Using enhanced data co-registration to update Spanish National Forest Inventories (NFI) and to reduce training data under LiDAR-assisted inference

Abstract

ABSTRACT The estimation of forest attributes at the local scale as well as in wall-to-wall approaches benefits from the integration of remote sensing data such as airborne laser scanning (ALS). A poor level of spatial co-registration between ALS data and ground National Forest Inventories (NFI) data has traditionally restricted the application of area-based (ABA) methods. Improving the reliability of the spatial co-registration in NFI can enhance the statistical inference. We evaluated the improvement of ABA models in six forest ecosystems in Spain when upgrading NFI plot positions using commercial-grade global navigation satellite systems (GNSS). The new ABA models based on more accurate NFI positioning reduced the root mean squared error (RMSE) for mean stand volume and basal area by 9.3% and 9.4%, respectively, and 2.1% for mean tree height compared to ABA models based on previous NFI positioning records. The model error and its variability showed a major decrement for the upgraded ABA models when iteratively fitting and using cross-validation. The variability of RMSE estimates decreased at a faster rate compared to ABA models based on less accurate NFI positions. Hence, we derived the minimum sampling intensity needed to control the variability of the RMSE. The results showed that upgraded ABA models required fewer training sample plots, although results were dependent on the assessed forest attribute and the forest ecotype. The observed marginal benefit of improving data co-registration can increase the operational efficiency of NFI designs. With this work, we provide further insights for the measuring of NFI samples considering both ALS-assisted inference and the effect of forest structure.