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

Abstract

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/m2 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.