Despite thorough preparations, flying the drone is still nervewracking.
Dr. Manoela Machado, a Research Scientist at Woodwell Climate, has double- and triple- checked her calculated flight path over a study plot in the Cerrado, Brazil’s natural savanna. The drone can essentially fly itself, and she’ll be monitoring its speed, altitude, and battery life from her handheld controller on the ground, but many things could still go wrong. High winds, an unforeseen obstruction, loss of connectivity— all could jeopardize the mission, potentially dropping the expensive equipment 40 meters into the woodland canopy below.
Aboard Machado’s drone sits a powerful piece of technology – a LiDAR sensor. Developed originally for use in meteorology, this remote sensing technique now has widespread applications across scientific fields, from archaeology, to urban planning, to climate science. At Woodwell Climate, Machado and others employ LiDAR to create detailed three dimensional models of landscapes, which provide valuable insight into the structure of ecosystems and the amount of carbon stored in them— all with just a few (million) pulses of light.
What is LiDAR?
LiDAR stands for Light Detection and Ranging. Put simply, it is a sensor that uses laser light to measure distance.
Similar to other technologies like sonar and radar, which use sound and radio waves, respectively, LiDAR is an example of an “active” sensor. “Passive” sensors like cameras collect ambient light, while LiDAR actively pings the environment with beams of laser light and records the time those beams take to bounce back. The longer the return time, the further away an object is. That distance measurement is then used to calculate the precise location in three-dimensional space for each reflection.
This process is repeated millions of times during a single scan, resulting in a dense cloud of point locations. With some advanced computing, the data can be assembled into a 3D picture of the landscape.
“It’s effectively three dimensional pointillism,” says Woodwell Climate Chief Scientific Officer, Dr. Wayne Walker, who has been using LiDAR in his studies for 25 years.
Far more detailed than an oil painting however, a LiDAR model can reconstruct nearly every leaf, twig, and anthill on a landscape.
“Once you construct that cloud of millions of points, you get to walk inside the forest again,” says Machado. “When you finish processing the data and see the cloud you go, ‘I remember that tree! I remember standing there!’ It’s mesmerizing.”
For a project like Machado’s, scanning a few dozen hectares, the sensor is usually placed on a drone. Larger study areas require sensors mounted on low-flying airplanes or even satellites, but for small ground-based applications there are sensors one can carry, mount on a tripod, or attach to a backpack. Some newer phone models even have LIDAR apps built in. Regardless of how LIDAR is deployed, it remains a straightforward method of data collection. Just point the sensor at what you want to scan and within minutes, you’ve captured the data for a detailed three-dimensional model of your area of interest.
Estimating the weight of a forest
What Machado and Walker are often after from a LiDAR scan is a measurement of biomass, or the total weight of the organic matter present in an ecosystem. Plants store carbon in the form of organic matter, so biomass measurements are an easy way to estimate an area’s carbon storage.
However, measuring a forest’s biomass directly would require cutting down all the trees, drying them out, and weighing what’s left — impractical and needlessly destructive— so scientists use proxy measurements. Walker likens the process to trying to estimate the weight of a human without access to a scale.
“What are the measurements you might use if you couldn’t actually physically measure weight? You might record height, waist size, inseam, and if you obtain enough of these measurements you can start to build a model that relates them to weight,” says Walker. “That’s what we’re trying to do when we estimate the biomass of an entire forest.”
Raw LiDAR data is only a measurement of distance, but by classifying each point based on its location relative to the cloud, researchers are able to extract the proxy measurements needed to model biomass across the ecosystem. Before LiDAR, these proxy measurements— things like trunk diameter, height, and tree species— had to be recorded entirely by hand, which limits data collection based on human time and resources. The time-saving addition of LiDAR has vastly expanded the possible scale of study plots. While field measurements are still essential to calibrate models, LiDAR is one of the only technologies that can give scientists enough detail and scope to assess carbon stocks over entire ecosystems.
“There is no other kind of sensor that even comes close to LiDAR,” says Walker.
The power and potential of LiDAR
At Woodwell Climate, researchers have employed the power of LiDAR to map biomass and carbon from Brazilian forests, to the Arctic tundra. Outside of the Center, the technology has found applications in archaeological surveys, lane detection for self-driving cars, and topographical mapping down to a resolution of half a meter.
But the detail that makes LiDAR so powerful can also make the data a challenge to work with. A single scan produces millions of data points. According to Geospatial Analyst and Research Associate, Emily Sturdivant, who analyzed LiDAR data for Woodwell’s Climate Smart Martha’s Vineyard project, that wealth of data often overwhelms our ability to extract the full potential of information available in one point cloud.
“LiDAR creates so much data that when you look at it, it’s hard not to be blown away imagining all the different things you could do with it. But then reality kicks in,” says Sturdivant. “It’s challenging to take full advantage of all those points with our current processing power. It’s a matter of the analysis technology catching up with the data.”
Processing LiDAR data requires large amounts of computing time and storage space, especially when performing more complex analyses like segmenting the data on the scale of individual trees. As machine learning and cloud computing technologies advance however, this becomes less of an obstacle, and the potential insights from LiDAR datasets will advance along with them.
