Ecological Indicators 11 (2011) 789–810
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
Original article
The landscape infrastructure footprint of oil development: Venezuela’s heavy
oil belt
Chris W Baynard ∗
Dept. of Economics and Geography, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, USA
a r t i c l e
i n f o
Article history:
Received 22 June 2009
Received in revised form 26 August 2010
Accepted 20 October 2010
Key words:
Venezuela
Landscape ecology
Infrastructure footprint
GIS and remote sensing
Heavy oil belt
a b s t r a c t
Oil exploration and production activities (OEPA) and other extractive endeavors can create large-scale
and permanent landscape alterations through the establishment of infrastructure features such as roads,
well pads, pipelines and production facilities. These structures can lead to or increase landscape fragmentation and degradation, reduce biodiversity, disrupt important ecosystem services and attract informal
settlements that further alter the landscape, deplete area resources and lead to social conflict. Aside
from regulatory standards, many energy (oil and gas) companies include voluntary environmental performance as part of their sustainability reporting. However, they do not account for these site-specific
alterations in a systematic, quantifiable and transparent way. This paper proposes a calculation of a modified Landscape Infrastructure Footprint (LIF) of OEPA based on landscape ecology metrics measured via
GIS and remote sensing techniques. Three Venezuelan heavy oil belt (HOB) operations were examined
with reference to the years 1990 and 2000. Results indicate that Ameriven displayed the smallest LIF.
Newer technologies, best practices, land cover, competing economic interests and type of management
may explain observed alterations.
LIF methods provide four important benefits. First, they can help to reduce surface disturbances by
informing planning practices in current as well as in new projects. Second, they fortify environmental
reporting by providing objective measures of environmental performance tied to extractive activities.
Third, by including LIF in their sustainability practices, extractive industries can improve their competitive
advantage. Finally, the LIF helps create a set of transparent environmental performance standards that
industry and regulators can adopt in order to measure and monitor landscape alterations resulting from
extractive activities.
© 2010 Elsevier Ltd. All rights reserved.
1. Introduction
The development of infrastructure features related to extractive industries such as oil and gas, mining and logging can lead
to large-scale and permanent land-use land-cover changes (LUCC).
These include habitat fragmentation, land degradation, soil erosion, loss of wildlife; the introduction of invasive species; river
siltation; large water draw-downs; air, soil and water pollution;
increased greenhouse gas emissions and the opening of corridors
for disease vectors (Nelleman and Cameron, 1996; Schneider et al.,
2003; Morton et al., 2004; Lee and Boutin, 2006; Boletta et al., 2006;
Lawrence et al., 2007; Turner et al., 2007; Fischer and Lindenmayer,
2007). The accessibility created by roads built to extract natural
resources can draw new settlers to previously inaccessible areas if
government policies and enforcement do not dissuade them, or to
contrary, attract them (Hiraoka and Yamamoto, 1980; Southgate,
∗ Corresponding author. Tel.: +1 904 620 1243.
E-mail addresses: [email protected], [email protected]
1470-160X/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ecolind.2010.10.005
1990; Pelache, 2001; Brandão and Souza, 2006). Colonization can
lead to further land clearing and degradation, increased demand
on natural resources, conflicts among competing interests (including indigenous groups) and population growth spurred by a service
sector meeting the needs of the new “boom” economy (Musinsky
et al., 1998; Schmink and Wood, 1992; Laurance et al., 2001;
Forman et al., 2003; Lee and Boutin, 2006; Wilderness Society,
2006; Brandão and Souza, 2006).
In addition to complying with regulatory standards, multinational oil companies (MNOCs) are increasingly engaged in
minimizing and monitoring both environmental and social impacts
as part of their corporate social responsibility (CSR) and environmental stewardship efforts (Moser, 2001; Curlee, 2007). After
all, implementing environmental best practices can have several
positive business benefits. These include: securing and maintaining operating licenses; preventing costly environmental disasters,
project terminations, work stoppages, lawsuits, negative press coverage, political protests, sabotage and hostage taking (Anderson,
1994; Orlitzky et al., 2003; Diamond, 2005; Lawrence, 2007;
Kakabadse, 2007; IPIECA, 2007; Edoho, 2008; GRI, 2010). They
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can also increase employee and customer loyalty, attract investors
(Morhardt, 2002; IPIECA, 2007), offset actual or impending government regulation (Dashwood, 2007), improve community relations
and help stabilize long-term costs (Reinhardt, 2000).
Monitoring and quantifying the size and pattern of LIF is a tangible implementation of environmental best business practices that
reduce surface disturbances and lessen accessibility to new lands.
While land change science (LCS) uses geospatial technologies to
monitor LUCC (Boletta et al., 2006; Rindfuss et al., 2007; Turner
et al., 2007; Verburg et al., 2009), these efforts are currently not
part of sustainability planning or reporting by energy companies
despite the fact that “oil, natural gas, and pipeline industries manage infrastructure and operations as meticulously as possible by
using geographic information system (GIS) technology” (Wyland,
2010).
Even the US Bureau of Land Management (BLM) “is already
recognizing the potential for using GIS analysis to evaluate development impacts” (Wilbert et al., 2008; see also BLM, 2010). The
main problem is that there are currently no codified standards in
either government or industry on what the metrics should be used
to quantify surface alterations related to extractive industries (Ellis,
2010).
The Landscape Infrastructure Footprint (LIF) presented by
Baynard (2009) attempted to address this deficiency. It considered the type and pattern of visible infrastructure features (roads
and well pads) and measured surface disturbances using GIS and
remote sensing techniques. It consisted of 5 measures: Normalized
Difference Vegetation Index (NDVI) change detection, OEPA density, edge-effect zones, core areas and number of rivers crossed. The
change detection component drew from LCS methods; the others
from the landscape matrix model of landscape ecology. The latter acknowledges that infrastructure features slice the landscape
into patches, thus increasing human transportation connectivity
while disturbing ecosystem goods and services and wildlife habitats (Forman et al., 2003; Coffin, 2007).
