College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
Satellite Application Center for Ecology and Environment, Beijing 100094, China.
Sensors (Basel). 2022 Jul 16;22(14):5322. doi: 10.3390/s22145322.
The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.
地下天然气泄漏对环境和安全都有负面影响。微量的气体泄漏浓度无法达到直接检测的阈值。低浓度的天然气会导致地表植被发生变化，因此可以利用遥感技术来间接检测微泄漏。本研究使用红外热成像技术结合深度学习方法，检测天然气微泄漏区域，并揭示了 2017 年至 2019 年期间四种植被（草、大豆、玉米和小麦）在天然气胁迫下的不同冠层温度特征。天然气浓度与冠层温度的相关分析表明，植被在气体胁迫下的冠层温度升高。针对天然气微泄漏胁迫下不同植被种类的分类问题，提出了一种带有双线性池化层（GLNB）的 GoogLeNet 模型（GLNB）。此外，还采用迁移学习来改进模型的训练过程和分类效率。所提出的方法对四种植被的胁迫分类平均准确率达到 95.33%，平均召回率为 95.02%，平均特异性为 95.52%。最后，基于 Grad-Cam 和受天然气胁迫区域的准圆形空间分布规律，检测了不同植被和胁迫持续时间下的天然气微泄漏胁迫区域范围。总之，本研究证明了利用热红外成像和深度学习识别受气胁迫植被的潜力，这对于检测天然气微泄漏的位置具有重要意义。
