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In this study, a methodology based on the analysis of MODIS (MODerate-resolution Imaging Spectroradiometer) time series was developed to estimate post-fire resilience of Alpine vegetation. To this end, satellite images of two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were used. The analysis was conducted on wildfire affected areas in the Lombardy region (Italy) between 2003 and 2007. Some land surface (LS) descriptors (i.e. mean and maximum VI, growing season start, end and length) were extracted to characterize the time evolution of the vegetation. The descriptors from a burned area were compared to those from an undisturbed adjacent control site by means of analysis of variance (one-way ANOVA). Post-fire resilience was estimated on the basis of the number of subsequent years exhibiting a statistical difference between burned area and control site. The same methodologies were also applied to events aggregated by main land cover (broadleaf forest, prairies and mixed forest). The averaged resilience of broadleaf forest was 5–6 years, whereas prairie ecosystems exhibited a faster response of 0–2 years. Phenological analysis revealed that fire induces a shift of the start and end of growing season in forest ecosystems but has no effect on prairies. The method provides a useful and quantitative insight into complex post-fire vegetation dynamics in the Alps from a remote sensing perspective; results can apply to post-fire forest management and to multi-risk analysis

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha-1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI), defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e., %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index/Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image. © 2014 by the authors.

Narrow-band multispectral remote sensing techniques and thermal imagery were investigated for water stress detection in cereal crops. Visible and near infrared AISA Eagle (Specim, Finland) and thermal AHS-160 (Sensytech Inc., USA) imageries were acquired with an airborne survey on a farm-level experimental site where maize (Zea mays L) and sorghum (Sorghum bicolor L.) were grown with three different irrigation treatments. Vegetation biophysical and eco-physiological measurements were collected concurrently with the airborne campaign. Leaf fluorescence yield (Delta F/F-m') resulted to be a good indirect measure of water stress. Therefore, Delta F/F-m' measurements were compared against remotely sensed indicators: (i) the Photochemical Reflectance Index (PRI), (ii) the sun-induced chlorophyll fluorescence at 760 nm (F-760), retrieved by the Fraunhofer line depth method and (iii) the canopy temperature (T-c) calculated decoupling soil and vegetation contributions. T-c was related to Delta F/F-m' with the highest determination coefficient (R-2=0.65), followed by PRI586 (reference band at 586 nm) (R-2=0.51). The relationship with F-760 was significant but weaker (R-2=0.36). The coefficient of determination increased up to 0.54 when pigment concentration was considered by multiplying Delta F/F-m' and chlorophyll content, confirming the close relationship between passive fluorescence signal, pigment content and light photosystem efficiency. PRI586, F-760 and Tc maps were produced in maize and sorghum plots. The differences in the average values of PRI586, F-760 and T-c extracted from the plots with different water treatments showed that water treatments were well discriminated in maize plots by the three remotely sensed indicators. This was confirmed by the visual observation of the PRI586, F-760 and Tc maps, while in sorghum plots, F760 and Tc appeared more sensitive to water stress compared to PRI586

Introduction: While integrated care for diabetes mellitus type 2 has achieved good results in terms of intermediate clinical and process outcomes, the evidence-based knowledge on its implementation is scarce, and insights generalisable to other settings therefore remain limited. Objective: This study protocol provides a description of the design and methodology of a mixed methods study on the implementation of integrated care for type 2 diabetes. The aim of the proposed research is to investigate the mechanisms by which and the context in which integrated care for type 2 diabetes has been implemented, which outcomes have been achieved and how the context and mechanisms have affected the outcomes. Methods: This article describes a convergent parallel mixed methods research design, including a systematic literature review on the implementation of integrated care for type 2 diabetes as well as a case study on two Dutch best practices on integrated care for type 2 diabetes. Discussion: The implementation of integrated care for diabetes type 2 is an under-researched area. Insights from this study could be applied to other settings as well as other chronic conditions to strengthen the evidence on the implementation of integrated care.

This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an Eddy Covariance (EC) flux tower which provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf Area Index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, f PARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) showed better correlations with LAI and f IPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r =0.91) and the Photochemical Reflectance Index (PRI551), computed as (R531 −R551)/(R531 +R551) with LUEg (r =0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on the light-use efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a

The first linguistic analyses of some features of an old stage of Berber attested in a medieval text, recently discovered by the author

