基于Sentinel-2/MSI数据的煤层气烃微渗漏植被异常区提取
Extraction of vegetation anomaly caused by coalbed methane hydrocarbon microseepage based on Sentinel-2/MSI
- 2023年27卷第7期 页码：1713-1730
纸质出版日期： 2023-07-07
DOI： 10.11834/jrs.20232208
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摘要
地下油气（含煤层气）的烃微渗漏会引起地表土壤、植被的光谱变化。利用遥感技术探测烃微渗漏是覆盖范围大而成本低廉的煤层气前期探测新方法。然而，目前此类方法的研究主要针对裸土矿物，而对大面积植被覆盖区却研究甚少，其中重要原因便是烃微渗漏对植被根系毒害的生物物理过程复杂，可用于提取植被异常区的光谱特征含糊不清。而根据少量采样光谱选取的光谱特征也有偶然性，导致提取结果的精度较低。因此，本文首先探讨烃微渗漏对植被根系毒害的机理并基于PROSAIL模型优选光谱特征，然后利用实验区大量煤层气采集井统计最受烃微渗漏影响的异常植被光谱，并与对照区植被光谱对比，最后利用Sentinel-2/MSI数据以及合适的特征组合阈值提取烃微渗漏区并验证。在山林区能达到采集井60 m缓冲区内植被样本80%召回率与对照区植被样本5%误分率左右的平衡，表明了本文方法的有效性。本文分析与优选烃微渗漏影响的植被光谱特征并利用多光谱数据构建光谱指数提取烃微渗漏植被异常的方法，可为遥感提取烃微渗漏植被异常研究提供参考。
Abstract
Hydrocarbon microleakage of oil and gas resources (including coalbed methane) may induce spectral changes in surface soil and vegetation. Detecting surface hydrocarbon microleakage using remote sensing technology
a new method for early exploration of coalbed methane
has a wide range of applications and low cost. At present
the studies of this kind of method mainly focus on bare soil minerals and seldom on widespread vegetated areas. The important reason is that the biophysical process of hydrocarbon microleakage toxicity to vegetation roots is complex
and the spectral characteristics that can be used to extract vegetation anomalies are vague. Moreover
the spectral features selected according to a small number of sampled spectra are accidental
leading to the low accuracy of the extraction results. Therefore
this work first discussed the mechanism of hydrocarbon microleakage poisoning to vegetation roots. Afterward
the vegetation spectral features that effectively reflect the effect of hydrocarbon microleakage were selected based on the PROSAIL model. Moreover
a red-edge position index based on Sentinel-2/MSI band configuration was proposed. Then
we marked the mine sites across our study area
the Qinshui basin
on Google Earth for long-term vegetation spectral characteristics statistics. We compared these mine sites with those of the control area to determine how these spectral features were affected by hydrocarbon microleakage. Finally
the marked samples were divided into training and test sets and then verified. These sets were used to find the optimal spectral feature threshold combination via the threshold space method. The statistical results show that
compared with the control area
the experimental area exhibited an obvious blue shift revealed by the red-edge position index of the mine samples. Moreover
the near-infrared reflectance decreased
and the red valley reflectance increased. These findings were consistent with the mechanism of hydrocarbon microleakage poisoning vegetation and the results of the spectral simulation. In the background mountain forest area
the 80% recall rate of vegetation samples in the mine buffer zone could be balanced with the 5% misclassification rate of vegetation samples
showing the rationality of this method. In this study
we analyzed and optimized the spectral characteristics of hydrocarbon microleakage affecting vegetation. We also used multispectral data to construct a spectral index and extract the hydrocarbon microleakage vegetation anomaly according to the spectral statistics of the mine buffer. This method combines theoretical simulation with large sample statistics
providing a reference for the research of extracting hydrocarbon microleakage vegetation anomaly by remote sensing.
