17-19 February 2025

LiDAR technology has become a powerful tool in ecological research, providing detailed three-dimensional information of different  ecosystems. Its ability to capture structure information, terrain models, and habitat complexity has made it indispensable in fields such as ecology, forestry, biodiversity conservation, and environmental monitoring. However, LiDAR data requires a solid understanding of its theoretical principles and expertise in specialized software tools for analysis.
This 12-hour course, held over three days, is designed to introduce participants to both the foundational principles of LiDAR technology and its practical applications in ecology using R. The course will begin with an overview of the key concepts behind LiDAR data acquisition, processing, and the ecological variables it can help measure. Participants will then engage in hands-on practical sessions focused on the lidR package in R, learning how to handle point clouds, generate terrain models, and extract different ecological metrics.
By the end of the course, participants will have gained both theoretical knowledge and the practical skills necessary to process, analyze, and interpret LiDAR data for ecological research.

-    Understand the fundamental principles of LiDAR technology and its ecological applications.
-    Gain proficiency in using the lidR package in R for LiDAR data analysis.
-    Develop skills to create different digital models and to extract ecological metrics
-    Design and execute a complete LiDAR analysis workflow in R.

Day 1 – Introduction and Basic LiDAR Data Handling . 1-5 PM Berlin time

•    Introduction to LiDAR technology for ecological applications
o    Overview of LiDAR principles and key concepts, full waveform vs discrete return systems, introduction to platforms (aerial, drone, terrestrial)
•    Introduction to the lidR package
o    Overview of package features and setting up the R environment for LiDAR analysis.
•    Reading and visualizing point clouds in R
o    Importing LiDAR data and basic visualization techniques for exploring point clouds.

Day 2 – LiDAR Data Processing and Classification. 1-5 PM Berlin time

•    Point cloud classification and normalization
o    Filtering and classifying point cloud data into ground, vegetation, and non-ground points.
•    Transition from point cloud to raster (DTM, DSM, CHM)
o    Generating Digital Terrain Models (DTM), Digital Surface Models (DSM), and Canopy Height Models (CHM) from classified point clouds.
•    Las catalog
o    Handling large LiDAR datasets using LasCatalog for efficient processing.
•    Individual tree detection and segmentation
o    Detecting individual trees within the point cloud and segmenting them for further analysis.

Day 3 – Advanced LiDAR Analysis and Forest/Vegetation Metrics. 1-5 PM Berlin time

•    Extracting forest/vegetation metrics at tree and pixel level
o    Calculating key metrics such as tree height, canopy cover, and biomass.
•    The Area-Based Approach (ABA) to forest/ecological modelling
o    Applying ABA for forest structure modeling and analysis.
•    Global CHM from satellite LiDAR (GEDI)
o    Working with satellite-derived Canopy Height Models (CHM) using data from the GEDI mission.
•    Forest structural heterogeneity with LiDAR data
o    Analysing forest/vegetation structural diversity and heterogeneity using LiDAR-derived metrics.