College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China.
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China.
J Environ Manage. 2024 Nov;370:122539. doi: 10.1016/j.jenvman.2024.122539. Epub 2024 Sep 21.
Natural gas leaks alter both the spectral reflectance and the structure of surface vegetation, which can be used to indirectly monitor microleakages in gas storage facilities. However, existing methods predominantly focus on the spectral rather than structural response of stressed vegetation, and it is not clear whether structure characteristic can be used to identify natural gas stressed vegetation. In this study, the utility of mobile LiDAR in detecting vegetation structure changes due to natural gas stress was demonstrated by analyzing LiDAR data from a field experiment with bean and grass plants in their growing phase. A method utilizing the Jeffries-Matusita distance criterion constrained K-means clustering (JCKC) algorithm was proposed, which comprises three main steps: First, response of vegetation structure characteristic to natural gas stress was quantitatively analyzed at plot and pixel scales using LiDAR data. Second, the optimal set of structure characteristic parameters indicating natural gas stressed vegetation was determined using hierarchical clustering algorithm. Third, the reduced LiDAR data was clustered using K-means algorithm, and the clusters were classified under constraint of Jeffries-Matusita distance criterion to identify stressed vegetation. The results indicated natural gas stress significantly changes vegetation structure (p = 0.05), decreasing parameters like height, projected leaf area, canopy relief ratio, coefficient of variation of vegetation height, and entropy, while increasing homogeneity, contrast, and dissimilarity. The set of structure characteristic parameters based on height, homogeneity, and contrast can stably indicate natural gas stress, with Jeffries-Matusita distance values for comparing healthy and stressed vegetation samples exceeding 1.8. The proposed model achieved pixel-level identification accuracies of 98.95% for bean and 96.22% for grass, with average localization accuracies of 0.15 m and 0.12 m, respectively. This study demonstrates the potential of vegetation's structure characteristic in reflecting response to natural gas stress and monitoring natural gas storage microleakage in vegetated areas.
天然气泄漏会改变地表植被的光谱反射率和结构，这可用于间接监测天然气储存设施的微泄漏。然而，现有的方法主要集中在受胁迫植被的光谱响应上，而不清楚结构特征是否可用于识别受天然气胁迫的植被。本研究通过分析生长阶段豆类和草本植物的野外实验中的 LiDAR 数据，证明了移动 LiDAR 用于检测因天然气胁迫而导致的植被结构变化的有效性。提出了一种利用 Jeffries-Matusita 距离准则约束 K-均值聚类（JCKC）算法的方法，该方法包括三个主要步骤：首先，在小区和像素尺度上利用 LiDAR 数据定量分析植被结构特征对天然气胁迫的响应；其次，利用层次聚类算法确定指示受天然气胁迫植被的最佳结构特征参数集；最后，利用 K-均值算法对简化的 LiDAR 数据进行聚类，并在 Jeffries-Matusita 距离准则的约束下对聚类进行分类，以识别受胁迫的植被。结果表明，天然气胁迫显著改变了植被结构（p=0.05），降低了高度、投影叶面积、冠层起伏比、植被高度变异系数和熵等参数，而增加了均匀性、对比度和相异性。基于高度、均匀性和对比度的结构特征参数集可以稳定地指示天然气胁迫，健康和受胁迫植被样本之间的 Jeffries-Matusita 距离值超过 1.8。该模型对豆类的像素级识别准确率达到 98.95%，对草本的识别准确率达到 96.22%，平均定位准确率分别为 0.15 m 和 0.12 m。本研究证明了植被结构特征在反映对天然气胁迫的响应和监测植被区天然气储存微泄漏方面的潜力。
