Assessing Trunk Dimensions of Complex, Large-Diameter Trees Using Mobile LiDAR: A Case Study of Giant Sequoia

Large trees are declining worldwide. For giant sequoia (Sequoiadendron giganteum), extreme wildfire events present an increasing risk. Recent research suggests that the relative size of burn scars predicts the probability of a tree-killing fire. We proposed a vulnerability index comparing the ratio of burn scar width to tree circumference. However, accurately measuring large tree circumference can be challenging using existing inventory protocols. This paper tested the feasibility of using a mobile device equipped with a light detection and ranging (LiDAR) sensor to measure circumference. We evaluated existing methods to measure large tree circumferences, namely the traditional tape method (TTM) and a LiDAR-based mobile app (ForestScanner). We developed a tailored LiDAR method (TLM) and validated it against a specialized tape method (STM) designed to accurately measure giant sequoia. Our results confirmed that the TTM and ForestScanner did not measure large giant sequoia circumference with sufficient skill. For TTM, 28% of the DBH measurements were biologically unrealistic; for ForestScanner, the DBH measurements grossly underestimated the STM DBH (mean percentage error = -72.5%). The TLM results only slightly overestimated the STM DBH (mean percentage error = 1.2%). The proposed vulnerability index can guide protection and improve sustainability of large giant sequoia in this forest.