自然资源遥感, 2024, 36(3): 1-12 doi: 10.6046/zrzyyg.2023193

综述

煤层气富集区遥感勘查研究进展与展望

秦其明,1,2, 吴自华1, 叶昕1,3, 王楠1,4, 韩谷怀1

1.北京大学地球与空间科学学院遥感与地理信息系统研究所,北京 100871

2.自然资源部地理信息系统技术创新中心,北京 100871

3.中国农业大学信息与电气工程学院,北京 100083

4.北京信息科技大学计算机学院,北京 100192

Remote sensing-based exploration of coalbed methane enrichment areas:Advances in research and prospects

QIN Qiming,1,2, WU Zihua1, YE Xin1,3, WANG Nan1,4, HAN Guhuai1

1. Institute of Remote Sensing and Geographical Information System, School of Space and Earth Sciences, Peking University, Beijing 100871, China

2. Geographic Information System Technology Innovation Center, Ministry of Natural Resources, Beijing 100871, China

3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

4. School of Computer Science, Beijing Information Science and Technology University, Beijing 100192, China

责任编辑: 张仙

收稿日期: 2023-07-3   修回日期: 2023-07-22  

基金资助: 国家自然科学基金项目“煤层气富集区地表参数遥感反演与综合勘查”(42071314)

Received: 2023-07-3   Revised: 2023-07-22  

作者简介 About authors

秦其明(1955-),男,博士,教授,研究方向为定量遥感。Email: qmqin@pku.edu.cn

摘要

煤层气是一种自生自储式非常规清洁能源,它赋存于煤层及其围岩中。传统勘查方法费时费力,而遥感技术提供了煤层气富集区快速勘查的新途径。煤层气富集区遥感勘查的基本原理是基于典型地物波谱特征与富集区烃微渗漏导致的地表物体异常波谱特征,包括岩矿蚀变、植被异常和热异常等波谱特征的对比,结合煤田地质、地震、大地电磁等物探方法获取的数据,进行多源信息提取与综合分析,逐步查明煤层气富集区分布范围与含气特性。文章综述了煤层气富集区烃类物质渗漏与地表岩矿波谱和植被波谱异常的响应机制,以及基于地表岩矿波谱与植被波谱参数反演的多种方法与地物波谱异常反演在煤层气潜在富集区的勘查应用; 阐述了含煤层气地层导致地表热异常的不同解释与提高地表温度反演准确率的主要方法与应用。未来,遥感与煤田地质数据、地震探测和大地电磁探测相结合,开展立体多元信息分析与信息提取,将成为实现煤层气富集区低成本快速勘查的主要途径。

关键词: 煤层气富集区; 烃微渗漏; 遥感勘查; 多源信息提取

Abstract

Coalbed methane (CBM), a type of self-sourced unconventional clean energy, occurs in coal seams and their surrounding rocks. Conventional exploration methods for CBM enrichment areas are laborious, while remote sensing provides a new approach to the rapid exploration of such areas. The basic principle behind the remote sensing-based exploration of CBM enrichment areas is as follows: ① The extraction and comprehensive analysis of multi-source data are conducted based on the comparison between the spectral features of typical surface features and those of surface feature anomalies, including rock and mineral alterations, vegetation anomalies, and thermal anomalies, caused by hydrocarbon micro-seepage in CBM enrichment areas, along with data obtained using geophysical prospecting methods like geological, seismic, and magnetotelluric methods; ② The distribution range and gas-bearing properties of CBM enrichment areas are gradually delineated. This paper reviews the hydrocarbon seepage in the CBM enrichment areas and the response mechanisms of spectral anomalies of surface rocks, minerals, and vegetation. It covers the applications of various methods based on the inversion of spectral parameters of surface rocks, minerals, and vegetation, together with the inversion of the spectral anomalies of surface features, in the exploration of potential CBM enrichment areas. Additionally, this paper elucidates the different explanations for surface thermal anomalies caused by CBM-bearing strata, as well as major methods to improve the accuracy of surface temperature inversion and their applications. In the future, the main approach to achieving low-cost, rapid exploration of CBM enrichment areas will be the analysis and information extraction of three-dimensional, multi-source information based on the combination of remote sensing technology with the geological data, seismic exploration, and geomagnetic prospecting of coalfields.

Keywords: coalbed methane enrichment area; hydrocarbon micro-seepage; remote sensing-based exploration; multi-source data extraction

PDF (2192KB) 元数据 多维度评价 相关文章 导出 EndNote| Ris| Bibtex  收藏本文

本文引用格式

秦其明, 吴自华, 叶昕, 王楠, 韩谷怀. 煤层气富集区遥感勘查研究进展与展望[J]. 自然资源遥感, 2024, 36(3): 1-12 doi:10.6046/zrzyyg.2023193

QIN Qiming, WU Zihua, YE Xin, WANG Nan, HAN Guhuai. Remote sensing-based exploration of coalbed methane enrichment areas:Advances in research and prospects[J]. Remote Sensing for Land & Resources, 2024, 36(3): 1-12 doi:10.6046/zrzyyg.2023193

0 引言

煤层气又称煤层瓦斯,是赋存于煤层及其围岩中的一种自生自储式非常规清洁能源[1],其富集程度与煤系地层的厚度、分布及含气量呈正相关关系。20世纪后期,煤层气作为清洁能源的价值开始受到重视[2],美国、加拿大和中国等国都开始了煤层气的商业化开采[3-4]。与此同时,在全球气候变化与我国“双碳”目标的时代背景下,作为一种重要的温室气体,甲烷的排放正受到越来越多的关注,而煤层气的开发正是控制煤炭开采甲烷排放的主要技术途径[5]

目前,我国煤层气的勘查方法主要是通过钻井技术来采集煤层样本[6],通过实验室测定煤层气储层参数,分析煤储层含气性,圈定煤层气富集靶区,这里靶区是预期可发现煤层气富集的地区。此外,也有运用三维地震等地球物理勘探方法来圈定煤层气富集靶区的应用实践[7]。但是,上述方法不仅勘探范围小,而且需要消耗大量的时间、人力、物力和财力,特别在地形条件比较复杂的地区,存在施工难度大,勘查成本高,勘查周期长等问题[7-11]。然而,地表烃微渗漏蚀变物质光谱异常与地表温度异常是煤层气富集区标志性指标,它们可以通过高光谱遥感与热红外遥感反演来获取。因此,利用遥感技术勘查,是快速发现煤层气富集靶区的一种新途径。

油气微渗漏现象是现代遥感地质勘查重要依据之一。地下烃类气体微渗漏机制有多种阐释[12-13],其中MacElvain等[12]提出的微气泡迁移机制被人们普遍肯定和接受。煤层气富集区的显著特征是烃微渗漏导致的地表岩矿组成和植被生长状况变化[14],以及潜在的地表热异常[15]。研究表明,即便盖层封闭完好,烃类物质依然会沿着上覆盖层的断裂、节理、微裂隙及孔隙向地表渗漏,这一现象被称为烃微渗漏。现有85%以上的油田中都存在着烃微渗漏现象[16]。煤层气在地质条件下,压力在垂直方向上的变化梯度是水平方向上的200倍以上,远远高于水平方向上的变化梯度,因而煤层气的压力降低总体上是朝向垂直向上的方向[17]。因此,地下含油或含气储层烃微渗漏,通常垂直向上,沿着地下断层、裂缝和解理等通道进行,形成所谓的“烟囱效应”。这些渗漏的烃类物质在地表聚集,就导致了岩矿蚀变和植被生长异常等变化。

利用上述特征,可以开展煤层气富集靶区的遥感勘查。与测井分析、岩芯分析、地震方法等[18-19]勘查方法相比,卫星遥感具有覆盖面积大、查验成本低、不受地形与交通条件限制等独特的优势[20-21],因此受到了自然资源勘查部门的广泛关注。特别是近年来,随着遥感技术的发展,遥感传感器光谱和空间分辨率不断提升,这进一步拓展了使用遥感技术对煤层气富集区进行勘查的应用范围,也提升了勘查的精度[22-23]。在这一背景下,本文将从岩矿蚀变、植被胁迫和地表热异常3个方面对煤层气富集区遥感勘查方法近年来的研究展开综述,并给出对未来发展方向的展望。

