DEM Generation from Laser Scanner Data Using Adaptive TIN Models

By P. Axelsson. 2000

1,479 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/98bbd72676af97cfd351506c650fc175c7317adb

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Citations in the corpus

Predicting the spatial pattern of trees by airborne laser scanning (2013, methodology): “First, points were classified as ground and non-ground hits according to the approach described by Axelsson (2000).”
Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification (2014, methodology): “DSC data were first classified into ground or non-ground points using the standard approach of the TerraScan based on the method explained in [46].”
An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation (2016, background): “Axelsson [17] proposed an adaptive triangulated irregular network (TIN) filtering algorithm that gradually densified a sparse TIN that was generated from the selected seed points.”
Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data (2009, methodology): “The data were captured using a scanning angle of ±11 degrees, which resulted in a swath width of about 350 m. The last pulse data were employed to generate a digital terrain model (DTM) by the method explained in Axelsson (2000) , using a grid size of 1 m.”
Estimation of stem attributes using a combination of terrestrial and airborne laser scanning (2012, methodology): “Laser returns were classified as ground or non-ground using a progressive triangular irregular network (TIN) densification method (Axelsson 1999, 2000) implemented in the TerraScan software (Soininen 2004).”
FILTERING OF LASER ALTIMETRY DATA USING A SLOPE ADAPTIVE FILTER (2001, methodology): “A test on a few samples showed the average ratio between the slope adaptive filtrate and that from the Terrascan filter to be around 6:1.”
Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data (2006, methodology): “fi) to separate the ground points from other points (the method is explained in Axelsson (2000)).”
Non-parametric prediction of diameter distributions using airborne laser scanner data (2009, methodology): “Ground echoes were found and classified using the progressive Triangular Irregular Network (TIN) densification algorithm (Axelsson, 2000) of the TerraScan software (Anon., 2005).”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, methodology): “Returns we classified as ground by iteratively filtering lower returns into a Delaunay-triangulated irregular network (c.f. Axelsson, 2000).”
Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands (2011, methodology): “A digital terrain model (DTM) with a grid size of 1 m was generated from the last returns using the method presented by Axelsson (2000). After that, the laser pulse z values were deducted from the DTM to obtain the AGL height values (referred to below as ALS point heights).”
International benchmarking of terrestrial laser scanning approaches for forest inventories (2018, methodology): “initial points and starts to densify the TIN by adding more ground points according to the given parameters (Axelsson, 2000).”
Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment (2015, methodology): “A Digital Terrain Model (DTM) with a spatial resolution of 1 m was created using ground returns and standard pre-processing routines as per Axelsson (2000) [24].”
Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data (2010, methodology): “Ground echoes were found and classified using the progressive triangular irregular network (TIN) densification algorithm (Axelsson, 2000) of the TerraScan software (Anon, 2005).”