Oil in the Sahara: mapping anthropogenic
threats to Saharan biodiversity from space
Clare Duncan1, Daniela Kretz1,2, Martin Wegmann3,4, Thomas Rabeil5
and Nathalie Pettorelli1
rstb.royalsocietypublishing.org
Research
Cite this article: Duncan C, Kretz D,
Wegmann M, Rabeil T, Pettorelli N. 2014 Oil in
the Sahara: mapping anthropogenic threats to
Saharan biodiversity from space. Phil.
Trans. R. Soc. B 369: 20130191.
http://dx.doi.org/10.1098/rstb.2013.0191
One contribution of 9 to a Theme Issue
‘Satellite remote sensing for biodiversity
research and conservation applications’.
Subject Areas:
ecology, environmental science
Keywords:
remote sensing, biodiversity, oil exploration,
desert, conservation, Landsat
Author for correspondence:
Nathalie Pettorelli
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rstb.2013.0191 or
via http://rstb.royalsocietypublishing.org.
1
Institute of Zoology, Zoological Society of London, Regent’s Park, London NW1 4RY, UK
BayCEER, University of Bayreuth, Bayreuth 95440, Germany
3
Department of Remote Sensing, University of Würzburg, Campus Hubland Nord 86, Würzburg 97074, Germany
4
German Remote Sensing Data Center, German Aerospace Center, Oberpfaffenhofen 82234, Germany
5
Sahara Conservation Fund, Niamey, Republic of Niger
2
Deserts are among the most poorly monitored and understood biomes in
the world, with evidence suggesting that their biodiversity is declining
fast. Oil exploration and exploitation can constitute an important threat to
fragmented and remnant desert biodiversity, yet little is known about
where and how intensively such developments are taking place. This lack
of information hinders local efforts to adequately buffer and protect desert
wildlife against encroachment from anthropogenic activity. Here, we
investigate the use of freely available satellite imagery for the detection of
features associated with oil exploration in the African Sahelo-Saharan
region. We demonstrate how texture analyses combined with Landsat data
can be employed to detect ground-validated exploration sites in Algeria
and Niger. Our results show that site detection via supervised image classification and prediction is generally accurate. One surprising outcome of our
analyses is the relatively high level of site omission errors in Niger (43%),
which appears to be due to non-detection of potentially small-scale, temporary exploration activity: we believe the repeated implementation of our
framework could reduce the severity of potential methodological limitations.
Overall, our study provides a methodological basis for the mapping of
anthropogenic threats associated with oil exploitation that can be conducted
across desert regions.
1. Introduction
The human population and its demand on the Earth’s natural resources have
increased exponentially over the past century, leading to a global biodiversity
crisis [1–4]. With present levels of biological diversity drastically altered from
their historic states [1] and declines predicted to continue under growing
pressures from land-use change and climate change [2,5,6], scientists and conservationists are now grappling with the immense challenge of monitoring and
halting the current rate of global loss [7]. A number of prominent articles have
made the case for targeting conservation funding at biodiversity hotspots, tending to fall predominantly within tropical biomes. The reasoning behind such
argumentation is that management actions there target the greatest number of
species per surface unit, thus maximizing cost to benefit ratios [8–13]. This
focus on hotspots has inevitably resulted in the neglect of less species-rich
biomes [14–16] where threats are poorly documented and monitored, and their
local, regional and global impacts on biodiversity are poorly known, leading to
a much reduced understanding of the potential need for conservation action in
these regions [14–16].
Deserts are one such biome that has previously been poorly studied and has
received little attention from conservation activities [14,15], despite covering
17% of global land mass and harbouring surprisingly high levels of biological
diversity, including many endemic species and some of the most endangered
species in the world [17 –20]. This neglect is worrying as (i) available
& 2014 The Author(s) Published by the Royal Society. All rights reserved.
2
Phil. Trans. R. Soc. B 369: 20130191
these potential consequences, little is currently known about
how and where oil exploration and exploitation activities are
taking place, and where these developments are likely to
negatively impact Sahelo-Saharan ecosystems.
A major issue in determining threats to biodiversity from
the expansion of oil exploration activities is the difficulty in
obtaining field data on the geographical locations of these
developments. Field monitoring can be notoriously costly,
and field data on oil exploration and exploitation activities,
while accurate, are difficult to obtain for large areas, especially
for remote regions where logistics and safety are real issues
[41,42]. In light of this, remote sensing technologies could provide a useful monitoring alternative. Global-scale low light
imaging satellite data from the US Air Force Defense Meteorological Satellite Program Operational Linescan System [43],
data from the European Space Agency’s Along Track Scanning
Radiometer sensors [44] and data from the recently launched
VIIRS instrument on the Suomi National Polar Partnership
satellite [45], can be used to derive global and national information on oil exploration activities. However, the spatial resolution
of these datasets is relatively coarse (0.56 km, 1 km and 750 m,
respectively); thus, information derived from these data may
not be applicable to identify fine-scale, local-level oil exploration threats to biodiversity. Very high-resolution satellite
imagery could instead be used to derive national information
on oil exploration and exploitation activities; yet, most organizations and institutions in need of such data are unable to afford
the cost of data acquisition and processing at such spatial
extents. Freely available satellite data such as data from the
National Aeronautics and Space Administration (NASA) and
United States Geological Survey’s Landsat satellites, however,
have shown promising applications in the fields of ecology
and conservation [46] and could potentially inform efforts to
detect and map oil exploration and exploitation activities at relevant scales [47,48]. Furthermore, the availability of Landsat
satellite data at a good and consistent temporal scale (at up to
16-day intervals) can allow for the continued and repeated
monitoring of areas of concern.