关键词
煤层气烃微渗漏植被遥感PROSAIL模型沁水盆地
Keywords
coalbed methanehydrocarbon microleakagevegetationremote sensingPROSAIL modelQinshui basin
references
Adamu B, Tansey K and Bradshaw M J. 2013. Investigating vegetation spectral reflectance for detecting hydrocarbon pipeline leaks from multispectral data//Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX. Dresden: SPIE [DOI: 10.1117/12.2028907http://dx.doi.org/10.1117/12.2028907]
Al-Abbas A H, Barr R, Hall J D, Crane F L and Baumgardner M F. 1974. Spectra of normal and nutrient-deficient maize leaves. Agronomy Journal, 66(1): 16-20 [DOI: 10.2134/agronj1974.00021962006600010005xhttp://dx.doi.org/10.2134/agronj1974.00021962006600010005x]
Ben-Dor E and Kruse F A. 1995. Surface mineral mapping of Makhtesh Ramon Negev, Israel using GER 63 channel scanner data. International Journal of Remote Sensing, 16(18): 3529-3553 [DOI: 10.1080/01431169508954644http://dx.doi.org/10.1080/01431169508954644]
Chen L. 2015. Study on Hyperspectral Remote Sensing and Electromagnetic Exploration Technology for Coalbed Methane Enrichment Region. Beijing: Peking University: 19-86
陈理. 2015. 煤层气富集区高光谱遥感和电磁探测技术与方法研究. 北京: 北京大学: 19-86
Clayton J L. 1998. Geochemistry of coalbed gas – A review. International Journal of Coal Geology, 35(1/4): 159-173 [DOI: 10.1016/S0166-5162(97)00017-7http://dx.doi.org/10.1016/S0166-5162(97)00017-7]
Collins R J, McCown F P, Stonis L P, Petzel G J and Everett J R. 1974. An evaluation of the suitability of ERTS data for the purposes of petroleum exploration
Crosta A P and Moore J. 1989. Enhancement of Landsat Thematic Mapper imagery for residual soil mapping in SW Minais Gerais State, Brazil: a prospecting case history in Greenstone belt terrain//Proceedings of the 7th ERIM Thematic Conference: Remote Sensing for Exploration Geology. Ann Arbor: Environmental Research Institute of Michigan: 1173-1187
Cui X, Zhao Y J, Tian F and Yang Y J. 2019. Hyperspectral hydrocarbon exploration and geological verification of the oil and gas micro-seepage in the northeastern margin of the Junggar Basin, China. Acta Geologica Sinica, 93(4): 928-944
崔鑫, 赵英俊, 田丰, 杨燕杰. 2019. 准噶尔盆地东北缘航空高光谱油气微渗漏探测及地质验证. 地质学报, 93(4): 928-944 [DOI: 10.3969/j.issn.0001-5717.2019.04.012http://dx.doi.org/10.3969/j.issn.0001-5717.2019.04.012]
戴金星, 戚厚发, 宋岩, 关德师. 1986. 我国煤层气组份、碳同位素类型及其成因和意义. 中国科学(B辑 化学 生物学 农学 医学 地学), (12): 1317-1326
Drew M C. 1983. Plant injury and adaptation to oxygen deficiency in the root environment: a review. Plant and Soil, 75(2): 179-199 [DOI: 10.1007/BF02375564http://dx.doi.org/10.1007/BF02375564]
Everett J R, Jengo C and Staskowski R J. 2002. Remote sensing and GIS enable future exploration success. World Oil, 223(11): 59-65
Gan G Y, Yu R J and Cui J. 2005. Micro percolation of hydrocarbons and formation of sumptomatic minerals in oil-gas pools. Xinjiang Petroleum Geology, 26(6): 707-710
