This whitepaper investigates the innovative application of the GatorEye drone-borne LiDAR system for monitoring tropical forest succession through detailed canopy structural attributes. These attributes include canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index, leaf area index (LAI), and understory LAI.

Focusing on nine tropical forest stands in the Caribbean lowlands of northeastern Costa Rica, the study evaluates the relationship between these variables and aboveground biomass (AGB) stocks and species diversity. The analysis reveals that while canopy height and AGB do not show a clear pattern with forest age, gap fraction and spatial heterogeneity increase, and understory LAI decreases as forests age.

Notably, canopy height is strongly correlated with AGB. The study demonstrates that drone-LiDAR systems significantly enhance the characterization of heterogeneous mosaics created by successional forest patches in human-managed landscapes. This technology offers a valuable tool for improving forest recovery assessments and developing mechanistic carbon sequestration models, crucial for tracking progress within the UN Decade on Ecosystem Restoration.

A few weeks ago we posted a video of a completed Ranger-XL ready for shipment. Here’s a peek at a recent dataset collected by that same Ranger-XL coupled with the 100MP Phase One 4-band Solution. This urban corridor was easily captured with a single pass, creating a stunning dataset with high Imagery and LiDAR resolution. Data specs: 80 Points Per Square Meter Average for LiDAR and 3cm GSD for imagery.

This whitepaper explores the challenges of obtaining high-quality forest structure information in tropical forests, particularly those in difficult-to-access areas. Traditional field-based approaches are often costly and time-consuming, prompting the use of LiDAR technology for efficient and comprehensive structural parameter estimates.

The study compares aboveground biomass (AGB) estimations and products from aircraft-borne LiDAR data collected in 2015 with data from the UAV-borne GatorEye Unmanned Flying Laboratory in 2017 across ten forest inventory plots in the Chico Mendes Extractive Reserve in Acre, southwestern Brazilian Amazon.

Findings indicate that both platforms produce comparable LiDAR products, with the GatorEye system demonstrating higher point density due to its lower and slower flight and increased returns per second. Despite these differences, ground point density remained similar between the systems.

Both sensors yielded analogous results for digital elevation models and estimated AGB. This study validates the effectiveness of UAV-borne LiDAR sensors in accurately quantifying AGB in dense tropical forests and highlights the potential for detailed analyses of crown structure and leaf area density distribution using dense point clouds from UAV systems.

The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficult to use classical statistical techniques to design accurate predictive models from these data for conducting forest inventories. Machine learning techniques have the potential to solve this problem of modeling forest attributes from remotely sensed data. This work tests four different machine learning approaches – namely Support Vector Regression, Random Forest, Artificial Neural Networks, and Extreme Gradient Boosting – on high-density GatorEye UAV-Lidar point clouds for indirect estimation of individual tree dendrometric metrics (field-derived) such as diameter at breast height, total height, and timber volume.

Mohawk College recently partnered with the Niagara Parks Commission on a critical project, utilizing their Phoenix LiDAR Scout 32 system to achieve two key objectives. First, they generated a precision high-resolution 3D model of the historic Toronto Power Generation Station. This data is instrumental for the rehabilitation efforts to preserve this architecturally significant structure.

Second, they accurately scanned and geo-located a century-old barge that had been lodged above Niagara Falls since 1918. Dislodged by a powerful storm, the barge moved 150 meters downstream, closer to Horseshoe Falls. Niagara Parks Officials are closely monitoring its potential movement.

UAV LiDAR technology enabled the team to geo-locate the barge with centimeter-level precision and create a detailed cultural heritage preservation model of the power station. This project underscores the critical role of advanced LiDAR technology in historical preservation and environmental monitoring.

Check out their fine work and some incredible footage from the Niagara Falls project here!

This whitepaper examines the potential of UAV-borne LiDAR technology, specifically the GatorEye system, for measuring essential forest parameters such as diameter at breast height (DBH) and total tree height. Traditionally, these parameters are measured manually using level gauges and hypsometers in sample plots, a process that is laborious, expensive, and spatially limited. Terrestrial laser scanning (TLS) has been used for remote DBH measurement, offering high-density point clouds for accurate inventory but facing spatial limitations due to manual deployment and field access challenges.

The study demonstrates the effectiveness of UAV-LiDAR in providing higher density point cloud data compared to aircraft-borne systems. Focusing on an integrated crop-livestock-forest system with Eucalyptus benthamii plantations, 63 trees were georeferenced and measured in the field. Algorithms typically used for TLS were applied to the UAV-LiDAR point cloud for automatic individual tree detection and measurement.

The UAV-LiDAR-derived DBH and total height showed strong correlations with field measurements, with correlation coefficients of 0.77 and 0.91, and RMSEs of 11.3% and 7.9%, respectively. This study underscores the potential of UAV-LiDAR systems to efficiently measure forest plantations on a broad scale, reducing field effort and enhancing forest management decision-making. Further exploration in diverse tree plantations and forest environments is recommended.

We’re excited to announce the release of LiDARMill v2! LiDARMill v2 takes automated post-processing to the next level. In our recent webinar, we covered some of the new improvements and features including:

• Imagery Processing in LiDARMill
• Ground Control Reporting and Adjustments
• Robust Coordinate System Handling
• A Workflow Overview and Demonstration
• Multi-Mission Processing Support
• Advanced Point Cloud Filtering Options
• RGB Thermal & Fusion
• Accuracy Reporting
• Automated LiDAR and Camera Calibration Options
• Near-Real Time (NRT) Reference Station Positioning for Projects Requiring Less Than 24 Hour Turn-around Time
• Trajectory Post-Processing Without Reference Stations

If you have any questions or would like to learn more about LiDARMill v2, please don’t hesitate to get in touch. We’d be happy to help!

We’re happy to announce the release of SpatialSuite 5.0! Make sure to check out our webinar led by Post Processing Manager, Conrad Conterno, to learn about the many, powerful new tools and features. In this webinar we cover:

• The .cloud format
• LiDARSnap Plugin
• CameraSnap Plugin
• Classification/Point Filtering Plugin
• Exporting Vector and Raster Products
• System Visualization Tools
• Other New Features (i.e. RXP to SDCX converter, LAS Viewer, and more.)

If you have any questions or would like to learn more about SpatialSuite 5.0, please don’t hesitate to get in touch. We’d be happy to help!