Background The LiBackpack is a recently developed backpack light detection and ranging (LiDAR) system that combines the flexibility of human walking with the nearby measurement in all directions to provide a novel and efficient approach to LiDAR remote sensing, especially useful for forest structure inventory. However, the measurement accuracy and error sources have not been systematically explored for this system. Method In this study, we used the LiBackpack D-50 system to measure the diameter at breast height (DBH) for a Pinus sylvestris tree population in the Saihanba National Forest Park of China, and estimated the accuracy of LiBackpack measurements of DBH based on comparisons with manually measured DBH values in the field. We determined the optimal vertical slice thickness of the point cloud sample for achieving the most stable and accurate LiBackpack measurements of DBH for this tree species, and explored the effects of different factors on the measurement error. Result 1) A vertical thickness of 30 cm for the point cloud sample slice provided the highest fitting accuracy (adjusted R2 = 0.89, Root Mean Squared Error (RMSE) = 20.85 mm); 2) the point cloud density had a significant negative, logarithmic relationship with measurement error of DBH and it explained 35.1% of the measurement error; 3) the LiBackpack measurements of DBH were generally smaller than the manually measured values, and the corresponding measurement errors increased for larger trees; and 4) by considering the effect of the point cloud density correction, a transitional model can be fitted to approximate field measured DBH using LiBackpack- scanned value with satisfactory accuracy (adjusted R2 = 0.920; RMSE = 14.77 mm), and decrease the predicting error by 29.2%. Our study confirmed the reliability of the novel LiBackpack system in accurate forestry inventory, set up a useful transitional model between scanning data and the traditional manual-measured data specifically for P. sylvestris, and implied the applicable substitution of this new approach for more species, with necessary parameter calibration.

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.

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