September 2014 

NASA/CR–2014-218525 

Central Africa Energy: Utilizing NASA Earth 

Observations to Explore Flared Gas as an 

Energy Source Alternative to Biomass in 

Central Africa 

Amber Jones, Charles White, Christopher Castillo, Emmanuel Hitimana, 

Kenny Nguyen, Shikher Mishra, and Walt Clark 

Langley Research Center, DEVELOP National Program, Hampton, Virginia 
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September 2014 

NASA/CR–2014-218525 

Central Africa Energy: Utilizing NASA Earth 

Observations to Explore Flared Gas as an 

Energy Source Alternative to Biomass in 

Central Africa 

Amber Jones, Charles White, Christopher Castillo, Emmanuel Hitimana, 

Kenny Nguyen, Shikher Mishra, and Walt Clark 

Langley Research Center, DEVELOP National Program, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
1 

I. Abstract 

Much of Central Africa’s economy is centered on oil production. Oil deposits lie below 

vast amounts of compressed natural gas. The latter is often flared off during oil 

extraction due to a lack of the infrastructure needed to utilize it for productive energy 

generation. Though gas flaring is discouraged by many due to its contributions to 

greenhouse emissions, it represents a waste process and is rarely tracked or recorded in 

this region. In contrast to this energy waste, roughly 80% of Africa’s population lacks 

access to electricity and in turn uses biomass such as wood for heat and light. In 

addition to the dangers incurred from collecting and using biomass, the practice 

commonly leads to ecological change through the acquisition of wood from forests 

surrounding urban areas. 

The objective of this project was to gain insight on domestic energy usage in Central 

Africa, specifically Angola, Gabon, and the Republic of Congo. This was done through 

an analysis of deforestation, an estimation of gas flared, and a suitability study for the 

infrastructure needed to realize the natural gas resources. The energy from potential 

natural gas production was compared to the energy equivalent of the biomass being 

harvested. A site suitability study for natural gas pipeline routes from flare sites to 

populous locations was conducted to assess the feasibility of utilizing natural gas for 

domestic energy needs. Analyses and results were shared with project partners, as well 

as this project’s open source approach to assessing the energy sector. Ultimately, 

Africa’s growth demands energy for its people, and natural gas is already being 

produced by the flourishing petroleum industry in numerous African countries. By utilizing 

this gas, Africa could reduce flaring, recuperate the financial and environmental loss 

that flaring accounts for, and unlock a plentiful domestic energy source for its people. 

II. Introduction 

Background 

Africa is home to numerous burgeoning economies; a significant number rely on oil 

production as their primary source of revenue. Relative to its size and population 

density, the continent has a wealth of natural resources, including oil and natural gas 

deposits. The exploration of these resources is not a new endeavor, but rather one that 

spans decades, up to a century in some places. Their resources, if realized, could 

provide a great means of economic and social mobility for the people of Africa. 

Currently, Africa represents about 12 % of the energy market, yet at the same time, 

consumes only 3 % of the world’s energy (Kasekende 2009). 

The higher energy density of oil makes it a preferred fossil fuel over gas. Furthermore, 

oil’s natural state as a liquid requires comparatively less substantial infrastructure to 

transport and produce than natural gas. Despite the fact that natural gas burns 
2 

comparatively cleaner, it is either re-injected to increase pressure in the oil well, vented 

(simply released), or flared due to economic considerations. Flaring not only represents 

a loss of energy production, it also adds to greenhouse gas emissions which amounts to 

400 x 106 tons of carbon dioxide (CO2) annually, or roughly 1.2 % of both natural and 

anthropogenic world carbon emissions (World Bank 2014). 

Partner/End Users 

Collectively speaking, management in Africa’s energy sector is currently a fragmented 

and inconsistent hybrid of mostly private projects and a burdened, underdeveloped 

public utility. Without a clear governing organization, several independent consulting 

agencies have ascended to serve speculators seeking analyses on the continent’s 

energy industries. 