This paper expands work by Baynard (2009) by examining the
relationship between NDVI change and oil roads and wellpads,
non-oil roads, and agriculture. Roads are an important metric for
measuring landscape alteration because they are the leading cause
of landscape fragmentation (Jaarsma and Willems, 2002; Coffin,
2007; Frair et al., 2008) and strongly influence the spatial distribution of human activities in forest frontier regions (Soares-Filho
et al., 2004). Agriculture is the main driver of LUCC in dry tropical forests and savannas (Daniels et al., 2008; Romero-Duque et al.,
2007; Fajardo et al., 2005; Jepson, 2005; Southworth et al., 2004;
Trejo and Dirzo, 2000) and a central activity and cause of land conversion in the Venezuelan llanos (Hernández-A et al., 2000; Barnola
and Cedeño, 2000; Castel et al., 2002; Dumith, 2004; Mauricio et al.,
2005; Thielen et al., 2008).
Meanwhile, “The NDVI represents a continuous variable related
to productivity of land cover or vegetation biomass, which varies
both in space and time,” note Southworth et al. (2006, 410). This
makes it an alternative choice to traditional land classification
schemes, and a preferred one in semi-arid environments (Funk
and Brown, 2006) and in the Orinoco llanos of Venezuela (ChacónMoreno, 2004).
While the methodology presented here provides a necessary
evaluation of environmental performance, measuring total ecological impacts would require complete ecological and socioeconomic
analyses (Wilbert et al., 2008) that are beyond the scope of this
work.
The study area comprises three of the four heavy oil operations in Venezuela’s heavy oil belt (HOB), an area with vast
reserves where OEPA are projected to expand by 600% in the
next two decades (Petroguia 2006–2007; PDVSA, 2005a). The
methods implemented utilize Landsat imagery that is readily avail-
able to NGOs, government, academia and industry. Furthermore,
the methodology can be readily replicated and easily understood
(Wilbert et al., 2008), since the aim is to create a set of standardized
and transparent procedures that industry and regulators can adopt
as part of their environmental performance standards.
2. Landscape infrastructure footprint and geospatial
technologies
2.1. Sustainability measures and indices
Various indices and guidelines exist to measure and report oil
companies’ environmental performance but none has been systematically adopted. They include the International Petroleum Industry
Environmental Conservation Association (IPIECA, 2010), the International Organization for Standardization (ISO)—14,000 (14031 for
example) (ISO, 2009), the Global Reporting Initiative (GRI 2009), the
American Petroleum Institute (API, 2009) and the Pacific Sustainability Index (PSI 2006, 2009) among others. While some of these
address biodiversity conservation and land remediation and reclamation (IPIECA, GRI and PSI), none addresses alterations related to
infrastructure expansion.
Furthermore, many organizations that have implemented sustainability into their operations focus more on the reporting aspect
than on discussing measurable indicators of their landscape alterations (Morhardt et al., 2002; GRI, 2008). Additionally, these
methodologies often change from year to year, making temporal
comparisons difficult. Finally, many third-party indices are themselves based on what companies report, often online, rather than
on what should be reported in terms of sustainability (Morhardt
et al., 2002).
One exception includes the Ecological Footprint (EF), introduced
in the early 1990s (Rees, 1992; Kitzes et al., 2009). This resource
accounting tool measures the amount of productive land and water
area required to support human consumption patterns, as well
as the wastes generated, into a single physical unit, land area,
measured by hectares per capita (ESI, 2005; Ewers and Smith,
2007; Kitzes et al., 2009). The EF relies on quantifiable measures of
demand for natural resources (Siche et al., 2008; Ewers and Smith,
2007; Kitzes and Wackernagel, 2009; Niccolucci et al., 2008; Moran
et al., 2008; Siche et al., 2008) by considering “how much of the
regenerative biological capacity of the planet is demanded by a
given human activity” (Kitzes et al., 2009: 813). In short, it compares human demands on nature with the available natural capital
(Kissinger et al., 2007).
But measuring and monitoring OEPA patterns at the landscape level at their specific locations requires another approach.
First, while EF objectives include reducing environmental alterations such as habitat loss, soil degradation and loss of biodiversity
(Kissinger and Rees, 2009), it often considers a macro-level
approach, global hectares or real hectares, to allow for crosscountry comparison, rather than a landscape-level analysis. Second,
the EF focuses primarily on the impact of society on nature (Siche
et al., 2010) and aims to “translate all human activities into area
demand so as to assess their environmental impact” (Stoeglehner
and Narodoslawsky, 2008: 421 – emphasis added). Lenzen and
Murray (2001: 231) point out that it tends to “reveal nothing about
regional impacts on land and the sustainability of regional land use”
and instead propose that the EF focus on land disturbance. Third,
EF has not been applied to analyze environmental performance at
site-specific extractive operations such as OEPA or mining.
Fourth, the EF can be complicated to measure, requiring large
amounts of data and expertise that may not be available to small
government agencies and NGOs alike. Fifth, the majority of EF
studies focus on consumption even though this index can be
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
applied to production and services as well (Kitzes and Wackernagel,
2009; Benyon and Munday, 2008). Furthermore, applications of
EF methods to industrial systems are rare and “unable to track
specific changes in local resource management” (Niccolucci et al.,
2008: 165). Some exceptions include work on Canadian agriculture (Kissinger and Rees, 2009), Canadian wood pulp production
(Kissinger et al., 2007) and Italian wine production (Niccolucci et al.,
2008).
Lastly, and perhaps more importantly—the EF excludes the use
of GIS and remote sensing in its methodology. This is quite surprising given the importance of spatial data and processes in
understanding and quantifying human demands on nature. After
all, LUCC and environmental alterations are site-specific and cannot
be decoupled from their location. “Effective sustainability planning
and management depend on the reliability of land-use monitoring
and modeling approaches” which combine geospatial methodologies with social and landscape dynamics, notes Baptista (2010:
139). Additionally, geospatial approaches “can give consistent and
repeatable results for natural resource assessment” (Brink and Eva,
2009: 502). Yue et al. (2006) acknowledge this, pointing out that an
important deficiency in EF accounting is that it provides a snapshot
in time and cannot account for spatiotemporal changes.
The aspatial component to EF calculations, however, is beginning to change. For example, Kitzes et al. (2009) derived local
hectare footprints from remote sensing applications, while Moran
et al. (2009) used GIS to map the EF of international trade. Yue et al.
(2006) used GIS analysis and geostatistics to map the per capita
ecological budget of Gansu, China, between 1991 and 2015.
