Course Details

Forest Canopy and Biomass Estimation using Remote Sensing Training Course

Introduction

Forests are vital ecosystems, providing essential ecological services such as carbon sequestration, biodiversity habitats, water regulation, and economic resources. Accurately assessing and monitoring Forest Canopy characteristics (e.g., canopy cover, height, structure) and Forest Biomass (the total mass of living organic matter, primarily above-ground biomass, which includes carbon stock) is crucial for sustainable forest management, climate change mitigation efforts (REDD+), biodiversity conservation, and understanding global carbon cycles. Traditional forest inventories are often labor-intensive, time-consuming, and limited in their spatial coverage, making it challenging to track dynamic changes across large forested landscapes. Remote Sensing technology provides an unparalleled capability for systematically and efficiently monitoring forest attributes from local to global scales. Satellite, airborne, and increasingly, drone-based sensors can capture detailed information about forest structure, health, and extent. Technologies like optical imagery, LiDAR, and Synthetic Aperture Radar (SAR) offer complementary strengths for characterizing forest canopies and estimating biomass, providing objective, repeatable, and cost-effective data. Without the specialized skills to acquire, process, and analyze this remote sensing data, forest managers, researchers, and policymakers struggle to obtain accurate and timely information for sustainable forest resource management, carbon accounting, and conservation efforts. Many professionals in forestry and environmental sectors recognize the potential of remote sensing but lack the practical expertise to translate raw imagery into actionable insights for effective forest assessment.

Conversely, mastering Remote Sensing for Forest Canopy and Biomass Estimation empowers professionals to quantify forest resources, track deforestation and degradation, assess carbon sequestration potential, and inform land management decisions with unprecedented accuracy and efficiency. This specialized skill set is crucial for transforming raw sensor data into precise, actionable intelligence that contributes directly to sustainable forestry, climate action, and biodiversity protection. Our intensive 5-day "Forest Canopy and Biomass Estimation using Remote Sensing" training course is meticulously designed to equip foresters, environmental scientists, conservation practitioners, climate change researchers, natural resource managers, GIS professionals, and researchers with the essential theoretical knowledge and practical, hands-on skills required to confidently apply various remote sensing techniques for comprehensive forest canopy and biomass estimation.

Duration

5 Days

Target Audience

The "Forest Canopy and Biomass Estimation using Remote Sensing" training course is ideal for a wide range of professionals and researchers involved in forest management, conservation, climate change, and environmental monitoring. This includes:

• Foresters and Forest Managers: For inventory, planning, and sustainable management.
• Environmental Scientists and Ecologists: For ecosystem health assessment, biodiversity monitoring, and carbon cycling studies.
• Conservation Practitioners: Working on protected areas, habitat preservation, and REDD+ initiatives.
• Climate Change Researchers: Focusing on carbon sequestration, deforestation, and climate impacts on forests.
• Natural Resource Managers: Overseeing forest resources at various scales.
• GIS Professionals and Analysts: Seeking to apply their skills in the forestry domain.
• Remote Sensing Specialists: Wishing to deepen their expertise in forest-specific applications.
• Researchers and Academics: In forestry, ecology, environmental science, and remote sensing disciplines.
• Policy Makers: Involved in land use planning, climate policy, and national forest monitoring.
• Anyone working with forest ecosystems who needs to quantify canopy characteristics or biomass.

Course Objectives

Upon successful completion of the "Forest Canopy and Biomass Estimation using Remote Sensing" training course, participants will be able to:

• Understand the fundamental principles of remote sensing for forest characterization.
• Identify and select appropriate remote sensing data (optical, LiDAR, SAR) for forest canopy and biomass estimation.
• Perform essential pre-processing steps for diverse forest remote sensing datasets.
• Apply various spectral indices and image classification techniques for forest cover mapping and health assessment.
• Utilize LiDAR data for precise forest structure metrics, including canopy height and density.
• Understand and apply methods for estimating forest biomass and carbon stock using remote sensing.
• Perform change detection to monitor deforestation, degradation, and forest growth.
• Formulate a comprehensive workflow for integrating remote sensing data into forest inventory and monitoring programs.

