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Oil in the : mapping anthropogenic threats to Saharan from space

Clare Duncan1, Daniela Kretz1,2, Martin Wegmann3,4, Thomas Rabeil5 and Nathalie Pettorelli1 rstb.royalsocietypublishing.org 1Institute of Zoology, Zoological Society of London, Regent’s Park, London NW1 4RY, UK 2BayCEER, University of Bayreuth, Bayreuth 95440, Germany 3Department of Remote Sensing, University of Wu¨rzburg, Campus Hubland Nord 86, Wu¨rzburg 97074, Germany 4German Remote Sensing Data Center, German Aerospace Center, Oberpfaffenhofen 82234, Germany 5Sahara Conservation Fund, Niamey, Republic of

Research are among the most poorly monitored and understood in the , with evidence suggesting that their biodiversity is declining Cite this article: Duncan C, Kretz D, fast. Oil and exploitation can constitute an important threat to Wegmann M, Rabeil T, Pettorelli N. 2014 Oil in fragmented and remnant biodiversity, yet little is known about the Sahara: mapping anthropogenic threats to where and how intensively such developments are taking place. This lack of information hinders local efforts to adequately buffer and protect desert Saharan biodiversity from space. Phil. against encroachment from anthropogenic activity. Here, we Trans. R. Soc. B 369: 20130191. investigate the use of freely available satellite imagery for the detection of http://dx.doi.org/10.1098/rstb.2013.0191 features associated with oil exploration in the African Sahelo-Saharan . We demonstrate how texture analyses combined with Landsat data One contribution of 9 to a Theme Issue can be employed to detect ground-validated exploration sites in and Niger. Our results show that site detection via supervised image classi- ‘Satellite remote sensing for biodiversity fication and prediction is generally accurate. One surprising outcome of our research and conservation applications’. 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, tempor- Subject Areas: ary exploration activity: we believe the repeated implementation of our , environmental science 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 Keywords: across desert . remote sensing, biodiversity, oil exploration, desert, conservation, Landsat

