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:
- Identify types of vegetation for mapping. Three major types of vegetation were identified in the tropical savanna: forest, savanna, and grassland.
- 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.
- 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.
- Take measurements. Using UAV LiDAR, scientists would collect measurements at each of the four locations.
- 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.
