In forests with dense mixed canopies, laser scanning is often the only effective technique to acquire forest inventory attributes, rather than structure-from-motion optical methods. This study investigates the potential of laser scanner data collected with a low-cost unmanned aerial vehicle laser scanner (UAV-LS), for individual tree crown (ITC) delineation to derive forest biometric parameters, over two-layered dense mixed forest stands in central Italy. A raster-based local maxima region growing algorithm (itcLiDAR) and a point cloud-based algorithm (li2012) were applied to isolate individual tree crowns, compute height and crown area, estimate the diameter at breast height (DBH) and the above ground biomass (AGB) of individual trees. To maximize the level of detection rate, the ITC algorithm parameters were tuned varying 1350 setting combinations and matching the segmented trees with field measured trees. For each setting, the delineation accuracy was assessed by computing the detection rate, the omission and commission errors over three forest plots. Segmentation using itcLiDAR showed detection rates between 40% and 57%, while ITC delineation was successful at segmenting trees with DBH larger than 10 cm (detection rate ~78%), while failed to detect trees with smaller DBH (detection rate ~37%). The performance of li2012 was quite lower with the higher detection rate equal to 27%. Errors and goodness-of-fit between field-surveyed and flight-derived biometric parameters (AGB and tree height) were species-dependent, with higher error and lower r² for shorter species that constitute the lowermost layer of the forest. Overall, while the application of UAV-LS to delineate tree crowns and estimate biometric parameters is satisfactory, its accuracy is affected by the presence of a multilayered and multispecies canopy that will require specific approaches and algorithms to better deal with the added complexity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Aim of study: To assess terrestrial laser scanning (TLS) accuracy in estimating biometrical forest parameters at plot-based level in order to replace manual survey for forest inventory purposes. Area of study: Monte Morello, Tuscany region, Italy. Materials and methods: In 14 plots (10 m radius) in dense Mediterranean mixed conifer forests, diameter at breast height (DBH) and height were measured in Summer 2016. Tree volume was computed using the second Italian National Forest Inventory (INFC II) equations. TLS data were acquired in the same plots and quantitative structure models (QSMs) were applied to TLS data to compute dendrometric parameters. Tree parameters measured in field survey, i.e. DBH, height, and computed volume, were compared to those resulting from TLS data processing. The effect of distance from the plot boundary in the accuracy of DBH, height and volume estimation from TLS data was tested. Main results: TLS-derived DBH showed a good correlation with the traditional forest inventory data (R²=0.98, RRMSE=7.81%), while tree height was less correlated with the traditional forest inventory data (R²=0.60, RRMSE=16.99%). Poor agreement was observed when comparing the volume from TLS data with volume estimated from the INFC II prediction equations. Research highlights: The study demonstrated that the application of QSM to plot-based terrestrial laser data generates errors in plots with high density of coniferous trees. A buffer zone of 5 m would help reduce the error of 35% and 42% respectively in height estimation for all trees and in volume estimation for broadleaved trees. © 2018 INIA.

Natural resources management requires several parameters estimate in order to support the identification of the best alternatives to forest areas management. In particular, forest ecosystems require a complex and increasing set of descriptive information, where forest inventories put up important information, however not in a continuous spatial way. Lately, several scientific researches have been focusing on establishing methodologies to relate data from field to those obtained from multispectral images. Modeling these relations can extend the estimates of forest inventory data to not sampled areas. This research evaluated performance of non-parametric analysis using the K-Nearest Neighbor (k-NN) on SPOT 5 images. It evaluated the results obtained from the spatialization of some forest attributes in a forest area located at Molise, Italy. Among several methodologies for spatial distance calculations, the use of multiregressive non-parametric distances revealed the best results. Density and number of species on the ground revealed a Pearson correlation coefficient of = 0.58 as compared to data obtained from multispectral images, lightly lower than the obtained for basal area and volume, which were = 0.62 and 0.71, respectively.

A spatially explicit knowledge of forest resources is essential to support the sustainable use of wood as a fuel for producing energy (firewood).This paper describes the integrated use of remotely sensed data and sample based forest inventories to derive a biomass map for coppice forest, resulted estimated potential biomass available is contrasted with local domestic consumptions at the municipality level. The test was carried out in an environmentally and socially homogeneous district of Apennine Mountains (Alto Molise, south-central Italy) coupling multispectral high resolution Landsat 7 ETM+ satellite imagery and a local forest inventory trough the application of the non-parametric estimation procedure k-Nearest Neighbours (k-NN). Several forest management scenarios were applied in order to evaluate their impact on the potential availability of firewood from coppice forests.The paper introduces data and methods used and presents the achieved results both in terms of the accuracy of the biomass map produced by k-NN and of the relationship between the potential availability and demand for firewood.These results demonstrated that k-NN is able to estimate the biomass of coppice forest in the test area with an accuracy level comparable with recent similar application of k-NN carried out in Boreal regions (RMSE of 25.6%).The application of different forest management scenarios have a significant impact on local estimated firewood balance between potential supply from coppice forests and demand for domestic consumption, depending of the scenarios the net balance changed up to 84%. © 2010 Elsevier Ltd.