甘贵元, 余瑞娟, 崔俊. 2005. 油气藏中烃类微渗漏与特态矿物形成. 新疆石油地质, 26(6): 707-710 [DOI: 10.3969/j.issn.1001-3873.2005.06.032http://dx.doi.org/10.3969/j.issn.1001-3873.2005.06.032]
Garain S, Mitra D and Das P. 2021. Detection of hydrocarbon microseepage prospects using Landsat 8-based vegetation stress analysis in part of Assam-Arakan Fold Belt, NE India. Arabian Journal of Geosciences, 14(19): 1984 [DOI: 10.1007/s12517-021-08376-6http://dx.doi.org/10.1007/s12517-021-08376-6]
Hu P, Tian Q J and Yan B K. 2009. The application of hyperspectral remote sensing to the identification of hydrocarbon alteration minerals in Qaidam basin. Remote Sensing for Land and Resources, (2): 54-61
胡畔, 田庆久, 闫柏琨. 2009. 柴达木盆地烃蚀变矿物高光谱遥感识别研究. 国土资源遥感, (2): 54-61
Huang B R, Johnson J W and Nesmith D S. 1997. Responses to root-zone CO2 enrichment and hypoxia of wheat genotypes differing in waterlogging tolerance. Crop Science, 37(2): 464-468 [DOI: 10.2135/cropsci1997.0011183X003700020026xhttp://dx.doi.org/10.2135/cropsci1997.0011183X003700020026x]
Huang S. 2015. Study on Spectral Model of Plants Stressed by Hydrocarbon Microseepage. Changchun: Jilin University: 16-38
黄爽. 2015. 烃类微渗漏胁迫植被光谱模型研究. 长春: 吉林大学: 16-38
Huang S, Chen S B, Wang D M, Zhou C, Van Der Meer F and Zhang Y Z. 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: 180-190 [DOI: 10.1016/j.jag.2018.09.012http://dx.doi.org/10.1016/j.jag.2018.09.012]
Hunt G R and Ashley R P. 1979. Spectra of altered rocks in the visible and near infrared. Economic Geology, 74(7): 1613-1629 [DOI: 10.2113/gsecongeo.74.7.1613http://dx.doi.org/10.2113/gsecongeo.74.7.1613]
Jacquemoud S and Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91 [DOI: 10.1016/0034-4257(90)90100-Zhttp://dx.doi.org/10.1016/0034-4257(90)90100-Z]
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, François C and Ustin S L. 2009. PROSPECT+SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment, 113(S1): S56-S66 [DOI: 10.1016/j.rse.2008.01.026http://dx.doi.org/10.1016/j.rse.2008.01.026]
Jamaludin M I, Matori A N and Myint K C. 2015. Application of NIR to determine effects of hydrocarbon microseepage in oil palm vegetation stress//2015 International Conference on Space Science and Communication (IconSpace). Langkawi: IEEE: 215-220 [DOI: 10.1109/IconSpace.2015.7283791http://dx.doi.org/10.1109/IconSpace.2015.7283791]
Jiang T, Zhao K B, Rong F Z and Zhang H Q. 2011. Vertical micro-migration of hydrocarbons from subsurface reservoirs and geological significance in near-surface geochemical exploration for oil and gas. Natural Gas Geoscience, 22(5): 901-908
蒋涛, 赵克斌, 荣发准, 张恒启. 2011. 油气藏烃类垂向微渗漏及近地表化探异常的油气地质意义. 天然气地球科学, 22(5): 901-908 [DOI: 10.11764/j.issn.1672-1926.2011.05.901http://dx.doi.org/10.11764/j.issn.1672-1926.2011.05.901]
Jia A L, He D B, Wei Y S and Li Y L. 2021. Predictions on natural gas development trend in China for the next fifteen years. Natural Gas Geoscience, 32(1): 17-27
