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比较多光谱和高光谱数据在监测溢油影响方面的潜力。

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact.

机构信息

Center for Spatial Technologies and Remote Sensing, Department of Land Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616, USA.

Department of Innovation, Environmental and Energy Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands.

出版信息

Sensors (Basel). 2018 Feb 12;18(2):558. doi: 10.3390/s18020558.

Abstract

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

摘要

海上钻探和沿海炼油厂的溢油事故经常导致沿海环境的严重恶化。如果对初始油扩散范围、油影响和恢复情况进行监测,早期的溢油检测可能会防止损失,并加快恢复速度。卫星图像数据可以为监测溢油对湿地的影响提供一种具有成本效益的替代方案,而无需进行昂贵的航空图像或劳动密集型实地考察。然而,鉴于这些卫星数据的光谱测量特性和时间频率,它们在检测和绘制溢油后生态系统恢复方面的能力可能受到限制。在本研究中,我们评估了空间和光谱分辨率以及其他传感器特性是否会影响检测和绘制因油而导致的植被压力和死亡率的能力。我们比较了三个卫星多光谱传感器(WorldView2、RapidEye 和 Landsat EMT+)与航空高光谱 AVIRIS 传感器在 2010 年墨西哥湾深水地平线溢油事件后绘制油诱导植被压力、恢复和死亡率的能力。我们发现,更精细的空间分辨率(3.5 m)能够更好地划定受油影响的湿地边界,并更好地检测盐沼湿地生态系统中沿受油海岸线的植被压力。随着空间分辨率变得更粗糙(3.5 m 至 30 m),准确检测和绘制受压力植被的能力下降。光谱分辨率确实提高了对受油湿地的检测和制图能力,但不如空间分辨率强,这表明宽带数据可能足以检测和绘制受油影响的湿地。AVIRIS 窄带数据在检测植被压力方面表现更好,其次是 WorldView2、RapidEye,然后是 Landsat 15 m(拼接锐化)数据。更高质量的传感器光学器件和更高的信噪比(SNR)也可能提高对受油湿地的检测和制图能力;我们发现,具有更高 SNR 的重新采样较粗糙分辨率的 AVIRIS 数据表现优于三个卫星传感器中的任何一个。在这些潮汐湿地中,能够在特定时间(中午、低潮等)或特定日期(无云等)获取图像也很重要;在高潮时捕获的 WorldView2 图像检测到受油影响的海岸线较窄,这可能是因为部分受影响的湿地位于潮汐线以下。这些结果表明,虽然多光谱数据可能足以检测受油影响的湿地范围,但高光谱和空间分辨率、高质量传感器特性以及控制图像获取时间的能力可能会提高溢油后植被压力和恢复情况的评估和监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9dc6/5855317/b9207044b179/sensors-18-00558-g001.jpg

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