Surprisingly, the potential for freely available satellite data to
inform conservation efforts in desert systems currently remains
largely unexplored (but see [47]). This study aims to fill this
knowledge gap, by assessing if and how open access satellite
data can be reliably used to monitor oil exploration and refinery
activity in the Sahelo-Sahara region. It aims to do so in a reproducible manner, so that methods and analyses developed
herein can be applied to conservation activities in other dryland
regions. Many approaches and attempts to detect the location
and distribution of oil exploration and extraction activities
focus on the detection of oil flares alone [43–45]. However,
sites also feature buildings and other man-made structures,
which may include water ponds, roadways or visible smoke
[46], and these features are associated with different spectral signatures than those generally found in the surrounding desert
[47,49]. As such, a framework that aims to detect not only oil
flares but also associated structures may improve the success
of detection of novel and established oil extraction activity.
This serves as the basis for our approach, which aims to validate the use of Landsat satellite data for the detection of
features typically associated with oil exploration activities. Our
study undertakes a two-step approach: firstly, field-derived
(known oil exploration site locations) and visually derived
(high-resolution Google Earth imagery) validation data, and satellite-based landcover classification are combined to establish a
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information suggests that desert and dryland biodiversity is
declining rapidly [14,18–22]; and (ii) predictions show that
the rate of climate change is likely to be particularly high in
the desert biome, with species potentially facing especially
difficult adaptation challenges in the near future [23]. This
is of particular concern, as the ecology and distribution of
desert-adapted species are under extreme climatic control
[24]. Other major threats to deserts include overgrazing,
woody-vegetation clearance, agricultural expansion, water
diversion and extraction, soil and water pollution, land conversion due to industrial activities and associated threats
from armed conflicts [19,21]. With potentially enormous
local- and regional-scale effects, a current major anthropogenic threat to desert biodiversity, likely to increase in
extent and intensity over the coming years through increasing
resource demand, is oil exploration activities [21,25,26].
Across the Sahara and Sahel, threat from oil exploration is
particularly great, with concerns for global oil stocks
having led to exploration being undertaken in increasingly
remote areas [27,28]. The future of Sahelo-Saharan biodiversity is considered to be largely dependent on anthropogenic
expansion in the region [21]. Countries like Niger, which
represents a stronghold for local desert species [21,29,30],
have undergone rapid increases in their oil extraction and
refinery activity in recent years [26,30]. Such increased oil
prospection activities have, for example, now led to the
endangerment of the last known viable population of
addax (Addax nasomaculatus), owing to habitat loss in important, high-quality grazing sites in the Tin Toumma desert
[31]. These largely unmonitored activities are expected to
occur across many parts of the region, with potential ancillary impacts including waste production, reduced water
availability through anthropogenic extraction, reduced habitat quality and ecosystem functioning, and increased
poaching and human –wildlife conflict [21,30,32,33]. These
potential threats could have an enormous impact on local biodiversity, as (i) much of the fauna in the Sahelo-Saharan
region is considered to be of global conservation concern
(e.g. the addax, Dorcas gazelle (Gazella dorcas), dama gazelle
(Nanger dama), Saharan cheetah (Acinonyx jubatus ssp. hecki)
and Kleinmann’s tortoise (Testudo kleinmanni); [18]) and (ii)
the distribution of biological diversity in the area is markedly
fragmented, with often highly localized hotspots [20,21] that
could become imperilled by the impacts of unmonitored oil
exploration activity. Hotspots of Sahelo-Saharan biodiversity
are often concentrated within protected areas (PAs) [21]. If
conducted close to or within these areas, the ancillary threats
and associated impacts of oil exploration activities could lead
to drastic reductions in wildlife populations, reducing the
effectiveness of PAs in conserving these remnant hotspots
of biodiversity. Across the African continent, the designation
of oil concessions within PAs is common, and particularly
within those with a high level of protection [34]. Threats
from oil exploration activities at PA boundaries could also
result in reduced connectivity of habitats between these
PAs and important resource areas by reducing the quality
of habitat corridors and producing physical barriers to movement, cutting off wildlife populations from wider ecosystems
and satellite populations [35– 38]. Such impacts on wide ranging animal species could have potential knock-on cascading
effects through other trophic levels within such systems
[39,40], producing broad-scale ecological impacts and
impacts to overall ecosystem health and functioning. Despite
3
study areas (Landsat coverage)
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PAs
oil sanctions for exploration:
open block
block under licence
Phil. Trans. R. Soc. B 369: 20130191
(b) (a)
(c)
N
0
150
300
km
600
Figure 1. An overview of the location of the study areas in Algeria and Niger. Oil concession blocks within Niger are shown; blocks currently under licence
( purchased) in dark grey and open blocks (un-purchased) in mid grey [29]. Overlap between purchased oil concession blocks and PAs can been seen for (a)
Addax Sanctuary, (b) Aı̈r & Ténéré National Nature Reserve and (c) TTTNNR.
