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Temporal Analysis of Land Deformatio...

Smilovsky, Danielle,

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Temporal Analysis of Land Deformation in the West Texas Hydrocarbon Production Fields and the Arizona Willcox Groundwater Basin Using Remote Sensing InSAR /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Temporal Analysis of Land Deformation in the West Texas Hydrocarbon Production Fields and the Arizona Willcox Groundwater Basin Using Remote Sensing InSAR // Danielle Smilovsky.
Author:	Smilovsky, Danielle,
Description:	1 electronic resource (133 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31060109
ISBN:	9798381386288

Temporal Analysis of Land Deformation in the West Texas Hydrocarbon Production Fields and the Arizona Willcox Groundwater Basin Using Remote Sensing InSAR /
Smilovsky, Danielle,

Temporal Analysis of Land Deformation in the West Texas Hydrocarbon Production Fields and the Arizona Willcox Groundwater Basin Using Remote Sensing InSAR /Danielle Smilovsky. - 1 electronic resource (133 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

This study used a remote sensing SBAS InSAR approach to detect and quantify land changes over time in two case study areas located in the West Texas hydrocarbon production fields and the Willcox Groundwater Basin in Arizona. Anthropogenic hydrocarbon production activities have caused land deformation in much of West Texas, including the Permian Basin, the country's largest producer of oil and gas. These extractive activities have resulted in increased seismic activity in the area and provide an opportunity to use InSAR to detect land deformation. The Willcox Groundwater Basin is a location in southeastern Arizona that has experienced land subsidence due to decades of local groundwater withdrawal. This area has seen the most significant land subsidence in the state, which has led to earth fissuring in addition to regional water resource issues. These case study areas are ideal landscapes for remote sensing InSAR studies given the aforementioned conditions. This research aims to understand how temporal scales of InSAR data affect the detection and monitoring of land deformation and to suggest an optimal scale of interferometric data to track and measure land deformation for both case study areas. Different SBAS InSAR processing approaches can result in varying coherence estimates, LOS displacement values, and land deformation characterization, ultimately leading to uncertainty. For these case studies, I evaluated and compared three different intervals of SBAS InSAR processing. These intervals consisted of 12-day, 24-day, and 36-day image-pair combinations spanning a five-year period between January 2017 and December 2021. I calculated and compared coherence estimates of image pairs and LOS displacement values for each processing interval using a statistical test known as single factor analysis of variance and Tukey's HSD. I then created and mapped comparative time series SBAS InSAR stack results for each interval. To help determine the best processing scale for this case study area, I used GIS-based methods, including calculating and comparing optimized hot spot analyses using Getis-Ord Gi* spatial statistics, extracting cross-sectional profiles across featured areas, and cross-checking deformation results with local survey elevations from the same time period. These results were used to help select an SBAS InSAR processing scale fit for each case study area.The analysis of variance and Tukey's HSD results showed significant differences between the sets of coherence means and variance estimates, and LOS displacement values. The 12-day interval had the closest mean to one (100%), and the 36-day interval had the farthest mean from one. However, I found that the 36-day LOS displacement values, Getis Ord GI* results, and cross-sectional profiles followed the survey readings' trends in active deformation areas during the same study period. Based on these findings, I selected the SBAS InSAR 36-day processing scale for both of these case study areas.

English

ISBN: 9798381386288Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

InSAR

Temporal Analysis of Land Deformation in the West Texas Hydrocarbon Production Fields and the Arizona Willcox Groundwater Basin Using Remote Sensing InSAR /
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245 1 0 $a Temporal Analysis of Land Deformation in the West Texas Hydrocarbon Production Fields and the Arizona Willcox Groundwater Basin Using Remote Sensing InSAR / $c Danielle Smilovsky.
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300 $a 1 electronic resource (133 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
500 $a Advisors: Jensen, Jennifer Committee members: Li, Yanan; Hagelman, Ronald R., III; Meyer, Franz J.
502 $b Ph.D. $c Texas State University - San Marcos $d 2023.
520 $a This study used a remote sensing SBAS InSAR approach to detect and quantify land changes over time in two case study areas located in the West Texas hydrocarbon production fields and the Willcox Groundwater Basin in Arizona. Anthropogenic hydrocarbon production activities have caused land deformation in much of West Texas, including the Permian Basin, the country's largest producer of oil and gas. These extractive activities have resulted in increased seismic activity in the area and provide an opportunity to use InSAR to detect land deformation. The Willcox Groundwater Basin is a location in southeastern Arizona that has experienced land subsidence due to decades of local groundwater withdrawal. This area has seen the most significant land subsidence in the state, which has led to earth fissuring in addition to regional water resource issues. These case study areas are ideal landscapes for remote sensing InSAR studies given the aforementioned conditions. This research aims to understand how temporal scales of InSAR data affect the detection and monitoring of land deformation and to suggest an optimal scale of interferometric data to track and measure land deformation for both case study areas. Different SBAS InSAR processing approaches can result in varying coherence estimates, LOS displacement values, and land deformation characterization, ultimately leading to uncertainty. For these case studies, I evaluated and compared three different intervals of SBAS InSAR processing. These intervals consisted of 12-day, 24-day, and 36-day image-pair combinations spanning a five-year period between January 2017 and December 2021. I calculated and compared coherence estimates of image pairs and LOS displacement values for each processing interval using a statistical test known as single factor analysis of variance and Tukey's HSD. I then created and mapped comparative time series SBAS InSAR stack results for each interval. To help determine the best processing scale for this case study area, I used GIS-based methods, including calculating and comparing optimized hot spot analyses using Getis-Ord Gi* spatial statistics, extracting cross-sectional profiles across featured areas, and cross-checking deformation results with local survey elevations from the same time period. These results were used to help select an SBAS InSAR processing scale fit for each case study area.The analysis of variance and Tukey's HSD results showed significant differences between the sets of coherence means and variance estimates, and LOS displacement values. The 12-day interval had the closest mean to one (100%), and the 36-day interval had the farthest mean from one. However, I found that the 36-day LOS displacement values, Getis Ord GI* results, and cross-sectional profiles followed the survey readings' trends in active deformation areas during the same study period. Based on these findings, I selected the SBAS InSAR 36-day processing scale for both of these case study areas.
546 $a English
590 $a School code: 1035
650 4 $a Remote sensing. $3 535394
650 4 $a Environmental science. $3 677245
650 4 $a Physical geography. $3 516662
650 4 $a Geographic information science. $3 3432445
653 $a InSAR
653 $a GIS
653 $a SBAS
653 $a Land deformation
653 $a Subsidence
653 $a Hydrocarbon production
653 $a Groundwater extraction
690 $a 0799
690 $a 0368
690 $a 0768
690 $a 0370
710 2 $a Texas State University - San Marcos. $b Geography and Environmental Studies. $e degree granting institution. $3 3829717
720 1 $a Jensen, Jennifer $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-07B.
790 $a 1035
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31060109