A low-cost MLS prototype for voxel-based above-ground biomass estimation in short-rotation plantations - Skladan, Michal; Singh, Arunima; Chudá, Juliana; Lieskovský, Martin; Masný, Matej; Vyboštok, Jozef (Frontiers in Remote Sensing, 2025)
Short-rotation plantations of fast-growing trees (FGT) offer a sustainable biomass source to mitigate climate change and boost rural energy self-sufficiency. Accurate estimation of woody above-ground biomass (AGB) is critical for efficient management and utilization of these plantations. This study evaluates modern mobile laser scanning (MLS) techniques for dry-weight AGB estimation, comparing a commercial MLS system with a low-cost prototype built on the Livox Mid-360 sensor. Research was carried out in a dense, second-rotation poplar clone plantation. Thirty-one research plots were scanned using both MLS setups, then harvested and oven-dried to obtain reference dry weights. Point clouds were processed via a voxel-based approach at four resolutions (5, 10, 15 and 20 cm) to develop regression models correlating total voxel volume with dry biomass. The low-cost prototype delivered its best performance at 5 cm voxel size (R2 = 0.84; rRMSE = 12.2%), markedly outperforming the commercial system at the same resolution (R2 = 0.68; rRMSE = 17.5%). The commercial MLS achieved its optimum at 20 cm voxels (R2 = 0.82; rRMSE = 12.9%). Predictive models were validated using 16 plots for training and 15 for testing. The prototype yielded the highest precision for dry weight prediction (R2 = 0.89; rRMSE = 12.9%) at 5 cm resolution, while the commercial MLS excelled in fresh-weight estimation at 15 cm resolution (R2 = 0.92; rRMSE = 12.0%). These results demonstrate that affordable MLS solutions can provide biomass estimates comparable to those of higher-cost systems for dry AGB assessment in high-density poplar stands. Implementing low-cost laser scanning improves monitoring frequency, reduces operational expenses, and enables large-scale application in short-rotation forestry. This approach supports evidence-based decision-making for sustainable bioenergy production. Future work will explore integrating multispectral data and automated processing pipelines to further enhance biomass estimation accuracy and scalability across diverse forest conditions.