Author for correspondence: 1. Introduction Nathalie Pettorelli The population and its demand on the ’s natural resources have e-mail: [email protected] 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 -use change and change [2,5,6], scientists and conser- vationists 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, tend- ing to fall predominantly within tropical biomes. The reasoning behind such argumentation is that management actions there target the greatest number of 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 that has previously been poorly studied and has Electronic supplementary material is available received little attention from conservation activities [14,15], despite covering at http://dx.doi.org/10.1098/rstb.2013.0191 or 17% of global land mass and harbouring surprisingly high levels of biological via http://rstb.royalsocietypublishing.org. 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. information suggests that desert and dryland biodiversity is these potential consequences, little is currently known about 2 declining rapidly [14,18–22]; and (ii) predictions show that how and where oil exploration and exploitation activities are rstb.royalsocietypublishing.org the rate of climate change is likely to be particularly high in taking place, and where these developments are likely to the desert biome, with species potentially facing especially negatively impact Sahelo-Saharan . difficult challenges in the near future [23]. This A major issue in determining threats to biodiversity from is of particular concern, as the ecology and distribution of the expansion of oil exploration activities is the difficulty in desert-adapted species are under extreme climatic control obtaining field data on the geographical locations of these [24]. Other major threats to deserts include , developments. Field monitoring can be notoriously costly, woody- clearance, agricultural expansion, water and field data on oil exploration and exploitation activities, diversion and extraction, soil and water pollution, land con- while accurate, are difficult to obtain for large areas, especially version due to industrial activities and associated threats for remote regions where logistics and safety are real issues from armed conflicts [19,21]. With potentially enormous [41,42]. In light of this, remote sensing technologies could pro- B Soc. R. Trans. Phil. local- and regional-scale effects, a current major anthropo- vide a useful monitoring alternative. Global-scale low light genic threat to desert biodiversity, likely to increase in imaging satellite data from the US Air Force Defense Meteoro- extent and intensity over the coming years through increasing logical Satellite Program Operational Linescan System [43], resource demand, is oil exploration activities [21,25,26]. data from the European Space Agency’s Along Track Scanning Across the Sahara and , threat from oil exploration is Radiometer sensors [44] and data from the recently launched particularly great, with concerns for global oil stocks VIIRS instrument on the Suomi National Polar Partnership 369 having led to exploration being undertaken in increasingly satellite [45], can be used to derive global and national informa- 20130191 : remote areas [27,28]. The future of Sahelo-Saharan biodivers- tion on oil exploration activities. However, the spatial resolution ity is considered to be largely dependent on anthropogenic of these datasets is relatively coarse (0.56 km, 1 km and 750 m, expansion in the region [21]. Countries like Niger, which respectively); thus, information derived from these data may represents a stronghold for local desert species [21,29,30], not be applicable to identify fine-scale, local-level oil explor- have undergone rapid increases in their oil extraction and ation threats to biodiversity. Very high-resolution satellite refinery activity in recent years [26,30]. Such increased oil imagery could instead be used to derive national information prospection activities have, for example, now led to the on oil exploration and exploitation activities; yet, most organiz- endangerment of the last known viable population of ations and institutions in need of such data are unable to afford (Addax nasomaculatus), owing to loss in impor- the cost of data acquisition and processing at such spatial tant, high-quality grazing sites in the Tin Toumma desert extents. Freely available satellite data such as data from the [31]. These largely unmonitored activities are expected to National Aeronautics and Space Administration (NASA) and occur across many parts of the region, with potential ancil- United States Geological Survey’s Landsat satellites, however, lary impacts including waste production, reduced water have shown promising applications in the fields of ecology availability through anthropogenic extraction, reduced habi- and conservation [46] and could potentially inform efforts to tat quality and functioning, and increased detect and map oil exploration and exploitation activities at rele- poaching and human–wildlife conflict [21,30,32,33]. These vant scales [47,48]. Furthermore, the availability of Landsat potential threats could have an enormous impact on local bio- satellite data at a good and consistent temporal scale (at up to diversity, as (i) much of the fauna in the Sahelo-Saharan 16-day intervals) can allow for the continued and repeated region is considered to be of global conservation concern monitoring of areas of concern. (e.g. the addax, Dorcas (Gazella dorcas), Surprisingly, the potential for freelyavailable satellite data to (Nanger dama), Saharan (Acinonyx jubatus ssp. hecki) inform conservation efforts in desert systems currently remains and Kleinmann’s tortoise (Testudo kleinmanni); [18]) and (ii) largely unexplored (but see [47]). This study aims to fill this the distribution of biological diversity in the area is markedly knowledge gap, by assessing if and how open access satellite fragmented, with often highly localized hotspots [20,21] that data can be reliably used to monitor oil exploration and refinery could become imperilled by the impacts of unmonitored oil activity in the Sahelo-Sahara region. It aims to do so in a repro- exploration activity. Hotspots of Sahelo-Saharan biodiversity ducible manner, so that methods and analyses developed are often concentrated within protected areas (PAs) [21]. If herein can be applied to conservation activities in other dryland conducted close to or within these areas, the ancillary threats regions. Many approaches and attempts to detect the location and associated impacts of oil exploration activities could lead and distribution of oil exploration and extraction activities to drastic reductions in wildlife populations, reducing the focus on the detection of oil flares alone [43–45]. However, effectiveness of PAs in conserving these remnant hotspots sites also feature buildings and other man-made structures, of biodiversity. Across the African , the designation which may include water ponds, roadways or visible smoke of oil concessions within PAs is common, and particularly [46], and these features are associated with different spectral sig- within those with a high level of protection [34]. Threats natures than those generally found in the surrounding desert from oil exploration activities at PA boundaries could also [47,49]. As such, a framework that aims to detect not only oil result in reduced connectivity of between these flares but also associated structures may improve the success PAs and important resource areas by reducing the quality of detection of novel and established oil extraction activity. of habitat corridors and producing physical barriers to move- This serves as the basis for our approach, which aims to val- ment, cutting off wildlife populations from wider ecosystems idate the use of Landsat satellite data for the detection of and satellite populations [35–38]. Such impacts on wide ran- features typically associated with oil exploration activities. Our ging species could have potential knock-on cascading study undertakes a two-step approach: firstly, field-derived effects through other trophic levels within such systems (known oil exploration site locations) and visually derived [39,40], producing broad-scale ecological impacts and (high-resolution Google Earth imagery) validation data, and sat- impacts to overall ecosystem health and functioning. Despite ellite-based landcover classification are combined to establish a 3 study areas (Landsat coverage) rstb.royalsocietypublishing.org PAs oil sanctions for exploration: open block block under licence hl rn.R o.B Soc. R. Trans. Phil. 369 20130191 :

(b) (a)

(c)

