Accurate estimates of C stocks and fluxes of soil organic carbon (SOC) are needed to assess the impact of climate and land use change on soil C uptake and soil C emissions to the atmosphere. Here, we present an assessment of SOC stocks in forests, shrublands and grasslands of peninsular Spain based on field measurements in more than 900 soil profiles. SOC to a depth of 1 m was modelled as a function of vegetation cover, mean annual temperature, total annual precipitation, elevation and the interaction between temperature and elevation, while latitude and longitude were used to model the correlation structure of the errors. The resulting statistical model was used to estimate SOC in the ∼8 million pixels of the Spanish Forest Map (29.3 × 106 ha). We present what we believe is the most reliable estimation of current SOC in forests, shrublands and grasslands of peninsular Spain thus far, based on the use of spatial modelling, the high number of profiles and the validity and refinement of the data layers employed. Mean concentration of SOC was 8.7 kg m−2, ranging from 2.3 kg m−2 in dry Mediterranean areas to 20.4 kg m−2 in wetter northern locations. This value corresponds to a total stock of 2.544 Tg SOC, which is four times the amount of C estimated to be stored in the biomass of Spanish forests. Climate and vegetation cover were the main variables influencing SOC, with important ecological implications for peninsular Spanish ecosystems in the face of global change. The fact that SOC was positively related to annual precipitation and negatively related to mean annual temperature suggests that future climate change predictions of increased temperature and reduced precipitation may strongly reduce the potential of Spanish soils as C sinks. However, this may be mediated by changes in vegetation cover (e.g. by favouring the development of forests associated to higher SOC values) and exacerbated by perturbations such as fire. The estimations presented here provide a baseline to estimate future changes in soil C stocks and to assess their vulnerability to key global change drivers, and should inform future actions aimed at the conservation and management of C stocks.

Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale.

Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest. We generated point clouds and multispectral orthomosaics from UAS through photogrammetric processes. These were used to automatically delineate individual tree crowns and calculate vegetation indices such as the normalized difference vegetation index (NDVI) and excess green index (ExG) to objectively quantify defoliation of trees previously identified. Overall, our research suggests that UAS imagery and its derived products enable robust estimation of tree crowns with acceptable accuracy and the assessment of tree defoliation by classifying trees along a gradient from completely defoliated to non-defoliated automatically with 81.8% overall accuracy. The promising results presented in this work should inspire further research and applications involving a combination of methods allowing the scaling up of the results on multispectral imagery by integrating satellite remote sensing information in the assessments over large spatial scales.

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.