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Nothofagus alessandrii forests in the Mediterranean region of Chile face significant threats from deforestation, fragmentation, wildfires, and climate change. This study investigated the temporal and spatial dynamics of N. alessandrii populations and their implications for habitat fragmentation and connectivity. Using remote sensing data and geographic information system analysis, we assessed deforestation patterns, fragmentation metrics, and connectivity indices for different scenarios between 2009 and 2022. A reduction in the N. alessandrii forest area, extensive fragmentation, and decreased connectivity were observed over time, with wildfires exacerbating these trends. Reforestation simulations showed the potential of targeted restoration efforts to improve connectivity, especially when focusing on proximate patches. However, careful consideration of seed dispersal distances and wildfire risk is essential for effective reforestation planning. Overall, our findings underscore the urgent need for conservation interventions to preserve N. alessandrii forests and their biodiversity in Chile's coastal mountain ecosystems. A proactive and collaborative approach is essential to ensure the survival of these forests for future generations.

The connectivity of forest ecosystems is increasingly recognized as a key factor in evaluating the sustainability of forest management, with significant implications for biodiversity conservation. This study examines the impact of afforestation programs on forest evolution, fragmentation, and connectivity in León province, Spain, over the past 25 years (1996–2020). Three scenarios were modeled across two periods (1996–2006 and 2006–2020), integrating data from the national forest inventories (IFN2, IFN3, and IFN4) and afforestation program records provided by the Junta de Castilla y León. The evolution of connectivity “with” and “without” afforestation was analyzed using Graphab 2.6 and graph theory, and several connectivity metrics were calculated. The first period analyzed, influenced by the two initial afforestation programs, corresponded to the end of a forest expansion phase, followed by a decrease in tree cover. Despite this reduction, a net positive balance of up to 24% of all connectivity metrics (NC, PC, Flux, and ECA) was observed throughout the study period. Afforestation in mountain areas enhanced tree cover continuity, resulting in a more homogeneous but less diverse landscape. Conversely, afforestation in agricultural lands increased landscape heterogeneity, diversifying and extending the ecological network of connections. These programs have played a crucial role in shaping the landscape, influencing its diversity and the evolution of forest connectivity. Legislation grounded in technical and ecological principles should be prioritized as a strategic tool to address pressing land management challenges and preserve natural values.

(1) Background: Carob tree (Ceratonia siliqua L.) pulp is of great interest nowadays due to its nutritional benefits and diverse utilization in the food process. The nutritional and antioxidant properties of carob pulp in the Mediterranean have been assessed in several studies. Still, few studies have combined, within the same work, a comprehensive analysis of the chemical composition of carob pulp from fruits of natural populations across different countries of the Mediterranean basin, while also incorporating new research areas. (2) Methods: In the present work, we evaluated the nutritional value, total phenolic compounds, and antioxidant activity of carob pulp derived from wild populations of carob trees from three Mediterranean countries: Lebanon, Spain, and Morocco; (3) Results: All assessed bromatological characteristics, with the exception of ash and fiber content, revealed significant differences in the carob pulp from the three countries under study. High variability was observed for the total polyphenols ranging between 5.05 mg/g and 12.70 mg/g. Sucrose was the predominant sugar quantified ranging between 13.70 g/100 g and 28.10 g/100 g. The lipid content was low (0.26–0.36%). The moisture content of carob pulp ranges between 4.36% and 6.40%. Carob pulp presented a rich composition in fiber, with an average of 35.87%. The ash content was between 2.52% and 3.28%. The percentage of the protein content of the carob pulp ranged between 4.40 and 5.52, with an average carbohydrate value of 74.71%; (4) Conclusions: Spanish wild carob pulp samples offered higher carbohydrates contents and values for sucrose, fructose, and glucose, polyphenol content, and antioxidant activity, whereas Moroccan samples had higher values of carbohydrates and in concrete, the monosaccharides fructose and glucose showed higher contents in proteins and lipids. In contrast, Lebanese samples exhibit a high content of the disaccharide sucrose. These findings could be exploited in breeding programs to improve varieties that balance both the agronomical quality and nutritional values of carob pulp.

