Point Clouds and Tree Metrics: Flying on Top of Trees with Remote Sensing

Data collection in forest inventory has come a long way. From measuring tree manually in the field to using satellite imagery, humans have always strived to make this process easier and faster. Field trips inside forests can be enjoyable, but they are also intensive and sometimes dangerous due to wildlife and unpredictable weather conditions. Consequently, foresters and researchers are constantly seeking better, non-invasive ways to conduct forest inventories.

This is where remote sensing comes into play. Remote sensing is the art of obtaining information about an object using satellites or aircraft equipped with sensors, without the need for physical contact. Have you seen drone shots where a person can stand on a surface, zooms out with a wide-angle lens, and captures a large area as the drone flies further? This is an example of remote sensing.

Remote sensing comes in various forms, from taking photos to collecting points for data analysis. Many satellites collect series of images of land surface, which can then be studied side by side to compare areas of interest. For example, if you want to see how the number of houses in your area has increased, you can compare photos from two different time periods, say, 20 years ago and today, and clearly observe the changes. With some tweaks and software, you can even extract numerical data from these images.

Similarly, in forests, drones equipped with sensors or cameras can collect information about trees. This is especially useful in areas that are hard to reach or cover large expanses. One such drone is called a UAV, which stands for Unmanned Aerial Vehicle. On its own, UAV isn’t very useful, but when equipped with a sensor or camera, it becomes a power tool.

There are many types of sensors available, thanks to advances in technology. One of these is LiDAR (Light Detection and Ranging), an active sensor that shoots laser toward the ground. Active sensors generate their own light for reflection, whereas passive sensors rely on naturally occurring light sources like the sun or moon.

So, how does LiDAR work?

LiDAR works by emitting laser pulses toward the ground and measuring the time taken for the light to return to the sensor. Since the laser hits multiple surfaces along its path, it collects reflections from every object it encounters. Imagine a tree with several branches. A LiDAR sensor emits laser pulses that hit the top of the tree, then branches underneath, shrubs, and the ground. LiDAR collects these reflected pulses and generates point clouds.

Point clouds consist of x, y, and z coordinates, representing the 3D location of each point. From these data, we can determine a tree’s height, the position of its highest branch and lowest branches, its diameter, and much more. Foresters use point clouds to extract information about individual trees and entire forests for forest management planning, tree measurement, and detecting changes over time.

Higher density point clouds provide more detailed information, but they take longer to process and require more storage space. Think of it as having too many high-resolution photos on your phone, it slows down your phone and takes up space. Conversely, lower density point clouds are quicker to process and easier to store but may not provide enough data for accurate analysis.

This raises the question: how many points are needed to get accurate data while saving time and money?

Drones, those with sensors, are expensive. Collecting many LiDAR points can be costly, and fewer points may not provide sufficient information. Other remote sensing methods, like photographs, complement LiDAR but may not always be available.

Therefore, finding a sweet spot for point density is crucial, enough points to measure key metrics accurately, but not so many that it wastes time or money. LiDAR point clouds are particularly powerful for forests: they allow us to measure tree height, diameter, crown cover (the leafy top area), and other forest metrics, such as tree density per area.

Finding tree metrics like height, diameter, crown cover, and stem count is super useful for forest thinning. By knowing these metrics, foresters and landowners can make informed decisions about how much to thin, which trees to remove, and which to keep. This helps maximize profit from harvested timber while maintaining forest sustainability, because thinning too much or too little can hurt the health of the forest. Basically, having accurate tree data lets you strike a balance between economic benefits and ecological responsibility.

Additionally, optimal level of LiDAR point clouds can also help identify tree species by pattern recognition and provide information about the number of stems in a tree.

In conclusion, research on the optimal LiDAR point cloud density for forest inventory is urgently needed. Accurate, efficient point clouds can reduce costs, speed up data collection, and help digitize the forest sector. This knowledge can improve forest management techniques, such as thinning, by providing precise measurements for before and after comparisons.