Closer to the work presented in this paper, Lenzen and Murray
(2001) used satellite imagery to classify land cover in Australia
and determine the EF based on land disturbance. These types of
approaches may prove a meeting ground between researchers
studying land change science and those concerned with EF accounting. After all, the aim for the LIF as well as many EF and ecological
economics studies is the same: “maintaining productivity while
minimizing ecological impact per unit of production” (Kissinger
et al., 2007: 553).
2.2. Oil exploration and production, gis and remote sensing
Geospatial technologies have been used for geologic exploration, drilling and oil production for over 30 years (Catoe, 1973;
Yatabe and Fabbri, 1986; Almeida-Filho, 2002; Almeida-Filho et al.,
2002; Jackson et al., 2002; Noomen et al., 2006; Zhang et al.,
2007). They have been applied to detecting hydrocarbon seepages
(Almeida-Filho et al., 2002; Noomen et al., 2006) and to identify
oil-bearing sands (Zhang et al., 2007). Other applications include
implementing least-cost analysis for pipeline constructions and allweather roads (Feldman et al., 1995; Gaddy, 2003; Osejo et al.,
2004; Atkinson et al., 2005); addressing operational costs of surface facilities (Pochettino and Kovacs, 2001; Jaggernauth, 2003) and
detecting spills and monitoring pollution (Kwarteng, 1998, 1999;
Espedal and Wahl, 1999; Chust and Sagarminaga, 2007; Ud-Din
et al., 2008).
Fewer studies address the role of OEPA on landscape-change.
These include Janks et al. (1995), Janks and Prelat (1994) who
assessed the health of vegetation in and around oil fields and
monitored remediation efforts on abandoned well sites. Musinsky
et al. (1998) examined the relationship between the construction
of oil roads and subsequent deforestation. Other investigators have
focused on measuring avoidance and stress responses by wildlife to
OEPA (Nelleman and Cameron, 1996; Bradshaw et al., 1998; Wolfe
et al., 2000; Dyer et al., 2001; Noel et al., 2006; Schneider et al.,
2007; Harron, 2007) as well as finding potential habitat locations
for wildlife affected by these activities (Danks and Klein, 2002).
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A final group of scientists use geospatial techniques to examine
habitat fragmentation and degradation created by oil and gas development (Morton et al., 2002; Thomson et al., 2005; Wilderness
Society, 2006; Wilbert et al., 2008) all with the goal of determining “the exact size and extent of the ecological footprint of
energy development” (Morton et al., 2004: 8). This present work
contributes to the limited but growing body of research that uses
spatial analysis to detect and measure landscape alterations created
by OEPA (Baynard, 2009). It promotes a modified LIF based on NDVI
change detection and its relationship to infrastructure features (oil
and non-oil), competing land uses (agriculture) and number and
location of rivers crossed.
3. Materials and methods
3.1. Site description
The 54,000 km2 heavy oil belt (HOB) is located north of the
Orinoco River in Venezuela and contains over one trillion barrels of
heavy and extra heavy crude oil of which 513 billion are considered
recoverable (meaning they can be extracted using current technologies) (Schenk et al., 2009). This recent US Geological service
assessment greatly expands previous figures of 270–300 billion
recoverable barrels in this area (Boza and Romero, 2001; Talwani,
2002; Bourg et al., 2007; Machado et al., 2009). If this unconventional crude (which requires greater processing) were counted in
official proved reserve statistics, Venezuela would boast the world’s
largest onshore oil deposits.
The four current operations are located in the llanos, or plains,
of southern Anzoátegui state, above the Orinoco River. This dry
tropical forest region is marked by grasslands and sandy soils,
a four-to-six month dry season, low and dispersed drought tolerant vegetation, gallery forests along river tributaries, and a
sparse human population comprised of small scattered settlements
(Hernández et al., 1999; Dumith, 2004; Fajardo et al., 2005; Thielen
et al., 2008). The terrain is mainly flat with a slope of less than 2%
(Castel et al., 2002). The main economic activities are agriculture
(cattle ranching, crop production, Caribbean pine tree plantations)
and oil extraction.
Due to its viscosity and low-grade quality (high sulfur, vanadium
and nickel) the heavy oil extracted in the study area concessions
is dewatered, desalted and degassed, then sent via pipelines to
the coastal upgrading complex called Jose (Tankersley and Waite,
2002; Total, 2003; Guerra, 2005; Operadora Cerro Negro, 2004).
There it is converted into a higher quality product, called syncrude,
then sold on the open market or sent primarily to US Gulf Coast
refineries (Guerra, 2005; Oil Company and Representative, 2005;
PDVSA, 2005b). Until 2007 these four HOB operations were called
Sincor, Petrozuata, Ameriven and Cerro Negro1 —though the latter is
not included in this study (see Fig. 1). These concessions consisted
of partnerships between the national oil company (NOC) and six
multinational oil companies, or MNOCs. This paper refers to the
names of these concessions as they were in 2005.
3.2. Data description and analysis
3.2.1. Data description
The study refers to the years 1990 and 2000. The first year, 1990,
is the baseline, which predates the establishment of these four
1
Sincor is now Petrocedeño, Ameriven is Petropiar, Cerro Negro is Petromonagas
and Petrozuata remains the same (PDVSA 2007). Also, another operation called Bitor,
also known as Orifuels Sinovensa, extracts heavy oil nearby Cerro Negro. However,
it does not upgrade it into syncrude, like the other four operations and instead sells
it as a cheap fuel for generating electricity.
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Fig. 1. The 54,000 km2 Orinoco Heavy Oil Belt (HOB) and the three concessions studied. From left: Sincor (51,240 ha), Petrozuata (29,966 ha) and Ameriven (66,981) ha.
operations (Boza and Romero, 2001; Talwani, 2002; Gipson et al.,
2002; Mommer, 2004). The year 2000 represents the early production phase, when an OEPA pattern is developed (see Fig. 2). Analysis
was accomplished using ESRI’s ArcGIS 9.2 and 9.3, Hawth’s Analysis Tools extension, as well as Leica Geosystem’s ERDAS Imagine
9.1 and 9.2 software.
