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Are you tired of sifting through endless options and specifications to find the right drone lidar sensor for your needs? Look no further! Our comprehensive selection guide takes the guesswork out of the process, helping you make informed decisions about which sensor is best suited for your project.

Our guide offers a detailed overview of the key features and benefits of various drone lidar sensors, including their range, accuracy, and data output capabilities.

In 2017, an international team of Brazilian and U.S. scientists used two UAVs, including a Phoenix LiDAR system, to monitor and analyze degraded parts of the Brazilian Savanna that had previously been surveyed with aircraft and LiDAR. Their goal: to protect the most biologically diverse savanna in the world from further human impacts, restore native species, and increase CO2 absorption to mitigate the effects of climate change, all while balancing the protection and restoration of natural resources with food production.

UAVs are already used extensively in Brazil for a number of other uses. This team gathered both physical and biological information about the vegetation cover using Phoenix LiDAR and also visual (RGB) sensors. Counting trees is a critical metric that enables them to estimate planted seedling survival rates, species density, plant spacing, etc.

This team’s objective was to demonstrate how both Phoenix LiDAR and RGB sensors helped them monitor the vegetation structure (including tree numbers and height) in the Cerrado savanna. Phoenix LiDAR’s accuracy in performing automated measurements of the number and height of the trees was superior to the RGB sensors, though RGB was also useful for identifying tree species.

Unmanned Aerial Vehicles (UAVs) have become much more useful due to concurrent technological advancements. Individual tree detection is important to managing forests, but changing from traditional data collection to UAVs to collect this data is daunting due to the huge amount of information available.

The team of international researchers wrote this article as a tutorial to help non-researchers understand the benefits of using UAVs to do research usually done using airplanes. They break down how to do Individual Tree Detection (ITD) using images and LiDAR maps from UAVs carrying Phoenix Scout LiDAR cameras, and also using open source software to process the data. The software created a canopy height model (CHM) and also performed ITD.

Because this approach is aimed at beginners to remote sensing (using drones to capture data), the methods are simplified and the tree areas studied are relatively easy to study due to relatively open canopies.

Doing this kind of work in a timely manner is important for understanding the forest’s response to climate change. Traditional study methods provide the data, but at a higher financial, time, and labor cost. Especially if there is a lot of forest to monitor. UAVs can provide the data at significant savings to all three costs.

The tutorial uses R studio software and the R programming language. 

The size of the first area studied is 11.95 out of approximately 700 hectares of the E.O. Siecke State Forest, East Texas, which is managed by the Texas A&M Forest Service. This aerial imagery was gathered in August 2020 using a DJI Mavic Pro quadcopter, using the

Pix4Dcapture flight planning app.

The size of the second area studied is 23.04 hectares of the Ordway-Swisher Forest Dynamics Plot at the Ordway-Swisher Biological Station in Florida. The station is a long-term research facility by the University of Florida and it is also part of the Global Earth Observation Network

(ForestGEO) (https://forestgeo.si.edu/). The LiDAR data were collected in June 2019 with the

GatorEye Unmanned Flying Laboratory (http://www.gatoreye.org/). This system is composed of a DJI M 600 Pro hexacopter with a Phoenix Scout Ultra core, which has a STIM300 inertial measurement unit (IMU) coupled with a differential GNSS antenna and integrates a Velodyne

Ultra Puck 32c, a 24 MP visual camera, and a Headwall Photonics Nano hyperspectral camera. The data were post-processed using the GatorEye multi-scale post-processing (GMSPP) workflow (v. 229 detailed at http://www.gatoreye.org/).

The tutorial covers the following:

Loading the data

Clipping the area of interest

Classifying ground points

Creating a DTM

Height normalization of the point cloud

Creating a CHM

“Smoothening” the CHM

Detecting the treetops

Assessing accuracy

The goal is to help lay people understand how UAVs can help researchers do their work. Reducing the costs and complexity of this kind of research by using a UAV with a Phoenix Scout Ultra LiDAR camera, instead of an airplane-mounted option, makes this type of research more accessible to other researchers.

An international team of scientists from Brazil and the University of Florida are working together to study urban forests as a means of mitigating climate change. The University of Florida’s Second Generation GatorEye UAV LiDAR system uses a Phoenix Scout Ultra system to provide scientific backing for policies to preserve the forest inventory in the urban landscape.

This team’s objective is to improve what has been a difficult and costly data gathering process by incorporating remote sensing technology, such as UAV LiDAR, and to determine the levels of point density that return the best data. Ultimately, their goal is to increase the amount of research done on a part of the urban landscape that is difficult to research, and to provide decision makers with better information on which to base policy decisions.

This study looked closely at how other studies used LiDAR in similar situations. They found LiDAR is great for measuring tree parameters and designing vegetation structure 3D maps, and it provides spatial and temporal flexibility that other methods don’t offer. The other researchers got high resolution data at significantly lower cost, and also indicated applicability for urban forests using LiDAR alone or integrated with optical images.