1 基于岩矿蚀变的勘查方法

煤层中自生自储的煤层气,在垂直向上烃微渗漏的过程中,烃类物质与少量的非烃物质(如二氧化碳、硫化氢等)不断向地表运移时,在与上覆岩层发生氧化还原反应。细菌等微生物在这一相互作用过程中起到了关键作用。首先,好氧菌利用土壤中的氧气,将烃类气体氧化为二氧化碳(式(1))或碳酸氢盐,并最终形成碳酸盐矿物(式(2))。随着土壤中氧气浓度的降低,厌氧菌开始占据主导地位。在这种情况下,甲烷与土壤中的硫酸盐(式(3))、三价铁、四价锰、亚硝酸盐等发生厌氧氧化反应,并产生二氧化碳和硫化氢等气体[24]。这一系列过程会显著改变土壤环境的氧化还原电位(Eh)[25-26]和pH值[27]。在弱酸环境下,长石等碱性矿物风化产生高岭石、伊利石和绿泥石等黏土矿物。另外,烃类与硫化氢气体的还原性还会将土壤矿物中呈现红色的三价铁还原为二价铁,从而导致红层褪色(式(4)—(6))[26]

CH4+2O2=CO2+2H2O,
CaAl2SiO6+CO2=CaCO3+SiO2+Al2O3,
CH4+CaSO4=CaCO3+H2S+H2O,
CmHn+Fe2O3(赤铁矿)→Fe3O4(磁铁矿)+FeO(方铁矿)+CO2+H2O,
CmHn+Fe(OH)3→Fe3O4(磁铁矿)+FeCO3(菱铁矿)+H2O,
Fe2O3+2H2S=FeS2(黄铁矿)+FeO+2H2O,

研究表明,在可见光和近红外波段(0.4~2.5 μm),上述过程中产生的各种蚀变矿物各自有着典型的光谱吸收和反射特征。其中,诊断性光谱特征主要体现在蚀变岩矿特定波长位置的吸收谷[28],如: 碳酸盐岩化的显著光谱特征是2.3~2.6 μm之间的“W”型吸收谷,如图1(a)所示。黏土矿化的显著光谱特征是2.2 μm附近的“V”型吸收谷,如图1(b)所示。含铁矿物一般在0.8 μm之前反射率逐升,但在0.9 μm附近有一个强的吸收谷,之后反射率又急剧上升。而二价铁矿物(如菱铁矿、黄铁矿)相比三价铁矿物(如针铁矿、赤铁矿、黄钾铁矾矿)通常吸收谷稍右移且更宽(1.0~1.3 μm),如图1(c)所示。Zhang等[21]利用蚀变岩矿特定波谱的吸收谷快速确定了煤层气勘探靶区; 崔鑫等[29]通过野外查证发现,在部分烃渗漏异常区具有地表晕环状分带、黏土化、碳酸盐岩化等地质特征。

图1

图1   USGS数据库若干典型矿物波谱特征

Fig.1   Spectral features of several typical minerals in the USGS database


根据上述光谱特征,可以通过波段运算构建光谱指数,从而有效地提取出光谱异常区域,也即潜在的煤层气富集靶区。表1中列出了一些常用的光谱指数,可用于监测烃微渗漏导致岩矿蚀变。

表1   可用于监测烃微渗漏导致岩矿蚀变的常用光谱指数

Tab.1  Common spectral indices used for monitoring rock and mineral alteration caused by hydrocarbon micro-seepage

指数名称表达式含义参考文献
三价铁指数1ρRedρBlue反映土壤中三价铁矿物丰度,也即土壤“红度”[30-31]
三价铁指数2ρRedρBlue·ρRed+ρSWIR1650ρNIR反映土壤中三价铁矿物丰度,适用于三价铁丰度较高的情形[30-31]
二价铁指数1ρGreen+ρSWIR1650ρRed+ρNIR反映土壤中二价铁矿物丰度[31]
二价铁指数2ρSWIR2167ρNIR+ρGreenρRed反映土壤中二价铁矿物丰度[15]
黏土和碳酸盐矿物指数ρNIRρSWIR1650反映土壤中黏土和碳酸盐矿物的丰度[31-32]

①式中: ρRed,ρGreen,ρBlue,ρNIR,ρSWIR1650ρSWIR2167分别为影像红光、绿光、蓝光、近红外、短波红外1 650 nm和短波红外2 167 nm波段处反射率值。

新窗口打开| 下载CSV


使用常见的光谱指数来监测烃微渗漏引起的岩矿蚀变时,需要考虑地表覆盖类型对高光谱遥感勘查的影响。一些研究表明[15,32],没有植被覆盖或仅有稀疏植被覆盖的地区有利于发现碳酸盐矿物沉积,如方解石、白云石、菱铁矿和文石,也有利于发现酸性环境下长石发生蚀变、导致碱性金属元素的流失而形成的黏土矿物[21]。此外,没有植被覆盖,还可以观察到地表及浅层的赤铁矿物质被还原,形成含有低价铁矿物的红层漂白现象。因此,对于卫星或航空高光谱遥感勘查来说,适宜进行蚀变矿物的遥感制图的地方,主要是植被稀少的荒漠戈壁等地,以及人类活动影响较小的丘陵山地(冬季植被落叶后)。通过蚀变矿物的遥感制图,可以为煤层气潜在富集区的勘查提供证据。除了构建光谱指数,也可以通过光谱降维的方法从多光谱和高光谱遥感数据中提取有用的信息。这之中最为常用的是主成分分析(principal component analysis,PCA)和最小噪声分离(minimum noise fraction,MNF)方法[33]表2中列出了一些常见的基于光谱降维的岩矿蚀变信息提取方法。

表2   可用于监测烃微渗漏导致岩矿蚀变的光谱降维方法

Tab.2  Spectral dimensionality reduction methods for monitoring mineral alteration caused by hydrocarbon micro-seepage

传感器波段选择降维方法研究对象参考文献
ASTERB1,B3,B4,B6PCA黏土矿物蚀变[15]
B1,B3,B4,B5碳酸盐岩蚀变
Landsat7 ETM+B1,B4,B5,B7PCA红层褪色[32]
B1,B3,B4,B5PCA二价铁丰度
B3,B4,B5,B7PCA黏土和碳酸盐矿物丰度
Landsat8 OLIB2,B4,B5,B6PCA
PCA
二价铁丰度[34]
B2,B5,B6,B7黏土矿物蚀变
Sentinel-2 MSIB3,B6,B8A,B11PCA

PCA
二价铁丰度[14]
B3,B8,B11,B12氢氧根丰度
EO-1 Hyperion全波段MNF像元纯度计算与端元提取[21,35]

新窗口打开| 下载CSV


在实际研究中,通常需要将多个光谱指数或PCA得到的主成分进行叠加以确定煤层气富集区的潜在位置。例如,李娜等[15]同时使用了二价铁指数2以及基于ASTER传感器的2个主成分,将二价铁、黏土矿物和碳酸盐岩蚀变同时存在的区域作为烃微渗漏的靶区。

对于应用高光谱遥感数据识别烃蚀变矿物,更常见的处理方法则是在使用MNF等方法进行降维处理后,进行端元提取,然后使用匹配滤波、混合调谐匹配滤波、光谱特征拟合、光谱角匹配等方法,计算像元光谱与参考光谱(端元光谱或标准光谱库中的光谱)间的相似程度,确定影像端元对应的矿物组分,进一步判定是否存在相应的岩矿蚀变[36],从而确定煤层气富集区的潜在位置[14,21,35]

2 基于植被胁迫的勘查方法

遥感可以很方便地识别裸露地表的岩矿蚀变,但在有植被覆盖时,蚀变矿物的特征光谱往往会被掩盖,这时,可以通过监测植被胁迫来进行煤层气潜在富集区的提取。烃微渗漏会改变土壤的化学组成和化学环境,并进一步影响植被生长。过量的烃类气体和二氧化碳会阻断植物根系的呼吸作用[37-38],部分烃类气体和硫化氢还会对植物根部产生毒害。植物根系缺氧条件下,叶绿体合成过程会受到阻碍,叶片叶绿素含量降低[39-40]。而当受到乙烷等烃类气体和硫化氢的毒害作用时,植被会变得稀疏,叶面积指数降低[41-42]。此外,在烃类气体的胁迫下,植被倾向于关闭气孔以减少蒸腾,叶片水含量因此升高[43]。上述变化都会表现为冠层光谱的异常,包括0.55~0.65 μm区间内的反射率升高,以及0.68~0.75 μm附近的反射率陡升线(一般称为“红边”)[44]向短波区域移动,也就是“红边蓝移”等现象[45]

与岩矿蚀变类似,烃微渗漏导致的植被胁迫也可以用光谱指数来衡量。表3中列出了一些可用于监测烃微渗漏导致的植被胁迫的光谱指数。实际应用中,常结合使用多种植被指数。韩谷怀等[46]利用红边位置植被指数,结合近红外反射率与红谷反射率构建三维特征空间,对沁水盆地的烃微渗漏植被异常区进行了提取; Kashyap等[47]使用归一化差值植被指数(normalized difference vegetation index,NDVI)和优化土壤调节植被指数(optimization soil-adjusted vegetation index,OSAVI)等多种植被指数进行权重组合,提取了印度阿萨姆和梅加拉亚地区附近受烃微渗漏影响的植被范围。