N

km 0 150 300 600

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)Aı¨r&Te´ne´re´ National Nature Reserve and (c) TTTNNR. framework for the detection of known features associated with oil exploration activities. On the other hand, desert habitat in oil exploration activities; this framework is then applied in Niger remains largely undisturbed by anthropogenic activities, areas for which an explicit landcover classification cannot be and the expansion of oil exploration and exploitation activities made, in to provide spatially explicit information about in the country has occurred only in recent years [30,50]. The recent development in these activities in relation to existing PAs. Niger region was selected for this study as it borders key PAs for the conservation of desert biodiversity; namely, the A¨ır& Te´ne´re´ National Nature Reserve, and the Termit & Tin Toumma National Nature Reserve (TTTNNR), which are cur- 2. Material and methods rently threatened by the expansion of oil exploration activities [30,50]. The study area in Niger encompasses part of the eastern (a) Study areas side of the TTTNNR and also the Agadem oil concession block, The location used for the development of our monitoring frame- which is the predominant site of emerging exploration activity in work is in Saharan Algeria. The area of interest reaches from the region to the east of TTTNNR (figure 1; [30]). Within the northwestern corner at 3184901200 N, 882201200 E to the south- TTTNNR and A¨ır&Te´ne´re´, much of the remaining Sahelo- eastern corner at 318401200 N, 98270000 E (figure 1). A second Saharan diversity thrives [18,30,50], including 18 large study area in Niger, where our landcover classification is then species, 19 species, 137 resident and several implemented, is delimited by a box reaching from the northwest- Afro-tropical and Palaearctic migrant species [51]. These ern corner at 1682201200 N, 128120000 E to the southeastern corner at two PAs protect important populations of many species of 1582703600 N, 1384901200 E (figure 1). Both study areas comprise global conservation concern: (the Saharan cheetah areas of desert, rocky terrain and sparse vegetation. Although (population estimate currently less than 10 individuals in the they both lie within the Sahara, Algeria and Niger represent area; [51]), striped hyaena (Hyaena hyaena) and the greatest diver- two extremes in a spectrum of anthropogenic impacts in the sity of small and medium sympatric carnivores in the Sahara; hyper- zone: Algeria has a great number of established oil [51]), (the Nubian ( nuba) and Arabian bus- and gas extraction sites, as as refineries [21], and here rep- tard ( )) and large (the addax, Dorcas resents areas with high anthropogenic pressure associated with gazelle, dama gazelle and Barbary (Ammotragus lervia); [18,30,50]). Importantly, the Termit massif and Tin Toumma 1. Overview of all field-derived (SCF) and visually derived (via high- 4 desert region hold the last remaining viable population of resolution Google Earth imagery; [60]) ‘permanent’ oil exploration and rstb.royalsocietypublishing.org addax in the world (ca 200 individuals; [52]). The direct ecosys- extraction site data for the study areas in Algeria and Niger. tem impacts of increased oil exploration activities [21,30,32,33] have the potential to produce profound effects on the local biodi- versity; habitat loss and water extraction at prospection sites may site locations Algeria Niger serve to affect floral abundance and composition, in turn redu- a cing habitat quality and resource availability for species at field derived (SCF) 1 7 higher trophic levels [21]. Moreover, increased poaching of visually derived (Google Earth) 2 0 large species [30] may serve to reduce their densities aOne of the original eight field-derived sites in Niger was no longer present in such areas, and in turn, coupled with increased human–wild- in November 2012 (a temporary site), while the status of the remaining life conflict, may result in the reduction of populations of key

