Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.免费开放的卫星数据流，以及在谷歌地球引擎（Google Earth Engine）等云计算平台上处理此类数据的能力，使得高频次、大规模高分辨率土地覆盖制图成为现实可能。本研究将此类技术与机器学习相结合，针对加拿大阿尔伯塔省北方森林自然区的泥炭地展开制图：泥炭地作为一类土地覆盖类型，对于保护生物多样性、缓解气候变化影响以及提供碳储存等生态系统服务均具有关键意义。本研究提出了一套数据驱动的科学框架，该框架整合了海量地球观测数据集（包括雷达、光学与激光雷达（LiDAR）数据），对提取的变量在泥炭地建模中的适用性进行评估，优化模型参数化流程，并最终以10米空间分辨率（对应阿尔伯塔省全域共39亿像素），对阿尔伯塔省大片北方林区（397958平方千米）的泥炭地分布进行预测。经验证数据集检验，所得泥炭地分布模型的准确率达87%，卡帕统计量（Kappa Statistic）为0.57；而区分泥炭地与矿质湿地的分类任务准确率为69%，卡帕统计量为0.37。该数据驱动方法可推广至省级、国家级等大型地缘政治尺度的湿地与土地覆盖清查工作，为长期负责任的资源管理提供支撑。