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TitleSpectral-analysis-based extraction of land disturbances arising from oil and gas development in diverse landscapes
AuthorZhang, Y; Lantz, N; Guindon, B; Jiao, X
SourceJournal of Applied Remote Sensing vol. 11, no. 1, 15026, 2017., https://doi.org/10.1117/1.JRS.11.015026
Year2017
Alt SeriesNatural Resources Canada, Contribution Series 20181038
PublisherSPIE-Intl Soc Optical Eng
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
Released2017 03 24
AbstractAccurate and frequent monitoring of land surface changes arising from oil and gas exploration and extraction is a key requirement for the responsible and sustainable development of these resources. Petroleum deposits typically extend over large geographic regions but much of the infrastructure required for oil and gas recovery takes the form of numerous small-scale features (e.g., well sites, access roads, etc.) scattered over the landscape. Increasing exploitation of oil and gas deposits will increase the presence of these disturbances in heavily populated regions. An object-based approach is proposed to utilize RapidEye satellite imagery to delineate well sites and related access roads in diverse complex landscapes, where land surface changes also arise from other human activities, such as forest logging and agriculture. A simplified object-based change vector approach, adaptable to operational use, is introduced to identify the disturbances on land based on red-green spectral response and spatial attributes of candidate object size and proximity to roads. Testing of the techniques has been undertaken with RapidEye multitemporal imagery in two test sites located at Alberta, Canada: one was a predominant natural forest landscape and the other landscape dominated by intensive agricultural activities. Accuracies of 84% and 73%, respectively, have been achieved for the identification of well site and access road infrastructure of the two sites based on fully automated processing. Limited manual relabeling of selected image segments can improve these accuracies to 95%. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
GEOSCAN ID311392