Automatic Filtering and Classification of Low-Density Airborne Laser Scanner Clouds in Shrubland Environments

The monitoring of shrublands plays a fundamental role, from an ecological and climatic point of view, in biodiversity conservation, carbon stock estimates, and climate-change impact assessments. Laser scanning systems have proven to have a high capability in mapping non-herbaceous vegetation by classifying high-density point clouds. On the other hand, the classification of low-density airborne laser scanner (ALS) clouds is largely affected by confusion with rock spikes and boulders having similar heights and shapes. To identify rocks and improve the accuracy of vegetation classes, we implemented an effective and time-saving procedure based on the integration of geometric features with laser intensity segmented by K-means clustering (GIK procedure). The classification accuracy was evaluated, taking into account the data unevenness (small size of rock class vs. vegetation and terrain classes) by estimating the Balanced Accuracy (BA range 89.15-90.37); a comparison with a standard geometry-based procedure showed an increase in accuracy of about 27%. The classical overall accuracy is generally very high for all the classifications: the average is 92.7 for geometry-based and 94.9 for GIK. At class level, the precision (user's accuracy) for vegetation classes is very high (on average, 92.6% for shrubs and 99% for bushes) with a relative increase for shrubs up to 20% (>10% when rocks occupy more than 8% of the scene). Less pronounced differences were found for bushes (maximum 4.13%). The precision of rock class is quite acceptable (about 64%), compared to the complete absence of detection of the geometric procedure. We also evaluated how point cloud density affects the proposed procedure and found that the increase in shrub precision is also preserved for ALS clouds with very low point density (<1.5 pts/m(2)). The easiness of the approach also makes it implementable in an operative context for a non-full expert in LiDAR data classification, and it is suitable for the great wealth of large-scale acquisitions carried out in the past by using monowavelength NIR laser scanners with a small footprint configuration.