framework for the detection of known features associated with
oil exploration activities; this framework is then applied in
areas for which an explicit landcover classification cannot be
made, in order to provide spatially explicit information about
recent development in these activities in relation to existing PAs.
2. Material and methods
(a) Study areas
The location used for the development of our monitoring framework is in Saharan Algeria. The area of interest reaches from
the northwestern corner at 318490 1200 N, 88220 1200 E to the southeastern corner at 31840 1200 N, 98270 000 E (figure 1). A second
study area in Niger, where our landcover classification is then
implemented, is delimited by a box reaching from the northwestern corner at 168220 1200 N, 128120 000 E to the southeastern corner at
158270 3600 N, 138490 1200 E (figure 1). Both study areas comprise
areas of desert, rocky terrain and sparse vegetation. Although
they both lie within the Sahara, Algeria and Niger represent
two extremes in a spectrum of anthropogenic impacts in the
hyper-arid zone: Algeria has a great number of established oil
and gas extraction sites, as well as refineries [21], and here represents areas with high anthropogenic pressure associated with
oil exploration activities. On the other hand, desert habitat in
Niger remains largely undisturbed by anthropogenic activities,
and the expansion of oil exploration and exploitation activities
in the country has occurred only in recent years [30,50]. The
Niger region was selected for this study as it borders key PAs
for the conservation of desert biodiversity; namely, the Aı̈r &
Ténéré National Nature Reserve, and the Termit & Tin
Toumma National Nature Reserve (TTTNNR), which are currently threatened by the expansion of oil exploration activities
[30,50]. The study area in Niger encompasses part of the eastern
side of the TTTNNR and also the Agadem oil concession block,
which is the predominant site of emerging exploration activity in
the region to the east of TTTNNR (figure 1; [30]). Within
TTTNNR and Aı̈r & Ténéré, much of the remaining SaheloSaharan diversity thrives [18,30,50], including 18 large
mammal species, 19 reptile species, 137 resident and several
Afro-tropical and Palaearctic migrant bird species [51]. These
two PAs protect important populations of many species of
global conservation concern: carnivores (the Saharan cheetah
( population estimate currently less than 10 individuals in the
area; [51]), striped hyaena (Hyaena hyaena) and the greatest diversity of small and medium sympatric carnivores in the Sahara;
[51]), birds (the Nubian bustard (Neotis nuba) and Arabian bustard (Ardeotis arabs)) and large ungulates (the addax, Dorcas
gazelle, dama gazelle and Barbary sheep (Ammotragus lervia);
Our methodology makes use of Landsat 7 Enhanced Thematic
Mapper Plus (ETMþ) imagery, available at 30 m spatial resolution
([56]; available for download at: http://earthexplorer.usgs.gov),
which has previously been found to have utility in desert areas
[47,57]. One minor limitation of the Landsat 7 ETMþ sensor is
that the Scan-Line-Corrector is off (SLC-off) owing to a technical
malfunction, causing approximately 22% of data from any given
Landsat 7 ETMþ image to be lost [58]; ‘striping’ of missing data
is apparent in the images. Typically Landsat 5 TM imagery
would be applied instead due to this error; however, owing to limited data availability over Niger, we were unable to obtain Landsat
site locations
Algeria
Niger
field derived (SCF)
1
7a
visually derived (Google Earth)
2
0
a
One of the original eight field-derived sites in Niger was no longer present
in November 2012 (a temporary site), while the status of the remaining
seven sites is not known from SCF; thus, these sites were considered to be
‘permanent’.
5 TM imagery for analysis here, and thus Landsat 7 ETMþ data
were used for both regions despite the SLC-off issue. The Landsat
7 ETMþ scenes considered for both study areas were affected by
the SLC-off issue; however, gap masks facilitated the masking
out of the affected region, and thus the SLC-off issue can be
resolved for use in landcover classification [56]. The images
obtained for these analyses were from 5 November 2012 for the
Algerian area (framework development area), and 2 November
2012 for Niger (figure 1), in order to avoid differences according
to varying seasonal solar elevation and soil–water–vegetation
dynamics [59].