LiDAR can be an expensive endeavor, too. Drones with the right equipment can cost tens of thousands of dollars, as can hiring a plane and pilot and paying for jet fuel, so data sharing has been important in making the method more cost effective. U.S. government agencies like NASA and the USGS have facilitated the collection of LiDAR data through satellites and plane flights, making the data available for public use. Woodwell Climate research has benefitted from these public datasets, using them to inform landscape studies and carbon flux models.
According to Sturdivant, the reliable production of public data has been greatly beneficial to advancing LiDAR-based studies, though it now faces risks from federal cuts to science agency funding.
“One of the greatest advantages of having publicly supported data is the consistency, but that’s exactly what’s now at risk,” says Sturdivant. “Public accessibility has been so important in allowing new scientists to learn and experiment and then help everyone else learn.”
Each new LiDAR scan represents a trove of information that could be used to better understand our changing planet, making it critical to continue supporting and collecting LiDAR data. Its intensely visual and highly detailed nature has made it one of the most powerful tools we have for understanding something as complex as a forest.
“And on top of that,” says Machado “It’s just visually beautiful.”
Under the thick forest of Mexico’s Yucatán Peninsula, the ancient ruins of a Maya City have been uncovered with the use of remote sensing.
Of course, that wasn’t the outcome that Woodwell Climate’s Chief Scientific Officer, Dr. Wayne Walker, anticipated when he and his team collected and processed the remote sensing dataset for an unrelated project nearly a decade ago.
Walker’s team was mapping the region as part of the Mexico REDD+ project, a collaborative, international effort to explore strategies for reducing emissions from deforestation and degradation in the country. Using a remote sensing technology called LiDAR, which scans terrain from a low-flying plane using pulses of laser light, Walker and project collaborators created a comprehensive map of forests—and the carbon they contain—across Mexico.
Walker and team coordinated the flights and processed the raw data for use in the project, uploading it afterwards to a website for public use. But, once the project ended, he all but forgot about the effort, apart from responding occasionally to researchers interested in downloading the dataset for their own work.
One of those researchers was Luke Auld-Thomas, a PhD candidate at Tulane University researching the Classic Maya civilization, which thrived in the Yucatan until the 9th century when much of the region was abandoned, though their culture and languages persist to this day. Because of its unique ability to provide a detailed three-dimensional picture of whatever features are present on the ground, LiDAR imagery is an incredibly powerful tool for a multitude of purposes, from climate science to archaeology. And while the Mexico REDD+ project was interested in documenting the forests, Auld-Thomas was interested in what might be hidden beneath them.
“One scientist’s noise is another’s entire field of study,” says Walker. “In our other projects, like Climate Smart Martha’s Vineyard, we see historical structures like stone walls that aren’t necessarily meaningful to our work but could be of interest to archaeologists.”
In Mexico, the large areas surveyed by Woodwell Climate revealed not just individual human-built structures, but the plazas, reservoirs, and ball courts of an entire, previously undocumented city. The discovery, published in the journal Antiquity, supported the theory that the region was, in fact, densely settled during the height of Classic Maya civilization.
“We knew that it was close to a lot of interesting sites and settlements— areas of large-scale landscape modification that had been mapped and studied— but none of the survey areas themselves were actually places that archeologists ever worked, making it a really exciting sample to work with,” said Auld-Thomas.
Auld-Thomas had specifically been on the hunt for a pre-existing LiDAR dataset like the one Walker helped create— a survey conducted for completely non-archaeological purposes and therefore free of any biases. Essentially a “random sample” of the region. That randomness, and the subsequent discovery of an entire city, allowed Auld-Thomas and his colleagues to more strongly argue their point about intense urbanization in the Yucatán.
“If you’re only going to places where you know there’s going to be something, then of course, you’re going to find something significant, right? These random samples, not collected for archeological purposes, are gold in some respects,” said Dr. Marcello Canuto, who co-authored the paper. Canuto directs the Middle American Research Institute at Tulane, where the research for this study was conducted.
The unexpected outcome of the LiDAR survey offers a textbook example of the value of open data access. Sharing data and resources both within and between fields of science can jumpstart discovery and distribute the costs of an otherwise expensive data collection effort.
“Just look at what came out of the moonshot,” says Canuto. Thousands of technologies, developed in humanity’s pursuit of the moon landing, have found unforeseen applications in today’s world— including LiDAR.
“Certainly, many of us have produced datasets that have led to incremental advances in closely related fields,” says Walker. “But here is a special case of open source data advancing discovery in an entirely unrelated field of study.”
Advancements across fields continue to better our understanding of the world around us. And the lessons learned from a civilization like the Maya have very real parallels to today’s climate crisis.
As Auld-Thomas and Canuto show, the Maya densely settled the Yucatán Peninsula, maxing out the capacity of the surrounding environment to support their population. And then the regional climate shifted. A long-term drought settled in, resources became scarcer, governments became unstable, people started leaving the cities, and the infrastructure of the larger civilization collapsed.
“The reason environmental scientists collect LiDAR data of the forest, is that they are trying to understand environmental processes in order to help human societies conserve the landscape,” says Auld-Thomas. “As archaeologists, we try to understand how people in these exact environmental contexts have confronted deforestation and climate change and all of these other things before.”
For Canuto, the lesson to be learned lies not just in the environmental perils, but in the societal ones. Because what complex societies hate— be they the Classic Maya or today’s modern culture— is a lack of predictability. If a system cannot adapt, it will fail.
“The collapse was more than just climate change,” says Canuto. “It was a failure of a political system to respond to climate change.”