The aim of this work is to study modeling of air pollution measured data that are remotely sensed through appropriate instrumentation. Modeling is basically important in order to validate measured data. We use spatial and bidimensional modeling to reduce uncertainty in recovering data. We also use Gaussian model and we study the possibility of decreasing recovering error by using mathematical parameters.
Modeling Study in Remote Sensing of Air Pollution Measured Data / Andria, Gregorio; D'Orazio, Antonella; Lay Ekuakille, A.; Notarnicola, Michele. - (2000), pp. 782-785. (Intervento presentato al convegno 17th IEEE Instrumentation and Measurement Technology Conference, IMTC/200 tenutosi a Baltimore, Maryland nel May 1-4, 2000) [10.1109/IMTC.2000.848842].
Modeling Study in Remote Sensing of Air Pollution Measured Data
ANDRIA, Gregorio;D'ORAZIO, Antonella;NOTARNICOLA, Michele
2000-01-01
Abstract
The aim of this work is to study modeling of air pollution measured data that are remotely sensed through appropriate instrumentation. Modeling is basically important in order to validate measured data. We use spatial and bidimensional modeling to reduce uncertainty in recovering data. We also use Gaussian model and we study the possibility of decreasing recovering error by using mathematical parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
