Quantifying, comparing, and contrasting forest change pattern from shale gas infrastructure development in the British Columbia’s shale gas plays

ABSTRACT

Understanding the interconnection between land use processes and land change patterns is yet to be fully achieved. Land change researchers have posited that the lack of understanding is not due to lack of effort but rather the difficulty in the task of seeking understanding, theoretically and practically. In this study, to understand forest change patterns from shale oil and gas (SOG) land use, we combine geospatial approach with metrics from landscape ecology to compare and contrast forest change from SOG infrastructure development in the four shale gas plays in British Columbia (BC). The study finds that cumulatively, between 1975 and 2017, the Cordova, Horn, Liard, and Montney shale gas plays have lost 0.30%, 0.25%, 0.14%, and 0.36% of forest cover, respectively, due to the construction of SOG well pads, access roads, and pipelines. Also, we find that the shale gas plays with the largest quantity of forest cover loss from SOG infrastructure construction have the highest amount of forest fragmentation and the vice versa. The results, however, suggest that differences in the intensity of forest cover change from shale gas land use is likely to create different ecologically significant forest fragmentation patterns. From a broader perspective, this study demonstrates how different levels of human-environment interactions yield different levels of anthropogenic land use impacts on the environment. The study provides land managers with a context for understanding the land use intensities and forest change pattern, which is relevant for sustainable land management in the shale gas plays in British Columbia.

KEYWORDS:

• Shale gas land uses
• land use processes
• land change pattern
• remote sensing of forest
• forest fragmentation
• landscape metrics
• Northeastern British Columbia

Acknowledgments

We express sincere gratitude to the University of Northern British Columbia for providing travel grants for a reconnaissance field survey we undertook in the study area. However, any ideas, opinions, findings, conclusions, and recommendations expressed in this paper are that of the authors and do not necessarily reflect the views of the University of Northern British Columbia. We are very grateful to Mr. Jerry Dogbey-Gakpetor for devoting time to assist in the statistical analysis performed in this paper. We also express our sincere appreciation to Mr. Daniel Kpienbaareh for his suggestions regarding the accuracy assessment of the classified Landsat image.

Disclosure statement

No potential conflict of interest was reported by the authors.