LiDAR is more convenient than airborne LiDAR—lower cost, more accessible transportation, higher density of points. UAV LiDAR provides models with higher resolution, even though the value of point density influence for individual tree parameters is still not understood and needs further research.

Based on other studies’ findings, combining field-based research and remote sensing data seems to be best practice, especially if high-resolution LiDAR is available.

In November 2019, this Brazilian and U.S. team performed a physical measurement of 171 Brazilian pines, including circumference, height, and geographical position, in an urban forest remnant located in Curitiba, State of Parana, southern Brazil. They then collected the same trees’ data using UAV LiDAR and compared the data.

An international team of scientists from the University of Florida (USA), University of São
Paulo (Brazil), Florida A&M University (USA), USDA Forest Service (USA), Federal University of
Paraná (Brazil), University of Maryland (USA), and Bangor University (UK) worked together to
use the University of Florida’s GatorEye unmanned aerial vehicle (UAV) LiDAR system to fight
climate change. The GatorEye system uses Phoenix LiDAR cameras and software to compare
and contrast tree crowns and forest structure over time.
When storms and hurricanes change the forest canopy structure, traditional LiDAR takes too
much time and logistical planning to quickly assess the damage. Phoenix UAV LiDAR can
autonomously and efficiently get high-resolution, quality data from relatively large areas
(hundreds to thousands of hectares) to help decision makers with post-storm recovery.
The team compared aircraft-borne LiDAR surveys of the Apalachicola National Forest, USA with
single-pass GatorEye UAV LiDAR surveys. They found the following:

● Digital Terrain Models (DTM) with less than 1 m differences between airborne and UAV
LiDAR, within a 145 degree field of view.

● Canopy height models (CHM) provided reliable information from the top layer of the
forest, allowing reliable treetop detection, though tree height underestimations occurred
at 175 m from the flightline.

● Crown segmentation was reliable only within a narrower field of view, from which the
shadowing effect made it unviable.

Because UAV LiDAR is limited by onboard battery capacity, this group is researching efficient
sampling methods using single-pass surveys that focus on samples of specific locations only.
Despite the limitations, UAV LiDAR systems cost less and are more flexible, enabling rapid
planning and response, as well as more frequent data collection and higher point density data.
Single-pass surveying has not been studied for airborne LiDAR, but using LiDAR for monitoring
forest health is well studied. Airborne LiDAR data is highly dependent on the flightline, which
determines pulse density and usually includes overlapping. Phoenix UAV LiDAR is more
efficient with a single pass, but closer to the objects being surveyed, mitigating the difference in
density. Scan angle makes a large difference, and further research is required to determine
accuracy based on scan angle.

This study included sample areas in the Apalachicola National Forest, public land in the sandhills of the Florida Panhandle that covers approximately 233,000 hectares.

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.

Understanding LiDAR

LiDAR 3D mapping is a versatile technology that unlike passive sensing methods such as photogrammetry can penetrate vegetation and operate in dark lighting situations. LiDAR is a more applicable, user-friendly technology providing data that is faster and easier to process.
Phoenix LiDAR’s aerial solutions allow you to scan area swaths faster and with more consistent results than using current ground scan technologies, whether they’re utilizing terrestrial or mobile mapping methods. We’ve created this section to help our customers and the public learn about LiDAR and what makes our solutions a unique, ground-breaking tool for a variety of applications.

The Basics of LiDAR
LiDAR, short for Light Detection and Ranging is an active, remote sensing method used for an array of applications. It uses light in the form of a pulsed laser to measure ranges (variable distances) through vegetation to the Earth. The system is able to capture accurate surface data by measuring the time it takes for the laser to return to its source.

LiDAR requires four basics parts to operate:

• The LiDAR unit itself, which emits pulses of light, when mounted to a UAV scanning a predefined swath below.
• A GPS receiver tracking the unit’s x,y,z coordinates.
• An IMU which stands for Inertial Measurement Unit that tracks the tilt of the unit in space to achieve accurate elevation measurements.
• A computer that records all transmitted data.

These light pulses and their capabilities to produce multiple returns — combined with other data recorded by a system — are processed to create highly accurate, three dimensional information about the surface it has scanned.

HOW DOES LIDAR WORK?

The technique we use to derive centimeter level precision is called Real Time Kinematic Global Navigation Satellite System (RTK GNSS). This system uses the satellite signal’s carrier wave in addition to the information content of the signal and relies on a single GNSS reference station to provide real-time corrections. Now what happens during short periods of GNSS outages? Enter the Inertial Navigation System (INS): the INS uses a computer, motion sensors (accelerometers) and rotation sensors (gyroscopes) to continuously calculate the position, orientation, and velocity of the system. In order to combine the two systems, a very sophisticated algorithm, known as a linear quadratic estimation (LQE), operates on streams of noisy sensor data to produce a statistically optimal estimate of the system’s position at any point in time. By fusing this information with the LiDAR data, a point-cloud is generated and visualized in real-time using Phoenix Aerial SpatialExplorer.

