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Proposal of integration of VGI created by smart applications within an OGC standard geospatial data infrastructure.

This paper presents the results obtained from the analysis of one recent Landsat TM image for the study of the status of some glaciers on Eastern Italian Alps. Then these results are compared with a set of multitemporal Landsat images from the Eighties. A fuzzy set based classification technique permitted to quantify snow and exposed ice extents in glaciated areas. Integration with topographic information allowed to derive the elevation of glacier terminus, although the result is overestimated due to some problems with digital elevation data and frontal debris coverage. A comparison of front altitude changes data derived from satellite image processing with elevation measured during field surveys supports the knowledge of the size of glacier reduction of the last decades. On Aurine and Pusteresi Alps glaciers, the changes in the last twenty years result to be a reduction corresponding to the 40% of the glaciated surface in the '80s, but a lot of the glaciers are now characterized by a new extent debris coverage in the frontal zones.

Il Report descrive obiettivi e contenuti dei 12 progetti finanziati nell'ambito dell'Accordo Quadro Regione Lombardia - Consiglio Nazionale delle Ricerche, firmato nel 2012 e riferito al biennio 2013-2015. La descrizione del Progetto S4A, cui hanno collaborato tutti i WP leader/collaboratori: Pietro Alessandro Brivio, Paola Carrara, Mirco Boschetti, Alba L'Astorina, Anna Basoni, Anna Rampini, Daniela Stroppiana, Marco Sacco, Gloria Bordogna, si trova nelle pagine 83-104.

Il settore agricolo si trova ad affrontare oggigiorno sfide sempre più importanti per la continua pressione della domanda mondiale di cibo, e per l'aumento della concorrenza a causa della globalizzazione dei mercati. Anche a livello regionale è sempre più evidente la necessità di sviluppare un sistema di conoscenze condiviso rivolto al monitoraggio dei sistemi agricoli lombardi con particolare attenzione allo stress idrico, anche a fronte degli impatti dovuti ai cambiamenti climatici, con episodi di crisi idriche sempre più frequenti. In questo quadro e in risposta al bando previsto dall'Accordo Quadro Regione Lombardia - CNR è stato approvato il progetto Space4Agri - Sviluppo di Metodologie Aerospaziali Innovative di Osservazione della Terra a Supporto del Settore Agricolo in Lombardia. Le finalità del progetto sono di contribuire allo sviluppo di strumenti adeguati per migliorare le capacità del sistema regionale e delle principali categorie di portatori di interesse, nella pianificazione e gestione del settore agricolo lombardo coniugando in maniera innovativa le osservazioni remote da satellite, le tecnologie aeronautiche per gli UAV e le tecnologie internet 2.0 per interscambio smart di dati - informazioni.

Land Surface Temperature (LST) is the radiation temperature measured at the interface between surface materials (top of plant canopy, water, ground, ice, or snow surface) and the atmosphere. LST is a key indicator of geothermal and geological activities below the Earth and recently, there has been an increasingly interest in geothermal exploration relying on monitoring the anomalies of the surface temperature for searching alternative energy sources. Spaceborne sensors carried by polar orbiting satellites, such as the NOAA AVHRR (Advanced Very High Resolution Radiometer) series and the NASA Terra and Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) after the year 2000, with spectral bands in the TIR domain of the electromagnetic spectrum, allowed scientists to observe and monitor LST over large areas and long periods of time. The satellite synoptic view of large areas can consequently offer a unique help to geologists. The measurements collected directly on the field, forcedly spotty obtained, can be integrated with the satelliite data and consequently enlarged and spread over the whole area of interest. Ten years of MODIS day-time and nighttime 1 km Land Surface Temperature (LST) data over Southern Italy (2001-2010) have been analyzed to identify those factors that are influential on LST spatial and temporal variations. We first investigated the quality of the MODIS LST estimates and then quantified the amount of spatial and temporal variability explained by topography, land cover and incoming solar radiation. Further we analyzed the correlation between LST time series and the Vegetation Indices (VIs) (Normalized Difference vegetation Index, NDVI and Enhanced vegetation Index, EVI). These analyses are preliminary to investigate LST variability of surface temperature's due to the presence of anomalies related to geothermal sources of low enthalpy.

Chapter 11 - This chapter will review the use of hyperspectral vegetation indices (HVIs) for estimating N content in herbaceous plants (crops and pastures) by focusing on the most common/recent advancements, describing and discussing their theoretical basis and the methodological approaches. Ideally, a method is required that is accurate, non destructive, simple to use, and from the canopy as opposed to the leaf point of view. Remote sensing techniques satisfy these requirements by measuring the optical properties of leaves and canopies which are driven by the biophysical and biochemical properties of the plant. Chapter 11 In Hyperspectral Remote Sensing of Vegetation (P.S. Thenkabail, J.G. Lyon, and A. Huete, Eds.)

This study presents the application of a Light Use Efficiency model that exploits MODIS satellite data to assess rice yield in Italy. Field experimental data acquired in 2003 and 2004 were used to define model’s parameters (Radiation Use Efficiency and Harvest Index) and to calibrate the relation between MODIS-EVI and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). Satellite images were used to spatially estimate fAPAR and key phenological rice stages that control biomass accumulation. The maps of rice yield estimates for the year 2002, 2003 and 2004 were compared to official statistics and showed a good agreement with an inter-annual relative RMSE ranging from 15 to 17%.

Vegetation cover plays a key role in various biophysical processes related to terrestrial ecosystems and global climate. In this chapter we review the main concepts and techniques for monitoring vegetation conditions at continental scale using time series of satellite optical observations. A new approach based on a synthetic indicator of anomalies in vegetated land cover is also presented. This indicator is obtained by aggregating through the use of fuzzy sets theory the contributions from several factors derived from the analysis of time series of satellite products. The application of the method in the detection of anomalous conditions over the African continent is presented.

This work presents a design-based validation and calibration scheme for the Global Burned Area 2000 (GBA2000) products. The objective of such a scheme is to assess the margins of uncertainty associated with the burned area products and to estimate calibration coefficients needed to convert burned pixel counts into areal estimates. As the validation of GBA2000 was performed long after 2000, and given the fact that burned areas are a predominantly nonpermanent land cover change, the reference data are obtained from a set of Landsat-7 Enhanced Thematic Mapper Plus high-resolution remotely sensed data. A stratified sampling scheme is presented, specifically designed for the retrospective validation of burned area data; the scheme is based on combining information from two low-resolution burned area products (GBA2000 itself and Globscar). The resulting stratification has been applied to the whole global GBA2000 dataset, and preliminary validation results are reported for Africa. The conclusions highlight the limits of a retrospective validation exercise, and summarize some of the open issues in the validation of global burned area maps.