Evaluation of the use of low-density LiDAR data to estimate structural attributes and biomass yield in a short-rotation willow coppice: an example in a field trial

Abstract

LiDAR data (low-density data, 0.5 pulses m −2 ) represent an excellent management resource as they can be used to estimate forest stand characteristics in short-rotation willow coppice (SRWC) with reasonable accuracy. The technology is also a useful, practical tool for carrying out inventories in these types of stands. This study evaluated the use of very low-density airborne LiDAR (light detection and ranging) data (0.5 pulses m−2), which can be accessed free of charge, in an SRWC established in degraded mining land. This work aimed to determine the utility of low-density LiDAR data for estimating main forest structural attributes and biomass productivity and for comparing the estimates with field measurements carried out in an SRWC planted in marginal land. The SRWC was established following a randomized complete block design with three clones, planted at two densities and with three fertilization levels. Use of parametric (multiple regression) and non-parametric (classification and regression trees, CART) fitting techniques yielded models with good predictive power and reliability. Both fitting methods were used for comprehensive analysis of the data and provide complementary information. The results of multiple regression analysis indicated close relationships (Rfit 2 = 0.63–0.97) between LiDAR-derived metrics and the field measured data for the variables studied (H, D20, D130, FW, and DW). High R 2 values were obtained for models fitted using the CART technique (R 2 = 0.73–0.94). Low-density LiDAR data can be used to model structural attributes and biomass yield in SRWC with reasonable accuracy. The models developed can be used to improve and optimize follow-up decisions about the management of these crops.