Saharan species, e.g. the Saharan cheetah, where ungu- seven sites is not known from SCF; thus, these sites were considered to be B Soc. R. Trans. Phil. late prey are a limiting factor to predator abundance [53]. In ‘permanent’. addition to the ecological uniqueness of the Termit massif and Tin Toumma desert area, it is also thought to be rich in cur- rently largely unstudied archaeological, anthropological and palaeontological history [30,50]. 5 TM imagery for analysis here, and thus Landsat 7 ETMþ data Despite the discovery of hydrocarbons within Niger in 1975, were used for both regions despite the SLC-off issue. The Landsat 369 little oil exploration and exploitation activity has previously 7ETMþ scenes considered for both study areas were affected by occurred in the country, due to the low ratio barrel price the SLC-off issue; however, gap masks facilitated the masking 20130191 : (T. Rabeil 2013, personal communication) and a lack of economic out of the affected region, and thus the SLC-off issue can be incentive caused by a historic wealth of providing the resolved for use in landcover classification [56]. The images country’s main economic export [30,50]. However, concerns obtained for these analyses were from 5 November 2012 for the over diminishing uranium stocks over the past two decades Algerian area (framework development area), and 2 November have led to the designation of oil concession blocks throughout 2012 for Niger (figure 1), in order to avoid differences according much of the country [26,30,50]. In June 2008, the government to varying seasonal solar elevation and soil–water–vegetation of Niger signed a contract with the Chinese National dynamics [59]. Corporation (CNPC) for the purchase of three oil concession blocks covering large areas of the A¨ır&Te´ne´re´ and the TTTNNR (figure 1; [30,50]), with oil prospection activities occur- (c) Field-derived and visually derived training ring within these areas from June 2008 onwards. With extremely little ecotourism in the country, the societies and government of and validation data Niger have little economic incentive to conserve the ecological, In order to produce and validate a landcover classification from archaeological and anthropological uniqueness of the Termit satellite imagery, field-derived and visually derived validation massif and Tin Toumma desert region [50]. At present, 20 oil points of specific landcover classes can be used. In both Algeria concession blocks have been designated across the country, and Niger, ground-truthed geo-referenced (GPS-logged) site val- with four currently under licence (including the three rented by idation point data were obtained from the Sahara Conservation CNPC and one by the Algerian corporation SONATRACH) cov- Fund (SCF) over the period 2009–2011, detailing the locations of ering much of the Termit massif and Tin Toumma desert region known oil exploration and extraction activity. These data included (figure 1; [30]). Although most designated concession blocks in one permanent site within the Algerian Landsat 7 ETMþ scene. In Niger remain open at present, depleting uranium stocks are addition, we located two further validation sites in the Algerian likely to result in an economic shift to an increased reliance on study area through inspection of very high-resolution QuickBird hycrocarbon concession rentals and exports [30,50], and thus and IKONOS imagery within Google Earth Pro [60] as performed increased sale of concessions to oil corporations, extending the in the work of Fritz et al. [61], resulting in a total of three validated current level of threat to local biodiversity. The expansion of oil exploration sites in this study area (table 1). Using the very high- these activities into the neighbouring PAs is of particular concern resolution Google Earth imagery, we were also able to identify for large ungulate species as (i) many of these are highly threat- a total of eight landcover types in the scene: desert, bare rock, ened remnant populations and (ii) there have been reports of vegetation, roads, dark-coloured settlements, light-coloured settle- poaching ( and birds) associated with the arrival of ments, oil flares and water ponds. These eight landcover classes workers for oil exploration activities in the region since 2008 were selected based on the study aims of differentiating human [54]. Ancillary impacts associated with industrial soil and infrastructure from natural landcover (i.e. bare rock, vegetation water pollution have also been found to be evident at exploration and desert versus roads, settlements, oil flares and water ponds), sites within the oil concession blocks [55]. in order to develop an appropriate landcover classification. An equal distribution of training point data samples were then cre- ated across all landcover classes to define their relative spectral information from the Landsat 7 ETMþ scene (200 random samples (b) Satellite data of pixels per class) and were then used to develop a landcover Our methodology makes use of Landsat 7 Enhanced Thematic classification model. Mapper Plus (ETMþ) imagery, available at 30 m spatial resolution Ground-truthed geo-referenced site validation point data ([56]; available for download at: http://earthexplorer.usgs.gov), detailed 11 sites within the scene for Niger, which included which has previously been found to have utility in desert areas both temporary and permanent oil exploration sites visited by [47,57]. One minor limitation of the Landsat 7 ETMþ sensor is SCF. Some sites were highly concentrated in two specific areas, that the Scan-Line-Corrector is off (SLC-off) owing to a technical giving a total of eight unique exploration sites in the Niger malfunction, causing approximately 22% of data from any given study area (table 1). Of these eight sites, field information from Landsat 7 ETMþ image to be lost [58]; ‘striping’ of missing data the SCF revealed that one site was a temporary exploration site is apparent in the images. Typically Landsat 5 TM imagery (table 1) and was no longer present in November 2012 (date of would be applied instead due to this error; however, owing to lim- the Landsat 7 ETMþ scene considered). The temporary/perman- ited data availability over Niger, we were unable to obtain Landsat ent status of other sites within the Niger study area is not known from SCF beyond 2011, and these are thus considered here as 3. Results 5 ‘permanent’ extraction sites. Recent very high-resolution Google Earth imagery was not available for the Niger study The Random Forest model accurately predicted the distrib- rstb.royalsocietypublishing.org area, and thus associated landcover classes could not be visually ution of all eight landcover classes for Algeria. Model identified and classified for this area, leading to the prediction of internal class errors ranged between 0 and 0.02%; bare rock, landcover here by using the classification model developed for oil flares and water all had 0.00% error; vegetation, roads, the Algerian study area scene. 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, (d) Statistical analyses the Landsat ETMþ bands providing the most important vari- All analyses were performed in GRASS v. 6.4.2 [62], QGIS ables were band 3 (red), followed by band 1 (), band 7

