As a result of contemporary ecological and sustainable movements, a number of constructions from wood, as the only renewable building material, is still increasing. In accordance with these trends, wood elements are often left untreated. The presented study is aimed to characterize and compare a surface degradation of untreated wood caused by weathering. Weathering characteristics of softwoods and hardwoods (spruce, pine, Douglas fir, larch, oak, black locust, maple, poplar and alder) during twelve months of exposure in the climatic conditions of Central Europe were determined. All the wood species were characterized by colour and gloss changes and increasing roughness. The lowest discoloration was observed for oak and the highest one for spruce wood, respectively. The lowest changes of roughness were observed for alder, the highest for spruce wood. The depth of colour changes was relatively similar among the softwoods. Regarding hardwoods, it varied more. The values of total colour changes had a significant correlation with the depth values of these colour changes. Maple, alder and poplar samples were characterized by the presence of mould and blue-stain fungi. Douglas fir wood was found to be the most liable for the formation of cracks.

Increasing fire size and severity over the last few decades requires new techniques to accurately assess canopy fuel conditions and change over larger areas. This article presents an analysis on vegetation changes by mapping fuel types (FT) based on conditional rules according to the Prometheus classification system, which typifies the vertical profile of vegetation cover for fuel management and ecological purposes. Using multi-temporal LiDAR from the open-access Spanish national surveying program, we selected a 400 ha area of interest, which was surveyed in 2010 and 2016 with scan densities of 0.5 and 2 pulses·m⁻², respectively. FTs were determined from the distribution of LiDAR heights over an area, using grids with a cell size of 20 × 20 m. To validate the classification method, we used a stratified random sampling without replacement of 15 cells per FT and made an independent visual assessment of FT. The overall accuracy obtained was 81.26% with a Kappa coefficient of 0.73. In addition, the relationships among different stand structures and ecological factors such as topographic aspect and forest vegetation cover types were analyzed. Our classification algorithm revealed that stands lacking understory vegetation usually appeared in shady slopes, which were mainly covered by beech stands, whereas sunny areas were preferentially covered by oak stands, where the understory reached greater height thanks to more light availability. Our analysis on FT changes during that 6 year time span revealed potentially hazardous transitions from cleared forests towards a vertical continuum of canopy fuels, where wildfire events would potentially reach tree crowns, especially in oak forests and southern slopes with higher sun exposure for lower fuel moistures and increased flammability. Accurate methods to characterize forest canopy fuels and change over time can help direct forest management activities to priority areas with greater fire hazard. Multi-date canopy fuel information indicated that while some forest types experienced a growth of the shrub layer, others presented an understory decrease. On the other hand, loss of understory was more frequently detected in beech stands; thus, those forests place lower risk of wildfire spread. Our approach was developed using low-density and publicly available datasets and was based on direct canopy fuel measurements from multi-return LiDAR data that can be accurately translated and mapped according to standard fuel type categories that are familiar to land managers.

Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5–6.5 returns m^-2) and the sampling intensity (0.3–3.4%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r = 18 m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable.

This article presents the performance of a vegetation fuel type (FT) classification based on conditional rules according to the Prometheus system, including an analysis of the effect of cell size and scan density on mapping vertical structural types, exemplified as FT, using exclusively LiDAR data. Since the Prometheus system does not specify any criterion for the minimum extension where those methodologies can be applied, we searched for the optimal classification cell size by gridding the study area at 20 and 40 m cell sizes. We also included a study of the effects of varying the scan density from 2 to 0.5 pulses·m-2. To validate the classification method, we used a stratified random sampling without replacement of 15 cells per FT and made an independent visual assessment of FTs. The best results in terms of precision were obtained for the combination of 0.5 pulses·m-2 and 20 m-resolution dataset, with an overall accuracy of 84.13%. It was also showed that an increase in scan density would not improve the global accuracy of the classification, but it would be desirable for a better detection of the shrub stratum.