Damiano Gianelle

Abstract: Seven years of continuous eddy covariance measurements at an alpine meadow were used to investigate the impacts of climate drivers and ecosystem responses on the inter-annual variability (IAV) of the net ecosystem exchange (NEE). The annual cumulative value of NEE was positive (source) in 2003, 2005 and 2009 (50, 15 and 112 g m�2 respectively) and negative (sink) in 2004, 2006, 2007 and 2008 (29, 75, 110 and 28 g m�2 respectively). The IAV of carbon dioxide fluxes builds up in two phenological phases: the onset of the growing season (triggered by snow melting) and the canopy re-growth after mowing. Respiratory fluxes during the non-growing season were observed to increase IAV, while growing season uptake dampened it. A novel approach was applied to factor out the two main sources of IAV: climate drivers� variability and changes in the ecosystem responses to climate. Annual values of carbon dioxide fluxes were calculated assuming (a) variable climate and variable ecosystem response among years, (b) variable climate and constant ecosystem response and (c) constant climate and variable ecosystem response. The analysis of flux variances calculated under these three assumptions indicates the occurrence of an important negative feedback between climate and ecosystem responses. Due to this feedback, the observed IAV of NEE is lower than one would expect for a given climate variability, because of the counteracting changes in ecosystem responses. This alpine meadow therefore demonstrates the ability to acclimatise and to limit the IAV of carbon fluxes induced by climate variability.

Abstract: Forest inventories are important tools for the management of forests. In this context, the estimation of the tree stem volume is a key issue. In this paper, we present a system for the estimation of forest stem diameter and volume at individual tree level from multireturn light detection and ranging (LIDAR) data. The proposed system is made up of a preprocessing module, a LIDAR segmentation algorithm (aimed at retrieving tree crowns), a variable extraction and selection procedure, and an estimation module based on support vector regression (SVR) (which is compared with a multiple linear regression technique). The variables derived from LIDAR data are computed from both the intensity and elevation channels of all available returns. Three different methods of variable selection are analyzed, and the sets of variables selected are used in the estimation phase. The stem volume is estimated with two methods: 1) direct estimation from the LIDAR variables and 2) combination of diameters and heights estimated from LIDAR variables with the species information derived from a classification map according to standard height/diameter relationships. Experimental results show that the system proposed is effective and provides high accuracies in both the stem volume and diameter estimations. Moreover, this paper provides useful indications on the effectiveness of SVR with LIDAR in forestry problems.

Abstract: In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref=15 °C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, P<0.001, n=104) even within each PFT. Besides LAIMAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (Ndepo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAIMAX) which performed well in predicting the spatio-temporal variability of RECO, explaining >70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands.

Abstract: Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km(2) of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 213% considering altitude and slope sets. (C) 2011 Elsevier Inc. All rights reserved.

Abstract: In this paper, we present a study on the efficiency of multi-return LIDAR (Light Detection Ranging) data in the estimation of forest stem volume over a multi-layered forest area in the Italian Alps. The goals of this paper are (1) to verify the usefulness of multi-return LIDAR data compared to single-return data in forest volume estimation and (2) to define the optimal resolution of a stem volume distribution raster map over the investigated area. To achieve these goals, raw data were segmented into a net, and different cell dimensions were investigated to maximize the relationship between the LIDAR data and the ground-truth information. Twenty predicting variables (e.g., mean height, coefficient of variation) have been extracted from multi-return LIDAR data, and a multiple linear regression analysis has been used for predicting tree stem volume. Experimental results found that the optimal resolutions of the net square cells were 40 m. The analysis indicated that in a mixed multi-layered forest, characterized by a complex vertical structure, the correct selection of the map spatial resolution and the inclusion of the secondary-return data were important factors for improving the effectiveness of the laser scanning approach in forest inventories. The experimental tests showed that the chosen model is effective for the estimation of stem volume over the analyzed area, providing good results on all the three considered validation methods.

