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RANGER-LR | Dense Vegetation River Corridor

Point Density: 320 points per m^2
Height: 80 m AGL 
Speed: 8 m/s  
Data Acquisition Time: 7 minutes

Revolutionizing Forest Analysis: Unleashing the Power of UAV Lidar to Map Aboveground Biomass Density in the Brazilian Savanna

A team of scientists from the Department of Forestry, the University of Brasilia, and other organizations invested in the study of climate change have been using sensors from Phoenix LiDAR Systems to map vegetation biomass by drone, providing key insights into the carbon cycle to help lower carbon emissions and better manage the impacts of climate change.

[Vegetation biomass is the total weight or quantity of plants present in a given area—terms like yield, plant matter, and plant production are also sometimes used in place of biomass.]

Traditionally, scientists collect vegetation biomass data in the field by walking through a sample area and making measurements. These sample measurements can then be extrapolated using mathematical models to create measurements for the entire environment. But this manual approach is incredibly time-consuming and expensive, not to mention potentially bad for the environment, since it requires researchers to walk through the area on foot as they collect data.

To solve the data collection problem, scientists have honed a new approach: using drones equipped with LiDAR sensors.

By using UAVs (Uncrewed Aerial Vehicles, also known as drones), researchers can fly over sample areas and collect detailed LiDAR data in a fraction of the time it would take to do so manually. This data can then be processed using mathematical models to estimate the total biomass for the entire system.

The UAV LiDAR approach has several benefits, including:

  • Speed. The approach is much faster than mapping and making vegetation biomass estimates via manual data collection. 
  • Accuracy. Using drone LiDAR data instead of manually-collected data improves the accuracy of outputs such as tree height, leaf area density, and—the key data point—biomass.
  • It’s better for the environment. This approach also minimizes the negative impact data collection can have on the environment, since the drone flies above the vegetation, avoiding the need for people to walk through it on the ground.

So far, the UAV LiDAR approach to mapping vegetation and making biomass estimates has primarily been used in forests, focusing solely on the biomass of trees. But there are other important types of ecosystems that contribute to the planet’s carbon cycles, such as the tropical savanna found in Brazil, called the Cerrado.

The Cerrado is the second largest habitat in South America, and a crucial environment for the global carbon cycle. The team of scientists decided to test UAV LiDAR there for vegetation mapping and biomass calculation, presenting one of the first times the approach has ever been used to study a tropical savanna habitat.

Keep reading to learn how the team adapted its UAV LiDAR data collection methods for the unique environments found in the tropical savanna, and whether the approach was a success.

Why Mapping the Savanna Is So Important

Although rain forests are often the focus when we talk about carbon capture and climate change, tropical savannas make up 20% of the Earth’s surface and also play a key role in the carbon cycle.

In recent years, these savannas have lost a huge amount of vegetation due to human encroachment and increases in fires caused by climate change. In Brazil, for example, the Cerrado has lost almost half of its original vegetation over the last few decades alone, a loss that can primarily be attributed to the growth in agricultural production in the area.

Although previous studies have highlighted the benefits of using UAV LiDAR for estimating biomass in forests by focusing on trees, most of the biomass in tropical savannas comes from things like grass, dead leaves, and plant material on the ground, all of which can have a big impact on the amount of carbon stored in the ecosystem.

To inform policymakers in developing strategies for carbon markets, it’s important to understand how the environment naturally captures and stores carbon, and how much of this is happening in different types of environments across the planet.

This information is crucial for reducing carbon emissions—and that’s why mapping the vegetation biomass in the Cerrado was a point of focus for the team of scientists. If they could develop an approach that worked there it could potentially be applied to other tropical savannas, presenting a major step forward in humanity’s understanding of the global carbon cycle.

How Scientists Used UAV LiDAR to Map the Cerrado 

Scientists had already established a method for mapping large areas of forests using UAV LiDAR. The approach involved collecting data in a sample area by drone, then using mathematical models to extrapolate the biomass for the entire environment. But the Cerrado presented a new environment, which meant new models would have to be developed. 

The end goal for the team was to estimate and map the total aboveground biomass density (AGBt) of woody, shrubs, and surface vegetation found in the Brazilian savanna—an ambitious endeavor given that the Cerrado spans over two million square kilometers.

