Impacts of light detection and ranging (LiDAR) data organization and unit of analysis on land cover classification

Airborne light detection and ranging (LiDAR) data have been used to generate land cover models for almost two decades. In this paper, three common processing decisions are assessed for their impact on the accuracy and configuration of the resultant land cover models. Using data acquired from a single-wavelength, discrete return system, this study compares six land cover models that investigate (i) the organization of data into tiles or flightstrips, (ii) the unit of analysis as either the individual LiDAR point or as a pixel in a rasterized model of the LiDAR data, and (iii) the use of either pixel- or object-based image analysis. Although the overall accuracies of the land cover models generated in this study are comparable, models disagree on up to 17% of the total study area. Class-specific metrics of recall and precision differ markedly between models, and the configuration of land covers are also affected. Models that employ pixel-based image analysis techniques tend to generate models with smaller, more dispersed patches of land cover. Data organization and choice of unit of analysis also influence the configuration of land cover, although effects differ depending on the land cover class. Comprehensive analyses of accuracy and precision are crucial to developing land cover models. This study demonstrates that it is also important to understand the potential influence of classification methodologies on the configuration of landscape features, especially when interpreting land cover models from an ecological or landscape genetic perspective.