Abstract: Ground reference data collection represents an important element in the prediction of stem volume with LiDAR-derived variables, and at present it is the most expensive part of such analyses. In this paper two aspects of ground reference data collection were analyzed: (1) the positioning error of the ground plots; and (2) the optimal number of training plots. A system for the prediction of stem volume at area-based level was adopted. LiDAR data were preprocessed and 13 variables describing both height and coverage were extracted. Models were defined using a stepwise ordinary least square (OLS) regression. Three experiments were conducted: (i) the role of the plots positioning error on prediction accuracy; (ii) the influence of random downsampling of plot numbers on prediction accuracy; and (iii) the influence of a stratified downsampling of plot numbers on prediction accuracy based on LiDAR-derived variables. A dataset comprising 799 ground plots was used. They were distributed throughout a mountainous area in the Southern Alps, where the presence of a complex landscape increases the uncertainty of the Global Positioning System (GPS) accuracy, and where a large variety of tree forest species and climatic environments make it necessary to have a large number of sample plots for accurate characterization of the study area. All the experiments provided important indications for LiDAR based forest inventories: the GPS error did not significantly influence the prediction accuracy and it was possible to reduce the number of training samples without compromising the generalization ability of the prediction model. Leading on from these findings, a new ground sampling protocol based on genetic algorithms was proposed. The new protocol allowed us to obtain promising results for the considered dataset: using only 53 training plots, instead of 534 in the original dataset, we obtained the same results for the validation set. These results, obtained in a complex mountainous area, are representative of Alpine environments and allow us to infer that similar (or better) results could also be obtained within non mountainous areas. No tags for this post.

Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Abstract: Winter CO2 fluxes represent an important component of the annual carbon budget in northern ecosystems. Understanding winter respiration processes and their responses to climate change is also central to our ability to assess terrestrial carbon cycle and climate feedbacks in the future. However, the factors influencing the spatial and temporal patterns of winter ecosystem respiration (R-eco) of northern ecosystems are poorly understood. For this reason, we analyzed eddy covariance flux data from 57 ecosystem sites ranging from similar to 35 degrees N to similar to 70 degrees N. Deciduous forests were characterized by the highest winter R-eco rates (0.90 +/- 0.39 gCm(-2) d(-1)), when winter is defined as the period during which daily air temperature remains below 0 degrees C. By contrast, arctic wetlands had the lowest winter R-eco rates (0.02 +/- 0.02 gCm(-2) d(-1)). Mixed forests, evergreen needle-leaved forests, grasslands, croplands and boreal wetlands were characterized by intermediate winter R-eco rates (g Cm-2 d(-1)) of 0.70(+/- 0.33), 0.60(+/-0.38), 0.62(+/-0.43), 0.49(+/-0.22) and 0.27(+/-0.08), respectively. Our cross site analysis showed that winter air (T-air) and soil (T-soil) temperature played a dominating role in determining the spatial patterns of winter R-eco in both forest and managed ecosystems (grasslands and croplands). Besides temperature, the seasonal amplitude of the leaf area index (LAI), inferred from satellite observation, or growing season gross primary productivity, which we use here as a proxy for the amount of recent carbon available for R-eco in the subsequent winter, played a marginal role in winter CO2 emissions from forest ecosystems. We found that winter R-eco sensitivity to temperature variation across space (Q(S)) was higher than the one over time (interannual, Q(T)). This can be expected because Q(S) not only accounts for climate gradients across sites but also for (positively correlated) the spatial variability of substrate quantity. Thus, if the models estimate future warming impacts on R-eco based on Q(S) rather than Q(T), this could overestimate the impact of temperature changes.

