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

A Tailored Approach for the Global Gas Flaring Investigation by Means of Daytime Satellite Imagery

Articolo
Data di Pubblicazione:
2022
Abstract:
The Daytime Approach for gas Flaring Investigation (DAFI), running in Google Earth Engine (GEE) environment, exploits a Normalized Hotspot Index (NHI), analyzing near-infrared and short-wave infrared radiances, to detect worldwide high-temperature gas flaring sites (GFs). Daytime Landsat 8--Operational Land Imager (OLI) observations, of 2013-2021, represents the employed dataset. A temporal persistence criterion is applied to a gas flaring customized NHI product to select the GFs. It assures the 99% detection accuracy of more intense and stable GFs, with a very low false positive rate. As a result, the first daytime database and map of GF sites, operating during the last 9 years at global scale, has been generated. For each site, geographical metadata, frequency of occurrence and time persistence levels, at both monthly and annual scale, may be examined, through the specific developed GEE App. The present database will complement/integrate existing gas flaring maps. The joint use of global scale daytime and nighttime GFs inventories, in fact, will allow for tracking gas flaring dynamics in a timely manner. Moreover, it enables a better evaluation of GF emissions into the atmosphere. Finally, the next DAFI implementation on Landsat 9 and Sentinel 2 data will further improve our capabilities in identifying, mapping, monitoring and characterizing the GFs.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
DAFI GEE App; daytime infrared observations; gas flaring; Landsat 8-OLI; NHI
Elenco autori:
Pergola, Nicola; Marchese, Francesco; Faruolo, Mariapia
Autori di Ateneo:
FARUOLO MARIAPIA
MARCHESE FRANCESCO
PERGOLA NICOLA
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/444680
Pubblicato in:
REMOTE SENSING (BASEL)
Journal
  • Dati Generali

Dati Generali

URL

https://www.mdpi.com/2072-4292/14/24/6319
  • Utilizzo dei cookie

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