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

  • role： First author第一作者
  • Affiliation：

    School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Beijing Key Laboratory of Spatial Information Integration and 3S Engineering Application, Beijing 100871, China

  • Email：1601110520@pku.edu.cn
  • Introduction：韩谷怀， 研究方向为煤层气富集区遥感与大地电磁探测。E-mail： 1601110520@pku.edu.cn
HAN Guhuai12,  
  • Affiliation：

    School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Maritime College of Dalian Maritime University, Dalian 116026, China

SUN Yuanheng13,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth and Space Sciences, Peking University, Beijing 100871, China

    Geographic Information System Technology Innovation Center of the Ministry of Natural Resources, Beijing 100034, China

  • Email：qmqin@pku.edu.cn
  • Introduction：秦其明，研究方向为定量遥感与遥感图像智能理解。E-mail： qmqin@pku.edu.cn
QIN Qiming14*

résumé

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.

mots-clés

coalbed methane;hydrocarbon microleakage;vegetation;remote sensing;prosail model;Qinshui basin

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