Pinus pinea is an important Mediterranean species due to its adaptability and tolerance to aridity and its high-quality pine nuts. Different forest types located in Mediterranean native and non-native environments provide the opportunity to perform comparative studies on the species’ response to climate change. The aims of this study were to elucidate growth patterns of the species growing in native and exotic habitats and to analyze its response to climatic fluctuations, particularly drought, in both geographical contexts. Understanding stone pine (Pinus pinea) growth responses to climate variability in native and exotic habitats by comparing natural stands and plantations may provide useful information to plan adequate management under climate change. By doing so, we enhance the understanding of P. pinea’s adaptability and provide practical approaches to its sustainable management. In this study, we reconstructed and compared the stem radial growth of seven stone pine stands, two in southern Spain and five in central–southern Chile, growing under different climatic conditions. We quantified the relationships between growth variability and climate variables (total rainfall, mean temperature, and SPEI drought index). Growth was positively correlated with autumn rainfall in plantations and with autumn–winter rainfall in natural stands. Growth was also enhanced by high autumn-to-spring rainfall in the driest Chilean plantation, whereas in the wettest and coolest plantation, such correlation was found in winter and summer. A negative impact of summer temperature was found only in one of the five Chilean plantations and in a Spanish site. The correlation between SPEI and tree-ring width indices showed different patterns between and within countries. Overall, exotic plantations showed lower sensitivity to climate variability than native stands. Therefore, stone pine plantations may be useful to assist in mitigating climate change.

Abstract Previous research indicates that some important cocoa cultivated areas in West Africa will become unsuitable for growing cocoa in the next decades. However, it is not clear if this change will be mirrored by the shade tree species that could be used in cocoa-based agroforestry systems (C-AFS). We characterized current and future patterns of habitat suitability for 38 tree species (including cocoa), using a consensus method for species distribution modelling considering for the first time climatic and soil variables. The models projected an increase of up to 6% of the potential suitable area for cocoa by 2060 compared to its current suitable area in West Africa. Furthermore, the suitable area was highly reduced (14.5%) once considering only available land-use not contributing to deforestation. Regarding shade trees, 50% of the 37 shade tree species modelled will experience a decrease in geographic rate extent by 2040 in West Africa, and 60% by 2060. Hotspots of shade tree species richness overlap the current core cocoa production areas in Ghana and Côte d’Ivoire, suggesting a potential mismatch for the outer areas in West Africa. Our results highlight the importance of transforming cocoa-based agroforestry systems by changing shade tree species composition to adapt this production systems for future climate conditions.

Thinning focused on achieving growth and diameter management objectives has typically led to stands with reduced climate sensitivity compared to unthinned stands. We integrated dendrochronological with Airborne Laser Scanner (LiDAR) data and growth models to assess the long-term impact of thinning intensity on Canary pine (Pinus canariensis) radial growth. In 1988, 18 permanent treatment units were established in 73-year-old Canary pine plantations and three thinning treatments were applied (C–control-unthinned; 0% basal area removal; MT–moderate thinning: 10% and 15% basal area removal, and HT–heavy thinning: 46% and 45% basal area removal on the windward and leeward slopes, respectively). Dendrochronological data were measured in 2022 and expressed as basal area increment (BAI). The impact of climate on growth was examined by fitting linear regression models considering two different Representative Concentration Pathway (RCP) climate scenarios, RCP 2.6 and RCP 4.5. Finally, LiDAR data were used for standing segmentation to evaluate changes in overall growth under different climatic scenarios. The LiDAR–stand attributes differed between aspects. The BAI of the most recent 20 years (BAI20) after thinning was significantly higher for the moderate and heavy treatments on the leeward plots (F = 47.31, p < 0.001). On the windward plots, BAI decreased after moderate thinning. Considerable thinning treatments resulted in stronger changes in growth when compared to RCP climatic scenarios. From a silviculture perspective, the mapping of canopy structure and growth response to thinning under different climatic scenarios provides managers with opportunities to conduct thinning strategies for forest adaptation. Combining dendrochronological and LiDAR data at a landscape scale substantially improves the value of the separate datasets as forecasted growth response maps allow improving thinning management plans.

