Evaluation of digital height models of vegetation derived from LiDAR data
DOI:
https://doi.org/10.5902/1980509889194Keywords:
Laser scanning, Spatial interpolation, Deterministic methods, Geostatistics, Forest structureAbstract
Vegetation Digital Height Models (DHM) are generated by subtracting the Digital Terrain Model (DTM) from the Digital Surface Model (DSM), both of which can be constructed through the interpolation of three-dimensional coordinates obtained from Light Detection and Ranging (LiDAR) data. However, these models may contain errors associated with terrain topography and the interpolation method employed. Therefore, this study aimed to evaluate DHM generated using four interpolation methods applied to LiDAR data through statistical analyses designed to identify differences among the models and assess the influence of topography. The models were generated using Inverse Distance Weighting, Spline, Natural Neighbor, and Ordinary Kriging interpolators for the Jacaré River basin, located in Niterói, Rio de Janeiro, Brazil. The study area covers approximately 6 km², with an elevation range of up to 400 m, and is predominantly covered by dense tropical rainforest. The results showed that the DHM exhibited similar data distributions, with no statistically significant differences among the models according to ANOVA. Furthermore, no significant linear relationship was observed between vegetation height variance and the analyzed topographic variables (slope and elevation). However, the Spline interpolator showed a greater occurrence of extreme values, whereas Ordinary Kriging exhibited greater surface smoothing and lower data variability. Thus, under the environmental conditions of the study area, characterized by dense forest cover and rugged terrain, the Natural Neighbor and Inverse Distance Weighting interpolators are recommended for generating DHM from LiDAR data, as they showed a lower tendency to under- or overestimate values.
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