Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries.

中文翻译：

---

使用 GEE 云平台内的 Sentinel 数据绘制南非南开普林区的燃烧面积图

在过去的四年里，南非的人工林受到越来越多的经济破坏性火灾的影响。它们对林业构成了重大威胁，占该国商业木材损失的 80% 以上。在南非典型的干燥干旱条件下，森林火灾更加频繁和严重。为了进行适当的森林管理，需要准确检测和绘制被烧毁区域的地图，但由于时间和费用的原因，很难在现场进行这项工作。既然现成的卫星数据可以在基于云的谷歌地球引擎 (GEE) 中免费访问，在本研究中，我们利用 Sentinel-2 衍生的差分归一化燃烧率 (dNBR) 来表征烧伤严重程度区域，并在 2018 年 10 月下旬西开普省东南海岸发生野火后使用 Sentinel-5 跟踪一氧化碳 (CO) 羽流。结果显示，该地区 37.4% 的区域被严重烧毁，其中大部分发生了在研究区的林地中。紧随其后的是 24.7% 的区域被烧毁在中高水平。大约 15.9% 有中低烧伤严重程度，而 21.9% 有轻微烧伤。采用随机森林分类器将燃烧类与未燃烧类分开，总体准确率超过 97%。分类中最重要的变量包括纹理、NBR 和 NIR 波段。CO 信号在火灾爆发期间急剧增加，标志着受影响地区的黑碳强度。我们的研究有助于了解南开普省林业景观动态中的森林火灾。此外，它还证明了 Sentinel-5 在监测 CO 方面的有用性。总而言之，Sentinel 卫星和 GEE 提供了一种有效的工具来绘制火灾图，即使在数据匮乏的国家也是如此。