The data set consisted of two satellite images using the Landsat
TM and ETM+ sensors for path 2, row 54. The spatial resolution
of the Landsat imagery was 30 m, meaning that one pixel in the
imagery represented 30 m on the ground. Given excessive cloud
cover and noise (due to Venezuela’s tropical location), and nonanniversary dates in some of the data (Jensen, 2007), three of the
Fig. 2. The three HOB concessions overlain on an April 30, 2000 Landsat ETM+ image, with a 4,3,2 band combination. Red indicates healthy vegetation—in this case riparian
forests found along Orinoco River tributaries. Cyan/greenish colors indicate cleared or dried vegetation. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of the article.)
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
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Fig. 3. The three HOB concessions overlain on a 1990–2000 NDVI change detection map. Red represents negative NDVI (vegetation loss). Green represents gains and black,
little/no change. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 4. An NDVI change detection map of the three concessions. Red represents NDVI (vegetation) loss, green represents gains and black, little/no change.
four concessions were examined for two time periods, April 19,
1990 and April 30, 2000.
3.2.2. Data analysis
The first step was to acquire and geometrically rectify the satellite imagery so that they conformed to the same projection and
coordinate system and thus were properly aligned. The second step
involved scanning a paper Energy Map of Venezuela (2006–2007),
then rectifying it and digitizing relevant features to produce polygons (shapefiles) of the heavy oil belt and the oil concessions. Third,
six subset (smaller) images were created of the three oil concession
polygons from the Landsat images (with the clouds masked out2 )
2
Robert Richardson, from the College of Computing, Engineering and Construction at the University of North Florida aided with tasks such as masking clouds and
digitizing OEPA features.
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Fig. 5. Sincor NDVI change detection overlain by OEPA features.
Fig. 6. Petrozuata NDVI change detection overlain by OEPA features.
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Fig. 7. Ameriven NDVI change detection overlain by OEPA features.
for the two years in the time series. The next step required digitizing the visible infrastructure features in the six subset images via
on-screen digitizing.
Distinguishing OEPA from non-OEPA was accomplished using
field data, expert knowledge, and high resolution Google Earth
imagery—following methods by Portillo-Quintero and SánchezAzofeifa (2010).3 With infrastructure features digitized, road
density (or route density) was calculated by dividing the length
of features by the area. This common metric is used to assess the
potential impact of roads and infrastructures on local environments
from a landscape and habitat fragmentation perspective (Forman
et al., 2003; Thomson et al., 2005). The higher the density, the more
infrastructure features on the landscape, particularly roads.
Step six involved creating a normalized difference vegetation
index (NDVI) for the 1990 and 2000 imagery (Band 4 (NIR)–Band
3 (R)/Band 4 (NIR) + Band 3 (R)). The NDVI is a ratio that measures the amount and condition of vegetation in a satellite image
(Jensen, 1996). It has been widely used in vegetation-change studies (Southworth, 2004; Mambo and Archer, 2007; Tarnavsky et al.,
2008) and is correlated to the expansion of the infrastructure features (Musinsky et al., 1998). Additionally, the NDVI provides an
alternative to the traditional land-cover classification methods that
create discrete categories in the landscape. Instead, the NDVI is a
continuous variable related to land cover productivity (Southworth
et al., 2006) that “effectively discriminates vegetated and nonvegetated areas” (Daniels, 2006: 2951).
To calculate change detection, the 1990 subset images were subtracted from the 2000s to produce change detection maps that
discerned healthy biomass from disturbed areas. Changes were
highlighted using a 25% threshold. This meant that if a given pixel’s
NDVI value changed by 25% or more between the two time peri-
ods, it was selected and colored. Red represented a loss of NDVI,
green represented gains and black meant little or no change (see
Figs. 3–7).
Next, following methods by Wilbert et al. (2008) a grid of cells
was created for each concession (using Hawth’s Analysis Tools for
ArcGIS). Grid size was selected by considering three criteria. The
first two involved balancing the fine resolution and smooth visual
display of small cells with the need to reduce computer-processing
time by using larger cells (Wilbert et al., 2008). The third criterion
was to account for the direct disturbance resulting from OEPA. The
BLM (2010) considers oil roads to have an initial direct disturbance
width of 12.19 m (40-ft). But Ellis (2008) incorporated road surface,
pipelines and areas cleared for right-of-way to calculate a standard
50 m width for oil roads.
3
Note, Google Earth imagery resides on the Google server and cannot be analyzed
using the NDVI or other remote sensing techniques.
Fig. 8. Rainfall amounts for October, November and December for the years 1989,
1999 and a times series 1921–2002 for Ciudad Bolívar, Venezuela.
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Fig. 9. NDVI changes occupied by Sincor OEPA features. Loss = 54.78%; little/no change = 44.42%; gains = 0.80%.
The latter, wider measure was selected in this paper to encompass OEPA features that include roads as well as well pads and
production stations whose dimensions are hard to discern in
medium-resolution imagery (30 m). Based on this road width, the
study area was divided into a grid of 50 m-wide cells (.25 ha). The
NDVI raster maps were converted to vector maps and spatially
joined to the grid files. This allowed for consistent and quantifiable
comparisons of vegetation change, direct disturbance (measured
at 50 m), agricultural land, core areas and number and location of
rivers crossed (see Figs. 6–24). In this case, edge-effect zones, or
Fig. 10. NDVI changes occupied by Petrozuata OEPA features. Loss = 33.84%; little/no change = 61.56%; gains = 4.6%.
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Fig. 11. NDVI changes occupied by Ameriven OEPA features. Loss = 12.33%; little/no change = 77.40%; gains = 10.27%.
Fig. 12. NDVI changes occupied by Petrozuata non-OEPA features. Loss = 12.33%; little/no change = 77.40%; gains = 10.27%.
areas whereby significant ecological effects extend outward from
infrastructure features (Forman and Deblinger, 2000), were not calculated. This would require a closer examination of the fauna and
their avoidance behaviors to roads and OEPA. Dyer et al. (2001),
Schneider et al. (2003), Morton et al. (2004), Thomson et al. (2005),
and the Wilderness Society (2006) have examined habitat fragmentation, wildlife behavior and energy development in the western US
and Canada. However, this information is underrepresented in the
literature for the Venezuelan llanos and should be a focus of further
research.4
4
One reason to explain the dearth of road ecology literature for Venezuela is that
“Empirically demonstrating the effects of road density, landscape context, range
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Fig. 13. NDVI changes occupied by Ameriven non-OEPA features. Loss = 34.62%; little/no change = 64.00%; gains = 1.38%.
