Effect of flying altitude, scanning angle and scanning mode on the accuracy of ALS based forest inventory

Airborne laser scanning (ALS) is a widely used technology in the mapping of environment and forests. Data acquisition costs and the accuracy of the forest inventory are closely dependent on some extrinsic parameters of the ALS survey. These parameters have been assessed in numerous studies about a decade ago, but since then ALS devices have developed and it is possible that previous findings do not hold true with newer technology. That is why, the effect of flying altitudes (2000, 2500 or 3000 m), scanning angles (+/- 15 degrees and +/- 20 degrees off nadir) and scanning modes (single- and multiple pulses in air) with the area-based approach using a Leica ALS70HA-laser scanner was studied here. The study was conducted in a managed pine-dominated forest area in Finland, where eight separate discrete-return ALS data were acquired. The comparison of datasets was based on the bootstrap approach with 5-fold cross validation. Results indicated that the narrower scanning angle (+/- 15 degrees i.e. 30 degrees) led to slightly more accurate estimates of plot volume (RMSE%: 21-24 vs. 22.5-25) and mean height (RMSE%: 8.5-11 vs. 9-12). We also tested the use case where the models are constructed using one data and then applied to other data gathered with different parameters. The most accurate models were identified using the bootstrap approach and applied to different datasets with and without refitting. The bias increased without refitting the models (bias%: volume 0 +/- 10, mean height 0 +/- 3), but in most cases the results did not differ much in terms of RMSE%. This confirms previous observations that models should only be used for datasets collected under similar data acquisition conditions. We also calculated the proportions of echoes as a function of height for different echo categories. This indicated that the accuracy of the inventory is affected more by the height distribution than the proportions of echo categories. (C) 2016 Elsevier B.V. All rights reserved.