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

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。

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.