Assessment of the Carbon Stock in Pine Plantations in Southern Spain through ALS Data and K-Nearest Neighbor Algorithm Based Models

Carbon Sequestration in Resin-Tapped Slash Pine (Pinus elliottii Engelm.) Subtropical Plantations

Every year more than 150,000 tons of resin used in a myriad of industrial applications are produced by Brazilian plantations of Pinus elliottii Engelm. (slash pine), which are also used for timber. A pine tree can be tapped for resin over a period of several years. Resin is a complex mixture of terpenes, which are carbon-rich molecules, presumably influencing pine plantation carbon budgets. A total of 270 trees (overall mean DBH of 22.93 ± 0.11 cm) of 14-, 24-, and 26-year-old stands had their C content measured. Three different treatments (intact, wounded panels, and wounded + chemically stimulated panels, 30 trees each) were applied per site. Above- and belowground biomass, as well as resin yield, were quantified for two consecutive years. Data were statistically evaluated using normality distribution tests, analyses of variance, and mean comparison tests (p ≤ 0.05). The highest resin production per tree was recorded in the chemically stimulated 14-year-old stand. Tree dry wood biomass, a major stock of carbon retained in cell wall polysaccharides, ranged from 245.69 ± 11.73 to 349.99 ± 16.73 kg among the plantations. Variations in carbon concentration ranged from 43% to 50% with the lowest percentages in underground biomass. There was no significant difference in lignin concentrations. Soils were acidic (pH 4.3 ± 0.10-5.83 ± 0.06) with low C (from 0.05% to 1.4%). Significantly higher C stock values were recorded in pine biomass compared to those reported for temperate zones. Resin-tapping biomass yielded considerable annual increments in C stocks and should be included as a relevant component in C sequestration assessments of planted pine forests.

Simulating diverse forest management options in a changing climate on a Pinus nigra subsp. laricio plantation in Southern Italy

Mediterranean pine plantations provide several ecosystem services but are vulnerable to climate change. Forest management might play a strategic role in the adaptation of Mediterranean forests, but the joint effect of climate change and diverse management options have seldom been investigated together. Here, we simulated the development of a Laricio pine (Pinus nigra subsp. laricio) stand in the Bonis watershed (southern Italy) from its establishment in 1958 up to 2095 using a state-of-the-science process-based forest model. The model was run under three climate scenarios corresponding to increasing levels of atmospheric CO₂ concentration and warming, and six management options with different goals, including wood production and renaturalization. We analysed the effect of climate change on annual carbon fluxes (i.e., gross and net primary production) and stocks (i.e., basal area, standing and harvested carbon woody stocks) of the autotrophic compartment, as well as the impact of different management options compared to a no management baseline. Results show that higher temperatures (+3 to +5 °C) and lower precipitation (-20 % to -22 %) will trigger a decrease in net primary productivity in the second half of the century. Compared to no management, the other options had a moderate effect on carbon fluxes over the whole simulation (between -14 % and +11 %). While standing woody biomass was reduced by thinning interventions and the shelterwood system (between -5 % and -41 %), overall carbon stocks including the harvested wood were maximized (between +41 % and +56 %). Results highlight that management exerts greater effects on the carbon budget of Laricio pine plantations than climate change alone, and that climate change and management are largely independent (i.e., no strong interaction effects). Therefore, appropriate silvicultural strategies might enhance potential carbon stocks and improve forest conditions, with cascading positive effects on the provision of ecosystem services in Mediterranean pine plantations.

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations.

Biomass Assessment and Carbon Sequestration in Post-Fire Shrublands by Means of Sentinel-2 and Gaussian Processes

In this contribution, we assessed the biomass and carbon stock of a post-fire area covered by a young oak coppice of Quercus pyrenaica Willd. associated with shrubs, mainly of Cistus laurifolius L. This area was burned during the fire event of Chequilla (Guadalajara, Spain) in 2012. Sentinel-2 imagery was used together with our own forest inventories in 2020 and machine learning methods to assess the total biomass of the area. The inventory includes plots of total dry weight ranging between 6 and 14 Mg·ha−1 with individuals up to 8 years old. Nonlinear, nonparametric Gaussian process regression methods were applied to link reflectance values from Sentinel-2 imagery with total shrub biomass. With a reduced inventory of only 32 plots covering 136 ha, the total biomass could be assessed with a root-mean-square error of 1.36 Mg·ha−1 and a bias of −0.04 Mg·ha−1, getting a relative error between 9.8% and 20.4% for the gathered biomass. This is a rather good estimation considering the little effort and time invested; thus, the suggested methodology is very suitable for forest monitoring and management.

Carbon Sequestration in Carob (Ceratonia siliqua L.) Plantations under the EU Afforestation Program in Southern Spain Using Low-Density Aerial Laser Scanning (ALS) Data

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.