Abstract
Abstract. Airborne small-footprint LiDAR is replacing field measurements in regional-level forest inventories, but auxiliary field work is still required for the optimal management of young stands. Waveform (WF) recording sensors can provide a more detailed description of the vegetation compared to discrete return (DR) systems. Furthermore, knowing the shape of the signal facilitates comparisons between real data and those obtained with simulation tools. We performed a quantitative validation of a Monte Carlo ray tracing (MCRT) -based LiDAR simulator against real data and used simulations and empirical data to study the WF recording LiDAR for the classification of boreal juvenile forest vegetation. Geometric-optical models of three common species were used as input for the MCRT model. Simulated radiometric and geometric WF features were in good agreement with the real data, and interspecies differences were preserved. We used the simulator to study the effects of sensor parameters on species classification performance. An increase in footprint size improved the classification accuracy up to a certain footprint size, while the emitted pulse width and the WF sampling rate had minor effects. Analyses on empirical data showed small improvement in performance compared to existing studies, when classifying seedling stand vegetation to four operational classes. The results on simulator validation serve as a basis for the future use of simulation models e.g. in LiDAR survey planning or in the simulation of synthetic training data, while the empirical findings clarify the potential of WF LiDAR data in the inventory chain for the operational forest management planning in Finland.
Highlights
The capability of small-footprint airborne LiDAR in producing precise estimates of quantitative forest characteristics is well demonstrated (Naesset et al, 2004)
The value of parameter a was set at the average automatic gain control (AGC) value in each dataset, and optimal values for b were searched for by minimizing the nested coefficient of variation (CV) of the WF peak amplitude data, using homogeneous targets, which covered a range of surface reflectivity
The height histograms of the simulated echoes were generally in good agreement with the real data (Figure 4), especially for birch, there was an overestimation of mean H by
Summary
The capability of small-footprint airborne LiDAR in producing precise estimates of quantitative forest characteristics is well demonstrated (Naesset et al, 2004). The inventory of seedling stands is a separate process in the field. The overall aims of our study were related to the validation of the model and to the examination of the potential of WF data in the mapping of seedling stand vegetation. Three specific research aims (RA) were formulated: RA1) To perform quantitative validation of the simulation model against real data and test the model sensitivity to vegetation parameters. RA2) To use the simulation model for testing the effects of different sensor parameters on the performance of WF data in the classification of three selected plant species. Theoretical simulations, not completely replacing the need for empirical training and validation data, can be useful in e.g. RA3) To study empirical WF signatures for the classification of seedling stand vegetation representative of a large number of species
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