Here is the approach they planned to use:

  1. Identify types of vegetation for mapping. Three major types of vegetation were identified in the tropical savanna: forest, savanna, and grassland.
  2. Develop a framework. Given that this type of environment hadn’t been mapped with UAV LiDAR before, new frameworks were needed that would allow the scientists to choose the best UAV-LiDAR metrics for building AGBt models.
  3. Identify areas for data collection. Four locations in the Cerrado biome were selected for data collection, each of which had unique vegetation structures and was representative of different types of vegetation found there in terms of height, width, and species diversity.
  4. Take measurements. Using UAV LiDAR, scientists would collect measurements at each of the four locations.
  5. Process the data and make conclusions. After collecting the data, scientists planned to process it using the frameworks and models they had developed. They hoped to make findings regarding the vegetation biomass in the Cerrado, as well as evaluate the UAV LiDAR approach itself to see if it could be used to map other tropical savannas.
Applying High-Resolution UAV LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock Forest System

This whitepaper delves into the innovative use of high-density LiDAR data and Quantitative Structure Modelling (QSM) to estimate individual tree attributes, traditionally predicted by field-derived allometric models. Leveraging LiDAR data collected by Unmanned Aerial Vehicles (UAVs), we evaluated the accuracy of QSM in determining key tree metrics such as diameter at breast height (dbh), tree height, volume, and aboveground biomass components (stem, branch, and total).

Our study compares two QSM approaches: integrating QSM-derived dbh and height into field-based equations for volume estimation, and deriving tree volume directly from QSM. Despite a slight overestimation tendency, the models demonstrated satisfactory performance, highlighting QSM’s potential to provide detailed and extensive tree attribute estimates.

This method offers a promising alternative for forest management decision-making, especially in analyzing tree architecture and biomass. The findings underscore the value of UAV-LiDAR and QSM in enhancing the precision and scope of forest attribute assessments.

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

This whitepaper presents a novel approach to forest inventory within integrated crop-livestock-forest systems using high-density UAV-LiDAR point clouds. Focusing on Eucalyptus benthamii seed forest plantations, we utilized the GatorEye UAV-LiDAR system to compare two forest inventory methods: Sampling Forest Inventory (SFI) with various plot arrangements and Individual Tree Detection (ITD).

By analyzing a point cloud with over 1400 points per square meter, we assessed basal area and volume estimates using both field and LiDAR-measured heights. We compared the number of trees, basal area, and volume per hectare across different scenarios, using statistical analysis to evaluate accuracy and equivalence. Our results show that the SFI approach with a 2300 m² area provides estimates comparable to the ITD method, with minimal error and improved processing efficiency.

This study offers valuable insights for selecting optimal plot sizes in forest inventories, enhancing precision in integrated crop-livestock-forest systems.

Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

This whitepaper explores a groundbreaking framework for quantifying fuel load in fire-prone regions, focusing on the Brazilian tropical savanna (Cerrado biome), using NASA’s GEDI full-waveform LiDAR sensor. Understanding fuel load is crucial for integrated fire management, preserving carbon stock, biodiversity, and ecosystem functioning, and assessing global climate regulation. Traditional remote sensing methods lack the capability to measure vertical vegetation structure accurately.

Our study leverages UAV-collected LiDAR data to simulate GEDI full-waveforms, from which we derive vegetation structure metrics. These metrics are then correlated with field-measured fuel load components using Random Forest models. The resulting models, which predict woody and total fuel loads with high accuracy (R² = 0.88 and 0.71, respectively), provide reliable estimates even for lower strata components.

This innovative approach allows for the creation of fuel load maps for the entire Cerrado and can be extended to other fire-prone regions, enhancing fire management and carbon monitoring efforts. This research showcases the potential of spaceborne LiDAR to revolutionize environmental management and climate initiatives in tropical savannas and beyond.

Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners

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.

Using high-density UAV-Lidar for deriving tree height of Araucaria Angustifolia in an Urban Atlantic Rain Forest

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.

Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

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.

Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and LiDAR fusion

This whitepaper investigates the innovative use of UAV-borne LiDAR and hyperspectral 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.