4.7 Article

Monitoring the Spatio-Temporal Dynamics of Shale Oil/Gas Development with Landsat Time Series: Case Studies in the USA

Journal

REMOTE SENSING
Volume 14, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/rs14051236

Keywords

shale oil; gas; long-term sequence; NDVI trajectory matching

Funding

  1. National Key Research and Development Program of China [2017YFB0504204]
  2. One Hundred Talents Program of the Chinese Academy of Science [Y674141001]

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Shale oil/gas development is crucial for the energy market and environmental protection, but tracking its spatio-temporal dynamics is challenging. This study proposes a new NDVI Trajectroy Matching algorithm to accurately track shale oil/gas development and extract its location and time.
Shale oil/gas extraction has expanded rapidly in the last two decades due to the rising energy prices and the advancement of technologies. Its development can have huge impacts on and, at the same time, is also deeply affected by energy markets, especially in an era with high economic uncertainty. Understanding and monitoring shale oil/gas development over large regions are critical for both energy policies and environmental protection. However, there are currently no applicable methods to track the spatio-temporal dynamics of shale oil/gas development. To fill this gap, we propose a new NDVI Trajectroy Matching algorithm to track shale oil/gas development using the annual Landsat NDVI composite time series from 2000 to 2020. The results reveal that our algorithm can accurately extract the location and time of shale oil/gas exploitation in Eagle Ford and Three Forks, with an accuracy of 83.80% and 81.40%, respectively. In the Eagle Ford area, accuracy for all disturbance year detection was greater than 66.67%, with the best in 2011 and 2019 at 90.00%. The lowest accuracy in the Three Forks area was 63.33% in 2002, while the highest accuracy was 93.33% in 2019. In conclusion, the algorithm can effectively track shale oil/gas development with considerable accuracy and simplicity. We believe that the algorithm has enormous potential for other applications, such as built-up regions, forests, farmlands, and water body expansion and contraction involving vegetation damage.

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