Light detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15° were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them.

Airborne Laser Scanning (ALS) is capable of estimating a variety of forest parameters using different metrics extracted from the normalized heights of the point cloud using a Digital Elevation Model (DEM). In this study, six interpolation routines were tested over a range of land cover and terrain roughness in order to generate a collection of DEMs with spatial resolution of 1 and 2 m. The accuracy of the DEMs was assessed twice, first using a test sample extracted from the ALS point cloud, second using a set of 55 ground control points collected with a high precision Global Positioning System (GPS). The effects of terrain slope, land cover, ground point density and pulse penetration on the interpolation error were examined stratifying the study area with these variables. In addition, a Classification and Regression Tree (CART) analysis allowed the development of a prediction uncertainty map to identify in which areas DEMs and Airborne Light Detection and Ranging (LiDAR) derived products may be of low quality. The Triangulated Irregular Network (TIN) to raster interpolation method produced the best result in the validation process with the training data set while the Inverse Distance Weighted (IDW) routine was the best in the validation with GPS (RMSE of 2.68 cm and RMSE of 37.10 cm, respectively).

Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels.

This research develops a bottom-up procedure to assess the potential of food-energy-water (FEW) systems on the rooftops of buildings in an urban district in Spain considering the urban morphology of the built environment and obtains accurate assessments of production and developmental patterns. A multicriteria decision-making technique implemented in a geographical information system (GIS) environment was used to extract suitable rooftop areas. To implement this method, the slope (tilt), aspect (azimuth), shading, and solar radiation of the rooftops were calculated using LiDAR (Light Detection and Ranging) data and building footprints. The potential of FEW system implementation was analysed at the building and morphology levels. The results showed several differences between residential and non-residential urban morphologies. Industrial areas contained the highest productivity for FEW systems. The production was 2.51 kg of tomatoes/m², 48 kWh of photovoltaic energy/m², and 0.16 l of rainwater/m². Regarding the residential urban morphologies, the more compact tents resulted in better performance. Among the FEW systems, although water could best benefit from the features of the entire roof surface, the best production results were achieved by energy. The food system is less efficient in the built environment since it requires flat roofs. The methodology presented can be applied in any city, and it is considered optimal in the European context for the development of self-production strategies for urban environments.

Little research has focussed on the use of low point density airborne laser scanning (ALS) technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest to derive key stand characteristics for forestry. The objective of this research was to develop models of Aleppo pine structural variables from ALS point clouds with a point density of 1 point m⁻² and field information, in an area located in north-eastern Spain. Field stand metrics were obtained within 45 plots, 30 m in diameter. A multiple regression analysis was performed in order to develop the models for mean height, squared mean diameter, basal area, timber volume, stand density and crown ratio, which led to the following R² values: 0.87, 0.84, 0.89, 0.89, 0.48 and 0.69, respectively. ALS-derived metrics included in the models were maximum height, the 99th, 95th and 50th canopy height percentiles, the skewness, the kurtosis, the standard deviation, the coefficient of variation and the percentages of all and first returns from above 1 m of the ground surface. With the exception of stand density, the final models are appropriate to be used in forest management operations in Mediterranean Aleppo pine forest. This study demonstrates the usefulness of low point density ALS data to accurately estimate key structural parameters of Aleppo pine forest using an area-based approach.

The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.