The carob tree (Ceratonia siliqua L.) is a significant fruit tree in the Mediterranean region with cultural, biological, and ecological importance. Despite its importance, intraspecific trait variability (ITV) in carob trees has been largely overlooked in previous studies. Understanding ITV and its relationship with environmental conditions is crucial for conservation and breeding programs. In this study, we investigated the variability of carob pod and seed-related traits across different ecological scales in 25 studied populations in Morocco. Significant differences in morphological traits were observed between carob populations at various ecological levels, and pod-related traits exhibited greater variability than seed traits. Correlation analysis revealed strong associations between carob morphological traits and environmental conditions, with altitude and aridity index playing an influential role. The aridity gradient was strongly related to changes in pod size, seed number, and size, as well as seed yield. Our findings highlight an important ITV reaching 45% at the intra-population level, 36.5% at the inter-geographic level, and 30% at the inter-population level. Overall, this study contributes valuable insights into the ecology and adaptation of carob trees, emphasizing the importance of considering intraspecific variability when studying this remarkable species. This knowledge is critical for addressing the challenges posed by climate change and human activities on the long-term survival and ecological functioning of carob populations.

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics.

In many tropical regions, national forests plantation programs have been promoted. Those plantations frequently contribute to habitat changes. However, the associated effects of forest plantations on habitat fragmentation and landscape connectivity are unclear. From 2008 to 2018, we examined land use changes, plantations, and deforestation of the Manabí province (Ecuador) provided by the Ecuadorian Ministry of the Environment. Four scenarios were created: (i) land uses in 2008, (ii) land uses in 2018, (iii) land uses in 2018 without deforestation, and iv) land uses in 2018 including reforestation. Fragmentation and connectivity metrics were analyzed using ArcGisPro and Graphad 2.6 software, respectively. Puma yagouaroundi was selected as the reference species. At regional scale, forest plantations had a significant effect on land uses changes and fragmentation during the study period. Forests decreased from 33.7% to 32.4% between 2008 and 2018, although other natural land uses, mostly those involving shrubs, increased by almost double (from 2.4% to 4.6%). Most of the deforestation affected native forests during this period, and most reforested areas in 2018 covered former agricultural land. Fragmentation decreased in the number of patches and increased in the average patch size. When considering reforestation, deforestation was higher than the reforested area (58 km2 of difference), increasing the number of patches but with smaller size. Reforestation increased connectivity with a higher number of links and distance, particularly in central and extreme northeast areas of Manabí province. The scenario without deforestation also increased connectivity for Puma yagouaroundi in the west part of the Manabí province. Our findings suggest that forest plantations contribute to forest conservation by increasing the connectivity between fragmented patches.

This study investigates the impact of fragmentation on Ecuador’s coastal mangrove forests. Fragmentation is identified as a primary cause of aquatic ecosystem degradation. We analyzed the relationship between habitat loss, fragmentation, and mangrove connectivity through a multitemporal approach using Global Mangrove Watch and fragmentation and connectivity metrics. The terrain was divided into 10 km2 hexagons, and six fragmentation metrics were calculated. A Getis–Ord Gi* statistical analysis was used to identified areas with the best and worst conservation status, while connectivity analyses were performed for a generic species with a 5 km dispersion. Findings revealed widespread mangrove fragmentation in Ecuador, with geographical differences between the insular region (Galapagos) and the mainland coast. Minimal loss or even expansion of mangrove forests in areas like the Galapagos Islands contrasted with severe fragmentation along the mainland coast. Transformation of forests into fisheries, mainly prawn factories, was the primary driver of change, while only a weak correlation was observed between mangrove fragmentation and conversion to agriculture, which accounts for less than 15% of all deforestation in Ecuador. Fragmentation may increase or decrease depending on the management of different deforestation drivers and should be considered in large-scale mangrove monitoring. Focusing only on mangrove deforestation rates in defining regional conservation priorities may overlook the loss of ecosystem functions and fragmentation.