Abstract: More than half of the solar energy absorbed by land surfaces is currently used to evaporate water1. Climate change is expected to intensify the hydrological cycle2 and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land�a key diagnostic criterion of the effects of climate change and variability�remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network3, meteorological and remote-sensing observations, and a machine-learning algorithm4. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1�±�1.0�millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

Abstract: Grasslands and agroecosystems occupy one-third of the terrestrial area, but their contribution to the global carbon cycle remains uncertain. We used a set of 316 site-years of CO2 exchange measurements to quantify gross primary productivity, respiration, and light-response parameters of grasslands, shrublands/savanna, wetlands, and cropland ecosystems worldwide. We analyzed data from 72 global flux-tower sites partitioned into gross photosynthesis and ecosystem respiration with the use of the light-response method (Gilmanov, T. G., D. A. Johnson, and N. Z. Saliendra. 2003. Growing season CO2 fluxes in a sagebrush-steppe ecosystem in Idaho: Bowen ratio/energy balance measurements and modeling. Basic and Applied Ecology 4:167�183) from the RANGEFLUX and WORLDGRASSAGRIFLUX data sets supplemented by 46 sites from the FLUXNET La Thuile data set partitioned with the use of the temperature-response method (Reichstein, M., E. Falge, D. Baldocchi, D. Papale, R. Valentini, M. Aubinet, P. Berbigier, C. Bernhofer, N. Buchmann, M. Falk, T. Gilmanov, A. Granier, T. Grünwald, K. Havránková, D. Janous, A. Knohl, T. Laurela, A. Lohila, D. Loustau, G. Matteucci, T. Meyers, F. Miglietta, J. M. Ourcival, D. Perrin, J. Pumpanen, S. Rambal, E. Rotenberg, M. Sanz, J. Tenhunen, G. Seufert, F. Vaccari, T. Vesala, and D. Yakir. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11:1424�1439). Maximum values of the quantum yield (α=75 mmol·mol�1), photosynthetic capacity (Amax=3.4 mg CO2·m�2·s�1), gross photosynthesis (Pg,max=116 g CO2·m�2·d�1), and ecological light-use efficiency (ecol=59 mmol·mol�1) of managed grasslands and high-production croplands exceeded those of most forest ecosystems, indicating the potential of nonforest ecosystems for uptake of atmospheric CO2. Maximum values of gross primary production (8600 g CO2·m�2·yr�1), total ecosystem respiration (7900 g CO2·m�2·yr�1), and net CO2 exchange (2400 g CO2·m�2·yr�1) were observed for intensively managed grasslands and high-yield crops, and are comparable to or higher than those for forest ecosystems, excluding some tropical forests. On average, 80% of the nonforest sites were apparent sinks for atmospheric CO2, with mean net uptake of 700 g CO2·m�2·yr�1 for intensive grasslands and 933 g CO2·m�2·d�1 for croplands. However, part of these apparent sinks is accumulated in crops and forage, which are carbon pools that are harvested, transported, and decomposed off site. Therefore, although agricultural fields may be predominantly sinks for atmospheric CO2, this does not imply that they are necessarily increasing their carbon stock.

Abstract: The aim of this inventory (acronym: INFOCARB) was to measure the organic carbon stored in the forest ecosystems of the Trento region (Provincia Autonoma di Trento, Northern Italy) in both above- and belowground pools, according to the Kyoto protocol and IPCC requirements. A total of 150 forest sampling points were selected on the entire regional area (6206 km2) with a statistical sampling approach, based on the timber volume as a proxy variable for a stratified sampling. Each sampling point was located with a GPS receiver and a 600 m2 circular plot was delimited around each point. Inside the plots, the biomass of trees, shrubs and herbaceous vegetation was measured, while litter was collected in systematically placed subplots. Topsoil (down to 30 cm depth) was sampled with the excavation method on three systematically located pits, to determine the organic carbon content, the bulk density and the volume occupied by stones and roots. The inventory estimated the regional total carbon content of the forests as 71.9 ± 5.2 Tg C, with an average carbon density of 207.01 ± 14.5 Mg C ha�1. The aboveground biomass and the soil had a similar carbon content, 43.2% and 44.6% of the total ecosystem carbon, respectively, whereas the root systems and the litter accounted for 9.6% and 2.6%, respectively. Due to the high inter-site variability, only weak statistical relationships were found between the soil carbon content and main ecosystem and climatic variables. However, when dividing the plots into different species-dominated forests, the beech sites differed significantly from the conifer sites in the carbon stock and the C/N ratio in the soil organic layers.