(c) Field-derived and visually derived training
and validation data
In order to produce and validate a landcover classification from
satellite imagery, field-derived and visually derived validation
points of specific landcover classes can be used. In both Algeria
and Niger, ground-truthed geo-referenced (GPS-logged) site validation point data were obtained from the Sahara Conservation
Fund (SCF) over the period 2009 –2011, detailing the locations of
known oil exploration and extraction activity. These data included
one permanent site within the Algerian Landsat 7 ETMþ scene. In
addition, we located two further validation sites in the Algerian
study area through inspection of very high-resolution QuickBird
and IKONOS imagery within Google Earth Pro [60] as performed
in the work of Fritz et al. [61], resulting in a total of three validated
oil exploration sites in this study area (table 1). Using the very highresolution Google Earth imagery, we were also able to identify
a total of eight landcover types in the scene: desert, bare rock,
vegetation, roads, dark-coloured settlements, light-coloured settlements, oil flares and water ponds. These eight landcover classes
were selected based on the study aims of differentiating human
infrastructure from natural landcover (i.e. bare rock, vegetation
and desert versus roads, settlements, oil flares and water ponds),
in order to develop an appropriate landcover classification.
An equal distribution of training point data samples were then created across all landcover classes to define their relative spectral
information from the Landsat 7 ETMþ scene (200 random samples
of pixels per class) and were then used to develop a landcover
classification model.
Ground-truthed geo-referenced site validation point data
detailed 11 sites within the scene for Niger, which included
both temporary and permanent oil exploration sites visited by
SCF. Some sites were highly concentrated in two specific areas,
giving a total of eight unique exploration sites in the Niger
study area (table 1). Of these eight sites, field information from
the SCF revealed that one site was a temporary exploration site
(table 1) and was no longer present in November 2012 (date of
the Landsat 7 ETMþ scene considered). The temporary/permanent status of other sites within the Niger study area is not known
4
Phil. Trans. R. Soc. B 369: 20130191
(b) Satellite data
Table 1. Overview of all field-derived (SCF) and visually derived (via highresolution Google Earth imagery; [60]) ‘permanent’ oil exploration and
extraction site data for the study areas in Algeria and Niger.
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[18,30,50]). Importantly, the Termit massif and Tin Toumma
desert region hold the last remaining viable population of
addax in the world (ca 200 individuals; [52]). The direct ecosystem impacts of increased oil exploration activities [21,30,32,33]
have the potential to produce profound effects on the local biodiversity; habitat loss and water extraction at prospection sites may
serve to affect floral abundance and composition, in turn reducing habitat quality and resource availability for species at
higher trophic levels [21]. Moreover, increased poaching of
large ungulate species [30] may serve to reduce their densities
in such areas, and in turn, coupled with increased human – wildlife conflict, may result in the reduction of populations of key
Saharan carnivore species, e.g. the Saharan cheetah, where ungulate prey are a limiting factor to predator abundance [53]. In
addition to the ecological uniqueness of the Termit massif
and Tin Toumma desert area, it is also thought to be rich in currently largely unstudied archaeological, anthropological and
palaeontological history [30,50].
Despite the discovery of hydrocarbons within Niger in 1975,
little oil exploration and exploitation activity has previously
occurred in the country, due to the low ratio barrel price
(T. Rabeil 2013, personal communication) and a lack of economic
incentive caused by a historic wealth of uranium providing the
country’s main economic export [30,50]. However, concerns
over diminishing uranium stocks over the past two decades
have led to the designation of oil concession blocks throughout
much of the country [26,30,50]. In June 2008, the government
of Niger signed a contract with the Chinese National Petroleum
Corporation (CNPC) for the purchase of three oil concession
blocks covering large areas of the Aı̈r & Ténéré and the
TTTNNR (figure 1; [30,50]), with oil prospection activities occurring within these areas from June 2008 onwards. With extremely
little ecotourism in the country, the societies and government of
Niger have little economic incentive to conserve the ecological,
archaeological and anthropological uniqueness of the Termit
massif and Tin Toumma desert region [50]. At present, 20 oil
concession blocks have been designated across the country,
with four currently under licence (including the three rented by
CNPC and one by the Algerian corporation SONATRACH) covering much of the Termit massif and Tin Toumma desert region
(figure 1; [30]). Although most designated concession blocks in
Niger remain open at present, depleting uranium stocks are
likely to result in an economic shift to an increased reliance on
hycrocarbon concession rentals and exports [30,50], and thus
increased sale of concessions to oil corporations, extending the
current level of threat to local biodiversity. The expansion of
these activities into the neighbouring PAs is of particular concern
for large ungulate species as (i) many of these are highly threatened remnant populations and (ii) there have been reports of
poaching (gazelles and birds) associated with the arrival of
workers for oil exploration activities in the region since 2008
[54]. Ancillary impacts associated with industrial soil and
water pollution have also been found to be evident at exploration
sites within the oil concession blocks [55].