贾爱林, 何东博, 位云生, 李易隆. 2021. 未来十五年中国天然气发展趋势预测. 天然气地球科学, 32(1): 17-27 [DOI: 10.11764/j.issn.1672-1926.2020.11.019http://dx.doi.org/10.11764/j.issn.1672-1926.2020.11.019]
Khan S D and Jacobson S. 2008. Remote sensing and geochemistry for detecting hydrocarbon microseepages. Geological Society of America Bulletin, 120(1/2): 96-105 [DOI: 10.1130/0016-7606(2008)120http://dx.doi.org/10.1130/0016-7606(2008)120[96:RSAGFD]2.0.CO;2]
Kuusk A. 1991. The hot spot effect in plant canopy reflectance//Myneni R B, Ross J, eds. Photon-Vegetation Interactions. Berlin: Springer: 139-159 [DOI: 10.1007/978-3-642-75389-3_5http://dx.doi.org/10.1007/978-3-642-75389-3_5]
Lammoglia T and De Souza Filho C R. 2013. Unraveling hydrocarbon microseepages in onshore basins using spectral-spatial processing of advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Surveys in Geophysics, 34(3): 349-373 [DOI: 10.1007/s10712-013-9225-3http://dx.doi.org/10.1007/s10712-013-9225-3]
Li Q Q, Chen X M, Lin X, Mao B J and Ni G Q. 2012. Study on oil and gas exploration in sparse vegetation areas by hyperspectral remote sensing data. Chinese Optics Letters, 10(S1): S11004 [OI: 10.3788/COL201210.s11004http://dx.doi.org/10.3788/COL201210.s11004]
Lü Y M, Liu Y H, Chen G H, Wang C W, Zhu X S, Guo G S and Liu J. 2020. Analysis of factors affecting productivity of CBM in horizontal wells in southern Qinshui Basin. Coal Science and Technology, 48(10): 225-232
吕玉民, 柳迎红, 陈桂华, 王存武, 朱学申, 郭广山, 刘佳. 2020. 沁水盆地南部煤层气水平井产能影响因素分析. 煤炭科学技术, 48(10): 225-232
Molan Y E, Refahi D and Tarashti A H. 2014. Mineral mapping in the Maherabad area, eastern Iran, using the HyMap remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 27: 117-127 [DOI: 10.1016/j.jag.2013.09.014http://dx.doi.org/10.1016/j.jag.2013.09.014]
Noomen M F. 2007. Hyperspectral Reflectance of Vegetation Affected by Underground Hydrocarbon Gas Seepage. Wageningen: Wageningen University
Noomen M F, van der Werff H M A and Van Der Meer F D. 2012. Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage. Ecological Informatics, 8: 55-64 [DOI: 10.1016/j.ecoinf.2012.01.001http://dx.doi.org/10.1016/j.ecoinf.2012.01.001]
Pirone P P. 1960. The response of shade trees to natural gas. Garden Journal of the New York Botanical Garden, 10: 25-29
Prasad M. 2004. Heavy Metal Stress in Plants: From Biomolecules to Ecosystems. Berlin: Springer [DOI: 10.1007/978-3-662-07743-6http://dx.doi.org/10.1007/978-3-662-07743-6]
Pyšek P and Pyšek A. 1989. Veränderungen der Vegetation durch experimentelle Erdgasbehandlung. Weed Research, 29(3): 193-204 [DOI: 10.1111/j.1365-3180.1989.tb00859.xhttp://dx.doi.org/10.1111/j.1365-3180.1989.tb00859.x]
Saunders D F, Burson K R and Thompson C K. 1999. Model for hydrocarbon microseepage and related near-surface alterations. AAPG Bulletin, 83(1): 170-185 [DOI: 10.1306/00AA9A34-1730-11D7-8645000102C1865Dhttp://dx.doi.org/10.1306/00AA9A34-1730-11D7-8645000102C1865D]
Shabala S. 2017. Plant stress physiology. Cabi.
