12A Description of Simandou Archaeological Potential Model 12A.1 Overview The most accurate and reliable way of establishing archaeological baseline conditions in an area is by conventional methods of pedestrian reconnaissance with subsurface testing. This involves systematic and intensive ground survey of a study area, along with hand excavation and dirt-screening for artefacts in areas where subsurface materials are deemed likely based on observed surface conditions. The cost and time required for this kind of investigation make it impractical for many large project planning applications. A more practical option, a method chosen for this study, is archaeological potential modelling. This approach has been used internationally for large transportation projects and pipelines for the past fifteen to twenty years. Archaeological potential modelling uses a land-classification system to highlight areas likely to contain undiscovered archaeological sites. Archaeological potential is determined using geospatial technologies such as Geographic Information System (GIS) programs and satellite remote sensing platforms along with various publically available thematic data sets that are uploaded and analysed. Data sets include topographic factors, land use, present settlement, agricultural potential, drainage patterns and water body locations, and, of course, settlement location. These models do not provide exact answers, nor do they make fieldwork unnecessary. From a certain perspective, what they do is systematise and map the results of standard archaeological judgment, showing where sites are likely to be found. The probabilistic results of this modelling exercise provide a significant advantage in the management of archaeological impacts for large infrastructural projects, reliably distinguishing areas with high, medium and low archaeological potential. High potential areas, once identified, can be avoided or subject to more focused ground study and potential rescue excavation. 12A.2 The Model Our model used for this assessment was developed through two geospatial platforms: ENVI and ArcGIS. ENVI was used to prepare satellite analyse images of all three project areas and surrounding regions, and ArcGIS was used for further analysis of the satellite images, analysis of Digital Elevation Model (DEM) and for the model execution. The satellite images and DEM, which contained geospatial information on the landscape of Guinea, serve as the foundation to the model s predictive capability. Archaeological data available at the time of this assessment were not evenly distributed throughout the study area, with a large majority of known sites falling in the area around the mining concession area. This clustering of known archaeological sites in the southeast of Guinea is a result of the ready availability of data from recent studies based on ground reconnaissance. It has thus far been more difficult to find information on known archaeological elsewhere in Guinea. As desk and field research continues, more data will be added to our list of known sites and these data can also be used to further refine the accuracy of the model. Among the enhancements now planned or considered are: 1) field reconnaissance as a model test; 2) refinement of the model based on the field test; 3) acquisition and incorporation of additional desktop data such as the distribution of artisanal-quality iron ore deposits, historical patterns applicable to specific periods of history such as refuge areas, or high value locations for pre-agricultural periods. In the meantime, the predictive model is generally applicable to all portions of the project corridor and is therefore our best comprehensive source of information about potential project archaeological constraints in otherwise poorly known regions within the three project areas. Because of the limited number of known Archaeological Cultural Heritage (ACH) sites within the project areas (of the 145 known cultural heritage sites used to make the model, only 57 can be considered ACH sites) the decision was made to not directly use the known ACH sites to develop the model. Besides the limited number of known ACH sites, other factors that went into the decision not to base the model on ACH site location were: 1) the consideration that almost all documented cultural heritage sites in the project areas are localised around the southern part of the mining concession area (or the first 40 km of the ~600 km long project area) and 2) the observations that the three project areas traverse multiple ecological and topographic zones, each with unique landscape factors that influence human settlement location choice. 12A-1
In lieu of a robust and detailed database of ACH sites for Guinea a decision was made to utilise modern rural village location to develop the model. The assumption that modern rural populations locate their villages on certain landscapes in a similar manner as ancient populations was tested in the region of the mining concession area where spatial information exists on both modern rural villages and ancient settlements identified in the Project social baseline. After the model was constructed using modern rural village location to guide the process the 56 documented ACH sites were overlaid on top of the model to assess its accuracy which confirmed the methodology s applicability. There are a total of 4365 modern villages within the three project component areas. Accounting for artificial settlement strategies, such as the phenomenon of population migration to newly build roads, 2599 villages were removed from the database as they fell within three kilometres of a major road. Therefore, 1806 rural villages were left for use in directing the predictive modelling effort. The sensitivity model consists of seven sub-models centred on different indicative geologic, land cover and landform features often used in archaeological site prediction in the region. The sub-models take account of are as follows 1) Cost-distance to water sources (degree of difficulty as a function of slope to access water sources). 