One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous Abies pinsapo–Pinus–Quercus–Juniperus with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompasses the three attributes to quantify the potential capacity for fire propagation, and (iii) generating a cartograph of potential propagation capacity. Our main findings showed that Biomass–ALS calibration models performed well for Abies pinsapo (R2 = 0.69), Juniperus spp. (R2 = 0.70), Pinus halepensis (R2 = 0.59), Pinus spp. mixed (R2 = 0.80), and Pinus spp.–Juniperus spp. (R2 = 0.59) forests. The highest values of biomass were obtained for Pinus halepensis forests (190.43 Mg ha−1). The structural complexity of forest fuels was assessed by calculating the LiDAR Height Diversity Index (LHDI) with regard to the distribution and vertical diversity of the vegetation with the highest values of LHDI, which corresponded to Pinus spp.–evergreen (2.56), Quercus suber (2.54), and Pinus mixed (2.49) forests, with the minimum being obtained for Juniperus (1.37) and shrubs (1.11). High values of the Fuel Desiccation Index (IDM) were obtained for those areas dominated by shrubs (−396.71). Potential Behaviour Biomass Index (ICB) values were high or very high for 11.86% of the area and low or very low for 77.07%. The Potential Behaviour Structural Complexity Index (ICE) was high or very high for 37.23% of the area, and low or very low for 46.35%, and the Potential Behaviour Fuel Desiccation Index (ICD) was opposite to the ICB and ICE, with high or very high values for areas with low biomass and low structural complexity. Potential Fire Behaviour Index (ICP) values were high or very high for 38.25% of the area, and low or very low values for 45.96%. High or very high values of ICP were related to Pinus halepensis and Pinus pinaster forests. Remote sensing has been applied to improve fuel attribute characterisation and cartography, highlighting the utility of integrating multispectral and ALS data to estimate those attributes that are more closely related to the spatial organisation of vegetation.

Airborne laser scanning (ALS) technology is fully implemented in forest resource assessment processes, providing highly accurate and spatially continuous results throughout the area of interest, thus reducing inventory costs when compared with traditional sampling inventories. Several approaches have been employed to estimate forest parameters using ALS data, such as the Area-Based Approach (ABA) and Individual Tree Crown (ITC). These two methodologies use different information processing and field data collection approaches; thus, it is important to have a selection criterion for the method to be used based on the expected results and admissible errors. The objective of this study was to compare the prediction errors of forest inventory attributes in the functioning of ABA and ITC approaches. A plantation of 500 ha of Pinus radiata (400–600 trees ha−1) in Chile was selected; a forest inventory was conducted using the ABA and ITC methods and the accuracy of both methods was analyzed. The ITC models performed better than the ABA models at low tree densities for all forest inventory attributes (15% MAPE in tree density—N—and 11% in volume—V). There was no significant difference in precision regarding the volume and basal area (G) estimations at medium densities, although ITC obtained better results for density and dominant height (Ho). At high densities, ABA performed better for all the attributes except for height (6.5% MAPE in N, 8.7% in G, and 8.9% in V). Our results showed that the precision of forest inventories based on ALS data can be adjusted depending on tree density to optimize the selected approach (ABA and ITC), thus reducing the inventory costs. Hence, field efforts can be greatly decreased while achieving better prediction accuracies.

There are many local fragmentation metrics, but most can be grouped into four types (composition/area, isolation, edge, and shape), and none of them alone determines the degree of fragmentation of a patch. Here, we grouped together the main fragmentation metrics (area, edge, shape, and isolation) in order to propose a new metric/index, the Patch Fragmentation Index (PFI), with which to determine fragmentation at patch scale. The index was subsequently verified with the Ecuadorian seasonal dry forest by employing geographic information layers and temporal land uses changes in 1990, 2000, 2008, and 2018. The PFI was applied to calculate the fragmentation per patch, spatial and temporal changes of fragmentation based on PFI were assessed, and the spatial patterns (Getis-Ord Gi * analysis) were calculated. The Ecuadorian seasonal dry forest obtained a mean PFI value of 0.88 (median = 0.99) in 2018. This value has increased by 8.6% since 1990, and 3451 patches of forest disappeared between 1990 and 2018. The Getis-Ord Gi * analysis was effective with regard to describing the spatial patterns, and 62% of the patches that were classified as hot patches in 1990 had disappeared by 2018. The PFI has proven to be a useful tool with which to describe fragmentation patterns at patch scale (regardless of its size) and can be extrapolated to other landscapes. The PFI will provide a new vision and can help in the decision-making related to the conservation and management of fragmented ecosystems.

