Original scientific paper
https://doi.org/10.15177/seefor.17-16
The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM
Mateo Gašparović
; University of Zagreb, Faculty of Geodesy, Chair of Photogrammetry and Remote Sensing, Kačićeva 26, HR-10000 Zagreb, Croatia
Ante Seletković
; University of Zagreb, Faculty of Forestry, Department of Forest Inventory and Management, Svetošimunska 25, HR-10000 Zagreb, Croatia
Alen Berta
; Oikon Ltd. Institute of Applied Ecology, Department of Natural Resources Management, Trg Senjskih Uskoka 1-2, HR-10000 Zagreb, Croatia
Ivan Balenović
orcid.org/0000-0001-7422-753X
; Croatian Forest Research Institute, Division for Forest Management and Forestry Economics, Trnjanska cesta 35, HR-10000 Zagreb, Croatia
Abstract
Background and Purpose: Unmanned aerial vehicles (UAVs) are flexible to solve various surveying tasks which make them useful in many disciplines, including forestry. The main goal of this research is to evaluate the quality of photogrammetry-based digital surface model (DSM) from low-cost UAV’s images collected in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of ground control points - GCPs) conditions.
Materials and Methods: The UAV-based DSMs without (DSMP) and with using GCPs (DSMP-GCP) were generated. The vertical agreement assessment of the UAV-based DSMs was conducted by comparing elevations of 60 checkpoints of a regular 100 m sampling grid obtained from LiDAR-based DSM (DSML) with the elevations of planimetrically corresponding points obtained from DSMP and DSMP-GCP. Due to the non-normal distribution of residuals (vertical differences between UAV- and LiDAR-based DSMs), a vertical agreement was assessed by using robust measures: median, normalised median absolute deviation (NMAD), 68.3% quantile and 95% quantile.
Results: As expected, DSMP-GCP shows higher accuracy, i.e. higher vertical agreement with DSML than DSMP. The median, NMAD, 68.3% quantile, 95% quantile and RMSE* (without outliers) values for DSMP are 2.23 m, 3.22 m, 4.34 m, 15.04 m and 5.10 m, respectively, whereas for DSMP-GCP amount to -1.33 m, 2.77 m, 0.11 m, 8.15 m and 3.54 m, respectively.
Conclusions: The obtained results confirmed great potential of images obtained by low-cost UAV for forestry applications, even if they are surveyed in non-optimal weather and environmental conditions. This could be of importance for cases when urgent UAV surveys are needed (e.g. detection and estimation of forest damage) which do not allow careful and longer survey planning. The vertical agreement assessment of UAV-based DSMs with LiDAR-based DSM confirmed the importance of GCPs for image orientation and DSM generation. Namely, a considerable improvement in vertical accuracy of UAV-based DSMs was observed when GCPs were used.
Keywords
stereo photogrammetry; unmanned aerial vehicle (UAV); digital surface model (DSM); Structure from Motion (SfM); light detection and ranging (LiDAR); vertical agreement assessment; forest inventory
Hrčak ID:
191507
URI
Publication date:
22.12.2017.
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