Meanwhile, the Planet Earth Institute (PEI) is striving for Africa’s independence in 

scientific research and governance by training the continent’s next generation of 

scientists and leaders through locally-led and technologically innovative projects. In 

Angola, PEI is currently establishing the Center for Excellence in Science for 

Sustainability in Africa (CESSAF), the country’s first ever doctoral program. 

Since its inception at the 2002 World Summit on Sustainable Development, the World 

Bank Group’s Global Gas Flaring Reduction (WBG-GGFR) public-private partnership has 

supported the efforts of oil producing countries and companies to increase the use of 

associated natural gas and thus mitigate climate change through the reduction of 

flaring and venting. This is accomplished in two main ways. First, the WBG-GGFR 

partnership currently relies on National Oceanic and Atmospheric Administration 

(NOAA) to perform satellite imagery analysis utilizing the Visible Infrared Imaging 

Radiometer Suite (VIIRS) to provide flaring calculations. U.S. government sequestration 

has put a delay in the processing of that information, and methodologies comparable 

to NOAA’s for calculating flared energy volumes were established by DEVELOP during 

the 2013 spring and summer climate and air quality projects at Stennis Space Center. 

Second, the GGFR team facilitates demonstrative projects for associated gas utilization. 

GGFR also provides advice to governments, oil companies, and potential gas 

customers. 

Goals and Objectives 

The objective of this project was to assist the WBG-GGFR by applying NASA Earth 

observations in order to gain insight on domestic energy usage in Central Africa, 

specifically Angola, Gabon, and the Republic of Congo. This was done through an 

analysis of deforestation, an estimation of gas flared, and a suitability study for the 

infrastructure needed to realize the natural gas resources. The study attempted to 

answer the questions (1) how much natural gas is being flared? And (2) can the gas 

currently being flared replace a significant portion of the biomass being burned for 

fuel? 

The results and methodologies were presented to PEI and the WBG-GGFR in order to 

help them achieve their goals. Additionally, the project provided PEI with curriculum 

materials for using open-source geographic information system software and public 

domain data from NASA Earth observations. 
3 

Study Area 

The countries of Angola, Gabon, and the Republic of Congo are all located in Central 

Africa. In addition to being areas of interest for PEI and WBG-GGFR, these countries 

were chosen for study because they simultaneously experienced poor domestic energy 

options and a high rate of gas flaring. Angola had the second largest proven oil and 

gas reserve in sub-Saharan Africa (EIA 2014), but biomass represented 60% of the 

energy supply (IRENA 2014). Approximately 75% of the natural gas produced in Angola 

was flared, contributing extensively to greenhouse gas emissions and wasting billions of 

dollars in viable energy (EIA 2014). In Gabon, oil production declined by a third since 

1997 leading to a renewed interest in natural gas. Despite this interest, 90% of natural 

gas produced was re-injected, flared or vented (EIA 2014). Oil production in the 

Republic of Congo was also declining. Similar to Gabon, 85% of associated natural gas 

was re-injected, flared or vented while 81% of the population relied on biomass for 

domestic energy (EIA 2014). Together these countries were home to 26.8 million people 

of which 18.75 million did not have access to electricity (World Bank 2014). 

Earth Observations and National Application Area 

The paradox of gas flaring in Africa is such that while the oil industry churns out 12 % of 

the world’s energy through African resources, the majority of the continent’s population 

still relies on inefficient (almost five times less so than natural gas) biomass for their 

energy needs (Partnership for Policy Integrity 2011). Despite recent advancements in 

producing natural gas, such as the Angola LNG project in Soyo, there is still a vast 

energy potential that is being wasted. The problem is further compounded by the poor 

record of gas flaring, particularly in developing nations. As flaring represents a waste 

process, it is often under reported or in some cases not reported at all. Estimating gas 

flaring from space was suggested as early as the 1970’s using the Defense 

Meteorological Satellite program (DMSP), but DMSP imagery is relatively coarse, both in 

spatial terms as well as terms of the radiometric bands it uses to sense the Earth (Elvidge, 

et al. 2013). The recently launched Visible Infrared Imaging Radiometer Suite (VIIRS) 

provides a superior method of detecting burning activity on Earth’s surface through its 

multitude of sensor channels. 

