New algorithm for detecting and measuring individual tree height in Eucalyptus plantations using mobile laser scanning

Eucalyptus species are widely cultivated globally, with over 22.5 million hafv planted, owing to their rapid growth, adaptability, versatility for cellulose, wood, and energy production, and their role in carbon fixation. Other species, including pine, acacia, teak, poplar, rubber tree, and birch, are also widely cultivated for various uses. Measuring tree height in plantations is fundamental for estimating volume and biomass and for assessing the plantation condition, a key variable in forest inventories. Although challenging, various techniques, including trigonometric methods and technologies such as Light Detection and Ranging (LiDAR) sensors, photogrammetry, and Mobile Laser Scanner (MLS), have been employed to obtain accurate height measurem​ents. This study presents a new algorithm for estimating the total height ( H ) of trees in forest plantations using MLS data. It incorporates characteristics of organized plantations to improve trunk search and uses the theory of natural tree taper to obtain more accurate height estimates. Validated in two study areas, it demonstrated reliability, especially for short trees, and high accuracy, achieving Root Mean Squared Error (RMSE) (%) values of 3.32% for area 1 and 3.26% for area 2. In contrast, traditional algorithms such as, TreeLS yielded RMSE (%) values of 14.70% for area 1 and 14.40% for area 2, whereas the 3DFin algorithm obtained RMSE (%) values of 12.49% for area 1 and 21.43% for area 2. Although it requires more processing time than the 3DFin algorithm, the proposed method provides more accurate results for forest inventories.