datasets.
• Processing workflows and automation for efficiency.
• Collaboration and data sharing among stakeholders.
• Cost-effectiveness of LiDAR vs. traditional methods.
• Regulatory and safety considerations for LiDAR drone operations.

Module 13: Advanced Applications & Future Trends

• Full-waveform LiDAR for more detailed vegetation insights.
• Integration of LiDAR with hyperspectral and thermal data.
• AI and Machine Learning for automated tree species classification and advanced analysis.
• Forest inventory automation using LiDAR and AI.
• The role of LiDAR in precision forestry and smart forest management.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.