![]() “We developed a new individual tree segmentation algorithm that can be used to do tree inventory for large areas,” said article co-author Jinha Jung, assistant professor of civil engineering. This initiative, one of the five strategic investments in Purdue’s Next Moves, leverages digital technology and multidisciplinary expertise to measure, monitor and manage urban and rural forests to maximize social, economic and ecological benefits. The work was partially supported by Purdue’s Integrated Digital Forestry Initiative. (Purdue University photo/Joshua Carpenter) The results of the algorithm (right) use color to segment each tree from the point cloud. The input data (left) is colored by elevation. ![]() This image shows the input and output data of the tree segmentation algorithm. It also could lead to making digital twins of forests, which could improve management planning in the face of climate change, disease outbreaks and population growth. The approach means the difference between mapping a few trees to mapping hundreds of acres at a time quickly and with high accuracy. So, we find the shortest route for tree nutrients from the canopy down to the ground.”Ĭarpenter and four Purdue co-authors published the details of their mapping methods recently in the journal Remote Sensing. “Every leaf in a tree needs to be supplied with nutrients, and nutrients come from the ground. The concept also works from a plant biology standpoint. “If I could somehow treat all of the points in this point cloud like a path of least resistance, that will tell me something about where the tree is located,” Carpenter said. That led him to think the same way of his digital forest data, or point cloud. “When lightning travels from the sky to the ground, it finds the path of least resistance through the atmosphere,” said Joshua Carpenter, a PhD student in Purdue’s Lyles School of Civil Engineering. ![]() How lightning travels from the sky to the ground inspired the concept behind a new algorithmic approach to digitally separate individual trees from their forests in automatic forest mapping. ![]()
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