Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

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

Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

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

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

Species dominance and above ground biomass in the Białowieża Forest, Poland, described by airborne hyperspectral and lidar data

The objective of this research is to test and evaluate hyperspectral and lidar data to derive information on tree species dominance and above ground biomass in the Białowieża Forest in Poland. This forest is threatened by climate change, fire, bark beetles attacks, and logging, with changes in species composition and dominance. In this conservation valuable area, the monitoring of forest resources is thus critical. Results indicate that vegetation indices from hyperspectral data can support species dominance detection: using a Classification and Regression Trees algorithm the three main plot types (dominated by Deciduous, Spruce, and Pines species) were classified with an Overall Accuracy > 0.9. The accuracy decreased when a ‘Mixed’ group was added to account for very heterogeneous plots, and plots dominated by Spruce were not correctly detected. Hyperspectral vegetation indices were also used to estimate the level of species dominance in the forest plots, using a Multivariate Multiple Linear Regression model; the obtained accuracy varied according to groups, being higher for Deciduous (R2 = 0.87), compared to Pines (R2 = 0.61), and to Spruce-dominated plots (R2 = 0.37). Lidar data were employed to estimate above ground biomass, using an exponential regression model; overall the R2 resulted equal to 0.66 but ranged from 0.57 to 0.78 when considering subgroups according to species dominance; the addition of hyperspectral vegetation indices improved the result only for Pines. The illustrated methods provide a reliable description of important forest characteristics and simplify resource monitoring, supporting local authorities to address the challenges imposed by climate change and other forest threats