Estimation of breast height diameter and trunk curvature with linear and single-photon LiDARs

Abstract

Key message New technologies can take us towards real precision forestry: the terrestrial single-photon avalanche diode (SPAD) light detection and ranging (LiDAR) has a great potential to outperform conventional linear mode LiDARs in measuring tree parameters at the stand level. Context Precision forestry together with new sensor technologies implies Digital Forest Inventories for estimation of volume and quality of trees in a stand. Aims This study compared commercial LiDAR, new prototype SPAD LiDAR, and manual methods for measuring tree quality attributes, i.e., diameter at breast height (DBH) and trunk curvature in the forest stand. Methods We measured 7 Scots pine trees (Pinus sylvestris) with commercial LiDAR (Zeb Horizon by GeoSLAM), prototype SPAD LiDAR, and manual devices. We compared manual measurements to the DBH and curvature values estimated based on LiDAR data. We also scanned a densely branched Picea abies to compare penetrability of the LiDARs and detectability of the obstructed trunk. Results The DBH values deviated 1–3 cm correlating to the specified accuracies of the employed devices, showing close to acceptable results. The curvature values deviated 1–6 cm implying distorted range measurements from the top part of the trunks and inaccurate manual measurement method, leaving space for improvement. The most important finding was that the SPAD LiDAR outperformed conventional LiDAR in detecting tree stem of the densely branched spruce. Conclusion These results represent preliminary but clear evidence that LiDAR technologies are already close to acceptable level in DBH measurements, but not yet satisfactory for curvature measurements. In addition, terrestrial SPAD LiDAR has a great potential to outperform conventional LiDARs in forest measurements of densely branched trees.

Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

“…Currently, harvesters are not capable of recording nonharvested trees, and therefore we were not able to observe the possible mismatches between the harvester and ALS data. Close-range remote sensing solutions, which are capable of measuring tree-level attributes rapidly and accurately in forest stands (Ahola et al 2021), could open possibilities to also keep track on trees left out of harvest operations.…”

Understanding the dynamic properties of trees using the motions constructed from multi-beam flash light detection and ranging measurements

“…The LiDAR sensor is supported by automated data processing, which facilitates the measurements over a large area. To date, the LiDAR sensor has been developed for forest inventory studies [25][26][27], including stem mapping [28], tree height measurement [29], leaf area and density estimation [30], diameter estimation of the trunk or branches [31], and canopy porosity and crown surface estimation [32], which are useful for assessing the biomass and carbon sink of trees [33,34]. Recently, Puttonen et al [35] developed a clustering-based method for quantifying branch motions from LiDAR measurements.…”