Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging

Key message We compared (lognormal) universal kriging with the area-based approach for estimation of forest inventory variables using LiDAR data as auxiliary information and showed that universal kriging could be an accurate alternative when there is spatial autocorrelation. Context Forest inventories supported by geospatial technologies are essential to achieve a spatially informed assessment of forest structure. LiDAR technology supplies comprehensive and spatially explicit data enabling the estimation of wide-scale forest variables. Aims To compare the area-based approach with universal kriging for estimation of the stem density, basal area, and quadratic mean diameter using LiDAR data as auxiliary information. Methods We used data from 202 inventory plots, distributed in four Forest Management Units with differences in structure and management, and a 6-points/m(2) resolution LiDAR dataset from a Pinus sylvestris L. forest in Spain to test the accuracy of the (lognormal) universal kriging and the area-based approaches. Results In those Forest Management Units where the analyzed variables showed spatial autocorrelation, kriging showed better results than the area-based approach in terms of RMSE and Pearson coefficient between observed and estimated values, although lognormal universal kriging provided slightly biased estimations (up to 2%). Conclusion Universal kriging is an accurate method for estimation of forest inventory variables with LiDAR data as auxiliary information for those variable exhibiting spatial autocorrelation.

Automated summary

The study compared universal kriging with the area-based approach for estimating forest inventory variables using LiDAR data, finding that universal kriging performed better in the presence of spatial autocorrelation.