Urban Tree Classification Using Full-Waveform Airborne Laser Scanning

Abstract

ABSTRACT:Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning

“…The collinearity analysis in combination with the RFE procedure allowed us to reduce the number of lidar metrics in the classification at each hierarchical level. A similar approach to metric reduction has been used in other lidar‐based vegetation classifications (Millard and Richardson, 2013; Alexander et al, 2016; Koma et al, 2016). In our study, only five lidar metrics related to vegetation structure where needed to separate different land cover types and habitats within this wetland.…”

A Method for Tree Detection Based on Similarity with Geometric Shapes of 3D Geospatial Data

“…However, the authors warn that the algorithms are over-adapted for the specific forest type. Studies using data derived from LiDAR or Unmanned Aerial Vehicles (UAV) cite slightly higher accuracies ranging from 80% to 90% [9,18,[66][67][68][69], while recent deep learning developments reached accuracies of >90% [4,70,71].…”

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