Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments.

中文翻译：

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使用 Google Earth Engine 进行灾后恢复监控

灾后恢复是一个复杂的过程，可以衡量灾难发生后的进展并了解其组成部分和影响因素。在此过程中，灾害规划者和政府需要可靠的信息来做出使受灾地区恢复正常（灾前）甚至改善条件的决策。因此，必须使用方法来了解灾后恢复过程的动态/变量，并使用快速且具有成本效益的数据和工具来监控该过程。谷歌地球引擎 (GEE) 提供免费访问海量遥感 (RS) 数据和云平台中强大的计算环境，使其成为分析地球表面数据的有吸引力的工具。在这项研究中，我们评估了 GEE 分析和跟踪恢复的适用性。为此，在 2013 年袭击菲律宾莱特岛的台风海燕之后，我们使用 GEE 评估了三年期间的恢复过程。我们开发了一种方法来 (i) 从 Landsat 7 生成云和无阴影的图像合成，以及8 卫星图像并使用随机森林方法生成土地覆盖分类数据，以及 (ii) 基于分类后变化分析生成损坏和恢复图。该方法生成的土地覆盖图精度>88%。我们使用该模型为莱特岛的 62 个城市生成了损坏和三个时间步长恢复图。结果表明，根据选定的土地覆盖变化分析，大多数城市在三年后恢复到灾前情况。然而，更多的分析（例如，功能评估）需要基于详细数据（例如，土地利用地图）来评估恢复的更复杂和微妙的社会经济方面。研究表明，GEE 具有监测大面积恢复过程的良好潜力。然而，最重要的限制是缺乏非常高分辨率的 RS 数据，这些数据对于详细评估过程至关重要，尤其是在复杂的城市环境中。