应用遥感图像和辅助产品提高北极行动的安全和后勤效率

Tiffany C. Carey, K. Soofi
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引用次数: 0

摘要

遥感图像及其衍生的辅助产品提高了阿拉斯加北坡上游油气作业的效率和安全性。这些北极地区非常偏远,一般来说很难进入,有时只有季节性才能进入。我们谨慎负责的北极行动要求进行区域侦察勘探,认真监测植被现状、地形、排水系统和湖泊的时序变化等环境。最后,我们还需要对实地行动进行非常详细的后勤规划。遥感图像及其衍生的辅助产品明显改善了我们北极行动的所有这些方面。对于北极行动,遥感数据包括各种光谱和空间分辨率的光学卫星和航空图像,数字高程和数字地表模型的高分辨率激光雷达数据以及合成孔径雷达图像(SAR)。使用内部和商业软件的组合来摄取和处理这些数据。使用各种光谱组合和高通滤波对光学图像进行处理和增强,以产生每个传感器尽可能高的空间分辨率。采用经典神经网络分析方法对植被光学影像进行分类。对SAR图像进行校准(针对所有偏振)并进行几何校正以消除中途停留的影响。处理后的光学和SAR图像、激光雷达和辅助产品共同注册并导入GIS系统进行最终分析和应用。光学图像提供了诸如湖泊轮廓、一般排水、科尔维尔河活动河道、一般湖泊冰况、植被类型分类等表面特征信息。激光雷达数据用于生成斜坡图(用于北极车辆)、一般地形条件和现场操作。当光学图像在北极夜间条件下不可用时,SAR图像被用于监测地表状况。SAR图像还用于计算最终现场操作的冰厚代理图。所有这些产品都直接有助于我们的环境基线研究,提高了我们的现场作业效率和北极作业的总体安全性。对于执业工程师(个人或团队),通过GIS系统提供北极作业的遥感数据和衍生产品。这使得与其他数据层的集成变得容易,并为所有不同的学科提供了一个共同的背景来监控进展,并将他们的学习和想法贡献给整个团队。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Remote Sensing Imagery and Ancillary Products to Improve Safety and Logistical Efficiency of Arctic Operations
Remote Sensing Imagery and the derived ancillary products improved the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. These Arctic regions are remote, very difficult to access in general and sometimes only seasonably accessible. Our prudent and responsible Arctic Operations require regional-reconnaissance exploration, diligent monitoring of environment such as current state of vegetation, temporal changes of terrain, water drainage system and lakes. Finally, we also need very detailed logistical-planning of field operations. Remote sensing imagery and its derived ancillary products demonstrably improved all these aspects of our Arctic Operations. For Arctic Operations, remote sensing data consisted of optical satellite and aerial imagery at various spectral and spatial resolutions, high resolution LIDAR data for digital elevation and digital surface models and synthetic aperture radar imagery (SAR). A combination of in-house and commercial software was used to ingest and process these data. The optical imagery was processed and enhanced using various spectral combinations and high pass filtering to generate the highest possible spatial-resolution for each sensor. Classic neural networks analysis was used to classify the optical imagery for vegetation. The SAR imagery was calibrated (for all polarizations) and geometrically corrected to remove layover effects. The processed optical and SAR imagery, LIDAR and ancillary products were co-registered and imported into a GIS system for final analysis and applications. The optical imagery provided information about surface feature such as lake outlines, general drainage, active channels in Colville River, general lake ice conditions, classification of vegetation types etc. The LIDAR data were used to generate slope maps (for arctic vehicles), general topographic conditions and field operations. The SAR imagery was used to monitor surface conditions when optical imagery was not available during the Arctic night conditions. SAR imagery was also used to calculate the ice thickness proxy maps for eventual field operations. All of these products contributed directly to our environmental baseline studies, improved our field operation efficiency and general safety of our Arctic Operations. For a practicing engineer (individual or team) The remote sensing data and derived products for Arctic Operations were made available via GIS system. This allowed easy integration with other data layers as well as a common background for all different disciplines to monitor progress and to contribute their learnings and ideas to the entire team.
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