Climate change is one of the environmental issues of global dominance and public opinion, becoming the greatest environmental challenge and of interest to researchers. In this context, planting trees on marginal agricultural land is considered a favourable measure to alleviate climate change, as they act as carbon sinks. Aerial laser scanning (ALS) data is an emerging technology for quantitative measures of C stocks. In this study, an estimation was made of the gains of C in biomass and soil in carob (Ceratonia siliqua L.) plantations established on agricultural land in southern Spain. The average above-ground biomass (AGB) corresponded to 85.5% of the total biomass (average 34.01 kg tree⁻¹), and the root biomass (BGB) was 14.5% (6.96 kg tree⁻¹), with a BGB/AGB ratio of 0.20. The total SOC stock in the top 20 cm of the soil (SOC-S₂₀) was 60.70 Mg C ha⁻¹ underneath the tree crown and 43.63 Mg C ha⁻¹ on the non-cover (implantation) area for the C. siliqua plantations. The allometric equations correlating the biomass fractions with the dbh and Ht as independent variables showed an adequate fit for the foliage (Wf, R²_adj = 0.70), whereas the fits were weaker for the rest of the fractions (R²_adj < 0.60). The individual trees were detected using colour orthophotography and the tree height was estimated from 140 crowns previously delineated using the 95th percentile ALS-metric. The precision of the adjusted models was verified by plotting the correlation between the LiDAR-predicted height (HL) and the field data (R²_adj = 0.80; RMSE = 0.53 m). Following the selection of the independent variable data, a linear regression model was selected for dbh estimation (R²_adj = 0.64), and a potential regression model was selected for the SOC (R²adj = 0.81). Using the segmentation process, a total of 8324 trees were outlined in the study area, with an average height of 3.81 m. The biomass C stock, comprising both above- and below-ground biomass, was 4.30 Mg C ha⁻¹ (50.67 kg tree⁻¹), and the SOC_20-S was 37.45 Mg C ha⁻¹. The carbon accumulation rate in the biomass was 1.94 kg C tree⁻¹ yr⁻¹ for the plantation period. The total C stock (W_-S and SOC_20-S) reached 41.75 Mg ha⁻¹ and a total of 4091.5 Mg C for the whole plantation. Gleaned from the synergy of tree cartography and these models, the distribution maps with foreseen values of average C stocks in the planted area illustrate a mosaic of C stock patterns in the carob tree plantation.

This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the defoliation levels of different Pinus spp. and the main environmental drivers of the defoliation due to the PPM. Four vegetation indexes were also calculated for remote sensing defoliation assessment, both inside the stand and in a 60-m buffer area. In the area of study, all Pinus species are affected by defoliation due to the PPM, with a cyclic behavior that has been increasing in frequency in recent years. Defoliation levels were practically equal for all species, with a high increase in defoliation levels 2 and 3 since 2014. The Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) exhibited similar overall (p < 0.001) accuracy in the assessment of defoliation due to the PPM. The synchronization of NDII-defoliation data had a similar pattern for all together and individual Pinus species, showing the ability of this index to adjust the model parameters based on the characteristics of specific defoliation levels. Using Landsat-based NDII-defoliation maps and interpolated environmental data, we have shown that the PPM defoliation in southeastern Spain is driven by the minimum temperature in February and the precipitation in June, March, September, and October. Therefore, the NDII-defoliation assessment seems to be a general index that can be applied to forests in other areas. The trends of NDII-defoliation related to environmental variables showed the importance of summer drought stress in the expansion of the PPM on Mediterranean Pinus species. Our results confirm the potential of Landsat time-series data in the assessment of PPM defoliation and the spatiotemporal patterns of the PPM; hence, these data are a powerful tool that can be used to develop a fully operational system for the monitoring of insect damage.

Sustainable forest management (FOMA) requires explicit knowledge of the ecosystem services (ES) provided by forests and how they can be improved by different FOMA alternatives, especially when threatened by climate change and the productivity/profitability is low. Decision Support Systems (DSS) have evolved as powerful tools that facilitate decision-making in multi-objective FOMA. However, their use is often limited, as they are mono-objective, are based on empirical relationships, consider limited ES or do not define which silvicultural actions can be deployed, where and when. Thus, decision-makers and forest planners cannot adjust them to local conditions or specific criteria. CAFE (Carbon, Aqua, Fire & Eco-resilience) is a multi-objective DSS for FOMA that quantifies and optimizes different ES that stem from forest management. Its main contribution is coupling eco-hydrological process-based models and multi-objective optimization with genetic evolutionary algorithms. The output of the DSS, shown in a user-friendly interface, is a selection of the best-performing solutions (Pareto Optimal Front) of FOMA that optimize the user-selected ES. This tool allows designing and planning silvicultural operations such as thinning or planting required for meeting multiple objectives in FOMA by answering four fundamental questions: How much (thinning intensity or plantation density), where (spatial allocation), when (year of next intervention) and how (target forest strata in which the stand is vertically divided) thinning. This paper presents the design and components of CAFE, and a practical demonstration in contrasted forests. CAFE might contribute to addressing current challenges of meeting global environmental policy goals by stating baselines and additionality in FOMA.

Adaptive forest management (AFM) is an urgent need because of the uncertainty regarding how changes in the climate will affect the structure, composition and function of forests during the next decades. Current research initiatives for the long-term monitoring of impacts of silviculture are scattered and not integrated into research networks, with the consequent losses of opportunities and capacity for action. To increase the scientific and practical impacts of these experiences, it is necessary to establish logical frameworks that harmonize the information and help us to define the most appropriate treatments. In this context, a number of research groups in Spain have produced research achievements and know-how during the last decades that can allow for the improvement in AFM. These groups address the issue of AFM from different fields, such as ecophysiology, ecohydrology and forest ecology, thus resulting in valuable but dispersed expertise. The main objective of this work is to introduce a comprehensive strategy aimed to study the implementation of AFM in Spain. As a first step, a network of 34 experimental sites managed by 14 different research groups is proposed and justified. As a second step, the most important AFM impacts on Mediterranean pines, as one of the most extended natural and planted forest types in Spain, are presented. Finally, open questions dealing with key aspects when attempting to implement an AFM framework are discussed. This study is expected to contribute to better outlining the procedures and steps needed to implement regional frameworks for AFM.

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery.

Assisted population migration in new forest plantations can enhance future success by using seed sources that are more adapted to future climate than the local seed source. It is particularly interesting in the Spanish Mediterranean, where the number of protective plantations with Pinus halepensis has increased. However, there is a lack of operational information on these practices. The aim of this work is to improve this gap and provide information to forest managers on how to accomplish this new practice. To this end, the response of six plantations in terms of growth and survival has been evaluated. These six sites are heterogeneous regarding: the seed source planted (between 7 and 11 different ones), the bioclimate (aridity gradient), and the period analysed (from one to 13 years). Twelve seed source selection approaches have been proposed and tested using these sites. The local-use approach performed well overall (54 % reliability) but showed 25 % less medium-term survival than the average. The genetic improvement approach achieved the highest growth (+13 %), although survival gains were limited (+2 %). The drought-duration approach showed the highest reliability (58 %) and favourable outcomes where differences appeared. In contrast, the use of population distribution models as an approach to select the suitable seed source, showed poor average reliability under climate change scenarios, but not for except the historical climate, and did not correlated to plantation performance. In conclusion, to improve the future performance of P. halepensis plantations, it is recommended to review traditional silvicultural practices and consider the use of seed sources from genetic improvement or from areas with longer drought periods than the target area.