v. 1.8.0 [63] and R v. 3.0.0 [64], using the packages ‘raster’ [65], (short-wave infrared-2 (SWIR-2)), band 4 (near-infrared), B Soc. R. Trans. Phil. ‘rgdal’ [66], ‘randomForest’ [67] and ‘spgrass6’ [68], the latter band 6–2 (thermal-infrared 2), band 6–1 (thermal-infrared 1), of which serves as an interface between R and GRASS. A con- band 2 (green) and band 5 (mid-infrared; figure 2; electronic version of digital numbers to top-of-atmosphere reflectance supplementary material, Appendix S1). The field-derived values was carried out on the raw Landsat 7 ETMþ scenes. refinery location in Algeria was accurately predicted by the Owing to a lack of strong elevational gradients across both Random Forest model and visually confirmed using high-res- study areas (Algeria: 100–300 m.a.s.l.; Niger: 300–400 m.a.s.l.),

olution Google Earth imagery, and the two sites located via 369 no further orthorectification of the images was necessary [69].

Texture analyses were conducted on the Algerian Landsat 7 high-resolution Google Earth imagery were also accurately 20130191 : ETMþ scene using a moving window approach, in order to predicted by the Random Forest model (table 2). Detecting emphasize features typical of oil refineries and highlight them the accurate location of these sites was made possible by the against the background of the Saharan desert [70–72]. A moving presence of concentrated areas of non-desert landcover classes: window size of 5 5 pixels has been found to be useful in high- oil flares, settlements, roads and water ponds. lighting changes in edges of landcover classes in arid When applying the identification framework to the scene environments [73]. Such a window size (approx. 150 150 m) from Niger, a total of seven oil exploration sites were identified would also encompass the area of a small oil refinery but is not (figure 3 and table 2). Of the detected sites, four oil refineries so large as to neglect roads; this size was thus considered in were field validated by existing geo-referenced field data these analyses. The texture analysis included the mean, maximum, from SCF; thus, three new oil exploration sites were discovered minimum, variance and standard deviation of all eight Landsat 7 from our analyses (figure 3 and table 2). RGB visualization (7, 4 ETMþ bands (band 6 is composed of two separate bands). All þ metrics are based on the neighbourhood statistics within the and 2 bands) of the Landsat 7 ETM image, confirmed that the ‘r.neighbors’ module of GRASS (with square orientation). Thus newly detected sites were consistent with other validated sites. in total, 40 parameters (eight bands five moving window However, some of the ground-truthed geo-referenced explor- variables each) were calculated. These were used as input data ation sites from SCF in Niger were not detected when for the supervised landcover classification of the acquired scenes applying the Random Forest model framework (omission [41]. Landcover classification was developed using a Random error 43%; figure 3 and table 2). In contrast to oil exploration Forest model approach [74], based on the model training data sites in the Algeria study areas, aside from desert there were (200 pixels for each landcover class) mentioned above, using no other discernible classes of landcover located near the oil the aforementioned calculated parameters (40 in total) as the flares identified in Niger. The oil flares class thus enabled the independent variables for Algeria [75]. detection of the oil exploration and exploitation sites in the The relative importance of model variables within the Random Niger region, which is due to the unique spectral and thermal Forest model (i.e. the contribution of each variable in the model in the construction of each decision tree) was assessed for each signal of the class; surface temperature (K, calculated from variable by examining the mean decrease Gini index (MDG; band 6–1 of the Landsat ETMþ sensor [77]; figure 4) was high- [76]); variables with higher MDG have greater importance in a est for this landcover class, and high IR values (maximum band Random Forest model. In addition to the Random Forest standard 7 (SWIR-2)), which contributed greatly to the Random Forest construction of 500 decision trees to determine the best model for classification (figure 2; electronic supplementary material, landcover classification, we also aimed to assess the stability of the Appendix S1), are associated with the heat signature of the variable importance across models. To do so, we ran the Random flame [47,57]. Forest model 200 times and observed changes in the variable Ground-truthed SCF point data for the Niger region show importance parameter of the MDG over the runs. Random Forest an existing overlap between PA boundaries and oil explor- model accuracy was assessed by examining the model internal ation sites (within TTTNNR; figure 3); however, the Random class error rates (defined as the percentage of the 200 pixels falsely Forest identification model applied here did not detect any classified) for each landcover class identified within the model, as well as the ‘out-of-bag’ estimate for the generalization error for the activity within the part of the PA that fell within our study overall model [74]. The Random Forest classification model was area and did not detect the two oil exploration sites within then further validated by comparing model-detected oil explor- the study area–PA overlap (10% of the study area overlapped ation sites with the previously mentioned validation data from with the PA; figure 3). The proximity of many of the model- ground-truthed geo-referenced data points. The Random Forest detected oil refineries to the nearest boundary of the model derived and validated on the scene acquired for Algeria TTTNNR was found to be less than 15 km (figure 3). was then applied to the Landsat 7 ETMþ scene for Niger [67]. Val- idation 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 4. Discussion and 2) image assessment of the original Landsat 7 ETMþ data Owing to limited funding and conservation attention [14–16], for the considered area. little is currently known about the location and intensity of 6 band 3 mean band 1 mean rstb.royalsocietypublishing.org 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 B Soc. R. Trans. Phil. 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 369 band 5 var band 2 SD band 2 var 20130191 : 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

