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

被引:0
|
作者
Wang, Yifang [1 ]
Liu, Di [1 ]
Zhang, Fushan [1 ]
Zhang, Qingling [1 ]
机构
[1] Sun Yat Sen Univ, Sch Aeronut & Astronaut, Shenzhen 518107, Peoples R China
关键词
shale oil; gas; long-term sequence; NDVI trajectory matching; CLOUD SHADOW DETECTION; IMAGE-ANALYSIS; OIL PALM; SEGMENTATION; TRENDS; FOREST; FORCE; PLUS;
D O I
10.3390/rs14051236
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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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页数:22
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