Fig. 14. NDVI changes occupied by Sincor agriculture. Loss = 3.32%; little/no change = 97.53%; gains = 0.95%.
fidelity, and their interactions on animal distributions would be difficult to achieve
without experimentation, and such experiments are feasible for only a limited number of species” (Frair et al., 2008).
Core areas refer to intact habitat areas, or patches that remain
after OEPA, non OEPA and agricultural lands are discounted. For
the study site, core areas consist of riparian forests and flood zones.
Portillo-Quintero and Sánchez-Azofeifa (2010) have observed that
as deforestation advances in the dry tropical forests of the Americas
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Fig. 15. NDVI changes occupied by Petrozuata agriculture. Loss = 11.22%; little/no change = 84.37%; gains = 4.21%.
(including Venezuela), gallery forests and those with more difficult
access are left intact. While size, shape and connectivity are important (Forman et al., 2003), the focus here is on overall amount (see
Figs. 16–18).
The final measure regards the number and location of rivers
crossed (calculated using Hawth’s Analysis Tools); see Figs. 19–24.
Reducing the amount of river crossings lessens the disturbance to
aquatic ecosystems (Forman et al., 2003). Since riparian systems
serve as the immediate interface between rivers and land, they help
stabilize stream banks and reduce erosion (Ivits et al., 2009). Furthermore, fluvial environments are very sensitive to oil spills and
contaminants from industrial activity (Stein et al., 2002; Beisl et al.,
2003) and therefore reducing river crossings minimizes potential
contamination and surface disturbance.
Fig. 16. NDVI changes occupied by Ameriven agriculture. Loss = 39.20%; little/no change = 59.75%; gains = 2.85%.
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Fig. 17. Sincor: non-yellow areas represent size and pattern of the natural vegetation core areas that remained after OEPA, non-OEPA and agricultural lands are discounted
from the concession. Hectares = 50,105.87 ha, or 97.79% of the concession. White areas represent where cloud and cloud shadows were masked out. (For interpretation of
the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 18. Petrozuata: non-yellow areas represent size and pattern of the natural vegetation core areas that remained after OEPA, non-OEPA and agricultural lands are discounted
from the concession. Hectares = 26,642.30 ha, or 88.91% of the concession. White areas represent where cloud and cloud shadows were masked out. (For interpretation of
the references to color in this figure legend, the reader is referred to the web version of the article.)
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
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Fig. 19. Ameriven: non-yellow areas represent size and pattern of the natural vegetation core areas that remained after OEPA, non-OEPA and agricultural lands are discounted
from the concession. White areas represent where cloud and cloud shadows were masked out. Hectares = 27,642.30 ha, or 41.61% of the concession.
Fig. 20. Sincor: number of river crossings (20) and the NDVI changes within a 300 m buffer of OEPA features.
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Fig. 21. Petrozuata: number of river crossings (21) and the NDVI changes within a 300 m buffer of OEPA features.
Fig. 22. Ameriven: number of river crossings (0) and the NDVI changes within a 300 m buffer of OEPA features.
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
803
Fig. 23. Sincor: 92.61% of NDVI gains were located within 300 m of a river. These gains which mostly run along the riparian forests bordering the rivers in the concession.
The expansion of these forests are likely linked to abnormally high precipitation that fell 1 month before the capture of this image. See Fig. 8.
Fig. 24. Petrozuata: 92.07% of NDVI gains were located within 300 m of a river. These gains which mostly run along the riparian forests bordering the rivers in the concession.
The expansions of these forests are likely linked to abnormally high precipitation that fell 1 month before the capture of this image. See Fig. 8.
804
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
Table 1
Modified LIF calculations for three HOB concessions.
Sincor
Concession size (ha)
NDVI loss (ha)
% of area
NDVI little/no change (ha)
% of area
NDVI gain (ha)
% of area
OEPA (length in km)
OEPA (density)
Non-oil roads (length in km)
Non-oil roads (density)
All infrastructure (length in km)
All infrastructure density
OEPA direct effects (50 m grid) (ha)
% of area affected
Non-oil direct effects (50 m grid) (ha)
% of area affected
All infrastructure direct effects (50 m grid) (ha)
% of area affected
OEPA and NDVI—loss (ha)
% of NDVI
OEPA and NDVI—little/no change (ha)
% of NDVI
OEPA and NDVI—gains (ha)
% of NDVI
Non-oil roads and NDVI—loss (ha)
% of NDVI
Non-oil roads and NDVI—little/no change (ha)
% of NDVI
Non-oil roads and NDVI—gains (ha)
% of NDVI
Agriculture (ha)
% of area
Agriculture and NDVI loss (ha)
% of loss
Agriculture and NDVI little/no change (ha)
% of little/no change
Agriculture and NDVI gain (ha)
% of gains
Core Areas (natural areas remaining after OEPA, non-OEPA and agriculture are discounted) (ha)
% of area
Number of rivers crossed
OEPA river crossings and NDVI loss (300 m buffer) (ha)
% of NDVI
OEPA river crossings and NDVI little/no change (300 m buffer) (ha)
% of NDVI
OEPA river crossings and NDVI gains (300 m buffer) (ha)
% of NDVI
Percent of NDVI gains located within 300 m of a river
Oil production (barrels/day)
OEPA disturbance ha/daily oil barrel production
4. Results
Table 1 shows all the data related to the LIF analysis conducted.
4.1. NDVI change detection
The NDVI change detection maps (Figs. 3 and 4) reveal a pattern whereby vegetation biomass decreased more than it increased
across three sites in the 10-year span between 1990 and 2000.
Table 2 shows the amount of land hectares (ha) per site that
exhibited NDVI losses, gains and little/no changes, as well as the
percentage of the site that was affected. Overall, gains appear in
the riparian (gallery) forests following the rivers which drain south
into the Orinoco. This is true for the region in general (see Fig. 3)
and for Sincor and Petrozuata (see Figs. 5 and 6).
