The proliferation of unmanned aerial vehicles (UAVs) over the past decade has been driven by advancements in structure-from-motion (SfM), machine learning, and robotics. A crucial application in forestry is individual tree detection (ITD), essential for calculating forest attributes like stem volume, forest uniformity, and biomass estimation.

This whitepaper addresses the challenges users face in adopting UAVs and algorithms for specific projects by providing a detailed tutorial for performing ITD. It covers the use of low-cost UAV-derived imagery and UAV-based high-density LiDAR, utilizing open-source R packages to develop a canopy height model (CHM) and implement the local maxima (LM) algorithm for ITD.

Accuracy assessments are derived through manual visual interpretation and field-data validation. Targeted at beginners in remote sensing, this guide employs a simple methodology and uses study plots with relatively open canopies. Supplementary materials include R codes and sample plot data to facilitate practical application.

This whitepaper explores the role of urban forest remnants in mitigating climate change by reducing carbon dioxide levels in urban areas. Specifically, it focuses on the potential of UAV-LiDAR systems to accurately measure individual tree heights in an Urban Atlantic Forest, using Araucaria angustifolia trees as a case study.

Through detailed analysis, the study assesses the impact of varying point densities (ranging from 2,500 to 5 returns per square meter) on the accuracy of tree height measurements. The findings reveal that higher point densities provide more precise tree profiles, while lower densities result in gaps in the Crown Height Model (CHM). The research highlights that the optimal point density for the highest agreement between UAV-LiDAR -derived and field-based tree heights is 100 returns per square meter, with the lowest relative root mean square error (rRMSE) observed at 50 returns per square meter.

This whitepaper underscores the effectiveness of UAV-LiDAR technology in urban forest management and its implications for developing policies to maintain essential ecosystem services.

This whitepaper delves into the pivotal role tropical savanna ecosystems play in the global carbon cycle, particularly focusing on the Brazilian Savanna (Cerrado). It examines the uncertain capacity of these ecosystems to store and sequester carbon due to the intertwined effects of human activities and climate change. Utilizing high-density UAV-LiDAR technology, this study provides a comprehensive analysis of the above ground biomass density (AGBt) across diverse vegetation formations in Cerrado, including forests, savannas, and grasslands.

The research highlights the development and validation of regression models to estimate AGBt, emphasizing the model that incorporates vegetation height and cover as the most effective, achieving an adjusted R2 of 0.79. This model was used to map AGBt over a vast area, demonstrating the feasibility and potential of UAV-LiDAR in accurately estimating biomass. Additionally, the study underscores the necessity for improved biomass estimation in grasslands to enhance the understanding of the global carbon balance and support integrated fire management.

The findings presented in this whitepaper provide critical insights and benchmarks for future research, aiming to generate precise biomass maps and inform effective carbon emission mitigation strategies in tropical savanna ecosystems.

This whitepaper investigates the innovative use of UAV-borne hyperspectral and LiDAR data to enhance our understanding of forest ecosystem restoration. Focusing on twelve 13-year-old restoration plots in the Brazilian Atlantic Forest, the study evaluates the effectiveness of these technologies in assessing tree diversity and structure.

By combining LiDAR -derived structural attributes—such as canopy height and leaf area index (LAI)—with hyperspectral variables, the research demonstrates the complementary nature of these data sources. The findings reveal that while LiDAR -derived canopy height is a strong predictor of above ground biomass (AGB), the integration of hyperspectral and LiDAR data provides a comprehensive approach to monitoring forest structural attributes and tree diversity. The study supports biodiversity theory, showing that higher species richness enhances biomass capture and canopy functionality.

This whitepaper underscores the critical role UAV-borne remote sensors can play in large-scale forest monitoring, particularly in the context of the UN Decade of Ecosystem Restoration, by providing high-resolution data essential for effective decision-making in restoration projects.

Join the Phoenix LiDAR Systems webinar on advanced imaging systems, held on April 21, 2021. Hosted by Conrad Conterno, Head of Post-Processing, and Justin Wyatt, VP of Sales at Phoenix LiDAR Systems, along with Nick Nelio, Inspection Sales Manager for Phase One, this session dives into how Phoenix LiDAR’s data collection tools integrate with Phase One’s cutting-edge imaging systems to enhance remote sensing capabilities.