MODIS products, predominantly MOD09, were also utilized in this study for biomass 

estimations owing to their versatility and the large scope of the study area. In addition 

to VIIRS and MODIS, the site suitability methodology utilized a digital elevation model 

(DEM) from the Shuttle Radar Topography Mission (SRTM). 

III. Methodology 

Gas Flaring Estimation 

In order to estimate the amount of natural gas that was flared and its contribution to 

greenhouse gas emissions, VIIRS Nightfire data provided by the National Geophysical 

Data Center (NGDC) were acquired, aggregated, and clipped to the region of interest 

before being analyzed. The Nightfire algorithm works by first identifying high radiance 

values in the roughly 1.55-1.65 μm window of the instrument’s M10 band. Pixel values 

are then pulled for all nine of the bands operating at night. The M10 identification is 
4 

then confirmed using the day night band (DNB), together with VIIRS’ M7, M8, M12, and 

M13 bands. Using the values in these spectral windows, a Planck curve is fitted to the 

radiance retrievals. At this point, many attributes can be estimated from the 

combustion source such as temperature, radiant heat, size of source, and for gas flares, 

the volumetric flow of methane (CH4) and carbon loading. The Nightfire system made 

these daily detections available as a Comma Separated Value (CSV) or as a Google 

Earth Keyhole Markup Language Zip (KMZ) format. For the purpose of this study, the 

amount of gas and energy equivalent was estimated from a year’s worth of Nightfire 

data. To maintain consistency among the data, the Prerun version of the Nightfire 

product was used and the year was defined as March 2013 – February 2014.

Since Nightfire data files are very large and some of the content was not useful for the 

purpose of this study, eliminating unnecessary data was imperative before aggregating 

daily data into a single file for the entire year. Using C-sharp syntax, a script was written 

in order to extract the applicable data and merge files. 

Using the Quantum Geographic Information Systems (QGIS) software, shape files for the 

areas of interest (Angola, Gabon, and the Republic of Congo) were created. QGIS was 

preferred for this particular research, because it is an open source software that is easily 

accessed by end users. Merged data files were imported into QGIS and clipped to 

each country of the study area. 

Combustion sources detected within the study area had temperatures ranging from 

300-16,000 degrees Kelvin (K). However, biomass primarily burns in the range of 600 to 

1,200 K, with a median near 1,000 K (Elvidge, et al. 2013). Gas flare temperatures tend 

to overlap with the high end of temperatures associated with biomass burning and 

have a median near 1,750 K (Elvidge, et al. 2013). For this reason, 1,400 K was taken to 

be the threshold, and data with temperatures less than 1,400 K were eliminated. 

In order to estimate the amount of natural gas that was flared during one year, 24 hours 

were assumed to be the average lifetime of a flare and flare reactants were assumed 

to be predominantly methane. Uncontaminated natural gas is composed of 97.5 % 

methane (Tan 1965). For the purpose of this study, a complete combustion of 

uncontaminated methane was also assumed (equation 1). Average volume of flared 

gas was then estimated from the average volumetric rate flow rate and assumed 

lifetime. Then, total methane volume was then converted into energy (Q) equivalent 

(equation 2). 

CH4 + 2O2 CO2 + 2 H2O Q Equation 1 

 

Q (MJ) = ΔHf CH4 (MJ/kg) *ρ (kg/m3)*Estimated volume (m3) Equation 2 

 Where, 

ΔHf CH4 (54 MJ/kg)is the heating value of methane, which is the amount of thermal 

energy released when one mole of methane undergoes a complete combustion (Tan 

1965). 
5 

Based on the stoichiometry of the balanced methane combustion equation, one mole 

of methane is equivalent to one mole of CO2, which implies that the volume of 

methane reactant can be equated to the volume of carbon dioxide emission. 