(d) Statistical analyses
5
The Random Forest model accurately predicted the distribution of all eight landcover classes for Algeria. Model
internal class errors ranged between 0 and 0.02%; bare rock,
oil flares and water all had 0.00% error; vegetation, roads,
and dark- and light-coloured settlements all had 0.01%; and
finally, desert had the highest error rate at 0.02%. Moreover,
the out-of-bag overall model error rate was 0.62%. On average,
the Landsat ETMþ bands providing the most important variables were band 3 (red), followed by band 1 (blue), band 7
(short-wave infrared-2 (SWIR-2)), band 4 (near-infrared),
band 6–2 (thermal-infrared 2), band 6–1 (thermal-infrared 1),
band 2 (green) and band 5 (mid-infrared; figure 2; electronic
supplementary material, Appendix S1). The field-derived
refinery location in Algeria was accurately predicted by the
Random Forest model and visually confirmed using high-resolution Google Earth imagery, and the two sites located via
high-resolution Google Earth imagery were also accurately
predicted by the Random Forest model (table 2). Detecting
the accurate location of these sites was made possible by the
presence of concentrated areas of non-desert landcover classes:
oil flares, settlements, roads and water ponds.
When applying the identification framework to the scene
from Niger, a total of seven oil exploration sites were identified
(figure 3 and table 2). Of the detected sites, four oil refineries
were field validated by existing geo-referenced field data
from SCF; thus, three new oil exploration sites were discovered
from our analyses (figure 3 and table 2). RGB visualization (7, 4
and 2 bands) of the Landsat 7 ETMþ image, confirmed that the
newly detected sites were consistent with other validated sites.
However, some of the ground-truthed geo-referenced exploration sites from SCF in Niger were not detected when
applying the Random Forest model framework (omission
error 43%; figure 3 and table 2). In contrast to oil exploration
sites in the Algeria study areas, aside from desert there were
no other discernible classes of landcover located near the oil
flares identified in Niger. The oil flares class thus enabled the
detection of the oil exploration and exploitation sites in the
Niger region, which is due to the unique spectral and thermal
signal of the class; surface temperature (K, calculated from
band 6–1 of the Landsat ETMþ sensor [77]; figure 4) was highest for this landcover class, and high IR values (maximum band
7 (SWIR-2)), which contributed greatly to the Random Forest
classification (figure 2; electronic supplementary material,
Appendix S1), are associated with the heat signature of the
flame [47,57].
Ground-truthed SCF point data for the Niger region show
an existing overlap between PA boundaries and oil exploration sites (within TTTNNR; figure 3); however, the Random
Forest identification model applied here did not detect any
activity within the part of the PA that fell within our study
area and did not detect the two oil exploration sites within
the study area–PA overlap (10% of the study area overlapped
with the PA; figure 3). The proximity of many of the modeldetected oil refineries to the nearest boundary of the
TTTNNR was found to be less than 15 km (figure 3).
4. Discussion
Owing to limited funding and conservation attention [14–16],
little is currently known about the location and intensity of
Phil. Trans. R. Soc. B 369: 20130191
All analyses were performed in GRASS v. 6.4.2 [62], QGIS
v. 1.8.0 [63] and R v. 3.0.0 [64], using the packages ‘raster’ [65],
‘rgdal’ [66], ‘randomForest’ [67] and ‘spgrass6’ [68], the latter
of which serves as an interface between R and GRASS. A conversion of digital numbers to top-of-atmosphere reflectance
values was carried out on the raw Landsat 7 ETMþ scenes.
Owing to a lack of strong elevational gradients across both
study areas (Algeria: 100 –300 m.a.s.l.; Niger: 300 – 400 m.a.s.l.),
no further orthorectification of the images was necessary [69].
Texture analyses were conducted on the Algerian Landsat 7
ETMþ scene using a moving window approach, in order to
emphasize features typical of oil refineries and highlight them
against the background of the Saharan desert [70–72]. A moving
window size of 5 5 pixels has been found to be useful in highlighting changes in edges of landcover classes in arid
environments [73]. Such a window size (approx. 150 150 m)
would also encompass the area of a small oil refinery but is not
so large as to neglect roads; this size was thus considered in
these analyses. The texture analysis included the mean, maximum,
minimum, variance and standard deviation of all eight Landsat 7
ETMþ bands (band 6 is composed of two separate bands). All
metrics are based on the neighbourhood statistics within the
‘r.neighbors’ module of GRASS (with square orientation). Thus
in total, 40 parameters (eight bands five moving window
variables each) were calculated. These were used as input data
for the supervised landcover classification of the acquired scenes
[41]. Landcover classification was developed using a Random
Forest model approach [74], based on the model training data
(200 pixels for each landcover class) mentioned above, using
the aforementioned calculated parameters (40 in total) as the
independent variables for Algeria [75].