10 20 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 and mapped remotely via freely available satellite data. Our study site in Algeria and when applied to the additional study site scene in results confirm that predictions from ground-verified land- Niger. Omission errors for model detection of oil exploration and extraction cover classification models (here Algeria) can be successfully sites from field-derived locations. Additional ‘novel’ model-detected sites in applied to detect activities in areas for which classifica- Niger that were not known from original field-derived location data are also tion models cannot be explicitly produced (here Niger). The listed; these were visually validated using an RGB visualization (7, 4 and 2 methodology developed herein thus makes the introduced bands) of the original Landsat 7 ETMþ scene. 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 field- model- imagery and ground-truthed data for validation. This is a situ- detected detected omission novel ation that is particularly common in many remote desert area sites sites error (%) sites regions. Owing to the free availability of NASA’s Landsat Algeria 1 (2)a 300satellite data, the framework developed in this study is inex- pensive and easily implementable across deserts, and thus Niger 7 4 43 3 may be used to monitor oil exploration activities over time in a Additional sites in brackets determined via high-resolution Google Earth a cost-effective manner in areas for which conservation fund- imagery. ing 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 specific threats to biodiversity in desert ecosystems. Despite employed for the detection of oil flares [43–45], and thus is being considered a major threat [21], and increasing licensing more appropriate for identifying anthropogenic structures, of concessions [26–30,50], especially in close proximity to while omitting small-scale variations (e.g. singular PAs (figure 1; [21,30,34,40]), at present no monitoring frame- rocks), in order to determine regional-scale expansion in the work exists for the expansion of oil exploration activities in distribution and intensity of activity. Detecting previously desert regions. As a result, there is presently a lack of real- unknown oil exploration sites in close proximity to PAs time information regarding where conservation efforts (figure 3), the framework developed herein enables the map- should be targeted in order to determine the potential impacts ping and continuous monitoring of novel and intensifying of these activities on local biodiversity. The results of this study threats to localized hotspots of desert biodiversity. provide a framework to assist in closing this knowledge gap, The successful application of the model classification and showing for the first time that current and expanding oil identification framework developed in this study can be attrib- exploration activities in desert ecosystems can be detected uted to the 40 input variables derived from the eight Landsat 7 field detected

field detected: temporary rstb.royalsocietypublishing.org RF detected Landsat coverage Termit & Tin Toumma oil sanctions for exploration: open block block under licence hl rn.R o.B Soc. R. Trans. Phil. 369 20130191 :

N km 01530 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, repre- senting 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 blocksdenoteoil 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 flares. Oil flares have indeed previously been found to be moving window variables, as was found by Ge et al. [41]. Over- good indicators of refineries in the Sahara [33]; here, these all, texture analyses have been found to enhance information could be visualized both in the raw satellite images (as in the from the raw satellite bands [71]; the lightness or darkness of Kuwait oil fires; [47]) and with classification results. Such certain objects in relation to neighbouring objects can be iden- detection is primarily due to the band 7 (SWIR-2) of Landsat tified for the different bands. In using texture analyses, the 7 ETMþ sensor, which covers a similar spectral range as the importance of variables is dependent upon the targeted thermal IR Advanced Very High-Resolution Radiometer object of the analyses [71,78]. However, the most important band that has been previously found to be successful in detect- variables for this framework (based on the MDG; figure 2; elec- ing oil fires [49]. The maximum value of band 7 (SWIR-2) was tronic supplementary material, Appendix S1) seem to be the particularly useful in the model (figure 2; electronic sup- band averages, which indicates that the bands themselves are plementary material, Appendix S1), which provides still highly important in predicting landcover classification. information on the heat signature of the oil flare [47,57]. The We do, however, acknowledge that taking the average values successful application of this identification framework is also also reduces the effective spatial resolution of the input vari- largely a result of the chosen study region; in desert ecosys- ables, producing a trade-off between the detection of large- tems, cloud contamination of satellite imagery is negligible scale features versus increasing classification error according [79], and anthropogenic structures are not masked by the pres- to mixed pixel signals. The minimum band values were also ence of a dense vegetation canopy as in other biomes. Data shown to play an important role in image classification, as acquisition and exploration site detection were therefore not landcover classes such as water, vegetation, dark buildings impeded by these potential limitations. and road tarmac typically have lower spectral values in com- The oil exploration site identification framework present- parison to other classes [59]. While in Algeria, identification ed here is, however, not without limitation. Although the of oil exploration and extraction sites was made possible by framework was successful in detecting the location of oil the presence of assemblages of multiple landcover types (oil exploration sites in Algeria and Niger, it was unable to detect flares, small water ponds, settlements and roads), within the some of the reported oil extraction in the Niger region Niger study area such associated landcover classes were not based on ground-truthed data (43% omission error; table 2 present and sites were made detectable by the presence of oil and figure 3). This may be attributed to both temporal and (a) likely not result in misdetections of temporary sites if the 8 time period between consecutive images considered were rstb.royalsocietypublishing.org 0.20 sufficiently narrow. Individual oil flares are also not large (maximum 150 m in size), and thus may only cover a few Land- sat image pixels. Smaller oil exploration sites may thus be difficult to distinguish from the surrounding desert, especially 0.15 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 sur- 0.10 rounding associated landcover classes, unlike the sites detected frequency in the Algerian study area (i.e. water pools, settlement and B Soc. R. Trans. Phil. roads). These results might also suggest that the oil flares 0.05 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 0 of some of these activities in Niger, there may be no large sur- 369