Shen J L, Ding S B, Qi X P and Xing X W. 2010. The progress of remote sensing technology in the detection of hydrocarbon micro-seepage. Remote Sensing for Land and Resources, 5(3): 7-11
申晋利, 丁树柏, 齐小平, 邢学文. 2010. 烃类微渗漏现象遥感检测研究进展. 国土资源遥感, (3): 7-11
Smith K L, Steven M D and Colls J J. 2004a. Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Environment, 92(2): 207-217 [DOI: 10.1016/j.rse.2004.06.002http://dx.doi.org/10.1016/j.rse.2004.06.002]
Smith K L, Steven M D and Colls J J. 2004b. Spectral responses of pot-grown plants to displacement of soil oxygen. International Journal of Remote Sensing, 25(20): 4395-4410 [DOI: 10.1080/01431160410001729172http://dx.doi.org/10.1080/01431160410001729172]
Su W, Wu J Y, Wang X S, Xie Z X, Zhang Y, Tao W C and Jin T. 2021. Retrieving corn canopy leaf area index based on Sentinel-2 image and PROSAIL model parameter calibration. Spectroscopy and Spectral Analysis, 41(6): 1891-1897
苏伟, 邬佳昱, 王新盛, 谢茈萱, 张颖, 陶万成, 金添. 2021. 基于Sentinel-2影像与PROSAIL模型参数标定的玉米冠层LAI反演. 光谱学与光谱分析, 41(6): 1891-1897 [DOI: 10.3964/j.issn.1000-0593(2021)06-1891-07http://dx.doi.org/10.3964/j.issn.1000-0593(2021)06-1891-07]
Van Der Meer F, Van Dijk P, Van Der Werff H and Yang H. 2002. Remote sensing and petroleum seepage: a review and case study. Terra Nova, 14(1): 1-17 [DOI: 10.1046/j.1365-3121.2002.00390.xhttp://dx.doi.org/10.1046/j.1365-3121.2002.00390.x]
Von Wettstein D, Gough S and Kannangara C G. 1995. Chlorophyll biosynthesis. The Plant Cell, 7(7): 1039-1057 [DOI: 10.1105/tpc.7.7.1039http://dx.doi.org/10.1105/tpc.7.7.1039]
Wang B. 2013. Coalbed Methane Enrichment and High-production rule and Prospective Area Prediction in Qinshui Basin. Xuzhou: China University of Mining and Technology: 19-22
王勃. 沁水盆地煤层气富集高产规律及有利区块预测评价. 徐州: 中国矿业大学: 19-22
Wang L J and Niu Z. 2014. Sensitivity analysis of vegetation parameters based on PROSAIL Model. Remote Sensing Technology and Application, 29(2): 219-223
王李娟, 牛铮. 2014. PROSAIL模型的参数敏感性研究. 遥感技术与应用, 29(2): 219-223 [DOI: 10.11873j.issn.1004-0323.2014.2.0219http://dx.doi.org/10.11873j.issn.1004-0323.2014.2.0219]
Wang P R, Zhang F T, Gao J X, Sun X Q and Deng X J. 2009. An overview of chlorophyll biosynthesis in higher plants. Acta Botanica Boreali-Occidentalia Sinica, 29(3): 629-636
王平荣, 张帆涛, 高家旭, 孙小秋, 邓晓建. 2009. 高等植物叶绿素生物合成的研究进展. 西北植物学报, 29(3): 629-636
Wei W. 2010. Qinshui Basin Coalbed Methane Well Production Prediction. Qingdao: China University of Petroleum (East China): 2-6
魏韦. 2010. 沁水盆地煤层气井产能预测研究. 青岛: 中国石油大学(华东): 2-6
Xie Q Y and Ding S B. 1994. Discussion of the some problems of the remote sensing technology for oil-gas resource. Remote Sensing for Land and Resources, (2): 55-62
谢青云, 丁树柏. 1994. 油气资源遥感技术若干问题的探讨. 国土资源遥感, (2): 55-62
Ye J P and Lu X X. 2016. Development status and technical progress of China coalbed methane industry. Coal Science and Technology, 44(1): 24-28, 46
