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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, R2adj = 0.70), whereas the fits were weaker for the rest of the fractions (R2adj < 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 (R2adj = 0.80; RMSE = 0.53 m). Following the selection of the independent variable data, a linear regression model was selected for dbh estimation (R2adj = 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 SOC20-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 SOC20-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.

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The main objective of this research work was to evaluate the current and future geographical environmental space of Q. ilex and P. halepensis plantations, as well as to analyze the use of agroforestry components based on afforestation techniques in degraded agricultural areas in the context of climate change in Andalusia Spain. Current survival was evaluated under current and foreseeable future climate change scenarios and by using Species Distribution Models (SDMs).For this purpose, the Geographic Information System, and the presence/absence database ofQ. ilex and P. halepensis, obtained from the Andalusian Forest Surveillance Network (RED SEDA), and the third National Forest Inventory of Spain were used. (IFN3); data on the presence and survival of afforestation with Holm oak and Aleppo pine in agricultural lands and environmental information in the form of a "raster" at a spatial resolution of 2000 and 200 m 2 were used. It was found that 25-38% of the Q. ilexandP. halepensis plantations planted between 1993 and 2000 were established in the optimal area of occurrence (probability of occurrence> 70%), but only 12,3% (Q. ilex) and 22,9% (P. halepensis) simultaneously presented an acceptable survival rate (> 50%).Furthermore, the volume of the environmental space defined by Q. ilex decreased, while that defined by P. halepensis remained constant in future climate change projections. The potential of SDMs to predict the survival rate distribution of Q. ilex and P. halepensis and to assess the future stability of each of these species has been confirmed. In the worst case, ~ 5% of Q. ilex and ~ 33% of P. halepensis in the planted area would withstand climate change.On the other hand, robust allometric models were determined to estimate the general carbon biomass and SOC stocks as a function of the height and diameter of Ceratonia siliqua trees obtained from field data. Measurements of individual trees were obtained derived from Low density ALS and dbh and SOC were also quantified, since dbh is the most reliable variable for estimating biomass; finally the total C stocks in the carob tree plantation were estimated and mapped. The values obtained for tree biomass in foliage, roots, branches, and stems for carob trees were 5,35, 17,06, 14,53 and 9,52%, respectively. The combination of allometric models allowed calculating the total C stock in the Ceratonia siliqua plantation with precision and at a lower cost than with field inventories.Finally, the landscape fragmentation processes associated with afforestation in agricultural lands between 1990 and 2018 in two localities in southern Spain (Andevalo and Guadix) were studied using the land cover databases of Andalusia (1990 and, The PatchAnalyst-ARCgis and Getis-Ord Gi analysis fragmentation metrics were calculated to quantify changes in ecosystem

&lt;p&gt;Anti-personal/tank landmines, improvised explosive devices (IED), unexploded ordinances (UXO), and other abandoned explosive ordinances (EO) all pose long-lasting threats that are detrimental to areas of conflict. From 2015 to 2021, a total of 49,050 deaths/injuries were caused by EOs, and this number is only increasing. Current demining methods heavily rely on ground-based electromagnetic-induction (EMI); however, this method is costly, time consuming and puts personnel at risk. Recent advances in drone and remote sensing technology have allowed for the development of alternative remote methods to improve the efficiency in locating EOs. We used a Velodyne VLP-16 light detection and ranging (LiDAR) sensor attached to a DJI Matrice 600 drone platform to remotely identify EOs, specifically PFM-1 and VPMA-3 anti-personnel mines, TM-62M anti-vehicle mines, and 3 meter long 122 mm multibarrel rockets (MBRL). LiDAR data was acquired in dual return acquisition mode at 300 rpm and a flight speed of 1 m/s. Several of these EOs are being used in the current Russo-Ukrainian war, including: TM-62 anti-vehicle mines, PFM-1 landmines, and the MBRL rockets. Our LiDAR sensor was calibrated with a 18 m swath width to acquire 4630 points/m2 &amp;#160;density and a 1.7 cm footprint resolution. The LiDAR data that was collected was post-processed to produce various derivative data such as: 3D point clouds, digital elevation models (DEM), digital surface models (DSMs), and derivative data products such as the total horizontal derivative (THD) filter. Processed data highlighted lateral spatial heterogeneity, which identified vertical and horizontal MBRLs, as well as surficial TM-62M anti-vehicle, TM62P anti-personnel mines and VPMA-3 landmines. PFM-1 landmines, the smallest of all EOs used, were not located, as the footprint resolution of the data collected was too small (1.7 cm) to clearly differentiate the ordinance from the environment. This pilot study allowed us to better understand the strengths and weaknesses of this method. We plan to further develop this technology by exploring the use of streamlined algorithms, applying alternative data processing workflows, and using sub-pixel techniques to improve the accuracy and efficiency of location. Refining data acquisition parameters, such as the speed and height of drone flight may also lead to further improvements in efficiency. In addition to location, a focus could also be placed on looking at intensity to identify material properties of EOs.&amp;#160;&lt;/p&gt;