Novo algoritmo para detecção automática e mensuração de alturas de árvores individuais em plantios florestais usando scanner a laser móvel

Abstract

Measuring the total height (H) of trees in forest plantations is crucial for several reasons. Tree height is a key variable in calculating tree volume and biomass. Accurate height measurements, combined with diameter at breast height (D) data, allow for accurate estimates of individual tree volume and, by extension, total volume and biomass of the plantation. This is essential for assessing timber yield, carbon sequestration potential, and overall forest productivity. The height distribution within a plantation reflects stand density and structure. Accurate measurements of tree height are vital for the economic assessment of the plantation. Knowing volume and biomass allows for more accurate estimates of timber value and potential plantation revenue. In the context of climate change, accurate estimates of forest biomass (influenced by height) are necessary for carbon accounting and monitoring carbon sequestration efforts. In summary, accurate measurement of tree height (H) is not merely a component of forest inventory; it is an integral part of the overall forestry inventory. is a fundamental parameter that underpins many crucial aspects of forest management, from economic assessment to ecological assessments and sustainable resource planning. This work presents an algorithm for measuring the total height (H) of trees in forest plantations efficiently and accurately using Mobile Laser Scanner (MLS) data. In the first part of the paper, the algorithm focuses on detecting individual tree trunks in plantations. It employs DBSCAN and RANSAC methods for accurate detection, achieving 100% accuracy under ideal conditions and approximately 96% in challenging scenarios. The efficiency of the algorithm was evaluated on various computer configurations. In the second part of the paper, the algorithm measures the total height (H) of trees found in the plantation by its trunk identification method. The algorithm achieved significant accuracy, particularly in the challenge of accurately measuring shorter measurements obtained using a tape measure and a total station, demonstrating superior accuracy compared to the algorithms used in the experiments (TreeLS and 3DFin), especially for shorter trees. The study also analyzes the error distribution for each method, and the proposed algorithm stands out by presenting a more normal and less skewed error distribution than the other algorithms. Although slightly slower than 3DFin, its improved accuracy makes it a valuable tool for forest inventory. Areas for future improvements include processing speed and handling for processing low-density point clouds.