SPE 12LAHS-P-156-SPE Identifying and Avoiding

SPE 12LAHS-P-156-SPE Identifying and Avoiding Sensitive Habitats in Petroleum Operations Jessica L. Deichmann, Smithsonian Conservation Biology Institute, Mark A. Higgins, Carnegie Institution for Science, Reynaldo Linares-Palomino, Farah Carrasco Rueda, Marcel Costa Faura, Fransisco Dallmeier and Alfonso Alonso, Smithsonian Conservation Biology Institute

Copyright 2013, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Latin American and Caribbean Health, Safety, Social Responsibility, and Environment Conference held in Lima, Peru, 26–27 June 2013. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of th e paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect an y position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract Defining and understanding the habitats in which a company is operating is a key step toward the reduction of impacts on biodiversity. Identification of vegetation types is a commonly-used method for mapping an area of operations, and these vegetation types are often used as a surrogate for plant and animal habitats, but defining these types too finely may result in limited biological importance of these types for plants and animals, and may complicate the conservation planning process. Instead, habitat maps based on more coarse-scale but biologically important data such as elevation and geologic history can result in more useful maps of plant and animal communities and can lead to better land management during operations. We created habitat maps for Blocks 39 and 57 in northeastern and south central Peru, respectively, using Landsat imagery and elevation data. In Block 39, three different geological formations, or habitat types, were identified in the map, while four were identified in Block 57. In order to confirm that the habitats identified in this study are biologically distinct in terms of plant and animal communities, CCES researchers assessed soil samples and a variety of taxonomic groups including ferns, birds, bats, amphibians and reptiles in each. The protocol requires a minimum of five days sampling for each taxonomic group in a minimum of four different areas within each distinct habitat in order to ensure thorough data collection. We then use this data to test and redefine the boundaries of habitats, and to identify habitats with communities of plants and animals of special conservation concern. In the cases of Blocks 39 and 57, recommendations were made to the company regarding where to avoid or limit operations, in order to reduce negative impacts on special habitats and improve the likelihood and cost-effectiveness of habitat restoration post-operations.

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SPE 12LAHS-P-156-SPE

Introduction Petroleum operations, particularly exploration and extraction, can take place across large expanses of land. In Peru, oil and natural gas exploration concessions are on average over half a million hectares in total size (PeruPetro S.A. 2013), offering companies large areas in which to conduct operations. These concessions are often located in complex landscapes and can include a variety of ecosystems of interest to biodiversity conservation. In order to ensure minimal impacts of operations on habitats, it is of the utmost importance to survey the different habitat types within a concession before undertaking operations. Doing so can help realize the best possible conservation outcomes, and avoid unnecessary delays and costs during initial as well as closing operations at a site. Importance of Habitat Mapping In order to attain a comprehensive spatial assessment of the impact of operations, and to plan for proper avoidance and mitigation of impacts, it is important to consider many different types of spatial data, including vegetation types, geomorphological units, elevational data, satellite imagery and aerial photographs. Vegetation mapping is the most widespread method of mapping for Environmental Impact Assessments (Geneletti 2002). Vegetation maps often identify fine-scale changes in vegetative cover and plant species distributions across the landscape. In areas that are particularly species rich, such as tropical lowland rainforests, such maps may be difficult to produce or use, and in some cases may not actually reflect significant turnover in overall biodiversity. However, such ambiguous distinctions as dense forest vs semi-dense forest; and hilly forest vs semi-hilly forest, for example, are likely not biologically important for the majority of flora and fauna inhabiting a given area. Habitat divisions based on broader-scale units—reflecting fundamental physical differences such as geological formation, soil type, and hydrology—can better predict differences in plant and animal communities that can be supported in these habitats. For this reason, broader-scale habitat maps based on these variables may be more useful than detailed vegetation maps in operations planning when the goal includes reduction of impacts on biodiversity. Recommended Habitat Mapping Practices Creating an Initial Map Data Acquisition Landsat Geocover imagery for sites around the world can be found and reviewed in the Global Landcover Facility at the University of Maryland (GLCF, Universidad de Maryland, USA, http://glcf.umiacs.umd.edu). The best images (no clouds, appropriate time periods) from the study area first are selected and downloaded. For Landsat images, only bands 4, 5, and 7 are needed for the purposes of processing, mosaicking, and interpretation. These bands represent near and mid-infrared wavelengths, and have been demonstrated to be the effective at identifying compositional variation in Amazonian forests (Tuomisto et al. 1994; Higgins et al. 2011; Higgins et al. 2012). These three bands are stacked into a single color image for use in later steps, with bands 4, 5, and 7 set to the colors red, green, and blue respectively. In addition, Shuttle Radar Topography Mission (SRTM) digital elevation data is used to identify geological formations (Rodriguez et al. 2006; Rossetti and Valeriano 2007; Higgins et al. 2012). SRTM data are well suited for habitat characterization because they are of high positional and elevational accuracy (9 m and 6 m, respectively). They are also freely available to the public. SRTM data for any given study area can be downloaded from the USGS National Map Seamless Server (http://seamless.usgs.gov) as set of non-overlapping tiles. If necessary, multiple tiles can then be mosaicked into a single image in ArcGIS and then masked to match the area of interest. Data Processing To improve the interpretability of Landsat data for a study area, images can be processed using a combination of contrast stretching and low-pass spatial convolution (i.e. smoothing). This processing increases the contrast in images (stretching) and averages local variation (smoothing), resulting in substantially easier-to-interpret imagery (Hill and Foody 1994, Tuomisto et al. 1994). More information describing how to apply this process can be found in Higgins et al. (2012). Contrast stretches can also be applied to the SRTM data mosaic to clarify interpretation and aid in creation of figures. Image Interpretation Landsat and SRTM data can be used to identify features in the study area including both human disturbance and intact forest habitat types, normally corresponding to variations in hydrology, soils, or underlying geology.