2) Landscape slope (i.e. steepness of mountains). 3) Geomorphology (i.e. soil types). 4) Topographical Position Index (TPI) (i.e. categorising the landscape into topographic features such as hills or valleys, etc). 5) Landscape curvature (classifying the landscape into positive or negative values representing relative curvature). 6) Aspect (the direction in which a slope is facing (N-E-S-W). 7) Terrain relief (identifies areas that are relatively higher or lower than the surrounding landscape). Each sub-model contains numerous sub-categories of information each with associated weighted values (see Table 12A-1). At present there is no sub-model to address locations of archaeological mining or ore reduction sties. Positive weighted values have a positive force on the final predictive model while negative weighted values have a negative force (see Table 12A-1). For example, within the TPI sub-model, the upper slope/mesa category had an assigned value of +1 since many of the modern rural villages are located on this type of topographic feature. For a negative example, within the TPI sub-model canyons/deep streams and mountain top/high ridges categories were given a value of -1 since very few modern rural villages occupied these topographic features. This makes sense since people tend to want to live in areas that are elevated enough to avoid swampy lowlands or flooding river banks, but not too far away as to limit access to resources found in lowlands. Once all the sub-models and their respective sub-categories were generated, the sub-models (and associated sub-categories) were set to overlay one another. Where sub-categories from different submodels overlapped, the positive or negative weighted values are summed together. In areas where many positive sub-categories overlap, a very high-summed value is created. In areas where many negative subcategories overlapped, a low-summed value is created. Highs and lows are averaged. The higher the summed value the more archaeological site potential an area is considered to have; the lower the summed value, the lower the archaeological site potential. This is represented in Figure 12A-1 below where areas of bright red represent very high summed values, while the yellow areas represent very low summed values; High and Low potential respectively. 12A-2
Moreover, different models were generated for different ecological zones. This is necessary when modelling a large region, as archaeological settlement strategy will differ from one region to the next. The entire project area was first divided into the 4 ecological zones identified by previous baseline studies (Forest, Upper, Mid, and Lower). Furthermore, each zone was further subdivided into additional zones based upon topography. For example, the eco-zone of Forest Guinea had two subdivisions: Simandou Mountain range, and areas outside of the mountain range. This is also important as most of the modelling is based upon digital elevation models (DEM) which change drastically between mountainous and flat regions. There were a total of 10 topographic zones distributed among the 4 ecological zones. The multiple final models for each topographic zone were eventually combined within their associated ecological zones (see Figure 12A-1). Finally all 4 models representing each of the 4 ecological zones were also combined to generate one single final model that retained its localised parameters and model specifics. Table 12A-1 Predictive Variables for the Archaeological Sensitivity Model Predictive Factor Predictive Sub-category Critical Value Predictive Weight Cost-Distance to water sources/ degree of difficulty as a function of slope to access water sources ***Measured in Cost Distance Units (CDU)*** Slope/ As percentage above zero Geomorphology/ soil type Topographical Position Index (TPI)/ categorising the landscape into topographic features such as hills or valleys Landscape curvature positive or negative values representing Cost-Distance 1 0 CDU 1 Cost-Distance 2 0-280 CDU 1 Cost-Distance 3 280-701 CDU 1 Cost-Distance 4 701-1261 CDU -1 Cost-Distance 5 1261-2103 CDU -1 Cost-Distance 6 2103-3224 CDU -1 Cost-Distance 7 3224-5327 CDU -1 Cost-Distance 8 5327-9253 CDU -2 Cost-Distance 9 9253-16123 CDU -2 Cost-Distance 10 16123-35752 CDU -2 Slope 0-6.5 o 1 6.5-90 o -3 Duricust or Crust Yes 1 Lithosol on Various Rocks Yes 1 Hardened Latosol Yes 1 Loose Latosol Yes -1 Tropical Ferruginous Soil Yes -1 Hydromorphic Soil Yes -1 Soil Rejuvenated by Erosion Yes -2 Soil on Particular Materials Yes -1 canyon/deep streams Yes -1 Midslope drainages/ shallow Yes 0 valleys upland drainages/ headwaters Yes 0 u-shaped valleys Yes 0 Plains Yes 1 open slopes Yes 0 upper slopes/mesas Yes 1 local ridges/ hills in valleys Yes 0 midslope ridges/small hills in Yes 0 plains mountain tops/high ridges Yes -1 Positive Curve Yes 1 12A-3
Predictive Factor Predictive Sub-category Critical Value Predictive Weight curvature Negative Curve Yes -1 Aspect/ the direction in which a slope is facing (N-E-S- W) Terrain Relief/ identifies areas that are relatively higher than the surrounding landscape N Yes -1 E Yes 1 S Yes 1 W Yes -1 Relief 0-3 meters 1 3-6 meters 1 6-7 meters -1 7-9 meters -1 9-10 meters -1 10-11 meters -1 11-14 meters -1 14-18 meters -1 18-29 meters -1 29-200 meters -1 Figure 12A-1 Unprocessed result of archaeological potential modelling Notes: Red indicates higher site potential and blue lower site potential. Different models were created for each ecological zone along project area. 12A-4