8 Course Modules

Module 1: Introduction to Forest Remote Sensing and Biomass Concepts

• Importance of forests: Ecosystem services, carbon cycle, biodiversity.
• Definition of forest canopy attributes (e.g., cover, height, structure) and biomass (e.g., Above-Ground Biomass - AGB).
• Challenges of traditional forest inventory and the advantages of remote sensing.
• Overview of remote sensing platforms and sensors relevant to forestry (optical, LiDAR, SAR).
• Introduction to forest carbon accounting and the role of remote sensing in REDD+.

Module 2: Optical Remote Sensing for Forest Cover and Health

• Characteristics of optical sensors for forest monitoring (e.g., Landsat, Sentinel-2, Planet).
• Spectral signatures of different forest types and health conditions.
• Vegetation indices for forest health assessment: NDVI, EVI, NDWI, Red Edge indices.
• Mapping forest cover/non-forest using image classification techniques.
• Analyzing defoliation, disease outbreaks, and insect infestations using optical data.

Module 3: LiDAR Remote Sensing for Forest Structure and Height

• Introduction to LiDAR (Light Detection and Ranging) principles: Active remote sensing, point clouds.
• Types of LiDAR data: Discrete return vs. full waveform.
• LiDAR data processing workflow: Ground classification, Digital Terrain Model (DTM) generation, Canopy Height Model (CHM) generation.
• Extracting forest structural metrics from LiDAR: Mean height, canopy density, Leaf Area Index (LAI).
• Applications: Precise tree height measurement, volume estimation, structural diversity.

Module 4: Biomass Estimation using Optical and LiDAR Data

• Relationship between forest structural metrics (derived from LiDAR/optical) and biomass.
• Methods for biomass estimation using remote sensing:
• Regression Models: Empirical relationships between remotely sensed data and field-measured biomass.
• Allometric Equations: Relating tree dimensions to biomass.
• Using CHM and other LiDAR metrics for direct biomass estimation.
• Combining optical vegetation indices with LiDAR for improved biomass mapping.
• Uncertainty and error assessment in remote sensing-based biomass estimates.

Module 5: SAR Remote Sensing for Forest Biomass and Dynamics

• Introduction to SAR (Synthetic Aperture Radar) principles for forest applications.
• Understanding SAR interaction with forest canopies: Sensitivity to woody biomass and moisture.
• SAR frequency bands (L-band, P-band) and their penetration capabilities for biomass.
• Applications of SAR: Biomass estimation, deforestation monitoring (through canopy disturbance), forest type mapping.
• Challenges of SAR data in forest environments (e.g., speckle, saturation at high biomass).

Module 6: Forest Change Detection and Disturbance Monitoring

• Techniques for detecting forest cover change: Deforestation, degradation, regeneration.
• Multi-temporal analysis of optical imagery for long-term forest change mapping.
• Using SAR data for rapid disturbance detection (e.g., illegal logging, fire damage).
• Change detection algorithms (e.g., image differencing, post-classification comparison, time-series analysis methods like LandTrendr/BFAST).
• Quantifying rates of deforestation and carbon emissions/sequestration.

Module 7: Advanced Topics and Integration

• Fusion of multi-sensor data: Combining optical, LiDAR, and SAR for comprehensive forest characterization.
• Object-Based Image Analysis (OBIA) for individual tree crown delineation and classification.
• Machine learning and deep learning applications in forest remote sensing (e.g., automated tree detection, species classification).
• Introduction to forest monitoring systems and platforms (e.g., Global Forest Watch, FAO FRA).
• Role of drone-based remote sensing for high-resolution forest inventory.

Module 8: Practical Workflow and Reporting for Forest Applications

• Designing a complete workflow for a forest canopy or biomass estimation project.
• Data acquisition strategy based on project objectives.
• Step-by-step processing and analysis using relevant software.
• Validation with field data and accuracy assessment of derived products.
• Creating professional maps, reports, and dashboards for forest monitoring and management.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com