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Deforestation and construction are visible in Landsat imagery as high reflectances in bands 5 and 7, making these features appear similar to deserts and beaches (sand). This is true for local communities, oil camps, airstrips, and other infrastructure. Partially-deforested and recovering areas such as rights-of-ways (RoW) and agricultural areas do not possess this bare-earth reflectance, but rather are apparent by high reflectance in all three bands (4, 5, and 7). While RoWs are often evident by their linear configuration, agricultural clearings are the most difficult to detect, and can frequently resemble young forest, or forest on rich soils. All types of human disturbance, however, cause rapid changes in forest cover, and can thus be detected as dateto-date change in Landsat imagery. Therefore, Landsat imagery from multiple dates should be used to identify and map areas of suspected human disturbance. Habitat types in intact forest are initially mapped on the basis of Landsat data using variations in tone and texture (Figure 1). SRTM elevation data can then be used to confirm and adjust interpretations in the initial map by comparing it to underlying geological formations (Figure 2). Identification of habitat types can be complicated by a number of factors, including the presence of site-specific or temporally variable characteristics such as unusual geological formations or bamboo. Once again, to deal with these types of complications, it may be necessary to consult multiple dates of Landsat imagery to interpret forest habitat types. Verification on the Ground Once an initial habitat map has been created, questionable areas and divisions between habitat types should be verified on the ground, i.e. ground-truthed. In addition, habitats should be sampled to determine whether any habitat supports particularly sensitive, range-restricted, or otherwise threatened populations of plant or animal species. Taxonomic groups that have proven to be useful in ground-truthing include ferns, birds and amphibians. Bats have also been suggested as a useful group, as they can be particularly abundant in tropical habitats. Soils should be sampled to evaluate differences in soil characteristics between habitat types, and are a particularly good indicator of distinct habitats as both plant and animal communities are known to differ with varying edaphic conditions (Deichmann et al 2011; Higgins et al 2011; Jones et al 2013; Pomara et al 2012). Ground-truthing assessments should be conducted in each of the identified habitat types and repeated at least four locations for each habitat. Sampling at any given site should be conducted for a minimum of five days to ensure an adequate evaluation of the species present. It is important to note that five days will not be sufficient time, particularly in tropical lowland forest, to guarantee that all species at a site have been identified, but should produce a reliable baseline for comparison between habitats. Example Using the methodology outlined above, habitat maps were created and ground-truthing was conducted in Blocks 39 and 57, both located in Amazonian Peru. The initial habitat map for Block 39 identified three habitat types: Late Miocene Islands, Miocene Pebas Formation Forest, and flood plain forest (Figure 3). The former two types were of particular biodiversity and conservation interest in terms of their plant and animal communities. Ground-truthing demonstrated differences in soil types and in both fern and avian communities between the two habitats identified in the initial map. These differences were used to finalize the delineations between habitat types in the map. They were also used to make recommendations for operation planning. Specifically, the Late Miocene Islands are composed of poor soils relative to the Miocene Pebas Formation Forests, and will likely be more costly to restore post-operations, so it was recommended that operations be avoided in this habitat where possible. This is particularly important due to the presence of restricted-range white-sand bird species that are otherwise known only from the Allpahuayo-Mishana protected area of Iquitos (Alvarez et al. 2013). In Block 57, the initial map documented four probable habitat types, including Late Miocene Islands and Miocene Pebas Formation Forest (the same two identified in Block 39), Holocene Alluvial Deposit Forest, and an Unknown Pre-Miocene Formation Forest. Ground-truthing in these is on-going, but the groups being assessed include birds (Figure 4), amphibians (Figure 5), bats (Figure 6) and ferns. Soils are also being sampled from the different habitats. Conclusions Detailed vegetation maps are useful for gaining information about the appearance of a landscape; however, we recommend the use of the habitat mapping method outlined herein for future operations planning. Mapping at this scale provides a more biologically useful tool for planning because the habitats defined within more accurately represent distinct plant and animal communities that may be affected as a result of operations. An accurate map that can be used to minimize impacts on biodiversity can strengthen a company’s ability to apply Best Practices in operations and can also save a company valuable time and money in the long run.

