Work

Assessing the Application of Mobile Light Detection and Ranging in Complex Mixed-Species Forest Inventory

Year: 2026

Type: article

Abstract: Understanding forest dynamics requires reliable inventories that assess tree- and stand-level characteristics. Traditionally, this has relied on field measurements such as diameter at breast height (DBH), height, and crown attributes, but these methods are labor-intensive and spatially limited. Remote sensing, particularly Light Detection and Ranging (LiDAR), has expanded forest inventory capacity by generating three-dimensional structural information. Mobile laser scanning (MLS), a recent adaptation, offers flexible, high-resolution data collection, though its performance across complex forests is still being evaluated. This study assessed the effectiveness of MLS in detecting individual trees and estimating DBH in mixed-species forests of the Northeastern United States. We also evaluated the influence of tree- and plot-level characteristics on detection accuracy and DBH estimation. Results showed an 85.2% tree detection rate, a 23.5% commission rate, and a DBH root mean square error (RMSE) of 1.98 cm (9.65%). Among the variables tested, tree DBH was the only significant predictor of detection probability; tree density and relative density had minimal effect. These findings demonstrate that MLS can achieve precise DBH estimation when trees are correctly identified, but false detections remain a limitation. Further methodological improvements are needed to enhance accuracy in structurally complex forests and advance MLS for operational forest monitoring.

Source: Remote Sensing

DOI: 10.3390/rs18091382

Authors: Hunter Moore, Mark J. Ducey, Benjamin T. Fraser, Olivia Fraser

Institutions: University of New Hampshire, US Forest Service

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,258