Quercus ilex L. is the dominant species in the Mediterranean forest and agrosilvopastoral ecosystem “dehesa.” Currently, this forest species is threatened by natural and anthropogenic agents, especially by the decline syndrome, which is caused by Phytophthora cinnamomi and drought periods. Although the morphological and physiological responses of Q. ilex to combined stress (P. cinnamomi and drought) have been examined already, little is known at the molecular level. In this study, we studied the effect and response of 8-month seedlings from three contrasting Andalusian populations (Seville [Se], Granada [Gr], and Almeria [Al]) to the individual and combined stresses of P. cinnamomi and drought from morphological, physiological, biochemical, and proteomics data. Whereas, seedling damage (leaf chlorosis and necrosis) and mortality were greater under the combined stresses in the three populations, the effect of each individual stress was population-dependent. Resilient individuals were found in all the populations at different percentages. The decrease in leaf chlorophyll fluorescence, photosynthetic activity, and stomatal conductance observed in undamaged seedlings was greater in the presence of both stresses, the three populations responding similarly to drought and P. cinnamomi. Biochemical and proteomic analyses of undamaged seedlings from the two most markedly contrasting populations (Se and Al) revealed the absence of significant differences in the contents in photosynthetic pigments, amino acids, and phenolics among treatments. The Se and Al populations exhibited changes in protein profile in response to the different treatments, with 83 variable proteins in the former population and 223 in the latter. Variable proteins belonged to 16 different functional groups, the best represented among which were protein folding, sorting and degradation, carbohydrate, amino acid, and secondary metabolism, photosynthesis, and ROS scavenging. While photosynthetic proteins were mainly downaccumulated, those of stress-responsive were upaccumulated. Although no treatment-specific response was observed in any functional group, differences in abundance were especially marked under the combined stresses. The following variable proteins are proposed as putative markers for resilience in Q. ilex, namely, aldehyde dehydrogenase, glucose-6-phosphate isomerase, 50S ribosomal protein L5, and α-1,4-glucan-protein synthase [UDP-forming].

The use of spatially interactive forest landscape models has increased in recent years. These models are valuable tools to assess our knowledge about the functioning and provisioning of ecosystems as well as essential allies when predicting future changes. However, developing the necessary inputs and preparing them for research studies require substantial initial investments in terms of time. Although model initialization and calibration often take the largest amount of modelers’ efforts, such processes are rarely reported thoroughly in application studies. Our study documents the process of calibrating and setting up an ecophysiologically based forest landscape model (LANDIS-II with PnET-Succession) in a biogeographical region where such a model has never been applied to date (southwestern Mediterranean mountains in Europe). We describe the methodological process necessary to produce the required spatial inputs expressing initial vegetation and site conditions. We test model behaviour on single-cell simulations and calibrate species parameters using local biomass estimations and literature information. Finally, we test how different initialization data—with and without shrub communities—influence the simulation of forest dynamics by applying the calibrated model at landscape level. Combination of plot-level data with vegetation maps allowed us to generate a detailed map of initial tree and shrub communities. Single-cell simulations revealed that the model was able to reproduce realistic biomass estimates and competitive effects for different forest types included in the landscape, as well as plausible monthly growth patterns of species growing in Mediterranean mountains. Our results highlight the importance of considering shrub communities in forest landscape models, as they influence the temporal dynamics of tree species. Besides, our results show that, in the absence of natural disturbances, harvesting or climate change, landscape-level simulations projected a general increase of biomass of several species over the next decades but with distinct spatio-temporal patterns due to competitive effects and landscape heterogeneity. Providing a step-by-step workflow to initialize and calibrate a forest landscape model, our study encourages new users to use such tools in forestry and climate change applications. Thus, we advocate for documenting initialization processes in a transparent and reproducible manner in forest landscape modelling.

We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots - six for each species - and three different fixed-radius designs (i.e. 7, 15, and 30 m) were established within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.