Rainfall amounts help explain some of the detected gains. Rainfall records for Ciudad Bolivar, the nearest metropolitan area with
relevant records and the same climatological regime as the study
area, show that October through December 1989 were markedly
51,239.61
5359.19
10.46
42,074.51
82.11
3805.92
7.43
149.55
0.29
0.00
n/a
149.55
0.29
870.74
1.70
0.00
n/a
870.74
1.70
476.95
54.78
386.79
44.42
7.00
0.80
n/a
n/a
n/a
n/a
n/a
n/a
263.00
0.51
8.75
3.32
251.75
95.73
2.50
0.95
50,105.87
97.79
20.00
163.50
25.47
445.75
69.43
32.75
5.10
92.61
180,000
0.004837444
Petrozuata
Ameriven
29,965.79
4780.79
15.96
21,153.51
70.59
4031.49
13.45
264.57
0.88
39.19
0.13
303.76
1.01
1566.32
5.23
231.22
0.77
1797.54
6.00
530.06
33.84
964.25
61.56
72.00
4.60
28.50
12.33
179.98
77.40
23.75
10.27
4514.22
15.06
506.37
11.22
3808.73
84.37
199.12
4.41
26,642.30
88.91
21.00
135.00
18.01
455.25
60.74
159.25
21.25
92.07
104,000
0.015060769
66,981.17
34,900.19
52.10
30,900.66
46.14
1180.31
1.76
13.00
0.02
598.57
0.89
611.57
0.91
94.77
0.14
3350.16
5.00
3444.93
5.14
48.31
50.98
46.45
49.02
0.00
n/a
1159.99
34.62
2144.11
64.00
46.07
1.38
39,112.82
58.39
15,349.17
39.20
22,692.87
57.95
1114.00
2.85
27,868.35
41.61
0.00
n/a
n/a
n/a
n/a
n/a
n/a
6.00
190,000
0.000498789
drier than average, while the same time period in 1999 was wetter
than average, see Fig. 8. These months with complete data, correspond to the dry season prior to the capture of the Landsat 1990
and 2000 scenes. The 1999 period was the wettest year in 68 years,
while March 2000 was the wettest March in 68 years. Such high
levels of rainfall during the normally driest months of the year
apparently had an impact on area rivers and tributaries, leading
to unseasonable flooding and vegetation growth along the riparian
Table 2
NDVI change detection figures for the concessions.
Sincor
Concession size (ha)
NDVI loss (ha)
% of area
NDVI little/no change (ha)
% of area
NDVI gain (ha)
% of area
51,239.61
5359.19
10.46
42,074.51
82.11
3805.92
7.43
Petrozuata
29,965.79
4780.79
15.96
21,153.51
70.59
4031.49
13.45
Ameriven
66,981.17
34,900.19
52.10
30,900.66
46.14
1180.31
1.76
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
Table 4
Direct effects of OEPA and non-OEPA features.
Table 3
OEPA and non-OEPA density measures.
Sincor
Concession size (ha)
OEPA (length in km)
OEPA (density)
Non-oil roads (length in km)
Non-oil roads (density)
All infrastructure (length in km)
All infrastructure density
805
51,239.61
149.55
0.29
0.00
n/a
149.55
0.29
Petrozuata
Ameriven
29,965.79
264.57
0.88
39.19
0.13
303.76
1.01
66,981.17
13.00
0.02
598.57
0.89
611.57
0.91
forests in 2000. Thus with a month’s lag time, the observed April
patterns of vegetation growth along the rivers is probably due to
excess rainfall.
Petrozuata showed the most gains (green) in 13% of the concession size, followed by Sincor at 7%. Ameriven displayed gains of
less than 2%—likely explained by fewer rivers running across this
concession and therefore less riparian forests, as well as the large
percentage of agriculture found here. The gains found in Ameriven
are likely explained by agricultural cycles in the Caribbean pine
plantations that occupy much of the farm land in the concession.
Regarding NDVI loss (red), Ameriven showed the greatest
amount, affecting 52% of its land. One explanation is that galleryforested land had been cleared at one time for agriculture. However,
rivers don’t run through agricultural land in Ameriven. Petrozuata
showed a 16% NDVI loss and Sincor 10%. While some of the agricultural land, such as portions of Caribbean pine plantations showed
gains in Ameriven, most of the other plots and non-forested land
in all concessions appeared cleared or dry, following the end of
the dry season and perhaps burning of fields. Other crops in this
region include: cotton, sorghum, corn, rice (Lehouck et al., 2009;
Hernández et al., 1999; Barnola and Cedeño, 2000; Castel et al.,
2002; Dumith, 2004; Mauricio et al., 2005; Parra, 2007).
4.2. OEPA and non-OEPA density
Petrozuata had the highest OEPA density of 0.88, followed by
Sincor (0.29) and Ameriven (0.02). This density number provides a
measure of habitat fragmentation and the closer to 1 the greater the
disturbance. Meanwhile Ameriven had the highest non-OEPA density at 0.89, caused mainly by agricultural roads. Petrozuata’s OEPA
density was 0.13, while Sincor did not appear to have one. When all
infrastructure features are combined (OEPA and non-OEPA), then
Petrozuata has the highest density at 1.01. Ameriven is next with
0.91, while Sincor’s is only 0.29 (see Table 3).
4.3. Direct effects of OEPA and non-OEPA (loss of natural capital)
As mentioned before, roads and well pads were assigned a direct
disturbance width of 50 m. Given rights of way, cleared road shoulders and pipelines, this figure more accurately accounts for surface
alterations caused by both oil and non-oil roads. This figure is also
useful given the difficulty of distinguishing paved from dirt roads
and precisely measuring individual road widths with a 30 m spatial resolution. Table 4 shows that 5% of Petrozuata’s area was
affected by OEPA, with less for Sincor and even less for Ameriven.
For non-OEPA—led by agriculture, Ameriven showed the highest
direct effects, 5% of its concession. Overall, the built-up landscape
was greatest in Petrozuata, with 1798 ha, or 6% of its concession.
Ameriven followed with 5% and Sincor with almost 2%.
