Airborne Laser Scanning Cartography of On-Site Carbon Stocks as a Basis for the Silviculture of Pinus Halepensis Plantations

Long-Term Carbon Sequestration in Pine Forests under Different Silvicultural and Climatic Regimes in Spain

Proactive silviculture treatments (e.g., thinning) may increase C sequestration contributing to climate change mitigation, although, there are still questions about this effect in Mediterranean pine forests. The aim of this research was to quantify the storage of biomass and soil organic carbon in Pinus forests along a climatic gradient from North to South of the Iberian Peninsula. Nine experimental Pinus spp trials were selected along a latitudinal gradient from the pre-Pyrenees to southern Spain. At each location, a homogeneous area was used as the operational scale, and three thinning intensity treatments: unthinned or control (C), intermediate thinning (LT, removal of 30–40% of the initial basal area) and heavy thinning (HT, removal of 50–60%) were conducted. Growth per unit area (e.g., expressed as basal area increment-BAI), biomass, and Soil Organic Carbon (SOC) were measured as well as three sets of environmental variables (climate, soil water availability and soil chemical and physical characteristics). One-way ANOVA and Structural Equation Modelling (SEM) were used to study the effect of thinning and environmental variables on C sequestration. Biomass and growth per unit area were higher in the control than in the thinning treatments, although differences were only significant for P. halepensis. Radial growth recovered after thinning in all species, but it was faster in the HT treatments. Soil organic carbon (SOC10, 0–10 cm depth) was higher in the HT treatments for P. halepensis and P. sylvestris, but not for P. nigra. SEM showed that Pinus stands of the studied species were beneficed by HT thinning, recovering their growth quickly. The resulting model explained 72% of the variation in SOC10 content, and 89% of the variation in silvicultural condition (basal area and density) after thinning. SOC10 was better related to climate than to silvicultural treatments. On the other hand, soil chemical and physical characteristics did not show significant influence over SOC10- Soil water availability was the latent variable with the highest influence over SOC10. This work is a new contribution that shows the need for forest managers to integrate silviculture and C sequestration in Mediterranean pine plantations.

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.

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

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 Mg C ha−1, ranging from 23.96 Mg C ha−1 for P. halepensis to 58.09 Mg C 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.

Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects

Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.