The relative importance of model variables within the Random
Forest model (i.e. the contribution of each variable in the model
in the construction of each decision tree) was assessed for each
variable by examining the mean decrease Gini index (MDG;
[76]); variables with higher MDG have greater importance in a
Random Forest model. In addition to the Random Forest standard
construction of 500 decision trees to determine the best model for
landcover classification, we also aimed to assess the stability of the
variable importance across models. To do so, we ran the Random
Forest model 200 times and observed changes in the variable
importance parameter of the MDG over the runs. Random Forest
model accuracy was assessed by examining the model internal
class error rates (defined as the percentage of the 200 pixels falsely
classified) for each landcover class identified within the model, as
well as the ‘out-of-bag’ estimate for the generalization error for the
overall model [74]. The Random Forest classification model was
then further validated by comparing model-detected oil exploration sites with the previously mentioned validation data from
ground-truthed geo-referenced data points. The Random Forest
model derived and validated on the scene acquired for Algeria
was then applied to the Landsat 7 ETMþ scene for Niger [67]. Validation of the Niger classification used a combination of the
aforementioned a priori-selected ground-truthed validation sites
and, in the absence of recent very high-resolution Google Earth
imagery for the area, a visual red–green–blue (RGB; bands 7, 4
and 2) image assessment of the original Landsat 7 ETMþ data
for the considered area.
3. Results
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from SCF beyond 2011, and these are thus considered here as
‘permanent’ extraction sites. Recent very high-resolution
Google Earth imagery was not available for the Niger study
area, and thus associated landcover classes could not be visually
identified and classified for this area, leading to the prediction of
landcover here by using the classification model developed for
the Algerian study area scene.
6
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band 3 mean
band 1 mean
band 1 min
band 2 mean
band 4 mean
band 7 max
band 3 min
band 7 min
band 7 mean
band 6-1 mean
band 4 min
band 6-2 mean
band 5 min
band 2 min
band 5 mean
band 1 max
band 5 max
band 6–2 min
band 7 SD
band 7 var
band 6–1 min
band 2 max
band 3 max
band 6–2 max
band 4 max
band 6–1 max
band 1 var
band 1 SD
band 5 SD
band 5 var
band 2 SD
band 2 var
band 6–2 var
band 6–2 SD
band 3 var
band 3 SD
band 6–1 var
band 6–1 SD
band 4 SD
band 4 var
Phil. Trans. R. Soc. B 369: 20130191
20
10
30
40
mean decrease Gini
Figure 2. Random Forest model variable importance; the MDG. All 40 variables are sorted according to their median importance in the model after 200 runs. The
most important variables in the Random Forest model have a median MDG between 30 and 40. Variables are depicted with decreasing importance from top
to bottom.
Table 2. Random Forest model classification results for the model training
study site in Algeria and when applied to the additional study site scene in
Niger. Omission errors for model detection of oil exploration and extraction
sites from field-derived locations. Additional ‘novel’ model-detected sites in
Niger that were not known from original field-derived location data are also
listed; these were visually validated using an RGB visualization (7, 4 and 2
bands) of the original Landsat 7 ETMþ scene.
area
fielddetected
sites
modeldetected
sites
Algeria
1 (2)a
3
0
0
Niger
7
4
43
3
omission
error (%)
novel
sites
a
Additional sites in brackets determined via high-resolution Google Earth
imagery.
specific threats to biodiversity in desert ecosystems. Despite
being considered a major threat [21], and increasing licensing
of concessions [26–30,50], especially in close proximity to
PAs (figure 1; [21,30,34,40]), at present no monitoring framework exists for the expansion of oil exploration activities in
desert regions. As a result, there is presently a lack of realtime information regarding where conservation efforts
should be targeted in order to determine the potential impacts
of these activities on local biodiversity. The results of this study
provide a framework to assist in closing this knowledge gap,
showing for the first time that current and expanding oil
exploration activities in desert ecosystems can be detected
and mapped remotely via freely available satellite data. Our
results confirm that predictions from ground-verified landcover classification models (here Algeria) can be successfully
applied to detect activities in areas for which classification models cannot be explicitly produced (here Niger). The
methodology developed herein thus makes the introduced
framework reproducible across other desert regions and in
areas for which location-specific classification models cannot
be constructed owing to limited availability of high-resolution
imagery and ground-truthed data for validation. This is a situation that is particularly common in many remote desert
regions. Owing to the free availability of NASA’s Landsat
satellite data, the framework developed in this study is inexpensive and easily implementable across deserts, and thus
may be used to monitor oil exploration activities over time in
a cost-effective manner in areas for which conservation funding is limited [15,16]. Moreover, the spatial resolution of the
input variables (150 m, based on the original 30 m Landsat 7
ETMþ bands) is much finer than other datasets traditionally
employed for the detection of oil flares [43–45], and thus is
more appropriate for identifying anthropogenic structures,
while omitting small-scale landscape variations (e.g. singular
rocks), in order to determine regional-scale expansion in the
distribution and intensity of activity. Detecting previously
unknown oil exploration sites in close proximity to PAs
(figure 3), the framework developed herein enables the mapping and continuous monitoring of novel and intensifying
threats to localized hotspots of desert biodiversity.
