<abstract xmlns="http://www.w3.org/1999/xhtml">Airborne laser scanning (ALS) technology delivers large amount of data collected from airborne level. These data are used for many different applications in forestry, civil engineering, environmental studies and others. To acquire the best possible results from the data, accuracy analysis is a necessary part of data processing chain. Therefore, considering the increasing interest worldwide in the use of laser scanning data, improving the quality control (QC) tools is a crucial pursuit.This study underlines the possible error sources, summarises the existing QC knowledge for ALS data and proposes an optimised QC procedure. The procedure was implemented in selected applications and evaluated for three different environments, namely, forests, rural areas and croplands.The proposed solution is almost fully automatic outside from the module that supports the operator in the classification examination. The workflow is scalable and can be expanded with new modules that enhance the functionality. The presented procedures can save up to 30 min of manual checks for every 1 km2 area.</abstract>

Airborne laser scanning (ALS) technology delivers large amount of data collected from airborne level. These data are used for many different applications in forestry, civil engineering, environmental studies and others. To acquire the best possible results from the data, accuracy analysis is a necessary part of data processing chain. Therefore, considering the increasing interest worldwide in the use of laser scanning data, improving the quality control (QC) tools is a crucial pursuit.This study underlines the possible error sources, summarises the existing QC knowledge for ALS data and proposes an optimised QC procedure. The procedure was implemented in selected applications and evaluated for three different environments, namely, forests, rural areas and croplands.The proposed solution is almost fully automatic outside from the module that supports the operator in the classification examination. The workflow is scalable and can be expanded with new modules that enhance the functionality. The presented procedures can save up to 30 min of manual checks for every 1 km2 area.

Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

Folia Forestalia Polonica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Airborne laser scanning (ALS) technology delivers large amount of data collected from airborne level. These data are used for many different applications in...

Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

Online veröffentlicht: 14. Dez. 2020

Seitenbereich: 317 - 326

Eingereicht: 05. Aug. 2019

Akzeptiert: 20. Aug. 2020

DOI: https://doi.org/10.2478/ffp-2020-0030

Schlüsselwörter
ALS, automation, accuracy, density, quality, control

© 2020 Bartłomiej Kraszewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

Online veröffentlicht: 14. Dez. 2020

Seitenbereich: 317 - 326

Eingereicht: 05. Aug. 2019

Akzeptiert: 20. Aug. 2020

DOI: https://doi.org/10.2478/ffp-2020-0030

SchlüsselwörterALS, automation, accuracy, density, quality, control

© 2020 Bartłomiej Kraszewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
ALS, automation, accuracy, density, quality, control