rounding structures yet constructed at these sites. As these 20130191 : (b) become developed into the future, the applicability of classifi- 0.30 cation of associated landcover types in locating and detecting their expansion will increase and will indeed be useful in moni- 0.25 toring 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 0.20 result in missing data values over the locations of oil explor- ation sites in desert regions, resulting in the lack of detection of sites and suggesting a potential limitation of the use of Land- 0.15 sat 7 ETMþ data in these analyses. However, with the recent launch of the Landsat 8 satellite in early 2013 [80], data avail- frequency 0.10 ability for the future implementation of this framework will not be hindered by this issue. Cross-scene atmospheric correc- tion, which would serve to further normalize features across 0.05 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 0 using texture metrics to detect the limited infrastructure in 295 300 305 310 315 the Niger scene and the few landcover classes present here, it is unlikely this correction would significantly affect the temperature (K) results of these analyses. Cross-scene correction as proposed Figure 4. The thermal signature of pixels classified as the oil flares landcover by Furby & Campbell [82] should be considered in future class (through band 6–1 of the Landsat ETMþ sensor; [77]) converted to implementation of this framework. Future and repeated surface temperature shows temperature between 303 and 312 K across implementation of the identification framework in other pixels of this class; panel (a) depicts the frequency of surface temperature areas will assist in determining the severity of these potential (K) for pixels of the class oil flares (N ¼ 2039) for the results of the frame- limitations. Moreover, given the largely cloud-free satellite cov- work classification in Algeria, and panel (b) depicts the values (N ¼ 221) for erage of desert ecosystems [79], the application of multi-date the application area in Niger. change detection analyses across multiple scenes to assess and predict temporal changes in the distribution of oil extrac- tion and exploration sites in these study sites may also help spatial limitations to our monitoring framework. Oil explor- to further determine the relative success of our framework. ation activity, which had recently commenced in Niger at the The methodology employed herein is only one of the many time of these analyses [30,50], is often temporary in nature options available to practitioners for the mapping and monitor- [33]. Indeed, four field-derived extraction sites in the Termit ing of oil exploration and oil exploitation activities in desert massif region (one within the study scene; figure 3) were ecosystems. Other possible methods include the use of purely known to no longer exist in November 2012. Accordingly, geographic information systems-based analyses, as employed some of the non-detected sites in Niger, for which further by Osti et al. [34]. However, this form of analysis relies heavily field validation beyond 2011 does not exist, could have simi- on global energy and environment data providers and consul- larly been no longer in existence in the November 2012 scene. tancies, such as the information handling services, which is not Thus, the relatively high omission rate for Niger may be due free of charge and may potentially be less objective than the use to non-detection of potentially temporary sites that were no of open access satellite imagery. Alternatively, another poten- longer present in the Landsat 7 ETMþ scene considered here. tial remote sensing approach could be to use an active However, for continued monitoring purposes, the application sensor, such as radar (e.g. TanDEM-X using synthetic aper- of the framework to time-series Landsat satellite data would ture radar digital elevation model (12 12 m resolution)). Felbier et al. [83] demonstrated the application of this sensor in severe potential impacts from anthropogenic water extraction 9 developing urban footprint maps (here human settlements), associated with oil exploration activities. Water scarcity in rstb.royalsocietypublishing.org which identify anthropogenic structures with relative accuracy arid regions has been found to cause large reductions in species in desert regions. Interestingly, the spatial resolution of these diversity and population size [21,96–98]. Resultant increases in data is very high, and the process is automated [83]. However, water-related conflict between oil exploration activity and local this method is targeted at settlements in general and would biodiversity under future climate change in desert regions [23] require additional information and methodological refinement could thus potentially lead to the collapse of already fragile in order to distinguish settlements from oil exploration sites, in populations (e.g. the red-fronted gazelle (Eudorcas rufifrons); addition to being costly and only implementable with the regu- [18]). Moreover, monitoring of the expansion of novel oil larity of the data (current data availability covers only 2011 and exploration across desert regions may be of great importance 2012; [84]). for determining sites for future reintroduction efforts for threat-