The successful application of the model classification and
identification framework developed in this study can be attributed to the 40 input variables derived from the eight Landsat
7
rstb.royalsocietypublishing.org
field detected
field detected: temporary
RF detected
Landsat coverage
Termit & Tin Toumma
oil sanctions for exploration:
open block
block under licence
Phil. Trans. R. Soc. B 369: 20130191
N
0
15
30
km
60
Figure 3. Ground-validated versus Random Forest model-detected oil exploration sites for the Niger study area. Geo-referenced field data from SCF are depicted, representing both temporary (circles with black crosses) and ‘permanent’ sites (white circles) visited between 2009 and 2011, within the study area (dashed black rectangle).
Black circles depict the model-detected sites from the Landsat 7 ETMþ scene. Model-detected sites were confirmed by inspection of the RGB visualization (7, 4 and 2
bands) of the Landsat 7 ETMþ scene. A total of seven sites were detected by the Random Forest model; four sites matched the geo-referenced field data and three new
sites were found. Three of the seven ground-validated ‘permanent’ sites within the study area were not detected by the identification framework. The TTTNNR is the area
to the left of the thick black line, and six ground-truthed observations of oil exploration activity can be found within the PA borders. Dark grey blocks denote oil
concessions that are currently under licence (purchased), while light grey blocks indicate open (unpurchased) blocks.
7 ETMþ spectral bands. Class separation was supported by the
moving window variables, as was found by Ge et al. [41]. Overall, texture analyses have been found to enhance information
from the raw satellite bands [71]; the lightness or darkness of
certain objects in relation to neighbouring objects can be identified for the different bands. In using texture analyses, the
importance of variables is dependent upon the targeted
object of the analyses [71,78]. However, the most important
variables for this framework (based on the MDG; figure 2; electronic supplementary material, Appendix S1) seem to be the
band averages, which indicates that the bands themselves are
still highly important in predicting landcover classification.
We do, however, acknowledge that taking the average values
also reduces the effective spatial resolution of the input variables, producing a trade-off between the detection of largescale features versus increasing classification error according
to mixed pixel signals. The minimum band values were also
shown to play an important role in image classification, as
landcover classes such as water, vegetation, dark buildings
and road tarmac typically have lower spectral values in comparison to other classes [59]. While in Algeria, identification
of oil exploration and extraction sites was made possible by
the presence of assemblages of multiple landcover types (oil
flares, small water ponds, settlements and roads), within the
Niger study area such associated landcover classes were not
present and sites were made detectable by the presence of oil
flares. Oil flares have indeed previously been found to be
good indicators of refineries in the Sahara [33]; here, these
could be visualized both in the raw satellite images (as in the
Kuwait oil fires; [47]) and with classification results. Such
detection is primarily due to the band 7 (SWIR-2) of Landsat
7 ETMþ sensor, which covers a similar spectral range as the
thermal IR Advanced Very High-Resolution Radiometer
band that has been previously found to be successful in detecting oil fires [49]. The maximum value of band 7 (SWIR-2) was
particularly useful in the model (figure 2; electronic supplementary material, Appendix S1), which provides
information on the heat signature of the oil flare [47,57]. The
successful application of this identification framework is also
largely a result of the chosen study region; in desert ecosystems, cloud contamination of satellite imagery is negligible
[79], and anthropogenic structures are not masked by the presence of a dense vegetation canopy as in other biomes. Data
acquisition and exploration site detection were therefore not
impeded by these potential limitations.
The oil exploration site identification framework presented here is, however, not without limitation. Although the
framework was successful in detecting the location of oil
exploration sites in Algeria and Niger, it was unable to detect
some of the reported oil extraction wells in the Niger region
based on ground-truthed data (43% omission error; table 2
and figure 3). This may be attributed to both temporal and
(a)
frequency
0.15
0.10
0
(b)
0.30
0.25
frequency
0.20
0.15
0.10
0.05
0
295
300
305
310
temperature (K)
315
Figure 4. The thermal signature of pixels classified as the oil flares landcover
class (through band 6 – 1 of the Landsat ETMþ sensor; [77]) converted to
surface temperature shows temperature between 303 and 312 K across
pixels of this class; panel (a) depicts the frequency of surface temperature
(K) for pixels of the class oil flares (N ¼ 2039) for the results of the framework classification in Algeria, and panel (b) depicts the values (N ¼ 221) for
the application area in Niger.
spatial limitations to our monitoring framework. Oil exploration activity, which had recently commenced in Niger at the
time of these analyses [30,50], is often temporary in nature
[33]. Indeed, four field-derived extraction sites in the Termit
massif region (one within the study scene; figure 3) were
known to no longer exist in November 2012. Accordingly,
some of the non-detected sites in Niger, for which further
field validation beyond 2011 does not exist, could have similarly been no longer in existence in the November 2012 scene.
