46 Spotlight on Environmental Impact of Energy Development
Figure 6: The scheme of the Natural landscapes acceptability for location of oil and gas objects (points). Undesirable areas are marked in red color, average-suitable areas are marked in yellow, and suitable areas - in green colour. The location of oil objects is marked in purple.
Area of dense vegetation cover to absorb carbon oxide. An area of a dense vegetation cover in the Russian research plot was defi ned by NDVI index (ENVI Software 5.2) using some early summer Landsat scenes. Areas with a high degree of biomass were singled out with NDVI values ranging from 0.6 to 1 (Myachina and Chibilyev 2015) (fi gure 7). Average indicators on exhibited 1 to 2 % dense vegetation, showing a low ability of landscapes within the area to absorb carbon dioxide.
We applied the following scale to “Area of dense vegetation cover” to compare the ecological situation of different oil and gas landscapes:
- mean value of woodland area < 1% - 5 scores - mean value of woodland area 1-5% - 4 scores - mean value of woodland area 5 - 10 % - 3 scores - mean value of woodland area 10-20% - 2 scores - mean value of woodland area 20-30% - 1 scores - mean value of woodland area > 30% - 0 scores
Conclusions
Utilising geospatial data and techniques, accompanied by fi eld data collection and observations allowed us to measure the energy development footprint in the eastern Pawnee National Grasslands in Colorado. Though oil and gas and wind energy created numerous disturbances in the study area, they nevertheless created a smaller footprint that anticipated. After all, wind energy occupied 0.0023% of the study area, while oil encompassed 0.006%.
Figure 7: The Russian research plot. Dense vegetation is marked in green.
In the Russian example, the proposed solution to the analysis of energy landscapes allows:
- the planning of production activities at the detailed engineering stage to optimise the location of objects in steppe landscapes;
- the regular monitoring of landscapes for timely detection of modifi cation or transformation;
- the comparison of different oil and gas production territories.
This publication is based on work supported by a grant from the U.S. Civilian Research & Development Foundation (CRDF Global) with funding from the United States Department of State. The opinions, fi ndings and conclusions stated herein are those of the authors and do not necessarily refl ect those of CRDF Global or the United States Department of State.
References
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Ksenya Mjachina Chris W. Baynard
Author Details Chris W. Baynard, Ph.D. Associate Professor • Dept of Economics and Geography, Coggin College of Business, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224 Tel: 904-620-1243 • Email:
cbaynard@unf.edu
Ksenya Mjachina Ph.D, Senior scientifi c researcher • Institute of Steppe Ural Branch Russian Academy of Sciences; 11, Pionerskaya st. Orenburg, Russia Tel: +7 961 942 5880 • Email:
mavicsen@gmail.com
Robert D. Richardson (University of North Florida) Alexander A. Chibilyev (Institute of Steppe, Ural Branch Russian Academy of Sciences)
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All of our articles are online! To view and download them, visit:
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