Biomass Estimation 

In order to determine the amount of biomass change occurring from 2002 to 2014, 

above ground woody biomass and forest area change were estimated. Knowing that 

leaf area index (LAI) and normalized difference vegetation index (NDVI) are both 

correlated to biomass (Ribeiro et al. 2008), a multivariate regression analysis was 

implemented. LAI and NDVI were the independent variables and above ground woody 

biomass from 2000 provided through the Woods Hole Research Center (Baccini et al. 

2008) was the dependent variable. The regression equation was then extrapolated to 

the future. To verify these results further, the forest area change was estimated using an 

unsupervised land classification using MODIS reflectance bands 1-7. This process was 

completed only for the country of Angola because of difficulty obtaining cloud free 

images over the more equatorial countries of Gabon and the Republic of the Congo. 

The northwest portion of Angola was removed from the study for the same reason. 

Normalized difference vegetation index (NDVI) change was also used to increase the 

accuracy of forest change and the two products were compared. 

 

The seven bands of the Terra MODIS level three eight day surface reflectance product 

(MOD09A1) were acquired through the Reverb data acquisition system with 500 m 

resolution along with the MODIS Vegetation Indices (MOD13Q1) with 250 m resolution. 

The images were downloaded for each year from 2002 to 2014 for dates near the end 

of May. Four image tiles were required for each date to cover all of Angola. 

Reflectance band 1 for each of the four tiles were added into QGIS and merged 

together. This was repeated for the last six bands. The merged MODIS bands were then 

layer stacked in QGIS and clipped to the specific study area of Angola. NDVI was 

processed similarly. The “Unsupervised KMeans image classification” from the Orfeo 

toolbox was used to create 25 classes from the stacked bands. Each band was 

reclassified into one of three classes: water, non-forested land, or forested land. The 

area covered by each class was calculated using zonal statistics for comparison 

between the dates. The NDVI percent change between the first (May 24, 2002) and last 

(June 1, 2014) dates was calculated using raster calculator. All values with a change 

percentage under 15% were assigned a value of zero while values with a change 

percentage of 15% or greater were assigned the value of one. This NDVI layer was 

multiplied by the forest change layer in order to remove all forest change areas without 

significant NDVI change. 

LAI and NDVI values for the date June 8, 2000 were used with the Woods Hole Research 

Center woody biomass data from 2000 to form a regression analysis. LAI data 

(MCD15A2) was downloaded from the Reverb data acquisition system with 1 km 

resolution and processed in the same fashion as the NDVI data. The “no data” values 

were removed from the files by setting them as null. The woody biomass dataset was 

downloaded from the Woods Hole Research Center (WHRC) (Baccini et al 2008) with 1 

km resolution. A set of 20,000 random points were generated in ArcMap covering the 

study area. The values of woody biomass, LAI, and NDVI were extracted at each point. 
6 

Points with “no data” values in any variable were removed before the comparison was 

exported as a text file. The text file was imported into Microsoft Excel and a regression 

analysis was performed resulting in an R2 value of 0.50 and a linear equation of: 

B = -102.106 + 0.022V + 2.062L Equation 3 

Where B is estimated biomass (Mg / hectare), V is NDVI, and L is LAI. The equation was 

then applied to each year from 2002 to 2014 using raster calculator resulting in a 1 km 

resolution dataset of estimated woody biomass (Mg / hectare). To avoid values less 

than zero in the statistics, each pixel of the results was converted into a point and all 

points with pixel values less than zero were removed. The total of the pixels was 

summated and multiplied by 100 to calculate the total in tons of biomass. This value of 

tons was also estimated in energy equivalent values using conversion factors (Biomass * 

0.3215 * 42) from the United Nations Economic Commission for Europe (UNECE). The 

trend per year was then compared to the amount of gas flared. 

