The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies

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 (W_t-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.

Forests are key elements in mitigating the effects of climate change due to the fact of their carbon sequestration capacity. Forest management can be oriented to optimise the carbon sequestration capacity of forest stands, in line with other productive objectives and the generation of ecosystem services. This research aimed to determine whether thinning treatments have a positive influence on the growth patterns of some of the main Mediterranean pine species and, therefore, on their Carbon (C) fixation capacity, both in terms of living biomass and soil organic carbon. The results obtained show that C sequestration capacity (biomass and SOC) increased at higher thinning intensities due to the induced alterations in tree growth patterns. We observed almost a 1.5-fold increase in P. nigra and P. sylvestris, respectively, and over a two-fold increase in P. pinaster under heavy thinning treatments; SOC stocks were affected by the intensity of the thinning treatments. These results can contribute to improving silvicultural practices aimed at C sequestration in forest plantations located in dry areas of the Mediterranean.

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.

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation.

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.