Figure 12A-2 Archaeological Potential Modelling for the Simandou Project Notes: Figure 12A-2: A) Processed model result seen in Figure 12.A.2. The models from each ecological zone have been combined and pixellated into blocks of a quarter hectare. They represent defined blocks of High (red) and Medium (yellow) Archaeological Interest. The large black transparent areas represent dense clustering of Areas of High Archaeological Interest (red blocks). These black transparent areas represent are considered to be Areas of High Archaeological Potential. Figure 12A-2: B) Final Archaeological Potential Map, which highlights Areas of High Archaeological Potential, is based upon an 2 extraction of all clusters of Areas of High Archaeological Interest greater than 2.5 km. 12A.3 Areas of High and Medium Archaeological Interest along the Project Corridor The Archaeological Potential Model, as it exists now, accurately accounts for about 85% of the modern rural villages. That is to say, about 85% of modern rural villages fall within the High and Medium Interest areas. The model is simply a planning tool based on current archaeological understanding and available data for the study area (see Figure 12A-2). Ground reconnaissance is needed to confirm actual conditions and will enhance the accuracy of the model. Models of this type are typically improved whenever new data becomes available, whether from desk sources or new fieldwork. When applied to an independent archaeological test (i.e. when tested against data that were not used to create the model), model accuracy typically falls within the 70-80% range. This means that models such as the one described here can be expected to have a 7080% accuracy of predicting undiscovered sites, after being readjusted to reflect findings of preliminary fieldwork. The model has identified specific areas within all three project areas that are most likely to contain undiscovered archaeological sites. These high potential areas, which constitute approximately 10% of the study corridor s areas, may contain as many as 50% of the undiscovered archaeological sites in the corridor. 12A-5
Specifically, the Project corridor encompasses a total area of 10,615.78 km 2. The total area of High Interest is 967 km 2, or about 9.1% of the total area. The total area of Medium Interest is 1787.75 km 2, or about 16.8% of the total area. The remaining 74.1% of the total area is Low Interest. Another factor that suggests the presence of additional undiscovered archaeological sites in the Project corridor is the issue of visibility. Dense vegetation cover will pose difficulties in discovering new sites and methodologies should be developed to improve the probability of finding sites in difficult terrain.. 12A.4 Areas of High Archaeological Potential Understanding and considering Areas of High Archaeological Potential serves as a viable method by which to plan possible port location, railway corridors and mining activity areas. Areas of High Archaeological Potential correspond to regions of contiguous and clustered instances of High Archaeological Interest. These areas are displayed in Figure 12A-2-A as a transparent black overlay, and are shown in Figure 12A-2- B as striped purple areas. On the ground, they are large areas of the landscape that are optimal for settlement given the seven variables (sub-models) that were used to identify such optimal areas. Areas of High Archaeological Potential indicate blocks of the landscape where numerous and dense ancient settlements are most likely to occur. The end result of the Archaeological Potential Model was 118 Areas of High Archaeological Potential, identifying the largest and most sensitive regions for yet undiscovered cultural heritage. Areas of High Archaeological Potential do not indicate the only areas with archaeological potential within the Project area, but rather suggest regions that are likely to contain more settlements per square kilometre than regions that only have limited instances of Areas of High Archaeological Interest. For this reason, Areas of High Archaeological Potential stand out as especially challenging archaeologically. They are areas where the Project is most likely to encounter dense archaeological resources. These are also areas where archaeological Chance Finds are most likely to occur during construction. Furthermore, sites found in these areas are expected, on average, to be larger and more important, and to represent use over a wider range of time periods. Assessment of findings presented in the Archaeological Potential Model provides further confirmation of the initial opinion that the Simandou Project could face significant cultural heritage issues including: 1) archaeological permitting; 2) threats to construction schedule and budget; and 3) negative impacts to the project reputation due to public concern about damage to cultural heritage. The first step towards reducing negative impacts on Project success would be to avoid Areas of High Archaeological Potential as much as possible. If the Project cannot avoid the Areas of High Archaeological Potential, then preconstruction archaeological survey should target these areas more intensely than others not highlighted by the Archaeological Potential Model. 12A.5 Archaeological Potential Maps The primary output of the model is a set of 65 maps following the Project area East to West; from the Mining Area, along the Railway Corridor, ending at the Port. Each map image shows Areas of High Archaeological Potential at a scale of 1:65,000 (see Figure 12A-3). Areas of High Archaeological Potential have been identified through the methodologies described above. All maps are located in Annex 12B. In addition to archaeological potential within the corridor, the maps include all of the known ACH and LCH sites. All sites have been labelled with unique Site Codes (CH-Num) which can be referenced in the inventory of sites for more detailed descriptions of each site, including site name, latitude/longitude, positional accuracy and time period, among others (see Annex 12D). 12A-6
Figure 12A-3 Example of Archaeological Potential Map 12A-7