Site-Suitability

In order to determine a suitable pipeline route to distribute gas across Angola, a 

combination of spatial analysis tools was used: raster calculator, accumulated cost grid 

(ACG), and least cost path (LCP) analysis. ACG and LCP relied on a scoring system 

known as an analytical hierarchy process (AHP) which ranked an arbitrary number of 

variables (Cimmery 2013). The first step in the AHP was to construct a pairwise 

comparison table which ranked each variable in comparison to another and assigned 

a weighted score based on whether the variable was more or less relevant than the 

other (Table 1). The individual scores were then summated for each variable to acquire 

the final weight. Prior to actual route design, parameters that may affect a pipeline 

were collected using various NASA satellites and other sources. These parameters 

included canopy height, road network, railroad network, inland waters, population 

data, seismic activity data, elevation, and protected or prohibited sites. See Table 2 in 

the Appendix for data sources. 

Least Important Most Important 

 1/7 = 0.14 1/5 = 0.20 1/3 = 0.33 1/1 = 1 3/1 = 3 5/1 = 5 7/1 = 7 

Slope of 

terrain

Primary 

Roads

Secondary 

Roads

Tertiary 

roads

Railway 

crossings

Water 

crossings

Seismic 

activity Canopy cover

Slope of terrain 1.00 1.00 0.33 0.14 0.20 0.20 0.14 0.14

Primary Roads 1.00 1.00 0.14 0.14 0.20 0.20 0.14 0.14

Secondary 

Roads 3.00 7.00 1.00 0.14 1.00 0.33 0.14 0.20

Tertiary Roads 7.00 7.00 7.00 1.00 5.00 5.00 0.14 1.00

Railway 

crossings 5.00 5.00 1.00 0.20 1.00 0.20 0.20 0.20

Water crossings 5.00 5.00 3.00 0.20 5.00 1.00 0.14 0.20

Seismic activity 7.00 7.00 7.00 5.00 7.00 7.00 1.00 3.00

Canopy Cover 7.00 7.00 5.00 1.00 5.00 5.00 0.33 1.00

SUM OF 

COLUMN 36.00 40.00 24.48 7.83 24.40 18.93 2.25 5.89

TOTAL 159.77
7 

Table 1 Pairwise comparison table

Once the weights were assigned to each variable, they were applied to their 

respective rasters and combined into a single cost grid using the raster calculator. 

Each pixel had a value and corresponding color so that overlapping factors were 

apparent. Figure 1 shows the resultant cost grid with weights applied, where some 

variables were more relevant than others (left), and where all variables were weighted 

equally (right). 

Figure 1 Weighted and 

un-weighted rasters 

resulting from single cost 

grid. The whited-out 

features represent 

prohibited areas. In the 

weighted raster, the 

darker shades represent 

more highly ranked 

variables, in this case 

roads and slope of 

terrain. In the unweighted raster, all 

variables were ranked 

equally. 

An ACG was then created by combining the raster calculator result with a destination 

point (or points). The twenty most populated cities in Angola were chosen as 

destination points for this study. With the ACG, higher cost was observed for points 

further from the destination, as was fluctuation due to the scores provided by the single 

cost grid of combined variables. 

Figure 2. Accumulated cost grid for a 

single destination. The further from the 

destination point, the more economic and 

environmental cost is accumulated. The 

white space represents a prohibited area 

through which a pipeline may not be 

routed.
8 

Finally, a LCP was calculated by inputting a source point and the ACG. When multiple 

destination points were used during creation of the ACG, the LCP was observed, more 

often than not, to favor the destination nearer to the source. In this study, the LCP did 

forfeit route length, but was ultimately more favorable than an unweighted approach 

since it avoided many areas where a pipeline route may be more costly to implement, 

for example, areas of more sloped terrain, or of dense vegetation. Accurate, efficient 

AHP application would require expert or client input, as well as a consideration for all 

possible variables. 

