Background and Aims:The goal of this study was to investigate the relationships between NDVI values at different phenological stages and measurements of grape parameters at two different harvest dates. Methods and Results: The research was done on a Sangiovese vineyard in Central Italy. Over four seasons, airborne NDVI measurements acquired between June and August were related to grape parameters (yield per vine, pH,°Brix, anthocyanins and polyphenols) at technological harvest (H1) and two weeks later (H2). Correlations were higher at H1 and decreased at H2 with a different rate depending on the parameter.°Brix and pH correlations showed a moderate rate of variation between H1 and H2; bigger differences and a different inter-annual dynamic were observed in anthocyanins and polyphenols between H1 and H2. Conclusions: The ability of NDVI to discriminate different grape classes was confirmed, but its efficacy substantially varies depending on the harvest date. These results suggest the existence, within the same vineyard, of different grape populations having specific timing and shape of ripening curve; as a consequence, distinct vigour zones of the vineyard show a different evolution of the content of grape parameters between the two harvests thus influencing the degree of correlation between grape quality and NDVI measurements. Significance of the Study: This is the first study in which harvest date has been considered for its influence on the predictive skill of RS. It therefore highlights not only the importance of spatial variation within the single vineyard, but also the importance of ripening dynamics. AbbreviationsH1 harvest date 1; H2 harvest date 2; NDVI normalised difference vegetation index; PAB photosynthetically active biomass; PV precision viticulture; RS remote sensing.

Floods have recently become a major hazard in West Africa (WA) in terms of both their magnitude and frequency. They affect livelihoods, infrastructure and production systems, hence impacting on Sustainable Development (SD). Early Warning Systems (EWS) for floods that properly address all four EWS components, while also being community and impact-based, do not yet exist in WA. Existing systems address only the main rivers, are conceived in a top-down manner and are hazard-centered. This study on the Sirba river in Niger aims to demonstrate that an operational community and impact-based EWS for floods can be set up by leveraging the existing tools, local stakeholders and knowledge. The main finding of the study is that bridging the gap between top-down and bottom-up approaches is possible by directly connecting the available technical capabilities with the local level through a participatory approach. This allows the beneficiaries to define the rules that will develop the whole system, strengthening their ability to understand the information and take action. Moreover, the integration of hydrological forecasts and observations with the community monitoring and preparedness system provides a lead time suitable for operational decision-making at national and local levels. The study points out the need for the commitment of governments to the transboundary sharing of flood information for EWS and SD.

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Abstract:A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets) are integrated through an open-source, interoperable SDI (spatial data infrastructure) based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

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

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