The continuous and automated monitoring of canopy phenology is of increasing scientific interest for the multiple implications of vegetation dynamics on ecosystem carbon and energy fluxes. For this purpose we evaluated the applicability of digital camera imagery for monitoring and modeling phenology and physiology of a subalpine grassland over the 2009 and 2010 growing seasons.We tested the relationships between color indices (i.e. the algebraic combinations of RGB brightness levels) tracking canopy greenness extracted from repeated digital images against field measurements of green and total biomass, leaf area index (LAI), greenness visual estimation, vegetation indices computed from continuous spectroradiometric measurements and CO2 fluxes observed with the eddy covariance technique. A strong relationship was found between canopy greenness and (i) structural parameters (i.e., LAI) and (ii) canopy photosynthesis (i.e. Gross Primary Production; GPP). Color indices were also well correlated with vegetation indices typically used for monitoring landscape phenology from satellite, suggesting that digital repeat photography provides high-quality ground data for evaluation of satellite phenology products.We demonstrate that by using canopy greenness we can refine phenological models (Growing Season Index, GSI) by describing canopy development and considering the role of ecological factors (e.g., snow, temperature and photoperiod) controlling grassland phenology. Moreover, we show that canopy greenness combined with radiation use efficiency (RUE) obtained from spectral indices related to photochemistry (i.e., scaled Photochemical Reflectance Index) or meteorology (i.e., MOD17 RUE) can be used to predict daily GPP.Building on previous work that has demonstrated that seasonal variation in the structure and function of plant canopies can be quantified using digital camera imagery, we have highlighted the potential use of these data for the development and parameterization of

This study compares MODIS NDVI 16-day (250 m) time series, acquired by Terra and Aqua platforms, for monitoring the phenological cycle of larch and grasslands in an alpine environment. The accuracy of MODIS 250 m Terra and Aqua phenological metrics was evaluated for larch forests through comparison with field data. At regional level it was carried out a correlation analysis between the mean dates of start and end of season detected from MODIS Terra and Aqua in different years. Regional maps of start and end of season were derived from MODIS data and the interannual phenological variability of both ecosystems was evaluated. Annual anomalies of the beginning of the growing season, obtained from satellite data, were related with air temperature anomalies, computed from meteorological stations, to evaluate the effects of recent climate variability on the vegetation phenological cycle. Comparison with field phenological observations showed that the start and the end of phenological cycle can be accurately determined from MODIS Terra and Aqua data and that an increase/decrease of 1°C in spring temperature lead to about 10 days in advance/ delay of the start of the growing season.

SpecCal software for the spectral calibration of high-resolution spectrometers is presented in this manuscript. The software, written in IDL 7.1, allows estimation of the channel central wavelength and the full width at half maximum of a selected spectrometer at several wavelengths across the VNIR range (350-1050. nm). This is achieved through comparison of the position and width of specific solar and terrestrial absorption features, as observed in the measured data, with those observed in simulated MODTRAN4 irradiance data. SpecCal is operated from a user-friendly graphical user interface that allows semiautomatic application of the spectral calibration algorithm at several wavelengths. The proposed software may be exploited as a useful in situ vicarious spectral calibration tool for field spectrometers operating in the VNIR range, which makes it possible to quickly analyze the spectral characteristics of the instruments and their possible variations with time. © 2011 Elsevier Ltd.

Reliable time series of vegetation optical properties are needed to improve the modeling of the terrestrial carbon budget with remote sensing data. This paper describes the development of an automatic spectral system able to collect continuous long-term in-field spectral measurements of spectral down-welling and surface reflected irradiance. The paper addresses the development of the system, named hyperspectral irradiometer (HSI), describes its optical design, the acquisition, and processing operations. Measurements gathered on a vegetated surface by the HSI are shown, discussed and compared with experimental outcomes with independent instruments. © 2011 American Institute of Physics.

Airborne hyperspectral remote observations, characterized by high spatial and spectral resolution, allow the estimation of quantitative vegetation variables useful in forest condition assessment. In this research, total chlorophyll (a + b) concentration (Cab), a biochemical variable describing crown discoloration rate, was mapped to assess oak (Quercus robur L.) condition in the Ticino Regional Park. A simulation experiment was conducted to evaluate the error in Cab estimation due to ecological variables (i.e. canopy leaf area index and understorey characteristics) and to sun-sensor configurations when optical indices are used. Canopy reflectance was simulated by means of the PROSPECT leaf radiative transfer model (Jacquemoud and Baret 1990) coupled with the SAILH canopy radiative transfer model, a variation of the SAIL (Scattering by Arbitrarily Inclined Leaves) model modified to include the hot spot effect (Verhoef 1984, Kuusk 1991). The vegetation was modelled as a two layer medium with oak canopy as the top layer and the understorey as the bottom layer. Simulations were performed for varying leaf Cab and canopy Leaf Area Index (LAI) of the top layer, yl (mean leaf inclination angle) and LAI of the bottom layer (LAIu) and sun-sensor geometry. Optical indices were calculated and used in Cab retrieval. Simulations demonstrated that errors in Cab estimation were negligible when MTCI (MERIS Terrestrial Chlorophyll Index) was used, thus indicating that MTCI was the most reliable index in mapping Cab in this forest environment. Empirical models based on optical indices were developed to map Cab from Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images acquired by an airborne survey on the ark forested area. A regression analysis between Cab concentration measured in leaves sampled in field and optical indices computed from hyperspectral MIVIS data was conducted. The MTCI index showed the highest performances and was therefore used to map Cab concentra

