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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. [ABSTRACT FROM AUTHOR]

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 (r2 = 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. [ABSTRACT FROM AUTHOR]

The use of three estimation methods was investigated for mapping forest volume over a complex Mediterranean region (Tuscany, central Italy). The first two methods were based on the processing of satellite images, specifically a summer Landsat Thematic Mapper scene. From this scene, information about forest volume was extracted through a nonparametric approach [k-nearest neighbor (k-NN)] and by means of locally calibrated regressions. The last method considered, kriging, instead used only the spatial autocorrelation of tree volume relying on geostatistical principles. The experiments performed demonstrated that, at the original sampling density, the three methods produced nearly equivalent accuracies. This was no more the case when reducing the sampling density to various levels. Whereas, in fact, this reduction marginally affected the performances of the two remote-sensing-based methods, it dramatically degraded that of kriging. Additionally, the investigation showed how per-pixel estimates of error variance were obtainable also by k-NN and locally calibrated regression procedures, in analogy with the same property of kriging. Such estimated error variances were utilized to optimally integrate the outputs of the methods based on remotely sensed data and spatial autocorrelation. In all cases, the integrated estimation outperformed the single procedures. These results are relevant to develop an operational strategy for mapping forest attributes in complex Mediterranean areas. Index Terms--Forest volume, k-nearest neighhor (k-NN), kriging, Landsat Thematic Mapper (TM), local regression, Mediterranean ecosystem.

The estimation of vegetation primary productivity is particularly important in fragile Mediterranean environments that are vulnerable to both natural and human-induced perturbations. The current work was aimed at using remotely sensed data taken by various sensors to infer information about a protected coastal pine forest in Tuscany (Central Italy), which could serve for driving a simplified model of carbon fluxes, C-Fix. Being based on the direct relationship between normalized difference vegetation index (NDVI) and fraction of absorbed photosynthetically active radiation (FAPAR), C-Fix uses satellite and standard meteorological data to simulate gross (GPP) and net (NPP) primary productivity of forest ecosystems. Due to the limited size of the study area, a major difficulty was in creating an NDVI dataset with suitable spatial and temporal resolutions, which was essential for the model functioning. To reach this objective, eight Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images of two years (2000 and 2001) were merged to low-resolution NDVI estimates taken by both the Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION (VGT) sensors. The C-Fix outputs for representative pine forest sites were evaluated by comparison to accurate estimates derived from a model of forest ecosystem processes previously calibrated in a similar environment (Forest-BGC). This analysis showed the potential of C-Fix for rapidly estimating GPP over wide forest areas when suitable NDVI inputs are provided. In particular, a slight superiority of VGT over AVHRR data was demonstrated, which could be reasonably attributed to the relevant higher radiometric and geometric properties. The estimation of NPP was instead quite inaccurate, due to the problematic simulation of forest respiration, which should necessarily rely on more complete modeling operations. Index Terms--Advanced Very High Resolution Radiometer (AVHRR), C-Fix, Forest-BioGeochemical Cycles (Forest-BGC), gross primary productivity (GPP), integration, net primary productivity (NPP), pine forest, Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), VEGETATION (VGT).

Several studies have investigated the utility of Land-sat 5 TM imagery to estimate forest parameters such as stand composition and density. Regression equations have generally been used to relate these parameters to the radiance responses of the TM channels. Such a method is not feasible in highly complex landscapes, where forest mixtures and terrain irregularities may obscure the existence of simple relationships. In the current paper a fuzzy approach to the problem is presented based on a multi-step procedure. First, some typical forest plots with known features are spectrally identified. A Maximum Likelihood fuzzy classification with nonparametric priors is then applied to the study images, so as to derive fuzzy membership grades for all pixels with respect to the typical plots. Finally, these grades are used to compute the estimates of the forest parameters by a weighted average strategy. The method was tested on a complex, rugged area in Tuscany mainly covered by deciduous and coniferous forests. Two TM scenes and accurate ground references taken in spring and summer 1991 were utilized for the testing. The first results, statistically evaluated in comparison with those of a more usual multivariate regression procedure, are quite encouraging. The possible application of the fuzzy approach to other cases of environmental monitoring is finally discussed.