The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM
| Autor: | Ante Seletković, Alen Berta, Mateo Gašparović, Ivan Balenović |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Mean squared error stereo photogrammetry unmanned aerial vehicle (UAV) digital surface model (DSM) Structure from Motion (SfM) light detection and ranging (LiDAR) vertical agreement assessment forest inventory 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Median absolute deviation lcsh:Forestry 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Forest inventory Forestry Photogrammetry Lidar Outlier lcsh:SD1-669.5 Environmental science Digital surface Quantile |
| Zdroj: | South-east European forestry : SEEFOR Volume 8 Issue 2 South-East European Forestry, Vol 8, Iss 2, Pp 117-125 (2017) |
| ISSN: | 1849-0891 1847-6481 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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