Many planted Pinus forests are severely affected by defoliation and mortality processes caused by pests and droughts. The mapping of forest tree crown variables (e.g., leaf area index and pigments) is particularly useful in stand delineation for the management of declining forests. This work explores the potential of integrating multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data for stand delineation based on selected tree crown variables in Pinus sylvestris plantations in southern Spain. Needle pigments (chlorophyll and carotenes) and leaf area index (LAI) were quantified. Eight vegetation indices and ALS-derived metrics were produced, and seven predictors were selected to estimate and map tree crown variables using a Random Forest method and Gini index. Chlorophylls a and b (Chla and Chlb) were significantly higher in the non-defoliated and moderately defoliated trees than in severely defoliated trees (F = 14.02, p < 0.001 for Chla; F = 13.09, p < 0.001 for Chlb). A similar response was observed for carotenoids (Car) (F = 14.13, p < 0.001). The LAI also showed significant differences among the defoliation levels (F = 26.5, p < 0.001). The model for the chlorophyll a pigment used two vegetation indices, Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI); three WV-2 band metrics, and three ALS metrics. The model built to describe the tree Chlb content used similar variables. The defoliation classification model was established with a single vegetation index, Green Normalized Difference Vegetation Index (GNDVI); two metrics of the blue band, and two ALS metrics. The pigment contents models provided R2 values of 0.87 (Chla, RMSE = 12.98%), 0.74 (Chlb, RMSE = 10.39%), and 0.88 (Car, RMSE = 10.05%). The cross-validated confusion matrix achieved a high overall classification accuracy (84.05%) and Kappa index (0.76). Defoliation and Chla showed the validation values for segmentations and, therefore, in the generation of the stand delineation. A total of 104 stands were delineated, ranging from 6.96 to 54.62 ha (average stand area = 16.26 ha). The distribution map of the predicted severity values in the P. sylvestris plantations showed a mosaic of severity patterns at the stand and individual tree scales. Overall, the findings of this work underscore the potential of WV-2 and ALS data integration for the assessment of stand delineation based on tree health status. The derived cartography is a relevant tool for developing adaptive silvicultural practices to reduce Pinus sylvestris mortality in planted forests at risk due to climate change.

Conversion of agricultural lands to forest plantations to mitigate rising atmospheric carbon dioxide (CO2) has been proposed, but it depends on accurate estimation of the on-site carbon (C) stocks distribution. The use of aerial laser scanning (ALS) data is a rapidly evolving technology for the quantification of C stocks. We evaluated the use of allometric models together with high-density ALS data for the quantification of biomass and soil C stocks in a 14-year-old Quercus ilex and Q. suber plantation in Southwestern Spain. In 2010, a field survey was performed and tree dasometric and biomass variables were measured. Forty-five soil profiles (N = 180 soil samples) were taken systematically and the soil organic C content (SOC) was determined. Biomass and soil organic C values were regressed against individual dasometric variables and total tree height was used as a predictor variable. Aerial laser scanning data were acquired with a point density of 12 points m−2. Relationships among ALS metrics and tree height were determined using stepwise regression models and used in the allometric models to estimate biomass and SOC C stocks. Finally, a C stock map of the holm-cork oak cover in the study area was generated. We found a tree total biomass of 27.9 kg tree−1 for holm oak and 41.1 kg tree−1 for cork oak. In the holm oak plantation, the SOC content was 36.90 Mg ha−1 for the layer 0−40 cm (SOC40) under the tree crown and 29.26 Mg ha−1 for the inter-planted area, with significant differences from the reference agricultural land (33.35 Mg ha−1). Linear regression models were developed to predict the biomass and SOC at the tree scale, based on tree height (R2 > 0.72 for biomass, and R2 > 0.62 for SOC). The overall on-site C stock in the holm-cork oak plantation was 35.11 Mg ha−1, representing a net C stock rise of 0.47 Mg ha−1 yr−1. The ALS data allows a reliable estimation of C stocks in holm and cork oak plantations and high-resolution maps of on-site C stocks are useful for silvicultural planning. The cost of ALS data acquisition has decreased and this method can be generalised to plantations of other Mediterranean species established on agricultural lands at regional scales. However, an increase of filed data and the availability of local biomass and, in particular, SOC will improve accurate quantification of the C stocks from allometric equations, and extrapolation to large planted areas.

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.

