Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • Persone
  • Pubblicazioni
  • Strutture
  • Competenze
  1. Pubblicazioni

On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space

Articolo
Data di Pubblicazione:
2018
Abstract:
An effective characterization of gas flaring is hampered by the lack of systematic, complete and reliable data on its magnitude and spatial distribution. In the last years, a few satellite methods have been developed to provide independent information on gas flaring activity at global, national and local scale. Among these, a MODIS-based method, aimed at the computation of gas flared volumes by an Italian plant, was proposed. In this work, a more general version of this approach, named RST-FLARE, has been developed to provide reliable information on flaring sites localization and gas emitted volumes over a long time period for the Niger Delta region, one of the top five gas flaring areas in the world. Achieved results showed a good level of accuracy, in terms of flaring sites localization (95% of spatial match) and volume estimates (mean bias between in 16% and 20%, at annual scale and 2-9% in the long period) when compared to independent data, provided both by other satellite techniques and national/international organizations. Outcomes of this work seem to indicate that RST-FLARE can be used to provide, at different geographic scales, quite accurate data on gas flaring, suitable for monitoring purposes for governments and local authorities.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
gas flaring; remote sensing; MODIS; RST-FLARE; BTE; flaring sites detection; gas flared volumes computation
Elenco autori:
Pergola, Nicola; Lacava, Teodosio; Faruolo, Mariapia
Autori di Ateneo:
FARUOLO MARIAPIA
LACAVA TEODOSIO
PERGOLA NICOLA
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/355209
Pubblicato in:
SENSORS (BASEL)
Journal
  • Dati Generali

Dati Generali

URL

https://www.mdpi.com/1424-8220/18/8/2466
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.5.0.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)