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Abstract: Recent European heatwaves have raised interest in the impact of land cover conditions on temperature extremes. At present, it is believed that such extremes are enhanced by stronger surface heating of the atmosphere, when soil moisture content is below average. However, the impact of land cover on the exchange of water and energy and the interaction of this exchange with the soil water balance during heatwaves is largely unknown. Here we analyse observations from an extensive network of flux towers in Europe that reveal a difference between the temporal responses of forest and grassland ecosystems during heatwaves. We find that initially, surface heating is twice as high over forest than over grassland. Over grass, heating is suppressed by increased evaporation in response to increased solar radiation and temperature. Ultimately, however, this process accelerates soil moisture depletion and induces a critical shift in the regional climate system that leads to increased heating. We propose that this mechanism may explain the extreme temperatures in August 2003. We conclude that the conservative water use of forest contributes to increased temperatures in the short term, but mitigates the impact of the most extreme heat and/or long-lasting events.

Abstract: Spectral Network (SpecNet) began as a Working Group in 2003 with the goals of integrating remote sensing with biosphere�atmosphere carbon flux measurements and standardizing field optical sampling methods. SpecNet has evolved into an international network of collaborating sites and investigators, with a particular focus on matching optical sampling tools to the temporal and spatial scale of flux measurements and ecological sampling. Current emphasis within the SpecNet community is on greater automation of field optical sampling using simple cost-effective technologies, improving the lightuse- efficiency (LUE) model of carbon dioxide flux, consideration of view and illumination angle to improve physiological retrievals, and incorporation of informatics and cyberinfrastructure solutions that address the increasing data dimensionality of cross-site and multiscale sampling. In this review, we summarize recent findings and current directions within the SpecNet community and provide recommendations for the larger remote sensing and flux communities. These recommendations include comparing the LUE model to other flux models driven by remote sensing, considering a wider array of biogenic trace gases in addition to carbon dioxide, adoption of standardized and automated field sensors and sampling protocols where possible, continued development of cyberinfrastructure tools to facilitate data comparison and integration, expanding the network itself so that a greater range of sites are covered by combined optical and flux measurements, and encouraging a broader communication between the flux and remote sensing communities.

Abstract: The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively. Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000�2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Abstract: Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate�carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45°N). The sensitivity of NEE to mean annual temperature breaks down at ~ 16 °C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.

Abstract: Small footprint Light Detection and Ranging (LiDAR) data have been shown to be a very accurate technology to predict stem volume. In particular, most recent sensors are able to acquire multiple return (more than 2) data at very high hit density, allowing one to have detailed characterization of the canopy. In this paper, we utilize very high density ( >8 hits per m2) LiDAR data acquired over a forest stand in Italy. Our approach was as follows: Individual trees were first extracted from the LiDAR data and a series of attributes from both the first, and non-first (multiple), hits associated with each crown were then extracted. These variables were then correlated with ground truth individual estimates of stem volume. Our results indicate that: (i) non-first returns are informative for the estimation of stem volume (in particular the second return); (ii) some attributes (e.g., maximum at the power of n) better emphasize the information content of returns different from the first respect to other metrics (e.g., minimum, mean); and (iii) the combined use of variables belonging to different returns slightly increases the overall model accuracy. Moreover, we found that the best model for stem volume estimation (adj - R2 = 0.77, P < 0.0001, SE = 0.06) comprised four variables belonging to three returns (first, second, and third). The results of this analysis are important as they underline the effectiveness of the use of multiple return LiDAR data, underling the connection between LiDAR hits different from the first and tree structure and characteristics.