Conrad Conterno opens with an overview of Phoenix LiDAR’s custom mapping solutions, emphasizing LiDAR sensor integration for superior data acquisition and analysis. He introduces various advanced camera options, including the lightweight custom A6K Light for UAV-based mapping, dual oblique cameras for enhanced colorization, multispectral solutions for detailed vegetation analysis, thermal mapping cameras for environmental monitoring, and hyperspectral sensors for precise spectral analysis.

Nick Nelio then showcases Phase One’s high-resolution, medium-format cameras, focusing on the 4-band solution that combines RGB and near-infrared imagery, ideal for crop analysis and environmental monitoring. He also presents the Phase One P3 payload for inspection applications and the IX Mach 5 controller designed for efficient geospatial missions.

Throughout the webinar, the benefits of direct geo-referencing and the seamless integration of multiple sensors into single payloads are highlighted. The hosts address audience questions on the accuracy of dual-camera systems, post-processing challenges, and the applications of hyperspectral imaging.

The session concludes with Justin Wyatt and Nick Nelio emphasizing their collaborative approach to delivering tailored solutions and inviting viewers to contact them for personalized consultations.

This whitepaper explores the application of high-density UAV-borne LiDAR technology for monitoring understory dynamics in the pine savannas of the southeastern United States, particularly in the context of prescribed fire management. Traditionally, understory characteristics such as height and biomass have been monitored through field sampling, but this study contrasts these conventional methods with advanced remote sensing techniques.

Utilizing the GatorEye UAV system, LiDAR data provided spatially explicit estimates of understory height and biomass before and after a prescribed fire. The results showed significant correlations between LiDAR-derived measurements and traditional field data, demonstrating the accuracy and efficiency of LiDAR technology.

Notably, LiDAR’s comprehensive spatial coverage revealed a smaller biomass reduction after the burn compared to in-situ measurements, highlighting the importance of capturing spatial variability. The findings underscore the potential of LiDAR as a powerful tool for land managers, offering enhanced spatial and temporal resolution in tracking understory biomass and its response to fire, ultimately supporting more effective ecosystem management practices.

This whitepaper examines the efficiency and accuracy of UAV-borne LiDAR, specifically the GatorEye system, for high-resolution forest data acquisition, comparing it to traditional aircraft-borne LiDAR in the Apalachicola National Forest, USA. The study assesses the effectiveness of single-pass flight plans for generating digital terrain models (DTMs) and canopy height models (CHMs).

Results indicate that DTMs derived from UAV LiDAR showed less than 1 meter difference compared to aircraft-derived DTMs within a 145° field of view (FOV). CHMs provided reliable treetop detection, though tree height underestimations occurred at distances over 175 meters from the flight line. Crown segmentation was effective within a 60° FOV, but shadowing effects hindered its accuracy beyond this range.

The study identifies optimal quality thresholds for various LiDAR products, supporting the development of efficient, cost-effective UAV flight plans for forest monitoring. These findings highlight the potential of UAV-borne LiDAR for detailed, multi-temporal forest structure assessment, offering valuable insights for forest management and conservation strategies.

This whitepaper explores the effectiveness of UAV-borne LiDAR technology for detecting small trails (less than 2.5 meters wide) in mixed forest canopy ecosystems. Accurate trail mapping is crucial for forest management, monitoring, and conservation, yet current sensor technology for sub-canopy detection is still evolving.

The study compares trail detectability using high-definition surface models from UAV LiDAR data and high-resolution satellite imagery from Google Earth. Through participatory mapping, respondents with limited geospatial experience identified trails on both map types. Results showed higher detection rates on the LiDAR-derived map compared to the satellite imagery. In satellite maps, trail detectability was positively correlated with wider trails and lower canopy cover, whereas LiDAR maps showed increased detectability with wider trails regardless of canopy cover.

This mixed-method approach, combining UAV-mounted LiDAR, satellite imagery, and participatory mapping, enhances the rapid detection of small trails under varying conditions, offering valuable insights for improving forest management and conservation efforts.

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.