表3   可用于监测烃微渗漏导致植被胁迫的常用光谱指数

Tab.3  Common spectral indices used for monitoring vegetation stress caused by hydrocarbon micro-seepage

指数名称表达式含义参考文献
NDVIρNIR-ρRedρNIR+ρRed综合反映植被长势[48-50]
绿光归一化差值植被指数(GNDVI)ρNIR-ρGreenρNIR+ρGreen对叶绿素含量敏感[47,51]
增强归一化差值植被指数(ENDVI)ρNIR+ρGreen-2ρBlueρNIR+ρGreen+2ρBlue综合反映植被长势,适用于各个生育期[52-53]
OSAVIρNIR-ρRedρNIR+ρRed+0.16综合反映植被长势,较好地消除了土壤背景的影响[47,54]
增强植被指数(EVI)2.5(ρNIR-ρRed)1+ρNIR+6ρRed-7.5ρBlue对叶面积指数敏感[52,55]
反转差值植被指数(IDVI)1+(ρNIR-ρRed)1-(ρNIR-ρRed)综合反映植被长势,在高植被覆盖情况下不易饱和[56]
红边位置植被指数(RPVI)(ρRed-b)/k+(ρNIR-b)/k2对叶绿素含量敏感[46]

①式中kb分别为Sentinel-2影像3个红边波段拟合线的斜率和截距。

新窗口打开| 下载CSV


无论是叶绿素含量、叶面积指数的下降,还是气孔的关闭,都会对植被的光合作用产生抑制。因此,作为光合作用强度的重要指标之一,日光诱导叶绿素荧光(sun/solar-induced chlorophyll fluorescence,SIF)被认为具有监测烃渗漏对植被胁迫的潜在应用前景[57-58]

植物所吸收的光合有效辐射(absorbed photosynthetically active radiation,APAR)一共有3个去向: 大部分能量被用于光合作用,称为光化学淬灭(photochemical quenching,PQ); 部分能量以热量的形式耗散,称为非光化学淬灭(non-photochemical quenching,NPQ); 剩余的少量能量被重新发射,即为叶绿素荧光[59-60]。叶绿素荧光主要分布在650~800 nm的波长范围内,并通常在685~690 nm和730~740 nm处呈现出2个峰值,这2个峰值的强度分别被称为F685F740[57]。其中,F685主要受到光系统Ⅱ(photosystem Ⅱ, PSⅡ)的影响,而F740同时受到PSⅡ和光系统Ⅰ(photosystem Ⅰ, PSⅠ)的影响[57,61]。荧光信号可以作为植被光合作用活性的一个指标。特别地,光源为日光时产生的叶绿素荧光就被称为SIF[57]

遥感观测得到的通常都是稳态(或称为“终态”)叶绿素荧光,它能够反映不同光照条件和环境胁迫之下,光化学和非光化学过程之间的动态平衡[57]。对于稳态叶绿素荧光,常用的度量指标包括2个荧光峰的幅度、幅度比值、位置、带宽以及荧光寿命等[57]。在卫星遥感的尺度上,叶绿素荧光是一个复合变量,受到多方面因素的影响[59],其计算公式为:

SIF (λ)=APAR·φF(λ)·fesc(λ)·τatm(λ)

式中: APAR为光合有效辐射吸收量,W/m2,即植物吸收的可以用于光合作用的辐射能量,与叶面积指数、叶绿素含量、冠层结构等有关; φF(λ)为荧光量子产率,与植被水分状况紧密相关; fesc(λ)为逸出冠层的荧光比例,与叶面积指数、叶片膨压和冠层结构等有关; τatm(λ)为在波长λ处的大气透过率。根据式(7),在植被未受到环境胁迫时,可以认为SIF和APAR的变化线性相关,进而与作物总初级生产力(gross primary productivity,GPP)线性相关[62]

当植被生长受到环境胁迫时,SIF也可以起到很好的指示作用[63-64],比如: 水分胁迫会造成叶片原初光能转化效率降低,直接影响光合作用的电子传递和二氧化碳同化过程; 荧光动力学参数还可以快速反映营养环境和病虫害的胁迫对光合器官的伤害程度[65]。荧光动力曲线可以直观地表明植物的光化学反应状态、正在遭受的胁迫以及自我保护能力[66]

目前SIF在植被环境胁迫监测方面的应用研究主要集中在水分和温度胁迫方面。Qin等[67]指出: 干旱是持续缺水的累积结果,与其他自然灾害相比具有缓慢发展的特点。在农业干旱发生早期进行监测,以便迅速采取行动防止作物受旱受损就变得至关重要。基于SIF数据可以构建早期干旱监测指标[68],有效监测到作物早期干旱[67]。同时,有研究发现,SIF在植被遭受温度胁迫时具有早期预警能力。Zhang等[69]研究发现,与APAR相比,SIF对温度更为敏感。对于不同植被覆盖类型而言,其对应的SIF对温度的响应程度也不一致。Wang等[70]指出,在热胁迫的早期阶段,SIF即表现出下降趋势,而此时传统植被指数还未表现出明显变化。这表明SIF可以更好地捕捉热胁迫发展的时空动态。

此外,SIF在病害胁迫和重金属胁迫方面也已得到应用。赵叶等[71]研究指出,SIF对小麦条锈病的敏感,能够实现作物病害的早期诊断; 瞿瑛等[72]和朱冰雪等[73]研究发现,农作物在受到铜胁迫时,其SIF的辐射特征会发生改变,因此SIF可以作为间接指示植物受铜胁迫强度的有效手段。

上述研究表明,叶绿素荧光能够及时反映植物受到生态环境胁迫的状态。在煤层气富集区,烃类气体在土壤中大量富集时,也会对植被生长造成生态环境胁迫,这种胁迫也可以采用SIF遥感来进行检测。

需要指出,造成生态环境胁迫的因素很多,实际应用中要结合具体地域进行分析,排除干扰因素。同时,还需要检查煤层气富集区与地下煤储层在三维空间上是否重合,该地区长时间序列SIF数据反映的生态环境胁迫是否持续存在,只有当上述2个条件同时具备,才能成为潜在煤层气富集靶区的重要线索。

3 基于地表热异常的勘查方法

大多数油气田都存在地表热异常[74],这是由于烃类物质渗漏至地表或近地表后氧化放热,以及烃微渗漏导致地层热容和热导率的改变[15,34]。其中,烃类物质以及其中包含的硫化氢等气体的氧化放热将导致地表温度的升高[75],而热导率的降低和比热容的增加,会导致地表温度在白天偏低而在夜晚偏高。在这方面,南鹏等[76]在地表能量辐射平衡理论分析的基础上,从岩石热传导、土壤热传导以及煤层气储层影响热导率等方面进行了分析,探讨了地球内部热量传递导致地表热异常的原因。

为了圈定地表热异常区域,可以使用热红外遥感进行地表温度和发射率反演,或进一步计算热惯量[77]。由于含气煤储层导致的热异常往往只有1~3 K的温度差异,必须将地表温度反演精度控制在1 K以内,才能有效识别地表热异常区域。另一方面,由于不同地表覆盖类型的发射率存在显著差异,在进行基于地表热异常的煤层气富集区勘查时,往往需要先进行地表分类,或至少需要根据NDVI等植被指数的数值进行分类[77]

在运用地表热异常方法勘查煤层气潜在富集区时,需要排除茂密植被等地表覆盖类型对热遥感反演地表温度的影响。一些研究者工作[77]表明,地表热异常勘查更适用于裸露地表; 另外,也有研究者建议,采用夜间热红外遥感数据进行地表温度反演,可以避免太阳在白天辐射造成的干扰。此外,将岩矿蚀变遥感勘查与地表热异常遥感反演的勘查方法相结合,可以提供互补的信息,增加煤层气潜在富集区识别和定位的准确性。

目前的地表温度反演方法,根据使用的热红外波段数量可以分为单波段算法和多波段算法2大类。单波段算法是一种利用大气窗口内的单一热红外波段进行地表温度反演的方法,在获取大气廓线或关键大气参数(例如: 水汽含量、平均大气温度或近地面气温等)以及地表发射率的基础上,通过求解热辐射传输方程计算地表温度[78-79],计算公式为:

R(T)=[εB(Ts)+(1-ε)R]·τ+R

式中: R(T)为大气层顶辐射; T为波段亮温; ε为地表发射率; Ts为地表温度; B(Ts)为普朗克函数计算得到的黑体热辐射; R为大气下行辐射; εB(Ts)+(1-ε)R为离地辐射; τ为大气透过率; R为大气上行辐射。