Figure 3 Methods summary for the satellite based gas pipeline site suitability study

IV. Results & Discussion 

Biomass Estimation 

The biomass estimations using NDVI and LAI showed similar results to the forest area 

change using MODIS reflectance. The areas along the coastline and in the 

southwestern portion of Angola near the Namib Desert had the highest levels of 

biomass change over the 12 year period. These results can be seen in figure 4 showing 

biomass and figure 5 showing forest area changes. 
9 

Figure 4 Biomass maps in Mg / hectare for 2002 and 2014

Figure 5 Forest area change from 2002 to 2014
10

Over the study period, approximately 996.3 billon metric tonnes of biomass were 

removed from the study area. The amount of biomass present using the methods 

presented produces some variability on an annual basis (Figure 6). 

Figure 6 Estimated biomass using LAI and NDVI from 2002 to 2014

This variability made it difficult to analyze annual changes so a trend line was used to 

determine the average annual change over the time period. This trend line estimated 

that an average of 61.7 million metric tonnes of biomass were lost each of years studied 

which equates to approximately 833 billion MJ of energy. 

For the same time period, it was estimated that 56,775 km2 of forested area had been 

lost within the study area. This value was significantly higher than the Global Forest 

Change (GFC) product from the University of Maryland which estimated forest area lost 

at 18,668 km2 (Hansen et al. 2013). The large estimation of forest cover lost is likely due 

to the methods used in this study. When determining the classes, areas that were 

borderline forest on a course MODIS reflectance image were added to the forest area. 

These same areas tended to have the most change in reflectance during the study 

period. For this reason, the forest area change from this study overestimated, but it was 

successful at identifying the areas of high biomass change. 

The annual biomass loss estimations were significantly higher than the Food and 

Agriculture Organization of the United Nations (FAO) at 16.15 million metric tonnes (FAO 
11

0

0.5

1

1.5

2

2.5

3

3.5

4

4.50 9.50 14.50 19.50 24.50 29.50 34.50 39.50 44.50

Energy Equivalent (109 MJ)

Mass of CO2 (103tons)

Energy lost in Angola

Vs CO2 emitted from

Mar 2013-Feb 2014

2010). This difference was likely due to the exclusive use of course remotely sensed 

reflectance data and its products. The woody biomass data from the WHRC was 

obtained using in situ, MODIS reflectance, and LiDAR data (Baccini et al. 2008). The 

FAO used similar methods to estimate biomass. This study used only coarse NDVI and LAI 

to data to estimate biomass resulting in variation from year to year. NDVI and LAI are 

both dependent on seasonal and climatic changes that may not have significant 

influence on the biomass present. 

Gas Flaring 

Preliminary results show that an estimated 109 million cubic meters (m3) of natural gas 

were flared in Angola, 46.2 million m3 in the Republic of Congo, and 20.3 million m3 in 

Gabon for a period of one year. This equates to about 4 billion MJ of energy equivalent, 

1.65 billion MJ, and 0.725 billion MJ in Angola, Republic of Congo, and Gabon 

respectively. However these estimates were obtained without taking into consideration 

various uncertainty factors. For instance, due to a U.S. government shutdown in 

October 2013, 24 out of 365 days of the study period did not have any reported data. 

