Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates

作者：Ruiz Luis A^*; Hermosilla Txomin; Mauro Francisco; Godino Miguel

来源：Forests, 2014, 5(5): 936-951.

DOI：10.3390/f5050936

• forest inventory
• LiDAR
• plot size
• data density
• forest structure
• forest attributes
• remote sensing

This paper assesses the combined effect of field plot size and LiDAR density on the estimation of four forest structure attributes: volume, total biomass, basal area and canopy cover. A total of 21 different plot sizes were considered, obtained by decreasing the field measured plot radius value from 25 to 5 m with regular intervals of 1 m. LiDAR data densities were simulated by randomly removing LiDAR pulses until reaching nine different density values. In order to avoid influence of the digital terrain model spatial resolution, eight different resolutions were considered (from 0.25 to 2 m grid size) and tested. A set of per-plot LiDAR metrics was extracted for each parameter combination. Prediction models of forest attributes were defined using forward stepwise ordinary least-square regressions. Results show that the highest R-2 values are reached by combining large plot sizes and high LiDAR data density values. However, plot size has a greater effect than LiDAR point density. In general, minimum plot areas of 500-600 m(2) are needed for volume, biomass and basal area estimates, and of 300-400 m(2) for canopy cover. Larger plot sizes do not significantly increase the accuracy of the models, but they increase the cost of fieldwork.