单波段算法形式简洁,只需要单一热红外波段即可反演地表温度,为早期的热红外数据提供了切实可行的地表温度反演方案。在后续研究中,学者们针对HJ-1B和Landsat8等数据从观测角度、大气参数和波段对比等方面进行了优化[80-82]。单波段算法依赖于经验关系,并且需要先验的地表发射率和大气参数。Suherman等[83]利用Landsat ETM+热红外数据进行单波段地表温度反演,结合监督分类与NDVI阈值划分地表覆盖类型,发现在马来西亚美里地区,受到烃微渗漏影响的区域在地表温度上与未受影响的区域间存在差异,但这一差异在不同地表覆盖类型中有不同表现。Garain等[84]利用Landsat8热红外数据进行单波段地表温度反演,在印度东北部阿萨姆地区发现了由潜在煤层气富集区导致的地表热异常现象。

多波段算法从热辐射传输方程出发,通过建立波段间的经验关系增加求解方程,从而实现地表温度反演,是目前使用最广泛的地表温度反演方法。其中,最具代表性的是劈窗算法和温度-发射率分离(temperature and emissivity separation,TES)算法。

劈窗算法的核心思想是根据大气窗口内热红外波段的大气吸收特性,通过组合相邻波段的亮度温度来削弱大气的影响。在地表发射率已知的前提下,对辐射传输方程进行Taylor展开,然后基于相邻的2个[79,85]或多个热红外波段[86-88]亮温进行线性或非线性组合构建。典型的双波段劈窗算法计算公式为:

Ts=Ti+α·(Ti-Tj)2+β·(Ti-Tj)+γ

式中: Ts为地表温度; TiTj分别为第i和第j波段的亮温; α,βγ均为算法系数。劈窗算法的系数受多种地表和大气因素的影响。数十年来,学者们已经根据不同热红外传感器提出了多种劈窗算法,综合考虑了地表发射率、水汽含量、观测天顶角等多种因素,在不同成像条件下的反演结果有明显的改进[89-90]。劈窗算法对大气条件不敏感,形式简单高效,但要求像元发射率已知。南鹏等[76]利用ASTER数据第13和14波段进行劈窗算法地表温度反演,指出在河南省焦作市的研究区内发现的热异常区与煤系地层资料中的含气煤层分布具有一致性; 李娜等[15]利用相似方法,结果表明,在陕西榆林气田地区发现的热异常区与使用多光谱影像提取出的矿物异常靶区位置基本吻合。

TES算法通过建立多个热红外波段的最小发射率与最大-最小相对发射率差之间的经验关系,增加了求解方程个数。在完成大气校正的情况下,可以实现地表温度和发射率的协同反演。TES算法由3个模块组成: 归一化发射率模块、发射率比值模块以及平均/最大最小差值模块。TES算法能够同时反演地表温度和多个热红外波段的地表发射率,对发射率光谱差异较大的地表类型(裸土、岩石、人工地类等)反演精度较高,发射率反演误差一般在0.015以内,地表温度反演误差一般在1.5 K以内[91-92],适用于识别含气煤储层导致的地表热异常。计算公式为:

εmin=a-b·MMDc

式中: εmin为最小波段发射率; MMD为最大-最小相对发射率差; a,bc均为算法经验系数。TES算法已被用于生产ASTER地表温度和发射率产品[93],并成功应用于MSG/SEVIRI,MODIS,GF-5和Sentinel-3等多种热红外数据[94-97]。不过,TES算法输入需要至少3个热红外波段数据,这在一定程度上限制了其应用范围。

近年来,得益于新型热红外数据在光谱与空间分辨率方面的优势,学者们还发展了多种创新性地表温度反演算法,包括四波段劈窗算法[88],能够在无参数输入的情况下同时反演得到地表温度和波段发射率的劈窗-TES结合算法[97]以及考虑地表三维结构的地表温度反演算法[98]等。这些新算法的应用将有望提升含气煤储层地表热异常识别的效果。

值得注意的是,由于烃类氧化以及热导率和比热容的改变对地表温度的影响并不完全一致,一些研究[76,84,99]发现的热异常为低值异常,而另一些研究[15,100]发现的热异常则为高值异常。这表明不同区域的潜在煤层气富集区提取地表热异常特征时,需要结合更多的相关资料进行综合分析。同时,使用长时间序列遥感影像进行地表温度平均,有助于克服偶然因素对地表温度的干扰,或者同时使用日间和夜间地表温度数据进行分析,也有助于得出更加准确的结果[101]

4 总结与展望

与传统勘查方法相比,基于遥感的煤层气富集区勘查方法具有独特的优势。在进行遥感勘查时,岩矿蚀变、植被胁迫与地表热异常3个方面的地表特征互相补充,互为印证,提高了结果的可靠性。随着遥感技术的进一步发展,遥感观测的波谱范围得到拓展,光谱分辨率得到提高,可以反映出更多、更精细的地表岩矿蚀变与植被受烃渗漏胁迫长势变化的特征; 另一方面,传感器的空间分辨率也在不断提升,这有利于煤层气富集区边界的区分与边界定位精度的提高。在数据处理方面,信息分离与增强技术日益成熟,新型定量遥感反演方法不断涌现,这也有助于提高烃类蚀变物质光谱特征提取与反演的精度。

由于地表参数,特别是植被参数受到的影响因素较多,如干旱、病虫害等,都可能造成遥感观测到的地表信息出现异常。为了排除这些因素的干扰,一个可行的做法是将遥感勘查与煤田地质、地震、大地电磁等物探方法相结合,综合进行多源信息提取与综合分析,逐步圈定与查明煤层气藏的地质特征和储层含气性的展布规律。为解决上述问题,未来研究发展方向的展望如下:

1)遥感勘查与煤田地质研究的综合分析。煤层既是煤层气的源岩,又是煤层气的储集层,这点与常规天然气藏不同。因此,把遥感勘查结果与煤田地质资料放在一起进行叠加分析,可以利用煤田地质资料提供了勘查区煤层分布、厚度、结构、岩性、煤质等特征及其形成演化过程等相关信息[4]。煤层的孔隙度、渗透率、厚度、煤质等地质条件对煤层气的储存具有重要影响,煤层中的断层和裂隙对地下煤层气三维空间中的分布与运移产生影响,这有助于预测煤层气的分布、含量和储量。此外,也可以结合烃蚀变地表参数遥感反演结果与煤层气热异常分布区,从地表到地下分析煤层气烃渗漏的三维空间的对应性与内在联系性,排除遥感勘查结果的其他因素的干扰,以提高煤层气富集区勘查的准确性。

2)遥感勘查与大地电磁探测的综合分析。大地电磁探测是一种非接触性的地球物理勘探技术,借助天然场源或人工场源产生的电磁波,经过地下介质的传播后,被接收器测量到[102]。地下不同介质对电磁场具有不同响应特征,其中煤层气的电导率远低于周围岩石和煤层,因此煤层气会对电磁场产生明显的衰减和相位延迟。通过分析煤层气对电磁场的响应,可以估算煤层气的分布和含气量。将遥感勘查与大地电磁探测数据相结合进行综合分析[103],可以在烃蚀变地表参数遥感反演与煤层气热异常分布区范围内,利用大地电磁探测数据获得地下介质的电磁场响应,分析煤层厚度、煤质等地下介质的电导率、地下水体分布区域的富集情况,结合煤田地质资料推断煤层气的富集区域,进一步提高遥感勘查的准确性。

3)遥感勘查与地震勘探的综合分析。地震勘探利用地下介质弹性和密度的差异,通过观测和分析大地对人工激发地震波的响应,推断地下岩层的性质、密度与形态的空间差异[104]。张留荣等[105]基于对煤层气富集主控地质因素的地球物理响应特征分析,认为煤田地质构造、煤层厚度、煤体结构、裂隙系统等在地震弹性参数上均有明确的响应特征。目前,三维地震勘探方法已经在煤层气勘探中得到初步应用[7]。三维地震勘探主要包括地震波反演和成像2种。地震波反演是指根据地震波数据,通过数学模型求解地下介质的物理参数,如速度、密度、泊松比等。地震波成像则是将反演得到的地下介质物理参数转换为图像,以显示地下介质的三维结构和属性[106],并在此基础上通过分析与解译获取地下煤层和煤层气等三维空间分布信息。开展遥感勘查与三维地震勘探的综合分析,可以利用烃蚀变地表参数遥感反演结果与煤层气热异常分布区作为三维地震勘探的控制参考区,凭借遥感技术获取地表高程、坡度、坡向等地形地貌特征来合理地布设地震测点,并利用遥感勘查结果辅助三维地震勘探资料的解译,进一步提高煤层气富集区三维空间勘查的准确性。

从发展趋势看,运用地表参数遥感反演方法获取煤层气富集区地表典型地物光谱特征与物理属性,开展煤层气富集区地表标志性高光谱信息提取、诊断与识别,以及煤层气富集区地表物体发射率和温度反演研究,利用高光谱遥感、热红外遥感数据反演获取的地表理化信息,结合煤田地质资料、大地电磁探测数据或三维地震勘探数据,构建多元信息判别模型,开展立体多元信息分析与信息提取,逐步圈定煤层气富集分布靶区,是实现煤层气富集区快速勘查的一条新途径,也是未来煤层气勘查技术发展的新趋势。

参考文献

王大锐.

