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Your Smartphone can be a Tree-Hugger 

Your Smartphone can be a Tree-Hugger 

Technology News |
By Wisse Hettinga


Phone-based measurements provide fast, accurate information about the health of forests

That super smart measurement tool on your smartphone, LiDAR, is not alone for gaming or augment reality purposes. Researchers from the University of Cambridge developed an algorithm, which gives an accurate measurement of tree diameter, an important measurement used by scientists to monitor forest health and levels of carbon sequestration.

This measurement can be do manually, easy; you hug the tree, take the measure lint around it and write down the result…but is takes time! A phone with LiDAR can do the same trick, but really fast and with the same accuracy. The algorithm uses low-cost, low-resolution LiDAR sensors that are incorporated into many mobile phones, and provides results that are just as accurate, but much faster, than manual measurement techniques.

The primary manual measurement used in forest ecology is tree diameter at chest height. These measurements are used to make determinations about the health of trees and the wider forest ecosystem, as well as how much carbon is being sequestered.

“When you’re trying to figure out how much carbon a forest is sequestering, these ground-based measurements are hugely valuable, but also time-consuming,” said first author Amelia Holcomb from Cambridge’s Department of Computer Science and Technology. “We wanted to know whether we could automate this process.”

“We wanted to develop an algorithm that could be used in more natural forests, and that could deal with things like low-hanging branches, or trees with natural irregularities,” said Holcomb.

The researchers designed an algorithm that uses a smartphone LiDAR sensor to estimate trunk diameter automatically from a single image in realistic field conditions. The algorithm was incorporated into a custom-built app for an Android smartphone, and is able to return results in near real-time.

To develop the algorithm, the researchers first collected their own dataset by measuring trees manually and taking pictures. Using image processing and computer vision techniques, they were able to train the algorithm to differentiate trunks from large branches, determine which direction trees were leaning in, and other information that could help it refine the information about forests.

Since their measurement tool requires no specialised training and uses sensors that are already incorporated into an increasing number of phones, the researchers say that it could be an accurate, low-cost tool for forest measurement, even in complex forest conditions.

The researchers plan to make their app publicly available for Android phones later this spring.

The research was supported in part by the David Cheriton Graduate Scholarship, the Canadian National Research Council, and the Harding Distinguished Postgraduate Scholarship.

The results are reported in the journal Remote Sensing.

https://www.mdpi.com/2072-4292/15/3/772


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