In case real-time corrections from the GNSS reference station are not available or longer outages prevent transmission of data to Rover, a third party software package called Inertial Explorer™ can still produce a precise trajectory in post-processing. Both types of trajectories (either generated in real-time from the INS or from Inertial Explorer™ in post-processing) can be fused with LiDAR data with Phoenix Aerial SpatialFuser to create point clouds in LAS format.

Phoenix Aerial LiDAR solutions are engineered to attach to almost any vehicle and for the first time, the accompanying software is just as flexible as the module. By splitting sensor control and user interface into separate parts, multiple mapping options are possible:

Aerial Mapping
Phoenix Aerial LiDAR solutions can be used for mapping with many different vehicles such as UAV’s, gyrocopters, fixed-wing aircraft, etc. As shown in the image above, the operator is typically on the ground and connected directly to the GNSS reference station. Using the Phoenix Aerial SpatialExplorer software, the operator transmits correction data to the Rover via a long range WiFi system. The Rover then fuses this data in real time and transmits a down-sampled point cloud back to the operator.

Ground Mapping
When the operator travels with the Rover in a car, boat or ATV, he/she can connect directly to the Rover using either WiFi or an ethernet cable. Correction data can then be transmitted from the GNSS reference station to SpatialExplorer software via long range WiFi or a public IP address (using e.g. 3G/4G). With the on-board 240 gig SSD hard drive, the operator can scan for 6 hours without having to stop to download the data.

Real-time Point Cloud Advantages
The ability to visualize real time point clouds brings several key advantages:

1) The operator can immediately determine if the results match the expectations. Previously, results were available only after landing, in which case it becomes very time consuming and expensive to make any changes.

2) The operator can visualize the growing point cloud on a computer screen in real time and with this data can locate areas yet to be scanned and quickly alter the UAV’s course.

3) Via 4G network, the operator can remotely share his/her screen with clients in real time to confirm/alter LiDAR point cloud.

Parameters for LiDAR Scanning Via UAV

Phoenix LiDAR Systems builds systems meant for mobile mapping. Surveying from a moving object is accompanied by certain parameters an operator must take into account: speed, scan area, altitude, frequency, pulse rate, scan angle and point density all play an integral role in capturing data. Note that you will obtain a scan swath of varying ranges and densities depending on these parameters. Actual accuracy is dependent on GPS processing options (RTK, PPK, WAAS), ionospheric conditions, satellite visibility, flight altitude (AGL) and other factors.

Phoenix LiDAR Workflow

Real-time vs Post Processed
In RTK (Real Time Kinematic) mode, about 500 bytes of differential corrections are sent from the reference station to the rover about once every second. Applying these corrections, the rover is able to solve its position with centimeter-accuracy.

The differences between RTK mode and post-processing are:

• post-processing requires extra software
• post-processing does not require a real-time connection between reference station and rover
• post-processing will often compute more accurate results, especially in environments with bad satellite visibility (ground scanning)
• post-processing allows the user to better judge the solution’s accuracy

Need More Help?

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The purpose of LiDAR

LiDAR, or light detection and ranging, is a tool used to quickly, accurately and densely measure 3D features from the world around us. 

LiDAR is a form of “remote sensing” – meaning we aren’t physically measuring things with our hands, but rather, using sensors.  To measure topography, vegetation or other data across large areas, we need remote sensing methods that can take many measurements quickly, easily, accurately.  LiDAR sensors can take hundreds of thousands of measurements each second and are represented in the form of a point cloud.

The point cloud can be used in various analytical processes to take distance measurements, compute volumes, and model objects in three dimensions.

So how does LiDAR work?

How does LiDAR measure distance? 

In addition to emitting a laser pulse, a  LiDAR system measures the time between when each pulse is sent from the system to the time it is reflected back to the sensor.

Since the speed of light is known, we can calculate distance using a simple mathematical formula (distance = speed x time).  With the help of positioning and orientation sensors within the lidar system, distance is then represented as an elevation.

Position Measurements are typically collected once every second, orientation measurements are typically collected between 125 to 400 times per second.  These positioning and orientation sensors are critical components and are integral for every aerial lidar system to precisely calculate where and when each point is collected.

In summary, a LiDAR system is an active remote sensing system, composed of components that emit the laser, receive a returned signal, and calculate the system’s position and orientation, to produce a geospatially accurate depiction of the world around us in the form of a 3D point cloud.

How Can LiDAR Measure the Ground Through Trees?

A laser pulse emits light energy as a bundle of photons.  As these photons move towards the ground, they hit objects, such as buildings, trees, and ground vegetation.  In the case of trees and vegetation, some of the photons reflect off the branch and return immediately to the sensor. 

However, some of the photons pass through gaps in the tree or vegetation, striking other features, like lower branches, or even the ground, before returning to the lidar sensor. 

In this way, multiple reflections may be recorded from one pulse of light.  In the lidar industry, this is known as multiple returns.

Some LiDAR Systems at Phoenix LiDAR Systems return two returns, some three, some four, and some even return up to 15 returns to the sensor. 

This depends on the power of the laser used in the system and a few other factors.  The result is a point cloud with a true 3D representation of both the vegetation and ground data.