我国煤层气工业前途远大——访我国著名天然气勘探专家、973项目首席科学家宋岩教授

[J]. 石油知识, 2005(3):2.

[本文引用: 1]

Wang D R.

The future of China’s coalbed methane industry is promising:Interview with professor Song Yan,a famous natural gas exploration expert and chief scientist of the 973 project in China

[J]. Petroleum Knowledge, 2005(3):2.

[本文引用: 1]

Flores R M.

Coalbed methane:From hazard to resource

[J]. International Journal of Coal Geology 1998, 35(1-4):3-26.

[本文引用: 1]

Flores R M. Coal and coalbed gas[M]//Fueling the future. Amsterdam: Elsevier, 2014:587-614.

[本文引用: 1]

Qin Y, Moore T A, Shen J, et al.

Resources and geology of coalbed methane in China:A review

[J]. International Geology Review, 2018, 60(5-6):777-812.

[本文引用: 2]

桑树勋, 袁亮, 刘世奇, .

碳中和地质技术及其煤炭低碳化应用前瞻

[M]. 煤炭学报, 2022:1-23.

[本文引用: 1]

Sang S X, Yuan L, Liu S Q, et al.

Geological technology for carbon neutrality and its application prospect for low carbon coal exploitation and utilization

[J]. Journal of China Coal Society, 2022:1-23.

[本文引用: 1]

潘海洋, 程爱国, 赵欣, .

地勘时期煤层瓦斯与煤层气测试标准对比研究

[J]. 中国煤炭地质, 2019, 31(4):30-34.

[本文引用: 1]

Pan H Y, Cheng A G, Zhao X, et al.

Contrastive study of coal seam gas and CBM testing standards during exploration

[J]. Coal Geology of China, 2019, 31(4):30-34.

[本文引用: 1]

邵林海, 刘池阳, 丁清香, .

韩城煤层气三维地震勘探区11# 煤层含气量预测

[J]. 地质科技情报, 2016, 35(1):147-151.

[本文引用: 3]

Shao L H, Liu C Y, Ding Q X, et al.

Predication of 11# coalbed gas content in Hancheng coalbed methane 3D seismic survey

[J]. Geological Science and Technology Information, 2016, 35(1):147-151.

[本文引用: 3]

何展翔, 贺振华, 王绪本, .

油气非地震勘探技术的发展趋势

[J]. 地球物理学进展, 2002, 17(3):473-479.

[本文引用: 1]

He Z X, He Z H, Wang X B, et al.

Tendency of non-seismic techniques of hydrocarbon prospecting

[J]. Progress in Geophysics, 2002, 17(3):473-479.

[本文引用: 1]

赵贤正, 朱庆忠, 孙粉锦, .

沁水盆地高阶煤层气勘探开发实践与思考

[J]. 煤炭学报, 2015(9):2131-2136.

[本文引用: 1]

Zhao X Z, Zhu Q Z, Sun F J, et al.

Practice and thought of coalbed methane exploration and development in Qinshui Basin

[J]. Journal of China Coal Society, 2015(9):2131-2136.

[本文引用: 1]

叶建平, 陆小霞.

我国煤层气产业发展现状和技术进展

[J]. 煤炭科学技术, 2016, 44(1):24-28.

[本文引用: 1]

Ye J P, Lu X X.

Development status and technical progress of China coalbed methane industry

[J]. Coal Science and Technology, 2016, 44(1):24-28.

[本文引用: 1]

吴海波, 徐宏杰, 张平松, .

基于地震反演参数的煤层气储层甜点区预测

[J]. 煤炭科学技术, 2018, 46(7):218-222.

[本文引用: 1]

Wu H B, Xu H J, Zhang P S, et al.

Sweet-spot prediction for CBM reservoir based on seismic inversion parameters

[J]. Coal Science and Technology, 2018, 46(7):218-222.

[本文引用: 1]

MacElvain R, Heroy W.

Mechanics of gaseous ascension through a sedimentary column

[J]. Unconventional Methods in Exploration for Petroleum and Natural Gas, 1969:15-28.

[本文引用: 2]

Saunders D F, Burson K R, Thompson C K.

Model for hydrocarbon microseepage and related near-surface alterations

[J]. AAPG bulletin, 1999, 83(1):170-185.

[本文引用: 1]

Chen L, Yang X, Zhen G.

Potential of Sentinel-2 data for alteration extraction in coal-bed methane reservoirs

[J]. Ore Geology Reviews, 2019, 108:134-146.

DOI:10.1016/j.oregeorev.2017.10.009      [本文引用: 3]

This study illustrates the potential of alteration extraction in coal-bed methane (CBM) reservoirs using the recently available Sentinel-2 data. This study then evaluates the capabilities for mapping the altered minerals and vegetation. In the alteration mapping process, we separately analyzed the key remote sensing signatures of altered minerals and geobotanical anomalies based on the hydrocarbon micro-seepage theory. The diagnostic spectral characteristics of irons, clays and altered vegetation were concentrated and demonstrated the distribution of hydrocarbon micro-seepage. In the bare soil region, the altered minerals, including irons and clays were extracted through band math and principal component analysis (PCA) methods. In the vegetation area mapping, the diagnostic spectral feature parameters, such as the locations and slopes of three feature edges, were calculated. In addition, the mapping accuracy was assessed based on the extraction results of Hyperion data through full spectral profile matching method and the X-ray diffraction (XRD) analysis results. The results show that: 1) compared with band math and PCA methods, the different extraction methods were suitable for different minerals; 2) the extraction results of iron and clay minerals were most accurate (78.33% and 76.67%, respectively) with XRD analysis; 3) the highest rate of change of the feature edge slope was up to 39% with a reference spectrum; and 4) the distribution of alteration information was consistent with the Hudi coal mining area in Jincheng, Shanxi province. The potential geological application of Sentinel-2 data was revealed to identify the direction of CBM exploration in a large scale, highly efficient, convenient, and inexpensive way.

李娜, 周萍.

基于ASTER数据和反射光谱分析的烃类微渗漏信息提取

[J]. 地质力学学报, 2015(2):218-227.

[本文引用: 8]

Li N, Zhou P.

Hydrocarbon microleakage information extraction based on ASTER data and reflectance spectroscopy

[J]. Journal of Geomechanics, 2015(2):218-227.

[本文引用: 8]

谢青云, 丁树柏.

油气资源遥感技术若干问题的探讨

[J]. 国土资源遥感, 1994(2):55-62.doi:10.6046/gtzyyg.1994.02.10.

[本文引用: 1]

Xie Q Y, Ding S B.

Discussion of the some problems of the remote sensing technology for oil-gas resource

[J]. Remote Sensing for Land and Resources, 1994(2):55-62.doi:10.6046/gtzyyg.1994.02.10.

[本文引用: 1]

姜洪训, 刘生福, 李英武. 综合地质物化探多参数直接找油理论方法与效果[M]. 西安: 陕西科学技术出版社, 1995.

[本文引用: 1]

Jiang H X, Liu S F, Li Y W, et al. Theoretical methods and effects of comprehensive geophysical and geochemical exploration with multiple parameters for direct oil exploration[M]. Xi’an: Shaanxi Science and Technology Press, 1995.

[本文引用: 1]

蒋洪波, 秦其明, 李百寿.

煤层气排采动态的被动式超低频频谱分析

[J]. 光谱学与光谱分析, 2011, 31(7):1898-1901.

[本文引用: 1]

Jiang H B, Qin Q M, Li B S.

Passive SLF spectrum analysis for tracing the mining dynamic of CBM

[J]. Spectroscopy and Spectral Analysis, 2011, 31(7):1898-1901.

[本文引用: 1]

Wang N, Qin Q, Chen L, et al.

Dynamic monitoring of coalbed methane reservoirs using super-low frequency electromagnetic prospecting

[J]. International Journal of Coal Geology, 2014, 127:24-41.

[本文引用: 1]

Lammoglia T, De Souza F C R.

Mapping and characterization of the API gravity of offshore hydrocarbon seepages using multispectral ASTER data

[J]. Remote Sensing of Environment, 2012, 123:381-389.