Abstract: In areas with complex three-dimensional features, slope and aspect interact with light conditions and significantly affect the spatial structure of images acquired by remote sensing instruments (for example, by changing the distribution of shadows and affecting the texture of high resolution imagery). In this scenario, this paper analyses the potential and the effectiveness of an automatic classification system to identify three fundamental vegetation classes (forest, grassland and crops) in the complex topography of the Italian Alps (Autonomous Province of Trento, Italy). This classification system is based on the fusion of spectral information provided by the SPOT-5 multi-spectral channels (Ground Instantaneous Field of View, GIFOV, equal to 10 m) and textural information extracted from airborne digital orthophotos (GIFOV equal to 1 m) and is designed to be user-friendly. The texture of the digital orthophotos was modelled using defined bidirectional variograms, thereby extracting additional information unavailable in first-order texture analyses. Using SPOT-5 multi-spectral information alone, the classification accuracy in the investigated alpine area was equal to 87.5%, but increased to 92.1% when texture information was included. In particular, the texture information significantly increased the classification accuracy for crops (from 68.9% to 87.9%), especially orchards that tend to be classified as lowland deciduous forests, and herbaceous crops (such as maize) that are often misclassified as grasslands. A further simple majority analysis increased the ability of detecting grassland, crops and urban zones. The combination of the majority analysis and the proposed automatic classification system seems an effective approach to classifying vegetation types in highly fragmented and complex Alpine landscapes on a regional scale.

Abstract: Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from a theoretical viewpoint [e.g. problems related to the Hughes phenomenon (Hughes, 1968) and from a computational perspective. Despite many previous investigations that have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only a few studies have analyzed the role of spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationship among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. We considered two different test sets characterized by images acquired by an AISA Eagle sensor over 126 bands with a spectral resolution of 4.6 nm, and we subsequently degraded its spectral resolution to 9.2, 13.8, 18.4, 23, 27.6, 32.2 and 36.8 nm. A series of classification experiments were carried out with bands at each of the degraded spectral resolutions, and bands selected with a feature selection algorithm at the highest spectral resolution (4.6 nm). The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis. From the experimental results, important conclusions can be made about the choice of the spectral resolution of hyperspectral sensors as applied to forest areas, also in relation to the complexity of the adopted classification methodology. The outcome of these experiments are also applicable in terms of directing the user towards a more efficient use of the current instruments (e.g. programming of the spectral channels to be acquired) and classification techniques in forest applications, as well as in the design of future hyperspectral sensors.

Abstract: Proximal and remote sensing measurements were used to calculate different vegetation indices that were applied as predictors of gross primary production (GPP), total ecosystem respiration (TER), net ecosystem production (NEP) and leaf area index (LAI). Reflectance data and carbon fluxes were collected during the 2005 growing season at a mountain grassland site in the Italian Alps. Significant relationships were found between GPP, TER, NEP, LAI and the most commonly used spectral vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Green-NDVI. Saturation of the spectral indices was evident in the estimation of both biophysical and ecophysiological parameters. Among the different indices, Green-NDVI was less affected by saturation on both a spatial and a temporal basis. Therefore, the use of an additional green-band sensor for spectral measurements at eddy covariance grassland sites is recommended. Concerning the bandwidth for the calculation of the indices, the highest predictive capacities among the sensor simulations included in the analysis were those of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the high-resolution hyperspectral instrument Hyperion, indicating the advantage of narrow bands for the prediction of plant parameters. Further analyses are, however, required to investigate the relationships between NEP, GPP and vegetation indices retrieved from satellite platforms, using the bands available on MODIS and Hyperion sensors.

Abstract: In remote sensing, the reflectance of vegetation has been successfully used for the assessment of grassland biophysical parameters for decades. Several studies have shown that vegetation indices that are based on narrow spectral bands significantly improve the prediction of vegetation biophysical characteristics. In this work, we analyse the relationships between the biophysical parameters of grasslands and the high-spatial-resolution hyperspectral reflectance values obtained from helicopter platform data using both a spectral vegetation index and a regression approach. The regression approach was favoured as it had optimal results with respect to producing higher R2 values than the spectral index approach (water content, 0.91 v. 0.90; leaf-area index, 0.88 v. 0.61; and green ratio, 0.90 v. 0.83). These three parameters were selected to obtain a fire risk map for the Bosco della Fontana grassland areas. The extreme spatial variability of the fire risk confirmed the hypotheses regarding the importance of obtaining scale-appropriate biophysical maps to model fire risk in fragmented landscapes and ecosystems. More studies are needed in order to investigate both the limits and the opportunities of high-spatial-resolution sensors in highly fragmented landscapes for the remote detection of fire risk and to generalise the obtained results to other grassland vegetation types.

