The aim of this study was to evaluate the use of high-resolution airborne laser scanner (ALS) data to detect and measure individual trees. We developed and tested a new mixed pixel- and region-based algorithm (using Definiens Developer 7.0) for locating individual tree positions and estimating their total heights. We computed a canopy height model (CHM) of pixel size 0.25 m from dense first-pulse point data (8 pulses m⁻²) acquired with a small-footprint discrete-return lidar sensor. We validated the results of individual tree segmentation with accurate field measurements made in 37 plots of Monterey pine (Pinus radiata D. Don) distributed over an area of 36 km². Fieldwork consisted of labelling all of the trees in each plot and measuring their height and position, for posterior integration of the data from both sources (field and lidar). The proposed algorithm correctly detected and linked 59.8% of the trees in the 37 sample plots. We also manually located the trees by using FUSION software to visualize the raw lidar data cloud. However, because the latter method is extremely time-consuming, we only considered 10 randomly selected plots. Manual location correctly detected and linked 71.9% of the trees (in this subsample the algorithm correctly detected and measured 63.5% of the trees). The R² values for the linear model relating field- and lidar-measured heights of the linked trees located manually and with the automatic location algorithm were 0.90 and 0.88, respectively.

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m${}^2$. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m${}^2$.

Aims of study: To evaluate the potential use of canopy height and intensity distributions, determined by airborne LiDAR, for the estimation of crown, stem and aboveground biomass fractions.To assess the effects of a reduction in LiDAR pulse densities on model precision.Area of study: The study area is located in Galicia, NW Spain. The forests are representative of Eucalyptus globules stands in NW Spain, characterized by low-intensity silvicultural treatments and by the presence of tall shrub.Material and methods: Linear, multiplicative power and exponential models were used to establish empirical relationships between field measurements and LiDAR metrics.A random selection of LiDAR returns and a comparison of the prediction errors by LiDAR pulse density factor were performed to study a possible loss of fit in these models.Main results: Models showed similar goodness-of-fit statistics to those reported in the international literature. R2 ranged from 0.52 to 0.75 for stand crown biomass, from 0.64 to 0.87 for stand stem biomass, and from 0.63 to 0.86 for stand aboveground biomass. The RMSE/MEAN · 100 of the set of fitted models ranged from 17.4% to 28.4%.Models precision was essentially maintained when 87.5% of the original point cloud was reduced, i.e. a reduction from 4 pulses m–2 to 0.5 pulses m–2.Research highlights: Considering the results of this study, the low-density LiDAR data that are released by the Spanish National Geographic Institute will be an excellent source of information for reducing the cost of forest inventories.Key words: Eucalypt plantations; airborne laser scanning; aboveground biomass; carbon stocks; remote sensing; forest inventory.

While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-automatic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.

Despite the large amounts of resources destined to developing filtering algorithms of LiDAR point clouds in order to obtain a Digital Terrain Model (DTM), the task remains a challenge. As a society advancing towards the democratization of information and collaborative processes, the researchers should not only focus on improving the efficacy of filters, but should also consider the users’ needs with a view toward improving the usability and accessibility of the filters in order to develop tools that will provide solutions to the challenges facing this field of study. In this work, we describe the Hybrid Overlap Filter (HyOF), a new filtering algorithm implemented in the free R software environment. The flow diagram of HyOF differs in the following ways from that of other filters developed to date: (1) the algorithm is formed by a combination of sequentially operating functions (i.e., the output of the first function provides the input of the second), which are capable of functioning independently and thus enabling integration of these functions with other filtering algorithms; (2) the variable penetrability is defined and used, along with slope and elevation, to identify ground points; (3) prior to selection of the seed points, the original point cloud is processed with the aim of removing points corresponding to buildings; and (4) a new method based on a moving window, with longitudinal overlap between windows and transverse overlap between passes, is used to select the seed points. Our hybrid filtering method is tested using 15 reference samples acquired by the International Society of Photogrammetry and Remote Sensing (ISPRS) and is evaluated in comparison with 33 existing filtering algorithms. The results show that our hybrid filtering method produces an average total error of 3.34% and an average Kappa coefficient of 92.62%. The proposed algorithm is one of the most accurate filters that has been tested with the ISPRS reference samples.