气候变化领域集成服务门户(CCPortal): Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model

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DOI	10.1016/j.jag.2018.09.012
	Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model
	Huang S.; Chen S.; Wang D.; Zhou C.; van der Meer F.; Zhang Y.
发表日期	2019
ISSN	15698432
起始页码	180
结束页码	190
卷号	74
英文摘要	Hydrocarbon micro-seepage can result in vegetation spectral anomalies. Early detection of spectral anomalies in plants stressed by hydrocarbon micro-seepage could help reveal oil and gas resources. In this study, the origin of plant spectral anomalies affected by hydrocarbon micro-seepage was measured using indoor simulation experiments. We analyzed wheat samples grown in a simulated hydrocarbon micro-seepage environment in a laboratory setting. The leaf mesophyll structure (N) values of plants in oil and gas micro-seepage regions were measured according to the content of measured biochemical parameters and spectra simulated by PROSPECT, a model for extracting hydrocarbon micro-seepage information from hyper-spectral images based on plant stress spectra. Spectral reflectance was simulated with N, chlorophyll content (C ab ), water content (C w ) and dry matter content (C m ). Multivariate regression equations were established using varying gasoline volume as the dependent variable and spectral feature parameters exhibiting a high rate of change as the independent variables. We derived a regression equation with the highest correlation coefficient and applied it to airborne hyper-spectral data (CASI/SASI) in Qingyang Oilfield, where extracted information regarding hydrocarbon micro-seepage was matched with known oil-producing wells. © 2018 Elsevier B.V.
英文关键词	Airborne hyper-spectral imaging; Hydrocarbon micro-seepage; Plant stressed spectra; PROSPECT model
语种	英语
scopus关键词	airborne sensing; computer simulation; hydrocarbon reservoir; multivariate analysis; numerical model; regression analysis; seepage; China; Henan; Qinyang; Triticum aestivum
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156569
作者单位	Jilin University, Faculty of Geo-exploration Science and Technology, Changchun, 130026, China; China Geological Survey, Division of Petroleum Geology, Beijing, 100029, China; National Marine Environmental Monitoring Center, Dalian, 116023, China; University of Twente, Faculty of Geo-information Science and Earth Observation, Department of Earth Systems Analysis, Enschede, Netherlands; Chinese Academy of Sciences, National Astronomical Observatories, Laboratory of Lunar Science and Deep-exploration, Beijing, 100101, China
推荐引用方式 GB/T 7714	Huang S.,Chen S.,Wang D.,et al. Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model[J],2019,74.
APA	Huang S.,Chen S.,Wang D.,Zhou C.,van der Meer F.,&Zhang Y..(2019).Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model.International Journal of Applied Earth Observation and Geoinformation,74.
MLA	Huang S.,et al."Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model".International Journal of Applied Earth Observation and Geoinformation 74(2019).