Abstract: Half-hourly measurements of the net exchanges of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere provide estimates of gross primary production (GPP) and evapotranspiration (ET) at the ecosystem level and on daily to annual timescales. The ratio of these quantities represents ecosystem water use efficiency. Its multiplication with mean daylight vapor pressure deficit (VPD) leads to a quantity which we call �inherent water use efficiency� (IWUE*). The dependence of IWUE* on environmental conditions indicates possible adaptive adjustment of ecosystem physiology in response to a changing environment. IWUE* is analyzed for 43 sites across a range of plant functional types and climatic conditions. IWUE* increases during short-term moderate drought conditions. Mean annual IWUE* varied by a factor of 3 among all sites. This is partly explained by soil moisture at field capacity, particularly in deciduous broad-leaved forests. Canopy light interception sets the upper limits to canopy photosynthesis, and explains half the variance in annual IWUE* among herbaceous ecosystems and evergreen needle-leaved forests. Knowledge of IWUE* offers valuable improvement to the representation of carbon and water coupling in ecosystem process models.

Abstract: The net ecosystem carbon dioxide (CO2) exchange (NEE) of nine European mountain grassland ecosystems was measured during 2002�2004 using the eddy covariance method. Overall, the availability of photosynthetically active radiation (PPFD) was the single most important abiotic influence factor for NEE. Its role changed markedly during the course of the season, PPFD being a better predictor for NEE during periods favorable for CO2 uptake, which was spring and autumn for the sites characterized by summer droughts (southern sites) and (peak) summer for the Alpine and northern study sites. This general pattern was interrupted by grassland management practices, that is, mowing and grazing, when the variability in NEE explained by PPFD decreased in concert with the amount of aboveground biomass (BMag). Temperature was the abiotic influence factor that explained most of the variability in ecosystem respiration at the Alpine and northern study sites, but not at the southern sites characterized by a pronounced summer drought, where soil water availability and the amount of aboveground biomass were more or equally important. The amount of assimilating plant area was the single most important biotic variable determining the maximum ecosystem carbon uptake potential, that is, the NEE at saturating PPFD. Good correspondence, in terms of the magnitude of NEE, was observed with many (semi-) natural grasslands around the world, but not with grasslands sown on fertile soils in lowland locations, which exhibited higher maximum carbon gains at lower respiratory costs. It is concluded that, through triggering rapid changes in the amount and area of the aboveground plant matter, the timing and frequency of land management practices is crucial for the short-term sensitivity of the NEE of the investigated mountain grassland ecosystems to climatic drivers.

Abstract: In this paper, we propose an analysis on the joint effect of hyperspectral and light detection and ranging (LIDAR) data for the classification of complex forest areas. In greater detail, we present: 1) an advanced system for the joint use of hyperspectral and LIDAR data in complex classification problems; 2) an investigation on the effectiveness of the very promising support vector machines (SVMs) and Gaussian maximum likelihood with leave-one-out-covariance algorithm classifiers for the analysis of complex forest scenarios characterized from a high number of species in a multisource framework; and 3) an analysis on the effectiveness of different LIDAR returns and channels (elevation and intensity) for increasing the classification accuracy obtained with hyperspectral images, particularly in relation to the discrimination of very similar classes. Several experiments carried out on a complex forest area in Italy provide interesting conclusions on the effectiveness and potentialities of the joint use of hyperspectral and LIDAR data and on the accuracy of the different classification techniques analyzed in the proposed system. In particular, the elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data.