[本文引用: 1]

Zhang C, Qin Q, Chen L, et al.

Rapid determination of coalbed methane exploration target region utilizing hyperspectral remote sensing

[J]. International Journal of Coal Geology, 2015, 150:19-34.

[本文引用: 5]

Asadzadeh S, De Souza Filho C R.

Investigating the capability of WorldView-3 superspectral data for direct hydrocarbon detection

[J]. Remote Sensing of Environment, 2016, 173:162-173.

[本文引用: 1]

Asadzadeh S, De Souza F C R.

Spectral remote sensing for onshore seepage characterization:A critical overview

[J]. Earth-Science Reviews, 2017, 168:48-72.

[本文引用: 1]

Thauer R K.

Anaerobic oxidation of methane with sulfate:On the reversibility of the reactions that are catalyzed by enzymes also involved in methanogenesis from CO2

[J]. Current Opinion in Microbiology, 2011, 14(3):292-299.

[本文引用: 1]

Adams R S Jr, Ellis R Jr.

Some physical and chemical changes in the soil brought about by saturation with natural gas

[J]. Soil Science Society of America Journal, 1960, 24(1):41-44.

[本文引用: 1]

Schumacher D.

Hydrocarbon-induced alteration of soils and sediments

[M/OL]//Schumacher D,Abrams M A. Hydrocarbon migration and its near-surface expression.American association of petroleum geologists [2022-05-09]. https://pubs.geoscienceworld.org/books/book/1308/chapter/107169077.

URL     [本文引用: 2]

Neue H U.

Methane emission from rice fields

[J]. BioScience, 1993, 43(7):466-474.

[本文引用: 1]

甘甫平, 王润生. 遥感岩矿信息提取基础与技术方法研究[M]. 北京: 地质出版社, 2004.

[本文引用: 1]

Gan F P, Wang R S. Research on the fundamentals and technical methods of remote sensing rock and mineral information extraction[M]. Beijing: Geological Publishing House, 2004.

[本文引用: 1]

崔鑫, 赵英俊, 田丰, .

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

[J]. 地质学报, 2019, 93(4):928-944.

[本文引用: 1]

Cui X, Zhao Y J, Tian F, et al.

Hyperspectral hydrocarbon exploration and geological verification of the oil and gas micro-seepage in the northeastern margin of the Junggar Basin,China

[J]. Acta Geologica Sinica, 2019, 93(4):928-944.

[本文引用: 1]

Rockwell B W.

Description and validation of an automated methodology for mapping mineralogy,vegetation,and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains,Colorado

[R]. Reston,Virginia: US Geological Survey, 2012:35.

[本文引用: 2]

Rockwell B W.

Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains,Colorado

[R]. Reston,Virginia: US Geological Survey, 2013:25.

[本文引用: 4]

Garain S, Mitra D, Das P.

Detection of hydrocarbon microseepage-induced anomalies by spectral enhancements of Landsat7 ETM+ images in part of Assam-Arakan Fold Belt,India

[J]. Journal of Petroleum Exploration and Production Technology, 2019, 9(4):2573-2582.

[本文引用: 3]

Luo G, Chen G, Tian L, et al.

Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising

[J]. Canadian Journal of Remote Sensing, 2016, 42(2):106-116.

[本文引用: 1]

Chen L, Li W, Chen L, et al.

Potential prediction of coalbed methane anomaly region utilizing Landsat8 OLI and GF-2 images

[C]// 2019 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2019:6819-6822.

[本文引用: 2]

El-Hadidy S M, Alshehri F, Sahour H, et al.

Detecting hydrocarbon micro-seepage and related contamination,probable prospect areas,deduced from a comparative analysis of multispectral and hyperspectral satellite images

[J]. Journal of King Saud University-Science, 2022, 34(6):102192.

[本文引用: 2]

胡畔, 田庆久, 闫柏琨.

柴达木盆地地表酸解烃含量的高光谱遥感估算

[J]. 天然气工业, 2009, 29(2):32-36.

[本文引用: 1]

Hu P, Tian Q J, Yan B K.

Estimating acidolysis hydrocarbon content in surface of Qaidam Basin through hyperspectral remote sensing

[J]. Natural Gas Industry, 2009, 29(2):32-36.

[本文引用: 1]

Drew M C.

Oxygen deficiency and root metabolism:Injury and acclimation under hypoxia and anoxia

[J]. Annual Review of Plant Physiology and Plant Molecular Biology, 1997, 48(1):223-250.

[本文引用: 1]

Drew M C, Cobb B G, Johnson J R, et al.

Metabolic acclimation of root tips to oxygen deficiency

[J]. Annals of Botany, 1994, 74(3):281-286.

[本文引用: 1]

Al-Abbas A H, Barr R, Hall J D, et al.

Spectra of normal and nutrient-deficient maize leaves 1

[J]. Agronomy Journal, 1974, 66(1):16-20.

[本文引用: 1]

邬登巍, 吴昀昭, 马宏瑞.

植物污染胁迫遥感监测研究综述

[J]. 遥感技术与应用, 2009, 24(2):238-245.

DOI:10.11873/j.issn.1004-0323.2009.2.238      [本文引用: 1]

我国工农业在过去几十年内迅速发展,随之而来的是大批环境问题的涌现,其中包括工农业对植物的污染问题。由于需要监测的范围越来越广、监测的对象越来越多,利用遥感技术监测受污植物受到越来越多的重视。首先提出了影响植被光谱的主要因素有叶绿素含量、细胞结构以及水含量,分析了植物在受到污染胁迫时光谱特征的变化,进而介绍了当前遥感监测植物污染胁迫的主要指标:植被指数和红边参数,并对主要植物污染物——重金属和烃类物质污染应用进行了详细阐述。指出了找到合适的植被指数和利用合适方法精确提取植被红边是监测的关键。最后对用遥感技术监测植物污染提出展望。

Wu D W, Wu Y Z, Ma H R.

Review on remote sensing monitoring on contaminated plant

[J]. Remote Sensing Technology and Application, 2009, 24(2):238-245.

[本文引用: 1]

Jamaludin M I, Matori A N, Myint K C.

Application of NIR to determine effects of hydrocarbon microseepage in oil palm vegetation stress

[C]// 2015 International Conference on Space Science and Communication (IconSpace).IEEE, 2015:215-220.

[本文引用: 1]

Noomen M F. Hyperspectral reflectance of vegetation affected by underground hydrocarbon gas seepage[D]. Wageningen: Wageningen University and Research, 2007.

[本文引用: 1]

Arellano P, Tansey K, Balzter H, et al.

Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images

[J]. Environmental Pollution, 2015, 205:225-239.

DOI:10.1016/j.envpol.2015.05.041      PMID:26074164      [本文引用: 1]

The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI705) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Horler D N H, Dockray M, Barber J.

The red edge of plant leaf reflectance

[J]. International Journal of Remote Sensing, 1983, 4(2):273-288.

[本文引用: 1]

申晋利, 丁树柏, 齐小平, .

烃类微渗漏现象遥感检测研究进展

[J]. 国土资源遥感, 2010, 22(3):7-11.doi: 10.6046/gtzyyg.2010.03.02.

[本文引用: 1]

Shen J L, Ding S B, Qi X P, et al.

The progress of remote sensing technology in the detection of hydrocarbon micro-seepage

[J]. Remote Sensing for Land and Resources, 2010, 22(3):7-11.doi: 10.6046/gtzyyg.2010.03.02.

[本文引用: 1]

韩谷怀, 孙元亨, 秦其明.

基于Sentinel-2/MSI数据的煤层气烃微渗漏植被异常区提取

[J]. 遥感学报, 2023, 27(7):1713-1730.

[本文引用: 2]

Han G H, Sun Y H, Qin Q M.

Extraction of vegetation anomaly caused by coalbed methane hydrocarbon microseepage based on Sentinel-2/MSI

[J]. National Remote Sensing Bulletin, 2023,2023, 27(7):1713-1730.

[本文引用: 2]

Kashyap R.

Vegetation stress as chief indicator for detection of hydrocarbon microseepage in regions of north-eastern India

[J]. Spatial Information Research, 2022, 30(5):571-584.

[本文引用: 3]

Ahmed M.

Exploration of hydrocarbon resources using remote sensing and geographical information system

[C]//AIP Conference Proceedings:2119. AIP Publishing LLC, 2019:020001.

[本文引用: 1]

Garain S, Mitra D, Das P.

Detection of hydrocarbon microseepage prospects using Landsat8-based vegetation stress analysis in part of Assam-Arakan Fold Belt,NE India

[J]. Arabian Journal of Geosciences, 2021, 14(19):1984.

[本文引用: 1]

Rouse J, Haas R H, Schell J A, et al.