4.4. OEPA, non-OEPA and NDVI changes
To test the relationship between the expansion of infrastructure
and vegetation change, NDVI figures underlying infrastructure features were examined (see Figs. 9–14 and Table 5). We would expect
OEPA direct effects (50 m grid) (ha)
% of area affected
Non-OEPA direct effects (50 m grid)
(ha)
% of area affected
Built-up direct effects (OPEA &
Non-OEPA-50 m grid) (ha)
% of area affected
Sincor
Petrozuata
Ameriven
870.74
1.70
0.00
1566.32
5.23
231.22
94.77
0.14
3350.16
n/a
870.74
0.77
1797.54
5.00
3444.93
6.00
5.14
1.70
Table 5
NDVI changes underlying infrastructure features.
OEPA and NDVI—loss (ha)
% of NDVI
OEPA and NDVI—little/no change (ha)
% of NDVI
OEPA and NDVI—gains (ha)
% of NDVI
Non-OEPA and NDVI—loss (ha)
% of NDVI
Non-OEPA and NDVI—little/no change (ha)
% of little/no NDVI
Non-OEPA and NDVI—gains (ha)
% of NDVI
Sincor
Petrozuata
Ameriven
476.95
54.78
386.79
44.42
7.00
0.80
n/a
n/a
n/a
n/a
n/a
n/a
530.06
33.84
964.25
61.56
72.00
4.60
28.50
12.33
179.98
77.40
23.75
10.27
48.31
50.98
46.45
49.02
0.00
n/a
1159.99
34.62
2144.11
64.00
46.07
1.38
these areas to show up as red, since the landscape was converted
to roads, or as black (little/no change) if the road features were
already there. This indeed appears to be the case. For Sincor and
Ameriven, over 50% of vegetation losses are tied to OEPA features.
Most of the remaining 50% are linked to little/no change. Petrozuata’s OEPA explains about 34% of vegetation loss while about 62% is
related to little or no change (see Figs. 9–11). This may indicate that
Petrozuata’s oil activities are older than the other two concessions.
Regarding non-OEPA and NDVI change, 35% of Ameriven’s vegetation loss is associated with non-oil roads and 64% with little or
no change. Again, this is related to the large network of agricultural roads found in the concession. In Petrozuata, 77% of non-oil
roads were associated with little/no vegetation change. Sincor did
not have such roads.
4.5. Agriculture (loss of natural capital)
As noted, much of Ameriven’s land has been converted to agriculture. This concession leads with 39,113 ha, or 58% of its land.
By contrast Petrozuata has 4514 ha of agricultural land (15%) and
Sincor much less, 263 ha (0.51%). Table 6 shows these values as
well as the amount of vegetation change encompassed by agricultural areas. Not surprisingly for Ameriven 39% of vegetation loss is
associated with agriculture. In this case it is likely related to crop
cycles rather than newly cleared land (see Fig. 16). Most of Petrozu-
Table 6
Agricultural land in the concessions and the relationship to NDVI.
Agriculture (ha)
% of area
Agriculture and NDVI loss (ha)
% of loss
Agriculture and NDVI little/no change (ha)
% of little/no change
Agriculture and NDVI gain (ha)
% of gains
Sincor
Petrozuata
263.00
0.51
8.75
3.32
251.75
95.73
2.50
0.95
4514.22
15.06
506.37
11.22
3808.73
84.37
199.12
4.41
Ameriven
39,112.82
58.39
15,349.17
39.20
22,692.87
57.95
1114.00
2.85
806
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
Table 7
Number of river crossings and relationship to NDVI.
Number of rivers crossed
OEPA river crossings and NDVI loss
(300 m buffer)—hectares
% of NDVI
OEPA river crossings and NDVI little/no
change (300 m buffer)—hectares
% of NDVI
OEPA river crossings and NDVI gains
(300 m buffer)—hectares
% of NDVI
Percent of NDVI gains located within
300 m of a river
Sincor
Petrozuata
Ameriven
20.00
163.50
21.00
135.00
0.00
n/a
25.47
445.75
18.01
455.25
n/a
n/a
69.43
32.75
60.74
159.25
n/a
n/a
5.10
92.61
21.25
92.07
This measure can help determine if the crossings are new, in which
case the surrounding area would show healthy vegetation. This
may be the case for Petrozuata, where 21% of these buffered river
crossings exhibit vegetation gains. For the most part, these crossings show little/no vegetation change for Sincor and Petrozuata
(Ameriven was not affected).
4.8. Rivers and riparian forests
n/a
6.00
ata’s and Sincor’s agricultural zones experienced little/no change.
In terms of loss of natural capital, Ameriven had the most, driven
by agriculture.
4.6. Core areas (remaining natural capital)
Core areas are the natural areas that remain after OEPA, nonOEPA and agricultural lands are discounted from the concession,
see Figs. 17–19. This can also be considered the remaining natural capital. These areas consist of riparian forests and dry savannah
vegetation. Patch number, size and connecting corridors are important since they determine whether wildlife species are isolated
(Colson et al., 2009). Here only the pattern and size are examined.
Sincor has the largest core areas, representing almost 98% of
the concession, since OEPA and agriculture occupy a small part.
The oil road network cuts the concession in half, possibly curtailing
animal movement. Petrozuata has a larger infrastructure footprint
(oil and non-oil) on both sides of the main river crisscrossing the
concession. It also has agricultural development, but its core areas
still comprise about 89% of the area. Animal movement appears
more curtailed and population density is likely higher given the
agricultural activities.
Ameriven, on the other hand, only has 42% of its concession
remaining as natural areas. This is because the remaining land has
been converted to agriculture. As mentioned previously, the cyclical flooding of the riparian forests and neighboring flood zones
make these forests less likely to be cut down (Portillo-Quintero and
Sánchez-Azofeifa, 2010)
4.7. Number of rivers crossed
Figs. 20–22 show the distribution of rivers crossing the concessions. Sincor and Petrozuata have more rivers than Ameriven does.
Therefore the river crossings created by OEPA features are greater:
20 for Sincor and 21 for Petrozuata (see Table 7). Ameriven has
none. Interestingly non-OEPA features did not cross the rivers in all
three concessions.