Thus, the relatively high omission rate for Niger may be due
to non-detection of potentially temporary sites that were no
longer present in the Landsat 7 ETMþ scene considered here.
However, for continued monitoring purposes, the application
of the framework to time-series Landsat satellite data would
Phil. Trans. R. Soc. B 369: 20130191
0.05
8
rstb.royalsocietypublishing.org
0.20
likely not result in misdetections of temporary sites if the
time period between consecutive images considered were
sufficiently narrow. Individual oil flares are also not large
(maximum 150 m in size), and thus may only cover a few Landsat image pixels. Smaller oil exploration sites may thus be
difficult to distinguish from the surrounding desert, especially
if not bordered by other associated landcover classes. This was
indeed the case for the sites detected in the Niger study area,
which (i) were smaller in size than those in the Algerian
study area and (ii) could not additionally be identified by surrounding associated landcover classes, unlike the sites detected
in the Algerian study area (i.e. water pools, settlement and
roads). These results might also suggest that the oil flares
class alone could be used in monitoring the distribution and
expansion of oil exploration and extraction activities in desert
regions over full landcover classification. However, we argue
that due to the recent commencement and temporary nature
of some of these activities in Niger, there may be no large surrounding structures yet constructed at these sites. As these
become developed into the future, the applicability of classification of associated landcover types in locating and detecting
their expansion will increase and will indeed be useful in monitoring expansion of these activities in other desert areas where
sites are more fully developed (as is the case in Algeria). The
SLC-off sensor issue with the Landsat 7 ETMþ data may
result in missing data values over the locations of oil exploration sites in desert regions, resulting in the lack of detection
of sites and suggesting a potential limitation of the use of Landsat 7 ETMþ data in these analyses. However, with the recent
launch of the Landsat 8 satellite in early 2013 [80], data availability for the future implementation of this framework will
not be hindered by this issue. Cross-scene atmospheric correction, which would serve to further normalize features across
scenes [81], was not applied in these analyses and may have
influenced the success of the implementation of this framework
to the scene in Niger. However, owing to the applied methods
using texture metrics to detect the limited infrastructure in
the Niger scene and the few landcover classes present here,
it is unlikely this correction would significantly affect the
results of these analyses. Cross-scene correction as proposed
by Furby & Campbell [82] should be considered in future
implementation of this framework. Future and repeated
implementation of the identification framework in other
areas will assist in determining the severity of these potential
limitations. Moreover, given the largely cloud-free satellite coverage of desert ecosystems [79], the application of multi-date
change detection analyses across multiple scenes to assess
and predict temporal changes in the distribution of oil extraction and exploration sites in these study sites may also help
to further determine the relative success of our framework.
The methodology employed herein is only one of the many
options available to practitioners for the mapping and monitoring of oil exploration and oil exploitation activities in desert
ecosystems. Other possible methods include the use of purely
geographic information systems-based analyses, as employed
by Osti et al. [34]. However, this form of analysis relies heavily
on global energy and environment data providers and consultancies, such as the information handling services, which is not
free of charge and may potentially be less objective than the use
of open access satellite imagery. Alternatively, another potential remote sensing approach could be to use an active
sensor, such as radar (e.g. TanDEM-X using synthetic aperture radar digital elevation model (12 12 m resolution)).
5. Conclusion
With mounting evidence of conflict between conservation
efforts and energy procurement, a cost-effective monitoring framework for these activities is clearly of enormous relevance and
utility in defining where adaptation and conservation actions
must be targeted under future expansion, in order to secure
the viability of unique biodiversity across global desert regions
in the face of associated threats. This will become increasingly
essential into the future, with projected increases for oil production and exports for many global dryland regions:
Northern Africa (including many Saharo-Sahelian countries;
i.e. Algeria, Egypt, Sudan), Central and Western Asia, Central
America and Australia [100,101]. Under such future increases
in hydrocarbon production, not only the utility but also the efficiency of the monitoring framework developed herein will
become critical. Currently, the framework is not automated,
and visual interpretation and data processing are required.
Automation of the framework to include data acquisition, satellite image processing, and classification model generation and
prediction across novel areas would serve to render the framework applicable across these widely spread geographical areas
in an effective, non-labour intensive manner. Moreover, in the
face of mounting conservation costs and the lack of funding targeted at global desert regions [15,16], the necessity for cheap
and easily implementable monitoring frameworks for anthropogenic activities will become increasingly great into the
future. We therefore highlight here the importance and relevance of the continued availability of open access satellite
imagery, such as that from NASA’s recently launched Landsat
8 satellite, to on-the-ground conservation monitoring and management direction. Implementation and automation of the
identification framework developed will enable the continued
monitoring of areas of concern, advancing current understanding of extremely under-studied and -represented global desert
regions, and forming a crucial resource in targeting on-theground management and conservation priorities in the face of
global environmental change.
Acknowledgements. We would like to thank J. Reise and two anonymous
reviewers for helpful comments on previous versions of this manuscript.
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