The Fraunhofer Line Discriminator (FLD) principle has long been considered as the reference method to quantify solar-induced chlorophyll fluorescence (F) from passive remote sensing measurements. Recently, alternative retrieval algorithms based on the spectral fitting of hyperspectral radiance observations, Spectral Fitting Methods (SFMs), have been proposed. The aim of this manuscript is to investigate the performance of such algorithms and to provide relevant information regarding their use. FLD and SFMs were used to estimate F starting from Top Of Canopy (TOC) fluxes at very high spectral resolution (0.12 nm) and sampling interval (0.1 nm), exploiting the O2-B (687.0 nm) and O2-A (760.6 nm) atmospheric oxygen absorption bands overlapping the fluorescence emissions at the red and far-red spectral window. Specific parameters affecting FLD and SFM performances are investigated and the accuracy of F estimation of the two methods is compared. The problem related to the lack of independent measurements of F at canopy level, which prevents the direct assessment of F estimation accuracy with actual measurements, is overcome in this study by using a modeled database of TOC reflectance spectra. In order to compute accuracy figures valid for operative applications the simulated spectra were perturbed by the addition of radiometric noise. An investigation was conducted to determine the best FLD channel configuration; it showed that violation of FLD assumptions results in a positive bias in F estimation at both oxygen absorption bands that cannot be avoided even at the high spectral resolution considered. SFMs were shown to be more accurate than FLD under any noise configuration considered. © 2009 Elsevier Inc. All rights reserved.

This research aims at developing a remote sensing technique for monitoring the interannual variability of the European larch phenological cycle in the Alpine region of Aosta Valley (Northern Italy) and to evaluate its relationships with climatic factors. Phenological field observations were conducted in eight test sites from 2005 to 2007 to determine the dates of completion of different phenological phases. MODerate Resolution Imaging Spectrometer (MODIS) 250 m 16-days normalized difference vegetation index (NDVI) time series were fitted with double logistic curves and the dates corresponding to different features of the curves were determined. Comparison with field data showed that the features of the fitted NDVI curve that allowed the best estimate of the start and end of the growing season were the zeroes of its third derivative (MAE of 6 and 4 days, respectively). The start and end of season were also estimated with the spring warming (SW) and growing season index (GSI) phenological models. MODIS start and end of season dates generally agreed with those obtained by the SW and GSI climate-driven phenological models. However, phenological models provided erroneous results when applied in years with anomalous meteorological conditions. The relationships between interannual variability of the larch phenological cycle and climate were investigated by comparing the mean start and end of season yearly anomalies with air temperature anomalies. A strong linear relationship (R2=0.91) was found between mean spring temperatures and mean start of season dates, with an increase of 1 °C in mean spring temperature leading to a 7-day anticipation of mean larch bud-burst date. Leaf coloring dates were found to be best related with mean September temperature (R2=0.77), but with higher spring temperatures appearing to lead to earlier leaf coloring. © 2010 Blackwell Publishing Ltd.

The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5 nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval

In this paper we present a methodology for the estimation of Gross Primary Production (GPP), Net Primary Production (NPP) and Net Ecosystem Production (NEP) for the main agricultural and forest ecosystems of the Lombardia Region (Italy). The MOD17 model was parameterized according to the different agro-forestry ecosystems and applied at regional scale by using satellite data with a spatial resolution of 250m. The high spatial resolution along with fine classification agro-forestry ecosystems has allowed to accurately analyze the carbon budget of an extremely fragmented and complex environment such as the Lombardia Region. Modeling results showed the role of the forests in the carbon budget at regional scale and represent important information layer for the spatial analysis and for inferring the inter-annual variability of carbon sequestration due to impacts of extreme events and recent climate change (e.g., drought, heat wave, flooding, fires).

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.

In this study we analyze the spatial and temporal variability of European Larch phenological cycle in mountainous areas (Val d'Aosta, Italy) by using a combination of satellite data, primary topographic attributes (altitude and aspect) and phenological models driven by climatic variables. We apply a remote sensing technique based on a time series of MODIS TERRA 250m 16-days NDVI data for mapping the inter-annual variability of phenological cycle. Starting from the satellite green-up maps we spatially explored the response of larch phenology at different terrain conditions varying for altitude and aspect. A retrospective analysis based on climate-driven phenological models was finally conductedd to evaluate the magnitude of temporal variability of Larch phenology in the last century.