The relevance and requirement for a monitoring frame- ened desert animal species under climate change (e.g. the B Soc. R. Trans. Phil. work for oil exploration such as that developed here is scimitar-horned (Oryx dammah); [99]). further highlighted by the findings of this study. Ground- truthed data from SCF suggest that an overlap between the borders of the TTTNNR and oil extraction activity, and a 5. Conclusion conflict of interest between conservation efforts and oil explor-

With mounting evidence of conflict between conservation 369 ation, already exists in Niger (figure 3). Moreover, while the efforts and energy procurement, a cost-effective monitoring fra- identification framework employed here did not detect any 20130191 : mework for these activities is clearly of enormous relevance and exploration sites within the PA, many of the sites detected utility in defining where adaptation and conservation actions were less than 15 km from the border of the TTTNNR and must be targeted under future expansion, in order to secure the framework detected a novel site in close proximity to its the viability of unique biodiversity across global desert regions boundary (figure 3), suggesting that potential and novel in the face of associated threats. This will become increasingly threats are encroaching into such areas. Increasing trends essential into the future, with projected increases for oil pro- in oil prospection activities are similarly already a concern duction and exports for many global dryland regions: for the PA network across other global dryland regions, with Northern (including many Saharo-Sahelian countries; concessions for oil exploration being sold within many PA i.e. Algeria, , ), Central and Western , Central boundaries [34]. Globally, the threats to dryland ecosystems America and [100,101]. Under such future increases from oil exploration and extraction activities are great in hydrocarbon production, not only the utility but also the effi- [84–86], causing reductions in species abundance and bio- ciency of the monitoring framework developed herein will diversity [87,88]. With much current conservation attention become critical. Currently, the framework is not automated, on the relative effectiveness of the global PA network for and visual interpretation and data processing are required. conservation efforts [89–95], methods to monitor threats Automation of the framework to include data acquisition, satel- to conservation and management action in PAs are of the lite image processing, and classification model generation and utmost importance. If the expansion of oil exploration activities prediction across novel areas would serve to render the frame- continues to be conducted at the edges of and within desert work applicable across these widely spread geographical areas PAs, they have the potential to increase pressures on the in an effective, non-labour intensive manner. Moreover, in the animal populations within through increased edge effects face of mounting conservation costs and the lack of funding tar- [37,38], reducing the effectiveness of buffer zones around geted at global desert regions [15,16], the necessity for cheap PAs and the resilience of animal populations and ecosystems and easily implementable monitoring frameworks for anthro- within from impacts of further processes of land-use and cli- pogenic activities will become increasingly great into the mate change. Moreover, these threats have the potential to future. We therefore highlight here the importance and rele- reduce the connectivity of the PA system to wider ecosystems vance of the continued availability of open access satellite [96], which may be of particular concern in desert ecosystems imagery, such as that from NASA’s recently launched Landsat due to both the patchy distribution of essential resources 8 satellite, to on-the-ground conservation monitoring and man- related to the high variability of the rainfalls in the Sahara agement direction. Implementation and automation of the [19,21], as well as the fragmented nature of desert biodiversity identification framework developed will enable the continued hotspots and highly endangered endemic species [14,18–21]. monitoring of areas of concern, advancing current understand- Indeed, increased anthropogenic activity within PAs and at ing of extremely under-studied and -represented global desert their boundaries may create barriers to nomadic and migratory regions, and forming a crucial resource in targeting on-the- species’ movement [35,36]. The importance of maintaining ground management and conservation priorities in the face of desert biodiversity hotspots within PAs and their surroundings global environmental change. has never been more important, as global climate change is predicted to produce among the highest temperature increases Acknowledgements. We would like to thank J. Reise and two anonymous in terrestrial biomes within these regions [23], highlighting reviewers for helpful comments on previous versions of this manuscript.

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