Abstract: This article examines how alternative rural funding scenarios might influence the pattern of functional land types in mountain areas. The study aims were to explore the use of stakeholders to predict landscape change and to provide a future policy context for other papers in the Carbomont program. EU rural funding policies could have a strong influence on land use and landscapes in mountain areas. At eight sites across Europe, groups of local stakeholders were asked to compare the possible effects of three contrasting funding scenarios over an imagined period of 20 years on (1) the importance of the main land-use sectors; (2) the areas of the main land functional land types; and (3) the management of individual land types. Stakeholders also listed their interests in the area to help define the perspective of the group. The protocols used were ranking and scoring procedures that permitted quantification of changes and of the degree of consensus within the group. The scenarios were (1) continuation of current rural funding (status quo), (2) rapid reduction of farm income support (reduce support), and (3) increasing rural diversification funding (diversification). The eight countries sampled included five established EU members (UK, Germany, Austria, Italy, Spain), two new accession members (Czeck Republic and Slovakia), and Switzerland. There were predicted to be widespread reductions in the importance of the agricultural sector across Europe and increases in the transport, built environment, and tourism sectors. In general, the status quo scenario was perceived to be unsatisfactory in various respects, reduce support was worse, but diversification offered opportunities for conservation and development of mountain communities and land use. Changes in the areas of land types would mainly involve loss of arable and grazing land and increases in scrub, and settlements. Some elements of the landscape such as most forests, mountain tops, and wetlands would, however, be little affected by any of the scenarios.

Abstract: Development of new methods for estimating biophysical parameters can be considered one of the most important targets for the improvement of grassland parameters estimation at full canopy cover. In fact, accurate assessment methods of biophysical characteristics of vegetation are needed in order to avoid the uncertainties of carbon terrestrial sinks. Remote sensing is a valid tool for scaling up ecosystem measurements towards landscape levels serving a wide range of applications, many of them being related to carbon-cycle models. The aim of this study was to test the suitability of satellite platform sensors in estimating grassland biophysical parameters such as LAI, biomass, phytomass, and Green herbage ratio (GR). Also, we wanted to compare some of the most common NIR and red/green-based vegetation indices with ones that also make use of the MIR band, in relation to their ability to predict grassland biophysical parameters. Ground-truth measurements were taken on July 2003 and 2004 on the Monte Bondone plateau (Italian Alps, Trento district) in grasslands varying in land use and management intensities. From satellite platforms, an IRS-1C-LISS III image (18/07/2003; 25 m resolution in the visible-NIR and 70 m resolution in the MIR) and a SPOT 5 image (27/07/2004, 10 m resolution in the visible-NIR and MIR) were used. LAI, biomass, and phytomass measurements showed logarithmic relationships with the investigated NIR and red/green-based indices. GreenNDVI showed the highest R2 values (0.59, IRS 2003; 0.60, SPOT 2004). Index saturation occurred above approximately 100�150 g m�2 of biomass (LAI 1.5�2). On the other hand, GR relationships were shown to be linear. MIR-based indices performed better than NIR and red/green-based ones in estimating biophysical variables, with no saturation effect. Biomass showed a linear regression with Canopy Index (MIR/green ratio) and with the Normalised Canopy Index (NCI) calculated as a normalised difference between MIR and green bands (IRS: R2 = 0.91 and 0.90, respectively. SPOT: R2 = 0.63 and 0.64). Similar correlations could also be found for LAI and phytomass, and GR predictability was shown to be higher than NDVI and GreenNDVI. According to these results obtained in the investigated areas, phytomass, biomass, LAI, and GR are linearly correlated with the investigated MIR band indices and as a result, these parameters could be estimated from the adopted satellite platforms with limited saturation problems.

Abstract: in this study, the suitability of spectral vegetation indexes for predicting green ratio (the percentage of green biomass with respect to the total phytomass) has been tested with the Italian Alps and New Zealand South Island grasslands. Considering three different datasets, green ratio (GR) was found to be negatively correlated with visible bands, while it was positively correlated in the NIR region (in total, R>0.80 in the 745-950 nm interval). GR proved to be more predictable than biomass and phytomass, both using hyperspectral single narrow bands and band ratios. Considering three different datasets, GR-index correlations were found to be linear and did not involve saturation problems. Many vegetation indices have been tested; they were well correlated with GR. Green Normalized Difference Vegetation Index (NDVIgreen) was the most stable index, with high values of R2 in all areas, low standard deviation and without significant differences in the slopes and intercepts of the linear correlations of the three datasets.

Abstract: In recent years, hyperspectral and multi-angular approaches for quantifying biophysical characteristics of vegetation have become more widely used. In fact, as both hyperspectral and multi-angle reflectance decrease the level of noise on retrieved geophysical parameter values, they increase their reliability by also reducing the saturation problem of the relationships between vegetation indices and biophysical characteristics. To test which is the best methodology in estimating some important biophysical grassland parameters (biomass, total and percent biomass nitrogen content, phytomass and its total and percent nitrogen content), nadir and off-nadir measurements were carried out, three times during the vegetative period of 2004, in a permanent flat meadow located in the experimental farm of the University of Padua, Italy. The two approaches and the broad band vegetation indices calculated using Landsat bands were compared considering both the best determination coefficients of five vegetation indices, calculated with the two analysis, and through a partial least squares regression using different spectral regions measured at different angles as predictive variables. Using nadir data the red edge region was the most useful for the prediction of biophysical variables, especially phytomass, but also nitrogen content. The off-nadir data did not provide any significance differences in results to that of data obtained in nadir view but both methods seem to be better adapted to describe biophysical parameters of vegetation than the use of broad band vegetation indices.

Abstract: Green herbage ratio (GR), the equivalent of the biomass/(biomass + necromass), is an important biophysical parameter as it is a fundamental indicator of photosynthetic activity of vegetation components, pedoclimatic conditions and the phenological state of vegetation. GR is strongly correlated with photosynthesis and respiration rates, the major processes that drive ecosystem simulation models. To compare grasslands GR predictability from remote sensing data and in order to test the possibility of producing spatially distributed maps, the GR estimation technique has been tested with data produced from sensors on aerial and satellite platforms. For this purpose, ASPIS (Advanced SPectroscopic Imaging System) aircraft sensor and IRS satellite LISS-III sensor imagery of the Viote of Monte Bondone (Italian Alps) grassland area has been compared. Ten differently managed grasslands reflectance was measured in the field and calculated from aircraft and satellite platforms. From these data 10 different vegetation indices were calculated to estimate GR predictability from aircraft and satellite platforms. At the aircraft platform, a significant linear regression could be found between GR and the calculated indices; nine of the 10 investigated indices showed an R2 > 0.70, all values being included within a small range of R2. With aircraft data, Green-NDVI (normalized difference vegetation index calculated using NIR and green bands) was one of the most correlated indices, the R2 value (0.74) being comparable to those found at ground level (R2 = 0.80). For satellite-derived data, only Green-NDVI showed a significant correlation (R2 = 0.63, p < 0.05). Green-NDVI aircraft and satellite-derived values correlated well with Green-NDVI ground values. According to these results, Green-NDVI was shown to be the only index that produced significant correlations with all the analyzed datasets.

Abstract: Microhabitat heterogeneity is considered to be one of the main factors affecting the structure and diversity of natural communities. This study evaluated: (i) whether it is possible to associate the distribution of four orthopteran species with small-scale spatial microhabitat heterogeneity based on floristic composition; and (ii) whether interspecific differences exist in microhabitat was among the different orthopteran species over a gradient of vegetation succession induced by abandonment of meadows. Orthoptera and plant species were sampled on 72 plots across an ecotonal area on Monte Bondone in the Southern Italian Alps. Microhabitats were identified based on grassland and undergrowth vegetation composition and by classifying sample plots using cluster analysis. Eight microhabitats were identified, each corresponding to a separate successional stage, and microhabitat use by each species was assessed. The distribution of orthopteran species revealed a different use of microhabitats. Species also had differing patterns of distribution, and a shift in distribution occurred following a change in microhabitat structure caused by mowing. The importance of the maintenance of a mosaic of microhabitats, with differently managed adjacent areas is discussed.