Monitoring vegetation systems in the Great Plains with ERTS

[J]. NASA special publication, 1974, 351:309.

[本文引用: 1]

Gitelson A A, Kaufman Y J, Merzlyak M N.

Use of a green channel in remote sensing of global vegetation from EOS-MODIS

[J]. Remote Sensing of Environment, 1996, 58(3):289-298.

[本文引用: 1]

Susantoro T M, Wikantika K, Saepuloh A, et al.

Utilization of vegetation indices to interpret the possibility of oil and gas microseepages at ground surface

[J]. IOP Conference Series:Earth and Environmental Science, 2018, 145:012012.

[本文引用: 2]

Maxmax. ENDVI[EB/OL].(2015-06-18) [2023-06-15]. https://www.maxmax.com/endvi.htm.

URL     [本文引用: 1]

Rondeaux G, Steven M, Baret F.

Optimization of soil-adjusted vegetation indices

[J]. Remote Sensing of Environment, 1996, 55(2):95-107.

[本文引用: 1]

Boegh E, Soegaard H, Broge N, et al.

Airborne multispectral data for quantifying leaf area index,nitrogen concentration,and photosynthetic efficiency in agriculture

[J]. Remote Sensing of Environment, 2002, 81(2-3):179-193

[本文引用: 1]

Sun Y, Ren H, Zhang T, et al.

Crop leaf area index retrieval based on inverted difference vegetation index and NDVI

[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11):1662-1666.

[本文引用: 1]

Mohammed G H, Colombo R, Middleton E M, et al.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation:50 years of progress

[J]. Remote Sensing of Environment, 2019, 231:111177.

[本文引用: 6]

Gholizadeh A, Kopačková V.

Detecting vegetation stress as a soil contamination proxy:A review of optical proximal and remote sensing techniques

[J]. International Journal of Environmental Science and Technology, 2019, 16(5):2511-2524.

[本文引用: 1]

Jonard F, De Cannière S, Brüggemann N, et al.

Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes:Current status and challenges

[J]. Agricultural and Forest Meteorology, 2020, 291:108088.

[本文引用: 2]

Magney T S, Frankenberg C, Fisher J B, et al.

Connecting active to passive fluorescence with photosynthesis:A method for evaluating remote sensing measurements of Chl fluorescence

[J]. The New Phytologist, 2017, 215(4):1594-1608.

[本文引用: 1]

Roháček K, Soukupová J, Barták M. Chlorophyll fluorescence:A wonderful tool to study plant physiology and plant stress[M]// Benoît S.Plant Cell Compartments-Selected Topics. Kerala: Research Signpost,2008:41-104.

[本文引用: 1]

Liu X, Liu Z, Liu L, et al.

Modelling the influence of incident radiation on the SIF-based GPP estimation for maize

[J]. Agricultural and Forest Meteorology, 2021, 307:108522.

[本文引用: 1]

张伟, 薛辉, 于磊.

日光诱导叶绿素荧光高光谱成像仪研究

[J]. 红外与激光工程, 2021, 50(10):20210311.

[本文引用: 1]

Zhang W, Xue H, Yu L.

Study on hyperspectral imager for the solar induced chlorophyll fluorescence observation

[J]. Infrared and Laser Engineering, 2021, 50(10):20210311.

[本文引用: 1]

Flexas J, Briantais J M, Cerovic Z, et al.

Steady-state and maximum chlorophyll fluorescence responses to water stress in grapevine leaves:A new remote sensing system

[J]. Remote Sensing of Environment, 2000, 73(3):283-297.

[本文引用: 1]

彭金龙, 李萌, 褚荣浩, .

日光诱导叶绿素荧光反演及其在植被环境胁迫监测中的研究进展

[J]. 江苏农业科学, 2021, 49(24):29-40.

[本文引用: 1]

Peng J L, Li M, Chu R H, et al.

Research progress of sun-induced chlorophyll fluorescence inversion and its application in vegetation environmental stress monitoring

[J]. Jiangsu Agricultural Sciences, 2021, 49(24):29-40.

[本文引用: 1]

张景波, 张金鑫, 卢琦, .

乌兰布和沙漠油蒿叶片PSⅡ叶绿素荧光动力学参数及其光响应曲线动态

[J]. 草业科学, 2019, 36(3):713-719.

[本文引用: 1]

Zhang J B, Zhang J X, Lu Q, et al.

Dynamic changes of leaf parameters of PSⅡ fluorescence kinetics and fast photosynthetic response curves in Artemisia ordosica

[J]. Pratacultural Science, 2019, 36(3):713-719.

[本文引用: 1]

Qin Q, Wu Z, Zhang T, et al.

Optical and thermal remote sensing for monitoring agricultural drought

[J]. Remote Sensing, 2021, 13(24):5092.

[本文引用: 2]

Zhang Z, Xu W, Qin Q, et al.

Monitoring and assessment of agricultural drought based on solar-induced chlorophyll fluorescence during growing season in North China Plain

[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:775-790.

[本文引用: 1]

Zhang L, Qiao N, Huang C, et al.

Monitoring drought effects on vegetation productivity using satellite solar-induced chlorophyll fluorescence

[J]. Remote Sensing, 2019, 11(4):378.

[本文引用: 1]

Wang X, Qiu B, Li W, et al.

Impacts of drought and heatwave on the terrestrial ecosystem in China as revealed by satellite solar-induced chlorophyll fluorescence

[J]. Science of the Total Environment, 2019, 693:133627.

[本文引用: 1]

赵叶, 竞霞, 黄文江, .

日光诱导叶绿素荧光与反射率光谱数据监测小麦条锈病严重度的对比分析

[J]. 光谱学与光谱分析, 2019, 39(9):2739-2745.

[本文引用: 1]

Zhao Y, Jing X, Huang W J, et al.

Comparison of sun-induced chlorophyll fluorescence and reflectance data on estimating severity of wheat stripe rust

[J]. Spectroscopy and Spectral Analysis, 2019, 39(9):2739-2745.

DOI:10.3964/j.issn.1000-0593(2019)09-2739-07      [本文引用: 1]

Stripe rust of wheat is one of the hazardous diseases which affects the wheat yield in China. It is more significant to early detect wheat stripe rust infection information for the prevention of wheat stripe rust and the improvement of yield and quality. Considering that reflectance spectra are sensitive to variations in the concentration of plant biochemical components, and the sun-induced chlorophyll fluorescence is sensitive to variations in plant photosynthetic physiology. In order to preferably detect the severity of wheat stripe rust disease by remote sensing, especially the earlier detection of wheat stripe rust disease, this study made a comparative analysis of the sensitivity of sun-induced chlorophyll fluorescence and reflectance spectrum data to monitor the severity of wheat stripe rust disease. First used the ASD Field Spec Pro NIR spectrometer to determine the wheat canopy spectral data of different illness severity, on the basis of the principle of fraunhofer line to extracted sun-induced chlorophyll fluorescence data by the method of 3FLD under different illness severity, then respectively induced by reflectance spectra data and sun-induced chlorophyll fluorescence data to construct at different conditions of wheat stripe rust of remote sensing detection model, and through the retained sample cross terms of inspection on the forecast model accuracy is evaluated. The result shows that; (1) when the severity of wheat stripe rust disease was less than 20%, the sun-induced chlorophyll fluorescence response of wheat stripe rust disease information was more sensitive than reflectance spectra data, and the sun-induced chlorophyll fluorescence as the independent variable to build the forecasting model of wheat stripe rust disease severity reached the extremely significant level. It can earlier diagnose the crop diseases by detecting the stress state of plants before the change of chlorophyll content or leaf area index, while it is hard to use the reflectivity spectrum data to detect wheat stripe rust damage information. (2) when the severity of wheat stripe rust disease is in the state of moderate incidence (20%<DI <= 45%), both sun-induced chlorophyll fluorescence and reflectance spectral data can detect the severity of wheat stripe rust disease, but the prediction model constructed by using sun-induced chlorophyll fluorescence data is superior to the reflectance spectral data model. (3)When the severity of wheat stripe rust reached the severe level (DI>45%), the prediction model of severity of wheat stripe rust disease constructed by using reflectance spectral data and sun-induced chlorophyll fluorescence data has reached the extremely significant level, both of which can preferably detect the severity of wheat stripe rust by remote sensing. The results of this study have great significance for improving the remote sensing detection accuracy of wheat stripe rust, and it provides reference basis for the earlier detection of stripe rust in wheat by using TanSat or other satellite fluorescence data.

瞿瑛, 刘素红, 李小文.

重金属Cu胁迫下典型农作物叶片日光诱导荧光辐射特征提取研究

[J]. 光谱学与光谱分析, 2012, 32(5):1282-1286.