Figure 7 The plot showing the non scaled amount of energy that could have been harvested 

from gas flaring in Angola from Mar 2013 to Feb 2014 versus the amount of carbon dioxide that 

was emitted into atmosphere 

Furthermore, a comparative study of biomass and natural gas is necessary. One of the 

useful factors to compare these two fuels is by combustion efficiency. The combustion 

efficiency of natural gas is approximately five times higher than combustion efficiency 

of woody biomass. Moreover, if it is assumed that harvested natural gas will be used for 

cooking, then heat transfer efficiency of biomass stove and natural gas stove should be 

taken into consideration. In developing countries such as Angola, woody biomass 

stoves are often inefficiently built. This renders a considerable amount of energy to be 

lost to the atmosphere as heat. 
12

After taking into consideration major uncertainty factors, preliminary results were scaled 

by the factor of five. This means that approximately 20 billion MJ of natural gas energy 

equivalent were flared in Angola per year, 8.25 MJ in the Republic of Congo, and 2.9 

MJ in Gabon. These results were derived from intermediary results provided in table 3 

(appendix). 

In figure 8, the monthly results show that July and August 2013 topped other months in 

gas flaring and the flares volume trend is the same for all three countries. This may be 

partially attributed to seasonal and climatic conditions. Generally in a tropical region, 

July and August are drier months with fewer clouds, and therefore data capture 

accuracy is higher. 

Figure 8 Graphical representation of rough estimation of monthly gas flaring volumes for each 

country from the study area 

0

2

4

6

8

10

12

14

16

18

20

22

FLARED GAS VOLUME ( X10

6 CUBIC 

METERS)

Gabon Angola Congo
13

(a) (b)

(c) (d)

a) Least Cost Path (LCP) derived with weighted parameters. Total 

route length is ~15816.44 KM.

b) LCP derived without weighted parameters. Total route length 

is ~15400.45 KM.

c) LCP derived without parameters. Total route length is 14465.35 

KM.

d) Comparison between three types of LCP for one destination.

 Total route length with weighted parameters: ~1238.44 KM.

 Total route length without weighted parameters: ~1210.82 

KM.

 Total route length without parameters: ~1052.96 KM.

Figure 9 Final site suitability results

It was observed that the optimal route for natural gas pipelines sacrificed route length, 

but studies have shown that it is ultimately much more cost efficient than an unweighted and no parameter approach. 

V. Conclusions 
14

The gas flared in Central Africa represented a significant amount of energy that could 

potentially be harvested. Estimations show that approximately 20 billion MJ of 

equivalent energy were flared in Angola over the study period when adjusted for 

combustion and heat transfer efficiency of natural gas compared to biomass. The 

amount of flared gas could replace 10% of the consumed biomass in 2011 as reported 

by the International Energy Agency (IEA). 

Although it was possible to create a methodology to estimate biomass loss, the values 

of biomass loss were not comparable to the amount of potential energy lost through 

gas flaring. 

AHP derived cost grid makes considerable amount of difference in LCP by giving due 

consideration to relative importance of parameters but it comes at the cost of route 

length. Desirable result depends on the selection of parameters & AHP implementation. 

VI. Acknowledgments 

The team would like to extend gratitude toward the following people: 

The science team at the NOAA National Geophysical Data Center behind the VIIRS 

Nightfire product, particularly those that extended their help to us, Drs. Feng Hsu and 

Mikhail Zhizhin 

The science advisors and mentors who took time out of their workloads to respond to 

our survey on pairwise comparisons for pipeline routing 

VII. References 

Baccini, A., Laporte, N., Goetz, S.J., Sun, M., and Dong, H. 2008. A first map of tropical Africa's 

above-ground biomass derived from satellite imagery. Environmental Research Letters 3. 

Elvidge, Baugh, Zhizhin, and Hsu. "Why VIIRS data are superior to DMSP for mapping nighttime 

lights." Asia-Pacific Advanced Network 36th Meeting. Korea Institute of Science and 

Technology Information, Daejeon. 20 Aug. 2013. Proceedings Paper.

Food and Agriculture Organization of the United Nations (FAO). 2010. Global forest resources 

assessment, 2010-Main report. FAO Forestry Paper 163. Rome, Italy.

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. 

V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, 

and J. R. G. Townshend. "High-Resolution Global Maps of 21st-Century Forest Cover 

Change." Science 342.6160 (2013): 850-53. 

