Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

Abstract

Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.

Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds

“…Above mentioned studies that have compared relevant filters [22][23][24][25][26][27] used different approaches to calibrating filters with custom settings, which depends strongly on the skillset of the operator. Some omitted calibration by using default settings to minimize the subjective influence of the operator [25].…”

“…Some omitted calibration by using default settings to minimize the subjective influence of the operator [25]. However, not only are the results more accurate when filters are calibrated, but some filters are also more susceptible to calibration than others [27]. Accordingly, most studies compare calibrated filters [22,23,26].…”

“…The analyzed filters are summarized in Table 2. To our knowledge PMF, SBF, SMRF, CSF, SegBF, and BMHF were not assessed in any of the previous studies [22][23][24][25][26][27].…”

“…In the past several studies have compared relevant filters implemented in free software [22][23][24][25][26][27]. However, none of them include newer filters, none of them use a standardized comparison method by Sithole and Vosselman [20], and none of them is archaeology-specific.…”

Conditional Random Field Features and Structure Assessment for Digital Terrain Modeling

“…Further, traditional methods are based on heuristic or statistical thresholds, which might vary or lead to errors in complex terrains (e.g. forested regions) [17]- [19].…”