115,107 research outputs found
Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping
No full textThe continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications
Optical instruments for measuring leaf area index in low vegetation : application in Arctic ecosystems
No full textAuthor Posting. © Ecological Society of America, 2005. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 15 (2005): 1462–1470, doi:10.1890/03-5354.Leaf area index (LAI) is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. We set out to develop a methodology for easy and accurate nondestructive assessment of the variability of LAI in low-stature vegetation. We developed and tested the methodology in an arctic landscape close to Abisko, Sweden.
The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance (NDVI) and light penetration through the canopy (gap-fraction analysis using a LI-COR LAI-2000). By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained. The combination of an NDVI sensor for sparser vegetation and a LAI-2000 for denser vegetation could explain 81% of the variance of LAI measured by destructive harvest. We used the method to quantify the spatial variability and the associated uncertainty of leaf area index in a small catchment area.This research was funded by U.S. National Science Foundation
grant DEB0087046
Estimation of leaf area index from PROBA/CHRIS hyperspectral multi-angular data
No full textLeaf Area Index (LAI) is a key structural and functional biophysical variable of the vegetated surfaces which is important in quantifying evapotranspiration rates and the energy exchange of terrestrial vegetation. Remote sensing offers a method of providing estimates of LAI through the analysis of the Bidirectional Reflectance Distribution Function (BRDF), an angular-dependent surface response. High-resolution, multi-angular and hyperspectral image data from PROBA/CHRIS (Project On-Board Autonomy/ Compact High Resolution Imaging Spectrometer) are used to estimate LAI. The retrieval of LAI is accomplished using the 1D turbid-medium canopy reflectance model, SAIL, coupled with the leaf reflectance model, PROSPECT REDUX. Look-up-tables are generated using scene-specific parameters required to invert the physically based model. Two experiments are performed to examine the contribution of multispectral versus hyperspectral reflectances (nadir direction) and single-look versus multi-look hyperspectral reflectances in deriving the LAI. Image data of the calibration/validation site at Chilbolton, Hampshire, UK are used for the inversion. In addition, ground measurements of LAI are compared with the retrieved LAI estimates. Retrieved LAI estimates using various spectral and directional sampling suggest that the spectro-directional reflectances from CHRIS provides more accurate results than their lower-resolution counterparts such as single-look and multispectral reflectances
Il Governo dei Rischi Aziendali tra Esigenze di Mercato e Fattori Istituzionali. Nuove Prospettive per il Board
No full textNegli ultimi anni il dibattito tra aziende, policy makers e standard setters su come implementare e comunicare le politiche di gestione del rischio è diventato sempre più acceso. Da un lato, le articolate esigenze degli stakeholder rendono necessaria un gestione olistica e responsabile dei rischi d’impresa, tramite l’adozione di adeguati sistemi di governo e controllo. Dall’altro, il riconoscimento che il rischio, se ben gestito, possa essere un fattore critico di successo ha fatto sì che si siano diffusi tra le aziende strumenti di risk management in linea con i processi di definizione delle strategie. In questo ambito la sostenibilità (economica, sociale e ambientale) di un modello di business risulta un elemento centrale e indispensabile del processo di creazione di valore. Tale sostenibilità non può prescindere da una accurata gestione dei rischi, rispetto alla quale gli organi di governo dell’azienda, e in particolare il Board, assumono un ruolo chiave. Da tali premesse prende spunto questo lavoro, che si propone di sintetizzare e divulgare, nella forma di executive summary, i risultati di un più ampio progetto di ricerca finanziato dal MIUR nell’ambito del PRIN 2009 e condotto da ricercatori appartenenti alle Università di Siena, Napoli, Padova, Udine e Verona. L’obiettivo principale è mettere in luce alcuni aspetti critici nel governo dei rischi aziendali, con particolare riferimento agli organi di governo, ai ruoli organizzativi, agli strumenti di misurazione delle performance e di disclosure verso gli utilizzatori delle informazioni aziendali. L’intento è delineare possibili soluzioni organizzative e offrire utili spunti di riflessione per aziende e policy makers
Dalla gestione dei rischi al valore d'impresa
No full textLo scritto, nel dare conto del processo di ricerca nell'ambito del PRIN 2009 di cui l'autore era responsabile nella sede di Verona, riflette sulla complementarità degli approcci allo studio dei rischi e sui risultati delle ricerche compiute in tema di: risk management nelle società quotate; relazione tra risk managament e performance aziendale; sistemi di managament del rischio e valore di mercato delle imprese italiane; risk disclosure nell'integrated report; gli strumenti per la gestione dei rischi protetti dai "modelli di organizzazione, gestione e controllo"; i compliance program, il rafforzamento dei sistemi di contenimento dei rischi nel c.d. modello a tre stadi; i rischi nei sistemi reticolari; i rischi nelle imprese di assicurazioni
Study on the relationship between hyperspectral reflectance and soybean LAI, aboveground biomass
No full textCanopy reflectances of soybean are measured with ASD FieldSpec in different growth stages with the fiber Probe 2.5m above the canopy surface; and simultaneously, LAI and Biomass are acquired. The relationship between canopy reflectance, the first derivative of reflectance and LAI, Biomass are analysed with every single band; determination coefficient (R2) was obtained with linear regression of soybean reflectance and the first derivative auainst LAI and Biomass; different regression model applied to establish the relationship between RVI and soybean LAI and Biomass. It was found that soybean canopy, reflectance has an intimate relationship with soybean LAI, Biomass in visible spectral region 350-680nm :and near infrared spectral region of 760-1050 nm M, our study, it found that RVI constructed by hyperspectral remote sensing reflectance from near infrared spectral region and visible spectral region regressing against soybean LAI, Biomass with power functions and expotential functions can greatly improve remote sensing abilitv for estimating soybean LAI, Biomass. Still more work need to be done in the future to simulate srectral band of remote sensors in orbit nowadays
Connessioni. Alexander von Humboldt precursore degli studi sull’Antropocene?
No full textIn this short article I try to support the idea that in Alexander von Humboldt’s works there are some
interesting insights into the effects of human activities in ecosystems and about the interrelationships between the
various elements of the ecosphere. Such insights are the result of his fieldwork during the exploration in various
South American regions between 1799 and 1804
Study of Environmental Vegetation Index Based on Environment Satellite CCD Data and LAI Inversion
No full textThe present study used the PROSAIL forward model to simulate vegetation canopy spectrum, introducing blue and green bands to amend the effects of atmosphere and soil background, and constructing HuanJing vegetation Index (HJVI) to avoid premature saturation. Based on ground observation data of different typical winter wheat, we established HJVI-LAI long time series inversion models and implemented different varieties cross-validation to the models. The results show that the LAI inversion model of HJVI has higher precision than similar vegetation index model, has good universality, and can be applied to remote sensing multi-temporal winter wheat growth monitoring and LAI inversion
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