The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. This strategy is ...based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013–2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m−2 year−1 in the first stand and rises to over 600 g C m−2 year−1 in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study, therefore, supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors.
•The study aims at constructing wall-to-wall estimates of forest growing stock (GSV).•We combine NFI plot data, remotely sensed and auxiliary variables.•We applied the methodology in Mediterranean ...Forest.•We create a wall-to-wall GSV forest map in a large test area.•The GSV map was used to produce model-based estimates of GSV at small scale.
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.
A recently proposed modelling strategy predicts the net primary production (NPP) of forest ecosystems by combining the outputs of a NDVI-driven model, Modified C-Fix, and a bio-geochemical model, ...BIOME-BGC. This combination strategy takes into account the effects of forest disturbances but still assumes the presence of a mixture of differently aged trees. The application of this strategy to even-aged forests, therefore, requires a methodological advancement aimed at properly modifying the modelling of main ecosystem processes. In particular, the adaptation of the method to even-aged forests is based on the use of high-spatial-resolution airborne laser scanning (ALS) datasets, which yields green and woody biomass estimates that regulate the simulation of photosynthetic and respiratory processes, respectively. This approach was experimented in a Mediterranean study area, San Rossore Regional Park (Central Italy), which is covered by even-aged pine stands in different development phases. The modelling strategy is driven by MODIS NDVI images and meteorological data across five years (2011–2015), which are combined with estimates of forest canopy cover and height obtained from ALS data taken in 2015. This allows the production of stand NPP estimates, which, when converted into respective current annual increment (CAI) values, reasonably reproduce the age dependency of the available ground observations. The CAI estimates also show a highly significant correlation with these observations (r = 0.773) and moderate error levels (RMSE = 2.03 m3 ha−1 year−1, MBE = −0.45 m3 ha−1 year−1). These results confirm the potential of the modified simulation method to yield accurate high-spatial-resolution NPP estimates, which can offer valuable insights into C cycling and storage, in even-aged forests.
A recent study has proposed and tested a semi-empirical method to estimate crop irrigation based on a water balance logic and Sentinel-2 Multi Spectral Instrument (MSI) NDVI imagery. The current ...paper aims at extending the same approach to the analysis of the main irrigation patterns occurred in Tuscany (Central Italy) during the 2000-2019 period. This operation was made possible by feeding the irrigation water (IW) estimation method with 250-m spatial resolution Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The results of this operation were first assessed versus various reference datasets available for the region; next, the annual maps of IW estimated for the 20 study years were analyzed at province scale in conjunction with relevant agricultural statistics. The use of MODIS in place of MSI images reduces the IW estimation accuracy irregularly at local scale, depending on the size and spatial arrangement of irrigated and non-irrigated fields; the reduction in accuracy is, however, marginal over relatively large areas. Irrigated crops are decreasing throughout most Tuscany provinces, while they are increasing in the most southern and driest province. The possible reasons and implications of these findings are finally discussed in relation to the main environmental issues affecting the region.
In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground ...measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson’s correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.
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.
Relative soil water content (RSWC) is widely used to characterize the impact of water stress (WS) on vegetation. In bi-layer ecosystems, such as olive groves, this impact must be primarily estimated ...for the tree component, which, having greater rooting depth, responds more slowly to WS than understory grass. This complicates the application of methods for RSWC prediction, which must be properly adapted to consider the deeper soil layer influential on olive trees. The current study investigates the modification of a recently proposed RSWC simulation method based on a combination of meteorological and satellite-derived normalized difference vegetation index (NDVI) data. The application of the method to an olive grove in Central Italy requires the estimation of both weather and NDVI contributions affecting solely olive trees, which is carried out through the use of appropriate data processing techniques. The RSWC estimates obtained reasonably reproduce the ground RSWC observations referred to the 1 m soil layer, which are representative of the WS affecting olive trees (r
2
= 0.795, RMSE = 0.15 and MBE = −0.09). The limits and prospects of this method are finally discussed with particular reference to the possible integration of the RSWC estimates within more complex ecosystem models.
This study simulates annual net primary production (NPP) of forests over peninsular Spain during the years 2005–2012. The modeling strategy consists of a linked production efficiency model based on ...the Monteith approach and the bio-geochemical model Biome-BGC. Recently produced databases and data layers over the study area including meteorological daily series, ecophysiological parameters, and maps containing information about forest type, rooting depth, and growing stock volume (GSV), were employed. The models, which simulate forest processes assuming equilibrium conditions, were previously optimized for the study area. The production efficiency model was used to estimate daily gross primary production (GPP), while Biome-BGC was used to simulate growth (RG) and maintenance (RM) respirations. To account for actual forest conditions, GPP, RG, and RM were corrected using the ratio of the remotely-sensed derived actual to potential GSV as an indicator of the actual state of forests. The obtained results were evaluated against current annual increment observations from the Third Spanish Forest Inventory. Coefficients of determination ranged from 0.46 to 0.74 depending on the forest type. A simplified dataset was produced by applying regular increments in air temperature and reductions in precipitation to the original 2005–2012 daily series with the goal of covering the range of variation of the climate projections corresponding to the different climate change scenarios reported in the literature. The modified meteorological series were used to simulate new GPP, RG, and RM through Biome-BGC and corrected using GSV. Precipitation was confirmed as the main limiting factor in the study area. In the regions where precipitation was already a limiting factor during 2005–2012, both the increment in air temperature and the reduction in precipitation contributed to a reduction of NPP. In the regions where precipitation was not a limiting factor during 2005–2012, the increment in air temperature led to an increment of NPP. This study is therefore relevant to characterize the growth of Spanish forests both in current and expected climate conditions.
The estimation of site water budget is important in Mediterranean areas, where it represents a crucial factor affecting the quantity and quality of traditional crop production. This is particularly ...the case for spatially fragmented, multi-layer agricultural ecosystems such as olive groves, which are traditional cultivations of the Mediterranean basin. The current paper aims at demonstrating the effectiveness of spatialized meteorological data and remote sensing techniques to estimate the actual evapotranspiration (ETA) and the soil water content (SWC) of an olive orchard in Central Italy. The relatively small size of this orchard (about 0.1 ha) and its two-layer structure (i.e., olive trees and grasses) require the integration of remotely sensed data with different spatial and temporal resolutions (Terra-MODIS, Landsat 8-OLI and Ikonos). These data are used to drive a recently proposed water balance method (NDVI-Cws) and predict ETA and then site SWC, which are assessed through comparison with sap flow and soil wetness measurements taken in 2013. The results obtained indicate the importance of integrating satellite imageries having different spatio-temporal properties in order to properly characterize the examined olive orchard. More generally, the experimental evidences support the possibility of using widely available remotely sensed and ancillary datasets for the operational estimation of ETA and SWC in olive tree cultivation systems.
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