[本文引用: 1]

Qu Y, Liu S H, Li X W.

A novel method for extracting leaf-level solar-induced fluorescence of typical crops under Cu stress

[J]. Spectroscopy and Spectral Analysis, 2012, 32(5):1282-1286.

[本文引用: 1]

朱冰雪, 陈圣波, 周超, .

Cu胁迫下油菜叶片日光诱导荧光辐射特征

[J]. 科学技术与工程, 2018, 18(10):1-5.

[本文引用: 1]

Zhu B X, Chen S B, Zhou C, et al.

Rape leaf solar-induced fluorescence radiation characteristics under Cu stress

[J]. Science Technology and Engineering, 2018, 18(10):1-5.

[本文引用: 1]

黄秀华, 李正文, 关燕宁, .

热红外遥感找油的时效性研究

[J]. 遥感学报, 2003, 7(2):142-145.

[本文引用: 1]

Huang X H, Li Z W, Guan Y N, et al.

Temporal effectiveness of thermal infra-red remote sensing applied to oil-gas exploration

[J]. Journal of Remote Sensing, 2003, 7(2):142-145.

[本文引用: 1]

Guo W, Parizek R R. Field research on thermal anomalies indicating sulflde-oxidation reactions in mine spoil[M]. Washington: ACS Publications, 1994.

[本文引用: 1]

南鹏, 秦其明, 姚云军.

地表温度异常机理及热红外遥感监测——以河南焦作煤层气富集区为例

[J]. 北京大学学报(自然科学版), 2009, 45(1):137-143.

[本文引用: 3]

Nan P, Qin Q, Yao Y.

Land surface temperature anomalies and monitoring through thermal remote sensing:A case of coal bed methane area at Jiaozuo,Henan Province

[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2009, 45(1):137-143.

[本文引用: 3]

Price J C.

Thermal inertia mapping:A new view of the Earth

[J]. Journal of Geophysical Research, 1977, 82(18):2582-2590.

[本文引用: 3]

Jiménez-Muñoz J C, Sobrino J A.

A generalized single-channel method for retrieving land surface temperature from remote sensing data

[J]. Journal of Geophysical Research:Atmospheres, 2003, 108(d22):4688.

[本文引用: 1]

Qin Z, Karnieli A, Berliner P.

A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region

[J]. International Journal of Remote Sensing, 2001, 22(18):3719-3746.

[本文引用: 2]

胡德勇, 乔琨, 王兴玲, .

单窗算法结合 Landsat8 热红外数据反演地表温度

[J]. 遥感学报, 2015, 19(6):964-976.

[本文引用: 1]

Hu D Y, Qiao K, Wang X L, et al.

Land surface temperature retrieval from Landsat8 thermal infrared data using mono-window algorithm

[J]. Journal of Remote Sensing, 2015, 19(6):964-976.

[本文引用: 1]

周纪, 李京, 赵祥, .

用HJ-1B卫星数据反演地表温度的修正单通道算法

[J]. 红外与毫米波学报, 2011, 30(1):61-67.

[本文引用: 1]

Zhou J, Li J, Zhao X, et al.

A modified single-channel algorithm for land surface temperature retrieval from HJ-1B satellite data

[J]. Journal of Infrared and Millimeter Waves, 2011, 30(1):61-67.

[本文引用: 1]

Cristóbal J, Jiménez-Muñoz J, Prakash A, et al.

An improved single-channel method to retrieve land surface temperature from the Landsat8 thermal band

[J]. Remote Sensing, 2018, 10(3):431.

[本文引用: 1]

Suherman A, Rahman M A, Busu I.

Albedo and land surface temperature shift in hydrocarbon seepage potential area,case study in Miri Sarawak Malaysia

[J]. IOP Conference Series:Earth and Environmental Science, 2014, 18:012148.

[本文引用: 1]

Garain S, Mitra D, Das P.

Landsat8-based surface temperature anomaly and hydrocarbon prospectivity:A study in part of Assam-Arakan Fold Belt,North East India

[J]. Current Science, 2020, 119(1):128.

[本文引用: 2]

Du C, Ren H, Qin Q, et al.

A practical split-window algorithm for estimating land surface temperature from Landsat8 data

[J]. Remote Sensing, 2015, 7(1):647-665.

[本文引用: 1]

Sun D, Pinker R T.

Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8)

[J]. Journal of Geophysical Research:Atmospheres, 2003, 108(d11):4326.

[本文引用: 1]

Sun D, Pinker R T.

Retrieval of surface temperature from the MSG-SEVIRI observations:Part I.Methodology

[J]. International Journal of Remote Sensing, 2007, 28(23):5255-5272.

[本文引用: 1]

Ye X, Ren H, LIU R, et al.

Land surface temperature estimate from Chinese Gaofen-5 satellite data using split-window algorithm

[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10):5877-5888.

[本文引用: 2]

Sobrino J A, Romaguera M.

Land surface temperature retrieval from MSG1-SEVIRI data

[J]. Remote Sensing of Environment, 2004, 92(2):247-254.

[本文引用: 1]

Atitar M, Sobrino J A.

A split-window algorithm for estimating LST from Meteosat 9 data:Test and comparison with in situ data and MODIS LSTs

[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(1):122-126.

[本文引用: 1]

Gillespie A R, Abbott E A, Gilson L, et al.

Residual errors in ASTER temperature and emissivity standard products AST08 and AST05

[J]. Remote Sensing of Environment, 2011, 115(12):3681-3694.

[本文引用: 1]

Sabol Jr D E, Gillespie A R, Abbott E, et al.

Field validation of the ASTER temperature-emissivity separation algorithm

[J]. Remote Sensing of Environment, 2009, 113(11):2328-2344.

[本文引用: 1]

Gillespie A, Rokugawa S, Matsunaga T, et al.

A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images

[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(4):1113-1126.

[本文引用: 1]

Hulley G C, Hook S J.

Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research

[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(4):1304-1315.

[本文引用: 1]

Jiménez-Muñoz J C, Sobrino J A, Mattar C, et al.

Temperature and emissivity separation from MSG/SEVIRI data

[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9):5937-5951.

[本文引用: 1]

Nie J, Ren H, Zheng Y, et al.

Land surface temperature and emissivity retrieval from nighttime middle-infrared and thermal-infrared Sentinel-3 images

[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 18(5):915-919.

[本文引用: 1]

Ren H, Ye X, Liu R, et al.

Improving land surface temperature and emissivity retrieval from the Chinese Gaofen-5 satellite using a hybrid algorithm

[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(2):1080-1090.

[本文引用: 2]

Chen S, Ren H, Ye X, et al.

Geometry and adjacency effects in urban land surface temperature retrieval from high-spatial-resolution thermal infrared images

[J]. Remote Sensing of Environment, 2021, 262:112518.

[本文引用: 1]

Tarish A B, Sali A, Mohamed Shariff A R, et al.

Thermal satellite imagery analysis and emissivity characteristics for the prediction of oil reservoirs existence

[C]// 2021 7th International Conference on Space Science and Communication (IconSpace). 2021: 227-232.

[本文引用: 1]

Muhammad I A.

Hydrocarbon exploration using thermal images west Baghdad

[J]. Engineering and Technology Journal, 2015, 33(2b):307-317.

[本文引用: 1]

Al Farid A. Knowledge and data driven approaches for hydrocarbon microseepage characterizations:An application of satellite remote sensing[D]. Lund: Lund University, 2020.

[本文引用: 1]

Wang N, Qin Q.

Natural source electromagnetic component exploration of coalbed methane reservoirs

[J]. Minerals, 2022, 12(6):680.

[本文引用: 1]

陈理. 煤层气富集区高光谱遥感和电磁探测技术与方法研究[D]. 北京: 北京大学, 2015.

[本文引用: 1]

Chen L. Study on hyperspectral remote sensing and electromagnetic exploration technology for coalbed methane enrichment region[D]. Beijing: Peking University, 2015.

[本文引用: 1]

Walton C, Evans B, Urosevic M.

Imaging coal seam structure using 3-D seismic methods

[J]. Exploration Geophysics, 2000, 31(3):509-514.

[本文引用: 1]

张留荣, 曾维望.

地震技术在煤层气勘探开发中的应用现状与发展趋势

[J]. 中国煤炭地质, 2017, 29(6):68-73.

[本文引用: 1]

Zhang L R, Zeng W W.

Application status quo and development trend of seismic prospecting technology in cbm exploitation

[J]. Coal Geology of China, 2017, 29(6):68-73.

[本文引用: 1]

Li Q, Peng S, Zou G.

High resolution processing of 3D seismic data for thin coal seam in Guqiao coal mine

[J]. Journal of Applied Geophysics, 2015, 115:32-39.

[本文引用: 1]

/

京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发