叶建平, 陆小霞. 2016. 我国煤层气产业发展现状和技术进展. 煤炭科学技术, 44(1): 24-28, 46 [DOI: 10.13199/j.cnki.cst.2016.01.005http://dx.doi.org/10.13199/j.cnki.cst.2016.01.005]
You J F. 2015. Research on Spectral Response Mechanisms of Oil Sands Components and Ore Prospecting by Remote Sensing Technology. Changchun: Jilin University: 19-78
尤金凤. 2015. 油砂组分光谱响应机理及遥感找矿研究. 长春: 吉林大学: 19-78
You J F, Xing L X, Pan J, Shan X L, Fan R X and Cao H. 2016. Application of Hyperion hyperspectral image for studying on the distribution of oil sands. Journal of Jilin University (Earth Science Edition), 46(5): 1589-1597
尤金凤, 邢立新, 潘军, 单玄龙, 樊瑞雪, 曹会. 2016. 基于Hyperion数据的油砂分布遥感研究. 吉林大学学报(地球科学版), 46(5): 1589-1597 [DOI: 10.13278/j.cnki.jjuese.201605307http://dx.doi.org/10.13278/j.cnki.jjuese.201605307]
Zhang C Y, Qin Q M, Chen L, Wang N, Zhao S S and Hui J. 2015. Rapid determination of coalbed methane exploration target region utilizing hyperspectral remote sensing. International Journal of Coal Geology, 150-151: 19-34 [DOI: 10.1016/j.coal.2015.07.010http://dx.doi.org/10.1016/j.coal.2015.07.010]
Zhang H L. 2014. Research on the New Energy Development in China. Changchun: Jilin University: 1-16
张海龙. 2014. 中国新能源发展研究. 长春: 吉林大学: 1-16
Zhang H Z, Wang L P and Liu J. 2013. Present conditions and development trend of CBM technology. Petroleum Science and Technology Forum, 32(5): 17-21, 27
张华珍, 王利鹏, 刘嘉. 2013. 煤层气开发技术现状及发展趋势. 石油科技论坛, 32(5): 17-21, 27 [DOI: 10.3969/j.issn.1002-302x.2013.05.004http://dx.doi.org/10.3969/j.issn.1002-302x.2013.05.004]
Zhang J C, Yuan L, Wang J H, Luo J H, Du S Z and Huang W J. 2012. Research progress of crop diseases and pests monitoring based on remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 28(20): 1-11
张竞成, 袁琳, 王纪华, 罗菊花, 杜世州, 黄文江. 2012. 作物病虫害遥感监测研究进展. 农业工程学报, 28(20): 1-11
Zhang Z. 2016. Coalbed Methane System and Drainage Optimization of Taiyuan Formation in Southern Qinshui Basin. Xuzhou: China University of Mining and Technology
张政. 2016. 沁水盆地南部太原组含煤层气系统及其排采优化. 徐州: 中国矿业大学
Zhao L Z, Wu L X and Guan S H. 2020. Influencing factors and countermeasures for the implementation of coalbed methane development and utilization planning. Coal Economic Research, 40(12): 65-69
赵路正, 吴立新, 管世辉. 2020. 煤层气开发利用规划实施影响因素与对策建议. 煤炭经济研究, 40(12): 65-69 [DOI: 10.13202/j.cnki.cer.2020.12.010http://dx.doi.org/10.13202/j.cnki.cer.2020.12.010]
Zheng X L, Wang R J, Zhao Q F, Liu Y P,Wang Y Y and Sun Z Q. 2017. Ecophysiological mechanisms of plant growth under the influence of rhizosphere oxygen concentration: A review. Chinese Journal of Plant Ecology, 41(7): 805-814
郑小兰, 王瑞娇， 赵群法, 刘勇鹏, 王媛媛, 孙治强. 2017. 根际氧含量影响植物生长的生理生态机制研究进展.植物生态学报, 41(7): 805-814 [DOI: 10.17521/cjpe.2017.0042http://dx.doi.org/10.17521/cjpe.2017.0042]
Zhou C. 2016. Research on Retrieval Method of Heavy Metal Content of Vegetation Using Hyperspectral Remote Sensing. Changchun: Jilin University
周超. 2016. 植被重金属含量高光谱遥感反演方法研究. 长春: 吉林大学
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