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In temperate forests, the highest plant richness is regularly found in the understorey, i.e. shrub, tree regeneration,\udherbal and moss covers, which provides important food and shelter for other plant and animal species.\udHere, Light Detection And Ranging (LiDAR) remote sensing was investigated as a surrogate to laborious field\udsurveys to improve understanding of the causal and predictive attributes of understorey.We designed a study\udin which we used a high-density LiDAR point cloud and applied a thinning algorithm to simulate two lower\uddensity point clouds including first and last returns and half of the remaining points (half-thinned data) and\udonly first and last returns (F/L-thinned data). From each dataset, several over- and understorey-related statistical\udmetrics were derived. Each of the three sets of LiDAR metrics was then combined with the forest habitat\udinformation to estimate the recorded proportions of shrub, herb and moss coverages.We used three different\udmodel procedures including zero-and-one-inflated beta regression (ZOINBR), ordinary least squares with logittransformed\udresponse variables (logistic model) and a machine learning random forest (RF) method. The logistic\udand ZOINBR model results showed highly significant relationships between LiDAR metrics and habitat types\udin explaining understorey coverage. The highest coefficients of determination included r2 = 0.80 for shrub\udcover (estimated by F/L-thinned data and ZOINBR model), r2 = 0.53 for herb cover (estimated by half-thinned\uddata and logistic model) and r2 = 0.48 for moss cover (estimated by half-thinned data and logistic model). RF\udmodels returned the best predictive performances (i.e. the lowest root mean square errors). Despite slight differences,\udno substantial difference was observed amongst the performances achieved by the original, halfthinned\udand F/L-thinned point clouds. Moreover, the ZOINBR models did not improve predictive performances\udcompared with the logistic model, which suggests that the latter should be preferred due to its greater simplicity\udand parsimony. Despite the differences between our simulated data and the real-world LiDAR point clouds\udof different point densities, the results of this study are thought to mostly reflect how LiDAR and forest habitat\uddata can be combined for deriving ecologically relevant information on temperate forest understorey vegetation\udlayers. This, in turn, increases the applicability of prediction results for overarching aims such as forest\udand wildlife management

Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data