Bellante Gabriel J, Powell Scott L, Lawrence Rick L, Repasky Kevin S, Dougher Tracy
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America.
Department of Electrical and Computer Engineering, Montana State University, Bozeman, Montana, United States of America.
PLoS One. 2014 Oct 20;9(10):e108299. doi: 10.1371/journal.pone.0108299. eCollection 2014.
Remote sensing of vegetation stress has been posed as a possible large area monitoring tool for surface CO2 leakage from geologic carbon sequestration (GCS) sites since vegetation is adversely affected by elevated CO2 levels in soil. However, the extent to which remote sensing could be used for CO2 leak detection depends on the spectral separability of the plant stress signal caused by various factors, including elevated soil CO2 and water stress. This distinction is crucial to determining the seasonality and appropriateness of remote GCS site monitoring. A greenhouse experiment tested the degree to which plants stressed by elevated soil CO2 could be distinguished from plants that were water stressed. A randomized block design assigned Alfalfa plants (Medicago sativa) to one of four possible treatment groups: 1) a CO2 injection group; 2) a water stress group; 3) an interaction group that was subjected to both water stress and CO2 injection; or 4) a group that received adequate water and no CO2 injection. Single date classification trees were developed to identify individual spectral bands that were significant in distinguishing between CO2 and water stress agents, in addition to a random forest classifier that was used to further understand and validate predictive accuracies. Overall peak classification accuracy was 90% (Kappa of 0.87) for the classification tree analysis and 83% (Kappa of 0.77) for the random forest classifier, demonstrating that vegetation stressed from an underground CO2 leak could be accurately discerned from healthy vegetation and areas of co-occurring water stressed vegetation at certain times. Plants appear to hit a stress threshold, however, that would render detection of a CO2 leak unlikely during severe drought conditions. Our findings suggest that early detection of a CO2 leak with an aerial or ground-based hyperspectral imaging system is possible and could be an important GCS monitoring tool.
由于植被会受到土壤中二氧化碳浓度升高的不利影响，因此植被胁迫遥感技术被视为一种可能用于大面积监测地质碳封存（GCS）场地地表二氧化碳泄漏的工具。然而，遥感技术用于二氧化碳泄漏检测的程度取决于由各种因素（包括土壤二氧化碳浓度升高和水分胁迫）引起的植物胁迫信号的光谱可分离性。这种区分对于确定远程GCS场地监测的季节性和适宜性至关重要。一项温室实验测试了受土壤二氧化碳浓度升高胁迫的植物与受水分胁迫的植物之间的区分程度。采用随机区组设计，将苜蓿植株（紫花苜蓿）分配到四个可能的处理组之一：1）二氧化碳注入组；2）水分胁迫组；3）同时遭受水分胁迫和二氧化碳注入的交互作用组；或4）接受充足水分且未注入二氧化碳的组。开发了单日期分类树以识别在区分二氧化碳和水分胁迫因素方面具有重要意义的各个光谱带，此外还使用了随机森林分类器来进一步理解和验证预测准确性。分类树分析的总体峰值分类准确率为90%（卡帕系数为0.87），随机森林分类器的准确率为83%（卡帕系数为0.77），这表明在某些时候，可以准确地从健康植被和同时存在水分胁迫植被的区域中辨别出因地下二氧化碳泄漏而受到胁迫的植被。然而，植物似乎达到了一个胁迫阈值，在严重干旱条件下不太可能检测到二氧化碳泄漏。我们的研究结果表明，使用航空或地面高光谱成像系统早期检测二氧化碳泄漏是可能的，并且可能成为一种重要的GCS监测工具。