While geological oil reserves guide where to drill, the advent
of lateral drilling allows oil to be extracted without having to drill
overhead. Both Sincor and Petrozuata have a noticeable petroscape
(see Figs. 5, 6, 17 and 18). Ameriven, on the other hand, has a
very small petroscape. This may be explained by its later start (it
is the newest concession), as well as that the area selected had
been partly converted to agricultural fields. Oil companies in the
region have to pay Caribbean pine plantations for lost hectares and
this may serve as an incentive to reduce land conversion. At the
same time, Ameriven may have implemented best practices which
include sharing existing roads rather than creating new ones (Lee
and Boutin, 2006).
Furthermore, by buffering the river crossing locations within
300 m it is possible to determine the NDVI changes occurring here.
Finally, to better understand gains in vegetation in the concessions given that OEPA and agriculture are the main economic
activities leading to LUCC, a 300 m buffer was placed along the
river courses. For both Sincor and Petrozuata about 92% of the
NDVI gains are located within 300 m of water courses. This indicates that the gains took place along the riparian forests and are
most likely linked to the abnormally high precipitation patterns
experienced in 2000. For Ameriven the figure is only 6% and Fig. 25
shows how the rivers and riparian forests are much smaller in this
concession.
5. Conclusion
This paper has applied a modified landscape infrastructure
footprint, or LIF, to measure surface alterations resulting from
oil exploration and production activities, or OEPA, in Venezuela’s
heavy oil belt between 1990 and 2000. Since agriculture is a central economic activity in these llanos (dry tropical forest), these
non-OPEA features were also measured. The aim is to include this
methodology into environmental performance standards for the oil
and gas and other extractive industries.
Landsat satellite images were analyzed using land change
science, landscape ecology and GIS methods and techniques.
Regarding OEPA the following petroleum-specific measures are
ranked in regard to environmental performance: OEPA length in
km; OEPA direct effects (based on 50 m-wide grid cells); OEPAdriven (NDVI) vegetation losses and gains; size of core areas and
the number of rivers crossed. A low number indicates better performance.
The OEPA length in km provides an indication of the expansiveness of petroleum-related infrastructure in the landscape and
the pattern of habitat fragmentation. Ranks: Ameriven 1, Sincor 2
and Petrozuata 3. OEPA direct effects refers to the amount of land
occupied by petroleum infrastructure features. Based on work by
the Wilbert et al. (2008), the BLM (Bureau of Land Management,
2010) and Ellis (2010), a 50 m-wide road measure was selected.
Here Ameriven ranked 1, Sincor 2 and Petrozuata 3.
For OEPA-driven vegetation losses and gains, the areas of direct
effect were analyzed in response to the underlying NDVI change
values (see Figs. 9–11). For vegetation losses Petrozuata ranked 1,
Ameriven 2 and Sincor 3. For gains, Ameriven had no values, so
Petrozuata ranked 1 and Sincor 2. While the core areas, or areas of
natural vegetation that remain, are not solely a function of OEPA,
they do indicate how much of the landscape has been converted and
what areas might be conserved. Sincor had the largest core areas
and ranked 1, followed by Petrozuata and Ameriven. For number
of rivers crossed, Ameriven ranked 1, followed by Petrozuata and
Sincor. When tallied, Ameriven has the best score, 8. Sincor comes
next at 12, closely followed by Petrozuata at 13.
Finally, if we take production figures, measured in thousands of
barrels of oil per day in each concession and account for the direct
area of OEPA disturbance, Ameriven ranks 1st, followed by Sincor
and finally Petrozuata (see Table 8).
These findings suggest that the management at Ameriven
(between 1990 and 2000) may have implemented environmental performance standards that were more rigorous than the
C.W. Baynard / Ecological Indicators 11 (2011) 789–810
807
Fig. 25. Ameriven: only 6.0% of NDVI gains were located within 300 m of a river. The rivers in this concession were much smaller and thus were the riparian forests. See
Figs. 2 and 3.
Table 8
Oil production and the amount of hectares disturbed by oil production.
Oil production
(barrels/day)
OEPA disturbance ha/daily
oil barrel production
Sincor
Petrozuata
Ameriven
180,000
104,000
190,000
0.004837444
0.015060769
0.000498789
other two concessions. Other factors may include site selection,
competing economic activities, indemnification for agricultural
lands, existing infrastructure network, and advanced extraction
methods among others. Further research is needed to better link the relationship between the observed LIF and land
managers.
In conclusion, if OEPA continues to expand as planned by the
Venezuelan government, then the HOB stands to experience alterations to 18,000 km2 , or 33% of the HOB land area. The types of
companies selected may likely affect the pattern and extent of the
LIF. Therefore the methods presented in this paper should be used
and refined to plan future development and monitor the expansion
of operations in order to reduce surface alterations. Such a strategy
can bring important social, environmental and economic benefits
and provide extractive industries and regulators with transparent
and replicable environmental performance standards and sustainability reporting.
Acknowledgments
I thank three anonymous reviewers who provided insightful and
useful comments on this manuscript, as well as to Jim Ellis, from
Ellis Geospatial, who shares my interest in establishing standards
by which extractive industries and regulators can measure surface
disturbances. Additionally, I thank Michael W. Binford, Jane Southworth, Tim Fik, Julie Silva, Eric Keys, Grenville Barnes, Peter Waylen,
Terry McCoy and Matt Marsik at the University of Florida for help
in data acquisition, analysis, and discussion of ideas and methods.
David Lambert and Robert Richardson at the University of North
Florida offered help and suggestions in geospatial analysis. Janice
Thomson, at the Wilderness Society, provided feedback and examples of current research regarding geospatial analysis and energy
development. I also thank Carlos Guerra and Rómulo Medina, at the
former Operadora Cerro Negro in Venezuela for their help in better
understanding the dynamics of heavy oil production and for logistical support. Representatives from ExxonMobil Venezuela, PDVSA,
Chevron Venezuela, Total, Shell Venezuela, BP Venezuela, ConocoPhillips USA, and Marathon Oil met and/or spoke with me about
heavy oil operations in eastern Venezuela, provided gray literature,
reports and maps, and insights into corporate social responsibility
practices in the petroleum industry. ExxonMobil Venezuela also
provided logistical support during fieldwork. Thanks also to Alicia
Moreau, Deud Dumith and José Acosta at the Instituto Geográfico
de Venezuela Simón Bolívar for their support and map data. Thanks
also to Víctor and Rosella Pérez and Elisa Pérez for providing housing and logistical support.
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