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Aug. 2014. 
15

<http://www.iea.org/statistics/statisticssearch/report/?&country=ANGOLA&year=2011&p

roduct=RenewablesandWaste>. 

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Web. 05 Aug. 2014. 

<http://www.irena.org/REmaps/countryprofiles/africa/angola.pdf#zoom=75>

Kasekende, Louis, and Elham Ibrahim. Oil and Gas in Africa. African Development Bank, 1 July 

2009. Web. 1 June 2014.

<http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/Oil%20and%20

Gas%20in%20Africa.pdf>.

Lanclos, A., King, R., Thompson, E., Monlezun, C., Jones, A., & Spruce, J. Determining the 

Feasibility of Using VIIRS Global Combustion Source Detection Products to Estimate the 

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2013. 

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Combustion. Prerun. March, 2013-February, 2014.

<http://www.ngdc.noaa.gov/eog/viirs/download_viirs_fire.html>. 

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No. 737227040 

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16

<http://siteresources.worldbank.org/INTGGFR/Resources/GGFR_NewBrochure%28Oct201

1%29.pdf>.

VIII. Appendix 

Table 2: Site Suitability Study Data Source 

S.No Parameter Source

Angola Administrative 

Boundaries, Roads, 

Rail-Roads and Inland 

Waters http://www.diva-gis.org/gdata

Angola SRTM Data

http://www.cgiar-csi.org/data/srtm-90m-digital-elevationdatabase-v4-1
17

Global Seismic Hazard 

Map

http://gmo.gfzpotsdam.de/pub/gshap_data/gshap_data_frame.html

Protected Areas http://www.globalforestwatch.org/

Global Canopy Heights http://www.nasa.gov/topics/earth/features/forest20120217.html

Protected Areas http://www.protectedplanet.net/

Population Count

http://sedac.ciesin.columbia.edu/data/set/grump-v1-

population-count/data-download

Table 3: Results of Gas Flaring Rough Estimations from March 2013 to February 2014 

Angola Gabon Republic of Congo

Flared Gas (m3 / year) 109,145,882 20,333,304 46,218,154

* Energy Equivalent (MJ) 3,889,959,241 724,678,955 1,647,215,014

Carbon Dioxide (tons / year) 201,047 37,454 85,134

Temperature Range (K) 1400-9946 1400-13506 1400-7483

* Non scaled results 
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Central Africa Energy: Utilizing NASA Earth Observations to Explore 

Flared Gas as an Energy Source Alternative to Biomass in Central Africa 

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NNX14AB604

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Jones, Amber; White, Charles; Castillo, Christopher; Hitimana, Emmanuel; 

Nguyen, Kenny' Mishra, Shikher; Clark, Walt

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NASA Langley Research Center 

Hampton, Virginia 23681 

 

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14. ABSTRACT

Much of Central Africa’s economy is centered on oil production. Oil deposits lie below vast amounts of compressed natural gas. The latter is 

often flared off during oil extraction due to a lack of the infrastructure needed to utilize it for productive energy generation. The objective of 

this project was to gain insight on domestic energy usage in Central Africa, specifically Angola, Gabon, and the Republic of Congo. This 

was done through an analysis of deforestation, an estimation of gas flared, and a suitability study for the infrastructure needed to realize the 

natural gas resources. The energy from potential natural gas production was compared to the energy equivalent of the biomass being 

harvested. A site suitability study for natural gas pipeline routes from flare sites to populous locations was conducted to assess the feasibility 

of utilizing natural gas for domestic energy needs. Analyses and results were shared with project partners, as well as this project’s open 

source approach to assessing the energy sector. Ultimately, Africa’s growth demands energy for its people, and natural gas is already being 

produced by the flourishing petroleum industry in numerous African countries.

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Biomass; Deforestation; GIS; Gas flaring; Greenhouse emission; Remote sensing

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