The mean and standard deviation (SD) of light detection and ranging (LiDAR)-derived canopy height are related to forest structure. However, LiDAR data typically cover a limited area and have a high economic cost compared with satellite optical imagery. Optical images may be required to extrapolate LiDAR height measurements across a broad landscape. Different spectral indices were obtained from three Landsat scenes. The mean, median, SD and coefficient of variation (CV) of LiDAR canopy height measurements were calculated in 30-m square blocks corresponding with Landsat Enhanced Thematic Mapper Plus (ETM+) pixels. Correlation and forward stepwise regression analysis was applied to these data sets. Mean and median LiDAR height versus normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR) and wetness Tasseled Cap showed the best correlation coefficients (R ² ranging between −0.62 and −0.76). Nineteen regression models were obtained (R ² = 0.65–0.70). These results show that LiDAR-derived canopy height may be associated with Landsat spectral indices. This approach is of interest in sustainable forest management, although further research is required to improve accuracy.

Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications—many times in combination—whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.

Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS) integrating medium- (MR) and very-high-resolution (VHR) images through the FAO’s Collect Earth platform. More than 11,150 plots were inventoried in the Temperate FAO ecozone in Europe to monitor LULUCF from 2000 to 2015. As a result, 2.19% (VHR) to 2.77% (MR/VHR) of the study area underwent LULUCF, including a 0.37% (VHR) to 0.43% (MR/VHR) net increase in forest lands. Collect Earth and VHR images have also (i) allowed for shaping a preliminary structure of the land-use network, showing that cropland was the land type that changed most and that cropland and grassland were the more frequent land uses that generated new forest land, (ii) shown that, in 2015, mixed and monospecific forests represented 44.3% and 46.5% of the forest land, respectively, unlike other forest sources, and (iii) shown that 14.9% of the area had been affected by disturbances, particularly wood harvesting (67.47% of the disturbed forests). According to other authors, the area showed a strong correlation between canopy mortality and reported wood removals due to the transition from past clear-cut systems to “close-to-nature” silviculture.

This paper compares two pairwise comparison methods, the analytic hierarchy process (AHP) and a utility theory based method (UTB method), for sustainability assessment in forest management at the local level. Six alternatives were ranked, corresponding to six different types of forest management in the Sierra de Guadarrama National Park in the Madrid Region in Spain. The methods were tested by postgraduate students enrolled in a “Decision Support Systems” course at Universidad Politécnica de Madrid. Three sustainability indicators were considered: structural diversity, timber yield, and amount of biomass. The utility theory based method was the first to be compared, which is implemented in the computer program SILVANET. For each pair of alternatives, the students were asked which one they considered to be more sustainable. In the case of the Analytic Hierarchy Process, the students compared the indicators and the alternatives for each indicator. The Spearman’s correlation coefficient indicated that there was no correlation between the rankings for most of the students. The results revealed that the convergence in opinion in the AHP method was higher than in the utility based method for a low number of participants, and distinguished the differences between the alternatives more accurately. However in the case of the UTB method, the participants considered sustainability as a whole and made a more context-based comparison.

The global distribution of forest trees is essential for monitoring land-use sustainability. For this reason, FAO developed the Global Forest Survey (GFS), a systematic regional level survey with a homogeneous sampling design. Preliminary results revealed an interest in analyzing “Trees Outside Forests” (TOFs). We analyzed more than 11,150 sample GFS plots in temperate FAO-ecozones in Europe through a photo-interpretation approach using FAO’s Collect Earth platform. Our results showed that forest land was the most predominant use of land (41%) in temperate FAO-ecozones in Europe in 2015. Forest IPCC land use followed a moderate upward trend from 2000 to 2015 (0.2%). Trees Outside Forests in temperate FAO-ecozones in Europe accounted for 22% of the inventoried area. Trees Outside Forests represent 30% and 48% of cropland and grassland IPCC land uses, respectively, as well as 75% in settlement uses. Comparing our results with previous studies on the Mediterranean, temperate FAO-ecozone TOFs showed a downward trend in Europe and an upward trend in the Mediterranean area, despite its smaller surface area. The greening of the Common Agricultural Policy may increase the extension of TOFs, although our results may indicate that this effect is not yet evident in the field.