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Acknowledgements We would like to extend our thanks to G. Joo, S. Castro and T. Pacheco for their administrative and logistical assistance with this project. We must also thank the field biologists who carried out the ground-truthing assessments in Blocks 39 and 57, in particular H.G. Andrade, J. Diaz, N. Elespuru, V. Gamarra, E. Perez, J.G. Vasquez Soto, V. Vargas, and H. Zamora. We also extend our gratitude to the members of the indigenous communities of Shivankoreni and Camisea who helped us carry out the field work in Block 57. The authors would like to acknowledge Repsol Exploración Peru for financial and logistical support, with a special thanks to the current staff dedicated to this work including E. Correa, R. Diaz, C. Videla, C. Ahumada and A. Watson. This is contribution 16 to the Peru Biodiversity Program.

References Alonso, J.Á., Metz, M.R., Fine, P.V.A., 2013. Habitat specialization by birds in western Amazonian white-sand forests. Biotropica, DOI: 10.1111/btp.12020. Deichmann, J.L., Lima, A.P., Williamson, G.B. 2011. Effects of geomorphology and primary productivity on Amazonian leaf litter herpetofauna. Biotropica 43:149-156. Geneletti D. 2002. Ecological evaluation for environmental impact assessment. Neth Geogr Stud (NGS 301). Utrecht7 Knag; 224 pp. Higgins, M. A. 2010. Geological control of floristic composition in Amazonian forests. Duke University, Durham, North Carolina. Higgins, M.A., Ruokolainen, K., Tuomisto, H., Llerena, N., Cardenas, G., Phillips, O.L., Vásquez, R., Räsänen, M. 2011. Geological control of floristic composition in Amazonian forests. Journal of Biogeography 38:2136-2149. Higgins, M. A., Asner, G. P., Perez, E., Elespuru, N., Tuomisto, H., Ruokolainen, K., Alonso, A. 2012. Use of Landsat and SRTM data to detect broad-scale biodiversity patterns in northwestern Amazonia. Remote Sensing 4:2401-2418. Hill, R. A., Foody, G. M. 1994. Separability of tropical rain-forest types in the Tambopata-Candamo Reserved Zone, Peru. International Journal of Remote Sensing 15:2687-2693. INGEMMET. 2000. Mapa Geológico del Perú. Instituto Geologico Minero Y Metalurgico, Lima. Jones, M.M., Ferrier, S., Condit, R., Manion, G., Aguilar, S., Pérez, R. 2013. Strong congruence in tree and fern community turnover in response to soils and climate in central Panama. Journal of Ecology 101:506-518. PeruPetro S.A. 2013. Mapa de lotes. http://www.perupetro.com.pe/wps/wcm/connect/perupetro/site/InformacionRelevante/MapaLotes/Mapa%20de%20Lotes Pomara, L.Y., Ruokolainen, K., Tuomisto, H., Young, K.R. 2012. Avian composition co-varies with floristic composition and soil nutrient concentration in Amazonian Upland Forests. Biotropica 44:545-553. Rodríguez, E., Morris, C. S., Belz, J. E. 2006. A global assessment of the SRTM performance. Photogrammetric Engineering & Remote Sensing 72:249-260. Rossetti, D.F., Valeriano, M.M. 2007. Evolution of the lowest Amazon basin modeled from the integration of geological and SRTM topographic data. Catena 70:253–265. Tuomisto, H., Linna, A., Kalliola, R. 1994. Use of digitally processed satellite images in studies of tropical rain-forest vegetation. International Journal of Remote Sensing 15:1595-1610.


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Figure 1. Processed Landsat imagery used to interpret habitat types in Block 39 in Peru. The yellow outline delimits the block and the different habitats distinguished using the imagery inside the block.

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Figure 2. Image interpretation of distinct habitats in Block 39 from the Landsat imagery overlaid on SRTM data.


Figure 3. Map of geologically defined habitat types for the Block 39 area of interest.

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Figure 4. Round-tailed manakin (Pipra chloromeros) documented in both Miocene Pebas Formation Forest and Unknown PreMiocene Formation Forest in Block 57. Photo by J. G. Vasquez Soto.


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Figure 5. Buckley’s broad-headed treefrog (Osteocephalus buckleyi) encountered during surveys in Miocene Pebas Formation Forest in Block 57. Photo by J. Deichmann.

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Figure 6. Big-eared wooly bat (Chrotopterus auritus) captured during sampling in Unknown Pre-Miocene Formation Forest in Block 57. Photo by F. Carrasco.