Detecting gas flares and estimating flaring volumes at individual flow stations using MODIS databy Obinna C.D. Anejionu, G. Alan Blackburn, J. Duncan Whyatt

Remote Sensing of Environment

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Year
2015
DOI
10.1016/j.rse.2014.11.018
Subject
Computers in Earth Sciences / Geology / Soil Science

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Gas flare

Thermal infrared remote sensing

MODIS flare detection

Niger Delta

Gas flaring volume l rec (GG n o y, typically at global scales. However, to adequately assess the environmental and local to regional scales, it is important that we have a means of acquiring informaongsid o detect and monitor petitive observations,

Remote Sensing of Environment 158 (2015) 81–94

Contents lists available at ScienceDirect

Remote Sensing o .eto begin to assess the environmental impacts of flaring in a coherent fashion, there is a pressing need for a robust, consistent and objective means of determining: where active flaring sites are located; what volmultispectral viewing capabilities, synoptic coverage, and their ability to retrieve information from hazardous locations. Four major classes of algorithm (single channel threshold, multi-channel threshold, con-to empirically assess the environmental impacts of flaring are frequently hampered by limited access to official information on flare locations and volumes, the heterogeneity in spatial and temporal sampling strategies and methods used to collect data and lack of auditability. In order 1.1. Fire detection using satellite imagery

Satellite systems have long been deployed t fires and their effects, due to their timely and recessing at a flow station where gas is separated from oil. One or a number of flares in the vicinity of the flow station are then used to burn off the gas. Flaring is commonly adopted by oil companies because it is more cost-effective than converting to commercial natural gas. Efforts attempted to map flares or estimate flaring volumes from space. The present study builds upon this work and presents an alternative and enhanced approach.ume of gas is being flared at each site; and h ⁎ Corresponding author at: Lancaster Environment Ce 2YQ, United Kingdom.

E-mail address: o.d.anejionu@lancaster.ac.uk (O.C.D. A http://dx.doi.org/10.1016/j.rse.2014.11.018 0034-4257/© 2014 Elsevier Inc. All rights reserved.e venting and reinjection, with extracted crude oil. initially gathered for proand remote sensing seems the most viable option. However, as explained below, whilst there have been several approaches developed for monitoring biomass fires, only a limited number of studies haveused to dispose of the natural gas associated

Crude oil from a group ofwells in an oil field is1. Introduction

Gas flaring is one of the processes, alwe developed an approach to the retrieval of such information using nighttime MODIS thermal imagery. The

MODIS flare detection technique (MODET) and theMODIS flare volume estimation technique (MOVET) both exploit the absolute and contextual radiometric response of flare sites. The levels of detection accuracy and estimation error were quantified using independent observations of flare location and volume. TheMODET andMOVET were applied to an archive of MODIS data spanning 2000–2014 covering the Niger Delta, Nigeria, a significant global hotspot of flaring activity. The results demonstrate the substantial spatial and temporal variability in gas flaring across the region, between states and between onshore and offshore sites. Thus, whilst the estimated total volume of gas flared in the region over the study period is large (350 Billion Cubic Metres), the heterogeneity in the flaring indicates that the impacts of such flares will be highly variable in space and time. In this context, the MODET andMOVET offer a consistent and objective means of monitoring flaring activity over an appropriate range of scales and it is now important that their robustness and transferability is tested in other oil-producing regions of the world. © 2014 Elsevier Inc. All rights reserved. volume of flares has changed over space and time. Consequently, there is a need to develop new methods of acquiring such information,Keywords: tion on the location of individual active flaring sites and the volume of gas combusted at these sites. In this studyAvailable online xxxx flares using satellite imager health impacts of flaring fromDetecting gas flares and estimating flaring stations using MODIS data

Obinna C.D. Anejionu ⁎, G. Alan Blackburn, J. Duncan W

Lancaster University, Lancaster Environment Centre, United Kingdom a b s t r a c ta r t i c l e i n f o

Article history:

Received 29 April 2014

Received in revised form 2 October 2014

Accepted 9 November 2014

Gas flaring has gained globa

Global Gas Flaring Reduction

Because auditable informatio j ourna l homepage: wwwow the distribution and ntre, Lancaster University, LA1 nejionu).olumes at individual flow yatt ognition as a prominent agent of pollution, leading to the establishment of the

FR) initiative, which requires an objective means of monitoring flaring activity. n flaring activity is difficult to obtain there have recently been attempts to detect f Environment l sev ie r .com/ locate / rsetextual and sub-pixel) have been developed to sense fires from satellite images (Li et al., 2000; Martín, Flasse, Downey, & Ceccato, 1999). The two main types of signals employed for this purpose are either direct (flames and heat) or indirect (smoke and burned surfaces). Direct signals are most commonly employed in fire detection studies (Justice 82 O.C.D. Anejionu et al. / Remote Sensing of Environment 158 (2015) 81–94et al., 2006; Movaghati, Samadzadegan, & Azizi, 2009; Weaver, Lindsey,

Bikos, Schmidt, & Prins, 2004), whilst indirect signals are employed for post fire assessment and management (Lanorte, Danese, Lasaponara, &

Murgante, 2011; Sedano, Kempeneers, San Miguel, Strobl, & Vogt, 2013). Most satellite-based fire detection studies have focused on forest/biomass fires, as their impacts draw considerable attention from the research community and investigations are facilitated by the availability of well-established fire-hotspot algorithms (ATPS, 2013;

Casadio & Arino, 2009; Dozier, 1981; Giglio, Descloitres, Justice, &

Kaufman, 2003; Prins & Menzel, 1992; Qian, Yan, Duan, & Kong, 2009;