Search

Quantifying the carbon-stocking contribution of forest plantations is a crucial but challenging and expensive process, usually performed through field analysis. For this reason, plantations’ carbon storage is often calculated and reported using generic and inaccurate functions relying exclusively on tree species and plantation age. This study introduces a new field-independent (FI) method for forest plantations’ carbon quantification and mapping through automatic analysis of Sentinel-2 data. The study area is a Guatemalan forest plantation of 20 hectares, for which we constructed a reference dataset measuring in the field the diameter and the height of all trees within 20 randomly selected plots (10-meter radius). The CO2 equivalent absorbed by the plantation was first estimated using ground data and a design-based (DB) approach. Then, to obtain CO2 equivalent estimates but also maps, we used both ground and Sentinel-2 data to compare a standard model-assisted (MA) approach relying on Random Forests with the FI approach. Our results demonstrate that the FI method provides carbon stock statistics comparable to those obtained using DB and MA methods and more accurate maps. Accordingly, the RMSE obtained using the FI method was 34% while that obtained by the MA method – exploiting random forest algorithm – was greater (RMSE = 39%). The 95% confidence interval estimates of the CO2 stored in the plantation were 100 ± 18 MgC ha−1 and 102 ± 8 MgC ha−1, for DB and MA respectively. Using the FI method, the CO2 ranged between 89 and 117 Mg C ha−1, all values within the DB confidence interval. In addition, the FI map was surprisingly consistent with the MA-derived map, making our approach a valid alternative for monitoring plantation status and carbon storage when ground data are not available.

Forest parameter estimation is required to support the sustainable management of forest ecosystems. Currently, forest resource assessment is increasingly linked to auxiliary information obtained from remote sensing (RS) technologies. In forest parameter estimation, airborne laser scanning (ALS) data have been demonstrated to be an invaluable source of information. However, ALS data are often not available for the entire forest area, whereas images from multiple satellite systems offer new opportunities for large-scale forest surveys. This study aims to assess and estimate the contribution of field plot data and ALS data along with Landsat data to the precision of growing stock volume (GSV) estimates. We compared different approaches for model-assisted estimation of mean forest GSV per unit area using different proportions of field sample data, ALS cover data, and wall-to-wall Landsat data. Model-assisted estimators were used with NFI sample data in an Italian study area using 10 RS predictors, specifically the seven Landsat 7 ETM+ bands and three fine-resolution metrics based on ALS-derived canopy height. We found that relative to the standard simple expansion estimator, the model-assisted estimators produced relative efficiency of 1.16 when using only Landsat data and relative efficiencies as great as 1.33 when using increasing levels of ALS coverage.

To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m. We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired. To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019. TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.

The development of laser scanning technologies has gradually modified methods for forest mensuration and inventory. The main objective of this study is to assess the potential of integrating ALS and TLS data in a complex mixed Mediterranean forest for assessing a set of five single-tree attributes: tree position (TP), stem diameter at breast height (DBH), tree height (TH), crown base height (CBH) and crown projection area radii (CPAR). Four different point clouds were used: from ZEB1, a hand-held mobile laser scanner (HMLS), and from FARO® FOCUS 3D, a static terrestrial laser scanner (TLS), both alone or in combination with ALS. The precision of single-tree predictions, in terms of bias and root mean square error, was evaluated against data recorded manually in the field with traditional instruments. We found that: (i) TLS and HMLS have excellent comparable performances for the estimation of TP, DBH and CPAR; (ii) TH was correctly assessed by TLS, while the accuracy by HMLS was lower; (iii) CBH was the most difficult attribute to be reliably assessed and (iv) the integration with ALS increased the performance of the assessment of TH and CPAR with both HMLS and TLS.

The application of Monteith’s models to estimate the gross primary production (GPP) of forest ecosystems is a common and efficient practice. A modified version of one of these parametric models, C-Fix, has been widely tested in Italy at daily to annual temporal scales. The current paper is a step forward demonstrating the capability of this model to reproduce also inter-annual forest GPP variations. To this aim, dendrochronological measurements collected in two study areas were utilized to validate the model. The first study area is a coastal Mediterranean pine forest and the second is a more temperate ecosystem dominated by the presence of European beech. The experiment was organized into the following main steps. Modified C-Fix was applied to simulate annual GPP over a 15-year period for both sites. Both the main driving factors used by the model and the estimated GPP were then analysed and assessed against the detrended tree-ring width series of the collected cores. The study yields indications on the factors most influential on GPP for the two forest ecosystems and supports the capability of the proposed modelling approach to reproduce relevant inter-annual GPP variations, which is useful for predicting the impacts of ongoing climatic changes.

This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates.