[Show abstract][Hide abstract] ABSTRACT: Properly tuned algorithms based on optical remote sensing data can provide estimates of chlorophyll-a (as a proxy for phytoplankton biomass) concentration in near real time, allowing the monitoring of phytoplankton dynamics for both neritic and oceanic areas. The main objective of this study was, through the use of ocean color satellite images, to offer a description of the interannual variability of chlorophyll-a and primary production both in coastal–neritic and in oceanic areas of the North Tyrrhenian and Ligurian Seas (NW Mediterranean). The second objective was to highlight the possible influence of land runoff on phytoplankton biomass variability in coastal–neritic waters. The results indicate that seasonal cycles of phytoplankton biomass and production were quite different in neritic areas potentially affected by freshwater runoff compared to offshore waters. Neritic areas are characterized by an anticipated bloom (winter–spring) and by higher spatial variability that appears to be linked with the distance from shore. Meanwhile, oceanic areas are dominated by a marked seasonal cycle and the typical bloom occurs in spring (March–April) in relation with vertical mixing. Finally, linear regression analysis suggests the influence of freshwater runoff in modulating the variability of chlorophyll-a in coastal–neritic areas. Overall, the results confirm previous observations on the dynamics of phytoplankton biomass and contribute to more realistic and lower estimates of both chlorophyll-a concentration and annual primary production.
Journal of Coastal Research 11/2014; · 0.76 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Integrazione di dati meteorologici, pedologici e telerilevati per la stima del bilancio idrico di aree semi-naturali Riassunto La stima del bilancio idrico di una regione è fondamentale per la programmazione e la gestione delle risorse idriche sul territorio, sia esso semi-naturale che agrario. Recentemente è stato messo a punto dall'IBIMET-CNR un metodo operativo, basato sull'utilizzo di dati telerilevati, per stimare l'evapotraspirazione reale (ET A), uno dei termini più importanti del bilancio idrico. L'unione di tale stima con informazioni relative ai suoli ricavabili dalla Banca Dati dei suoli di Regione Toscana realizzata dal Consorzio LaMMA, quali la profondità del suolo esplorabile da parte delle radici, la capacità di campo ed il punto di appassimento, permette di arrivare a stimare il contenuto idrico del suolo. Una valutazione preliminare di questa metodologia è stata effettuata confrontando le stime ottenute con misure di contenuto idrico del suolo raccolte in una querceta mista a Barbialla (Toscana). I risultati ottenuti sono promettenti e incoraggiano l'applicazione del metodo in altre aree. Abstract The estimation of water balance on a regional scale is a fundamental requirement towards the management of water resources of semi-natural or agricultural areas. Recently the research group of IBIMET-CNR developed an operational method, based on the use of remotely sensed data, to estimate actual evapotranspiration (ET A), one of the major terms of the water balance. These estimates are currently combined with information derived from the Soil Database of Tuscany Region created by the LaMMA Consortium, such as rooting depth, soil water capacity and wilting point, in order to predict soil water content. A preliminary test of the current methodology is performed by comparison with measurements of soil water content collected in an hilly area of Tuscany (Barbialla), covered by a mixed deciduous forest. The first results obtained are promising and encourage the application of the method in other areas. Introduzione Il contenuto d'acqua del suolo è un importante parametro ambientale che interessa un gran numero di processi ecosistemici. Nella maggior parte degli ecosistemi terrestri la quantità e la variazione stagionale in acqua disponibile è uno degli elementi più importanti che determina funzioni biologiche, come la crescita della vegetazione, lo sviluppo, la fruttificazione e la composizione delle specie. Da un punto di vista pratico, la valutazione del contenuto idrico del suolo è sempre più utilizzata nella pianificazione territoriale, soprattutto a scala di bacino per la stima dei deflussi e dell'erosione del suolo. La stima del contenuto d'acqua del suolo richiede informazioni su tre principali fattori che concorrono a determinare il bilancio idrico, ovvero la meteorologia, le caratteristiche del suolo e della vegetazione. I metodi operativi più semplici per simulare il bilancio idrico del suolo sono basati sul concetto di coefficiente colturale (Kc), definito come rapporto tra
XVIII Conferenza Nazionale ASITA, Firenze; 10/2014
[Show abstract][Hide abstract] ABSTRACT: This study was designed to compare the performance – in terms of bias and accuracy – of four different parametric, semiparametric and nonparametric methods in spatially predicting a forest response variable using auxiliary information from remote sensing. The comparison was carried out in simulated and real populations where the value of response variable was known for each pixel of the study region. Sampling was simulated through a tessellation stratified design. Universal kriging and cokriging were considered among parametric methods based on the spatial autocorrelation of the forest response variable. Locally weighted regression and k-nearest neighbor predictors were considered among semiparametric and nonparametric methods based on the information from neighboring sites in the auxiliary variable space. The study was performed from a design-based perspective, taking the populations as fixed and replicating the sampling procedure with 1000 Monte Carlo simulation runs. On the basis of the empirical values of relative bias and relative root mean squared error it was concluded that universal kriging and cokriging were more suitable in the presence of strong spatial autocorrelation of the forest variable, while locally weighted regression and k-nearest neighbors were more suitable when the auxiliary variables were well correlated with the response variable. Results of the study advise that attention should be paid when mapping forest variables characterized by highly heterogeneous structures. The guidelines of this study can be adopted even for mapping environmental attributes beside forestry.
Remote Sensing of Environment 09/2014; 152:29–37. · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Time-varying crop coefficients (Kc) can be obtained from remotely sensed data and combined with daily potential evapotranspiration estimates for the operational prediction of actual evapotranspiration (ETA). This approach, however, presents relevant limitations when applied in mixed, water stressed ecosystems. The current paper addresses these issues by introducing two innovations. First, fractional vegetation cover (FVC) is derived from NDVI and utilized to split evaporating and transpiring surfaces, whose behavior is simulated under fully watered conditions by the use of generalized Kc. Next, the short term effect of water shortage is taken into account by means of downregulating factors which are based on meteorological observations (potential evapotranspiration and rainfall) and act differently for vegetated and not vegetated surfaces. The new method is tested against latent heat of evaporation (LE) measurements taken by the eddy covariance technique in six sites of Central Italy representative of various forest and herbaceous ecosystems. In this experiment the method is driven by 1-km meteorological data obtained from a pan-European archive and by 250 m MODIS NDVI imagery. Satisfactory accuracies are obtained in all experimental situations, which encourages the application of the method for the operational monitoring of ETA on regional scale.
Remote Sensing of Environment 09/2014; 152:279–290. · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Phytoplankton is the main primary producer of organic matter in the sea. Chlorophyll concentration represents a standard index of phytoplankton biomass abundance, and therefore of organic carbon contained and produced (Primary Production). In the last decades the estimation of Primary Production (PP) through simulation models has found the support of satellite images that provide spatial and temporarily distributed information about the concentration of chlorophyll and other major marine bio-physical parameters: sea surface temperature (SST), photosynthetic radiation (PAR), etc..
The present work aims at developing a procedure for obtaining PP maps of the sea area in front of the Tuscany region. A semi-analytical model of the Tuscany marine waters is used to estimate the concentration of chlorophyll, suspended sediments and yellow substance, from the measured MODIS Aqua radiances. The pelagic primary production is then assessed by using these satellite estimates and other information within a bio-optical semianalytical model resolved in wavelength and depth. In this preliminary phase some PP maps have been obtained, that represent the end product prototype, specifically calibrated for the study waters.
[Show abstract][Hide abstract] ABSTRACT: Recent investigations have highlighted the dependence of Mediterranean forest production on spring rainfall. The current work introduces the concept of the start of the dry season (SDS) and performs a three-step analysis to determine the effect of SDS on Mediterranean forest production.
Seven forest zones of Tuscany (Central Italy), which present differently pronounced Mediterranean features, are considered. First, a statistical analysis investigates the influence of spring water budget on forest Normalized Difference Vegetation Index (NDVI) inter-annual variations during July–August. The analysis is then extended to assess the impact of inter-annual SDS variability on forest gross primary production (GPP) simulated by a NDVI driven parametric model, modified C-Fix. These simulations lead to rank the considered forest types according to the relevance of SDS in regulating inter-annual GPP variations. The application of similar statistical analyses to detrended tree ring-width time series of typical Tuscany forests confirms the existence of an eco-climatic gradient in the functional relevance of SDS. The influence of SDS on tree growth is attenuated moving from Mediterranean arid to temperate humid environments. These findings are examined and interpreted from an eco-physiological viewpoint taking into consideration the peculiarity of Mediterranean forest ecosystems. Next, relevant implications are discussed in view of the possible consequences of ongoing climate change.
Agricultural and Forest Meteorology 01/2014; 194:197–206. · 3.89 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: a b s t r a c t The acquisition of information about growing stock is a fundamental step in the framework of forest management planning and scenario modeling, besides being essential for assessing the amount of carbon stored within forest ecosystems. Gallaun et al. (2010) produced a pan-European map of forest growing stock by the combination of ground and remotely sensed data. The first objective of the current paper is to assess the accuracy of this map versus the ground data collected during the latest Italian National Forest Inventory (INFC). Next, a new wall-to-wall estimation of growing stock is obtained by combining ground measurements of four regional forest inventories with the CORINE land cover map of Italy and the global canopy height map derived from Geoscience Laser Altimeter System (GLAS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. More particularly, the growing stock measurements of the four inventories are stratified by ecosystem type and extended over all Italian forest areas through the application of locally weighted regressions to the GLAS/MODIS canopy height map. When compared to the INFC measurements, the new map shows higher accuracy than that by Gallaun et al., particularly for high growing stock values. The coefficient of determination between estimated and INFC growing stocks is improved by about 0.5, whilst the mean square error is reduced from 90 to 48 m 3 ha −1 .
International Journal of Applied Earth Observation and Geoinformation 01/2014; 26:377-386. · 2.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Scopo del presente articolo è illustrare il meccanismo proattivo ed integrato di monitoraggio e previsione della siccità che il Consorzio LaMMA e l'Istituto di Biometeorologia (CNR-IBIMET) hanno implementato e stanno validando ed adattando per la regione Toscana, nell'intento di ridurre il gap esistente fra il verificarsi di un evento siccitoso e le risposte degli utenti finali nel gestire le emergenze legate al protrarsi del fenomeno, fornendo informazioni e mappe in tempo quasi reale. Il framework si compone di tre parti: il sistema di monitoraggio, basato su un set di indici di siccità legati a dati a terra di pioggia e a indici derivati da immagini satellitari; il sistema di previsione stagionale dei parametri meteorologici pioggia e temperatura e dell'indice SPI-Standardized Precipitation Index a tre mesi; il sistema di diffusione delle elaborazioni ed analisi basato sull'uso spinto del web. Vengono poi descritte le ulteriori azioni in atto e i possibili sviluppi futuri per integrare e potenziare l'operatività del servizio. Abstract Aim of this paper is to illustrate the proactive, integrated drought monitoring and seasonal forecasting mechanism that the LaMMA Consortium and the Institute of Biometeorology (IBIMET-CNR) are implementing and adjusting for Tuscany region to fill the temporal gap between the development of a dry period and the response of final users in managing drought-related emergencies, by delivering maps and information in quasi-real time. The framework is constituted of three parts: the monitoring system, based on a set of drought indices both from rainy gauges and from remote sensing; the seasonal forecast system, predicting precipitation and temperature parameters and SPI index at three months; the web based system of final products dissemination. In the last part of the paper are described ongoing implementations and possible future developments, useful to integrate and enhance the efficiency of the operational service.
XVII Conferenza Nazionale ASITA, Riva del Garda; 11/2013
[Show abstract][Hide abstract] ABSTRACT: A recent paper of our research group has proposed a simplified “water balance” model which predicts actual evapotranspiration (ETA) based on ground and remotely sensed data. The model combines estimates of potential evapotranspiration (ET0) and of fractional vegetation cover derived from NDVI in order to separately simulate transpirative and evaporative processes. The new method, named NDVI-Cws, was validated against latent heat measurements taken by the eddy covariance technique over various vegetation types in Central Italy. The current paper extends this validation to three other test sites in Tuscany for which reference data are obtained from different sources. In the first two sites (non-irrigated winter wheat and irrigated maize fields) seasonal reference ETA data series are obtained by the WinEtro model. In situ transpiration measurements are instead used as reference data for a deciduous oak forest stand. The ETA and transpiration estimates of the NDVI-Cws method are very similar to the reference data in terms of both annual totals and seasonal evolutions. Examples are finally provided of the model application for operationally monitoring ETA in Tuscany.
European Journal of Remote Sensing 10/2013; 46:675-688. · 0.97 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A method has been recently presented to predict the net primary production NPP of Mediterranean forests by integrating conventional and remote-sensing data. This method was based on the use of two models, C-Fix and BIOME-BGC, whose outputs are combined with estimates of stem volume and tree age to predict the NPP of the examined ecosystems. This article investigates the possibility of deriving these two forest attributes from airborne high-resolution lidar data. The research was carried out in the San Rossore pine forest, a test site in Central Italy where several investigations have been conducted. First, estimates of stand stem volume and tree age were obtained from lidar data by application of a simplified method based on existing literature and a few ground measurements. The accuracy of these stand attributes was assessed by comparison with the independent ground data derived from a recent forest inventory. Next, the stem volume and tree age estimates were used to drive the NPP modelling strategy, whose outputs were evaluated against the inventory measurements of current annual increment CAI. The simplified lidar data processing method produces stand stem volume and tree age estimates having moderate accuracy, which are useful to feed the modelling strategy and predict CAI at a stand level. This method's success raises the possibility of integrating ecosystem modelling techniques and lidar data for the simulation of net forest carbon fluxes.
International Journal of Remote Sensing 04/2013; 34(7):2487-2501. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010–2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.
International Journal of Applied Earth Observation and Geoinformation 01/2013; 23:29-36. · 2.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In arid and semi-arid environments, the characterization of the inter-annual variations of the light use efficiency ɛ due to water stress still relies mostly on meteorological data. Thus the GPP estimation based on procedures exclusively driven by remote sensing data has not found yet a widespread use. In this work, the potential to characterize the water stress in semi-natural vegetation of three spectral indices (NDWI, SIWSI and NDI7) – from MODIS broad spectral bands – has been analyzed in comparison to a meteorological factor (Cws). The study comprises 70 sites (belonging to 7 different ecosystems) uniformly distributed over Tuscany, and three eddy covariance tower sites. An operational methodology, which combines meteorological and MODIS data, to characterize the inter-annual variations of ɛ due to summer water stress is proposed. Its main advantage is that it relies on existing series of meteorological data characterizing each site and allows calculating a typical Cws profile that can be “updated” () for the actual conditions using MODIS spectral indices. The results confirm that the modified can be used as a proxy of water stress that does not require concurrent information on meteorological data.
International Journal of Applied Earth Observation and Geoinformation 01/2013; 26:246-255. · 2.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The current paper presents the development and testing of a multi-step methodology which integrates remotely sensed and ancillary data to estimate olive (Olea europaea L.) fruit yield in Tuscany (Central Italy). The processing of very high resolution (Ikonos) and high resolution (Landsat ETM+) images provides a map of olive tree canopy cover fraction for all Tuscany olive yards, which is used to extract olive tree NDVI values from MODIS imagery. The combination of these values with standard meteorological data within a modified parametric model (C-Fix) enables the prediction of daily olive tree gross primary production (GPP) for ten years (2000–2009). These GPP estimates are then joint to the respiration estimates of a bio-geochemical model (BIOME-BGC) to simulate olive tree net primary production (NPP). The NPP accumulated over proper periods of the ten growing seasons is finally converted into olive fruit yield, whose accuracy is assessed through comparison with provincial statistics. The methodology is only partly capable of capturing spatial and temporal olive fruit yield variability at province level, but can accurately reproduce inter-year yield variation over the entire region. The paper concludes with a discussion of the results achieved and with considerations on the research prospects.
[Show abstract][Hide abstract] ABSTRACT: The availability of daily meteorological data extended over wide areas is a common requirement for modeling vegetation processes on regional scales. The present paper investigates the applicability of a pan-European data set of daily minimum and maximum temperatures and precipitation, E-OBS, to drive models of ecosystem processes over Italy. Daily meteorological data from a 10 yr period (2000 to 2009) were first downscaled to 1 km spatial resolution by applying locally calibrated regressions to a digital elevation model. The original and downscaled E-OBS maps were compared with meteorological data collected at 10 ground stations representative of different eco-climatic conditions. Additional tests were performed for the same sites to evaluate the effects of driving a model of vegetation processes, BIOME-BGC, with measured and estimated weather data. The tests were carried out using 10 BIOME-BGC versions characteristic for local vegetation types (Holm oak, other oaks, chestnut, beech, plain/hilly conifers, mountain conifers, Mediterranean macchia, olive trees, and C3 and C4 grasses). The experimental results indicate that the applied downscaling performs best for maximum temperatures, which is the most decisive factor for driving BIOME-BGC simulation of vegetation production. The downscaled data set is particularly suitable for the modeling of forest ecosystem processes, which could be further improved by the use of information obtained from remote sensing imagery.
[Show abstract][Hide abstract] ABSTRACT: The estimation of chlorophyll concentration in marine waters is
fundamental for a number of scientific and practical purposes. Standard
ocean color algorithms applicable to moderate resolution imaging
spectroradiometer (MODIS) imagery, such as OC3M and MedOC3, are known to
overestimate chlorophyll concentration ([CHL]) in Mediterranean
oligotrophic waters. The performances of these algorithms are currently
evaluated together with two relatively new algorithms, OC5 and SAM_LT,
which make use of more of the spectral information of MODIS data. This
evaluation exercise has been carried out using in situ data collected in
the North Tyrrhenian and Ligurian Seas during three recent oceanographic
campaigns. The four algorithms perform differently in Case 1 and Case 2
waters defined following global and local classification criteria. In
particular, the mentioned [CHL] overestimation of OC3M and MedOC3 is not
evident for typical Case 1 waters; this overestimation is instead
significant in intermediate and Case 2 waters. OC5 and SAM_LT are less
sensitive to this problem, and are generally more accurate in Case 2
waters. These results are finally interpreted and discussed in light of
a possible operational utilization of the [CHL] estimation methods.
Journal of Applied Remote Sensing 09/2012; · 0.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In the last decades researchers investigated the possibility of extending the information collected in sampling units during a field survey to wider geographical areas through the use of remotely sensed images. One of the most widely adopted approaches is based on the non-parametric k-Nearest Neighbors (k-NN) algorithm. This contribution describes the software K-NN FOREST we developed to provide a complete tool for the implementation of the k-NN technique to generate spatially explicit estimations (maps) of a response variable acquired in the field by sampling units through the use of remotely sensed data or other ancillary variables. K-NN FOREST is designed to guide the user through a graphic user interface in the different phases of the process. K-NN FOREST is freely available for download and it is designed to run under Windows environment in conjunction with the GIS software IDRISI.
European Journal of Remote Sensing 01/2012; 45:433 - 442. · 0.97 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The biogeochemical model BIOME-BGC is capable to estimate the main eco- physiological processes characterising all terrestrial ecosystems. To this aim it needs to be properly adapted to reproduce the behaviour of each biome type through a calibration phase. The aim of this paper is to adapt BIOME-BGC to re- produce the evapotranspiration (ET) and photosynthesis (GPP) of Mediter- ranean macchia spread all over Italy. Ten different sites were selected in the Centre-South of Italy and their gross primary production (GPP) was estimated by applying a parametric model, C-Fix, based on remotely sensed data for ten years (1999-2008). These monthly data were then used to calibrate BIOME-BGC through an iterative process which led to reproduce the spatial and temporal GPP variations found by C-Fix. The calibrated model was then applied to simu- late the ET and GPP of two Italian sites characterised by the presence of an eddy flux tower; its performances were evaluated against ground data by com- mon statistics. The results obtained indicate that, after a proper calibration phase, BIOME-BGC can be applied to estimate the evapotranspiration and pho- tosynthesis of Mediterranean macchia with a good accuracy, strictly dependent on the input data utilised.
iForest - Biogeosciences and Forestry 01/2012; · 1.06 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Daily values of gross primary production (GPP) derived from an eddy-covariance flux tower have been used to analyze the information content of the MODIS Photochemical Reflectance Index (PRI) on the light-use efficiency (ε). The study has been conducted in a Mediterranean Pinus pinaster forest showing summer water stress. Advanced processing techniques have been used to analyze the effect of various external factors on ε and PRI temporal variations. The intra-annual correlation between these two variables has been found to be mostly attributable to concurrent variations in sun and view zenith angles. The PRI has been normalized from these angular effects (NPRI), and its ability to track ecosystem ε response to prolonged summer water limitations has been analyzed. The observed shift between ε and NPRI reveals that, for the study area and at MODIS spatial resolution, NPRI is informative on changes in pigments and canopy structure related to the vegetation response to prolonged water stress.
Remote Sensing of Environment 01/2012; 123:359-367. · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Daily values of light use efficiency (LUE) of a Mediterranean forest throughout five years have been analyzed in terms of different spectral indices obtained from MODIS products and which are informative on the water stress conditions. Although correlations between LUE and the different indices are rather high, the inter-annual variation of LUE due to the summer water stress is not well identified in most of them. In particular, the PRI (photochemical reflectance index) inter-annual variation has been found to be mostly attributable to concurrent variations in sun and view zenith angles. For the study area and at MODIS spatial resolution, the different indices are informative on changes in pigments and canopy structure related to the vegetation response to prolonged water stress.