Evaluating the effects of environmental changes on the gross primary production of italian forests

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the interyear GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes

Wall-to-Wall Mapping of Forest Biomass and Wood Volume Increment in Italy

Several political initiatives aim to achieve net-zero emissions by the middle of the twenty-first century. In this context, forests are crucial as a carbon sink to store unavoidable emissions. Assessing the carbon sequestration potential of forest ecosystems is pivotal to the availability of accurate forest variable estimates for supporting international reporting and appropriate forest management strategies. Spatially explicit estimates are even more important for Mediterranean countries such as Italy, where the capacity of forests to act as sinks is decreasing due to climate change. This study aimed to develop a spatial approach to obtain high-resolution maps of Italian forest above-ground biomass (ITA-BIO) and current annual volume increment (ITA-CAI), based on remotely sensed and meteorological data. The ITA-BIO estimates were compared with those obtained with two available biomass maps developed in the framework of two international projects (i.e., the Joint Research Center and the European Space Agency biomass maps, namely, JRC-BIO and ESA-BIO). The estimates from ITA-BIO, JRC-BIO, ESA-BIO, and ITA-CAI were compared with the 2nd Italian NFI (INFC) official estimates at regional level (NUT2). The estimates from ITA-BIO are in good agreement with the INFC estimates (R2 = 0.95, mean difference = 3.8 t ha−1), while for JRC-BIO and ESA-BIO, the estimates show R2 of 0.90 and 0.70, respectively, and mean differences of 13.5 and of 21.8 t ha−1 with respect to the INFC estimates. ITA-CAI estimates are also in good agreement with the INFC estimates (R2 = 0.93), even if they tend to be slightly biased. The produced maps are hosted on a web-based forest resources management Decision Support System developed under the project AGRIDIGIT (ForestView) and represent a key element in supporting the new Green Deal in Italy, the European Forest Strategy 2030 and the Italian Forest Strategy.8n

wall to wall spatial prediction of growing stock volume based on italian national forest inventory plots and remotely sensed data

Abstract Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy. For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model. Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI

Estimation of Mediterranean forest transpiration and photosynthesis through the use of an ecosystem simulation model driven by remotely sensed data

Aim This paper investigates the use of an ecosystem simulation model, FOREST-BGC, to estimate the main ecophysiological processes (transpiration and photosynthesis) of Mediterranean coastal forest areas using remotely sensed data. Location Model testing was carried out at two protected forest sites in central Italy, one of which was covered by Turkey oak (Circeo National Park) and the other by holm-oak (Castelporziano Estate). Methods At both sites, transpiration and photosynthesis measurements were collected in the field during the growing seasons over a four-year period (1999 and 2001 for the Turkey oak; 1997, 1999 and 2000 for the holm-oak). Calibration of the model was obtained through combining information derived from ground measurements and remotely sensed data. In particular, remote sensing estimates of the Leaf Area Index derived from 1 x 1-km NOAA AVHRR Normalized Difference Vegetation Index data were used to improve the adaptation of the model to local forest conditions. Results The results indicated different strategies regarding water use efficiency, 'water spending' for Turkey oak and 'water saving' for holm-oak. The water use efficiency for the holm-oak was consistently higher than that for the Turkey oak and the relationship between VPD and WUE for the holm-oak showed a higher coefficient of determination (R-2 = 0.9238). Comparisons made between the field measurements of transpiration and photosynthesis and the model estimates showed that the integration procedure used for the deciduous oak forest was effective, but that there is a need for further studies regarding the sclerophyllous evergreen forest. In particular, for Turkey oak the simulations of transpiration yielded very good results, with errors lower than 0.3 mm H2O/day, while the simulation accuracy for photosynthesis was lower. In the case of holm-oak, transpiration was markedly overestimated for all days considered, while the simulations of photosynthesis were very accurate. Main conclusions Overall, the approach offers interesting operational possibilities for the monitoring of Mediterranean forest ecosystems, particularly in view of the availability of new satellite sensors with a higher spatial and temporal resolution, which have been launched in recent years