Accurate estimation of forest biomass to enable the mapping of forest C stocks over large areas is of considerable interest nowadays. Airborne laser scanning (ALS) systems bring a new perspective to forest inventories and subsequent biomass estimation. The objective of this research was to combine growth models used to update old inventory data to a reference year, low-density ALS data, and k-nearest neighbor (kNN) algorithm Random Forest to conduct biomass inventories aimed at estimating the C sequestration capacity in large Pinus plantations. We obtained a C stock in biomass (Wt-S) of 12.57 Mg·ha−1, ranging significantly from 19.93 Mg·ha−1 for P. halepensis to 49.05 Mg·ha−1 for P. nigra, and a soil organic C stock of the composite soil samples (0−40 cm) ranging from 20.41 Mg·ha−1 in P. sylvestris to 37.32 Mg·ha−1 in P. halepensis. When generalizing these data to the whole area, we obtained an overall C-stock value of 48.01 MgC·ha−1, ranging from 23.96 MgC·ha−1 for P. halepensis to 58.09 MgC·ha−1 for P. nigra. Considering the mean value of the on-site C stock, the study area sustains 1,289,604 Mg per hectare (corresponding to 4,732,869 Mg CO2), with a net increase of 4.79 Mg·ha−1·year−1. Such C cartography can help forest managers to improve forest silviculture with regard to C sequestration and, thus, climate change mitigation.

High levels of ‘Candidatus Phytoplasma pini’ have produced extensive forest mortality on Pinus halepensis Mill forests in eastern Spain. This has led to the widespread levels of forest mortality. We used archival Landsat imagery and shapes algorithm implemented in the Google Earth Engine to explore the potential of the LandTrendr algorithm and its outputs, together with field observations, to analyze and predict the health status in P. halepensis stands affected by ‘Candidatus Phytoplasma pini’ in Andalusia (south-eastern Spain). We found that the Landsat time series algorithm (LandTrendr) has captured both long- and short-duration trends and changes in spectral reflectance related to phytoplasma disturbance in the Aleppo pine forest stands investigated. The normalized burn ratio (NBR) trends were positively associated with environmental variables: Annual precipitation, mean temperature, soil depth, percent base saturation and aspect. Environmental variables were tested for their contributions to the mapping of changes in Aleppo pine cover in the study area, as an empirical modeling approach to disturbance mapping in forests of south-eastern Spain. The methodology outlined in this paper has produced valuable results that indicate new possibilities for the use in forest management of remote-sensing technologies based on spectral trajectories associated with pest-diseases defoliation. Given the likely increase in pest risks in the forests of southern Europe, accurate assessment and map of pest outbreaks on forests will become increasingly important, both for research and for practical applications in forest management.

Aim of the study: We assessed the ability of the 3-PG process-based model to accurately predict growth of Pinus sylvestris and P. nigra plantations across a range of sites, showing declining growth trends, in southern Spain. Area of study: The study area is located in “Sierra de Los Filabres” (Almería). Material and methods: The model was modified in fifteen parameters to predict diameter (DBH, cm), basal area increment (BAI, cm2 yr-1) and leaf area index (LAI, m2 m-2) in healthy trees and trees showing declining growth. We assumed that a set of specific physiological parameters (stem partitioning ratio-pFS20, maximum litterfall rate-γFx, maximum canopy conductance-gCx, specific leaf area for mature aged stands-σ1, age at which specific leaf area = ½ (σ0 + σ1), age at full canopy cover-tc, and canopy boundary layer conductance-gB) included in 3-PG would be suitable for predicting growth decline related to climate conditions. The calibrated model was evaluated using dendrochronological and LAI data obtained from plots. Main results: Observed and simulated DBH showed a high correlation (R2 > 0.99) between modelled and measured values for both species. In contrast, modelled and observed BAI showed lower correlation (R2 < 0.68). Sensitivity analysis on 3-PG outputs showed that the foliage parameters - maximum litterfall rate, maximum canopy conductance, specific leaf area for mature aged stands, age at which specific leaf area, and age at full canopy cover - were important for DBH and BAI predictions under drought stress. Research highlights: Our overall results indicated that the 3-PG model could predict growth response of pine plantations to climatic stress with desirable accuracy in southern Spain by using readily available soil and climatic data with physiological parameters derived from experiments. Keywords: Hybrid process model; forest management models; growth prediction; Pinus spp, Parameterization; forest decline.