South Africa is reported to experience timber shortages as a result of growing timber demands and pulp production, coupled with the government’s reluctance to grant new forestry permits. Rampant timber theft in the country makes these circumstances worse. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has greatly improved the accessibility and usability of high spatial and temporal Sentinel-1 and -2 data, especially in data-poor countries that lack high-performance computing systems for forest monitoring. Here, we demonstrate the potential of these resources for forest harvest detection. The results showed that Sentinel-1 data are efficient in detecting clear-cut events; both VH and VV backscatter signals decline sharply in accordance with clear-cutting and increase again when forest biomass increases. When correlated with highly responsive NDII, the VH and VV signals reached the best accuracies of 0.79 and 0.83, whereas the SWIR1 achieved –0.91. A Random Forest (RF) algorithm based on Sentinel-2 data also achieved over 90% accuracies for classifying harvested and forested areas. Overall, our study presents a cost-effective method for mapping clear-cut events in an economically important forestry area of South Africa while using GEE resources.

中文翻译：

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使用Google地球引擎使用Sentinel-1和-2数据检测人工林中的收获事件

据报道，由于木材需求和纸浆产量的增长，加上政府不愿发放新的林业许可证，南非的木材短缺。该国猖timber的木材盗窃使这些情况更加恶化。基于云的平台（例如Google Earth Engine（GEE））的出现极大地改善了高时空Sentinel-1和-2数据的可访问性和可用性，尤其是在缺少高性能计算系统的数据贫乏国家/地区用于森林监测。在这里，我们展示了这些资源在森林采伐检测中的潜力。结果表明，Sentinel-1数据可以有效地检测出清晰事件。VH和VV反向散射信号均会按照清晰的幅度急剧下降，并在森林生物量增加时再次增加。当与高响应性NDII相关时，VH和VV信号达到0.79和0.83的最佳精度，而SWIR1达到–0.91。基于Sentinel-2数据的随机森林（RF）算法也实现了90％以上的准确度，可对采伐和森林区域进行分类。总体而言，我们的研究提出了一种经济有效的方法，可以在利用GEE资源的同时绘制南非经济上重要的林业地区的明晰事件。