Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m⁻² and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m⁻², ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

Understorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric relationship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ϵ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to estimate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates.

Aim of study: An individual-tree growth system was developed for Aleppo pine (Pinus halepensis Mill.) plantations. Area of study: Aragón region (Northeast Spain). Material and methods: Two datasets were used: Second and Third Spanish National Forest Inventories (104 plots with 1,678 trees), and ad hoc permanent plots (58 plots with 1720 trees, including 36 dead trees). Individual tree growth system was based on nine models. Different combinations of yield classes, initial stocking rates, thinning parameters, rotation periods, and age at first thinning were evaluated through the three most representative scenarios: timber production; soil conservation and biodiversity enhancement. Main results: The nine models demonstrated a significant explanatory power for the data, with R2 values ranging from 0.71 to 0.99. These findings are consistent with previous research, indicating a strong goodness of fit. Additionally, yield tables were developed for the three prevalent silvicultural scenarios. To enhance usability, all models within the system were seamlessly integrated into a web-based application SIMANFOR. Research highlights: To date, Aleppo pine forest managers in Aragon could only simulate silvicultural scenarios in natural stands. This study provides a new tool for plantations.