Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon

Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature into national decision making by estimating the multiple services obtained from ecosystems using the natural capital accounting framework. In this study, we produced 30-m resolution 10 classes land cover maps for the 2015 epoch for Liberia and Gabon using the Google Earth Engine (GEE) cloud platform to support the ongoing natural capital accounting efforts in these nations. We propose an integrated method of pixel-based classification using Landsat 8 data, the Random Forest (RF) classifier and ancillary data to produce high quality land cover products to fit a broad range of applications, including natural capital accounting. Our approach focuses on a pre-classification filtering (Masking Phase) based on spectral signature and ancillary data to reduce the number of pixels prone to be misclassified; therefore, increasing the quality of the final product. The proposed approach yields an overall accuracy of 83% and 81% for Liberia and Gabon, respectively, outperforming prior land cover products for these countries in both thematic content and accuracy. Our approach, while relatively simple and highly replicable, was able to produce high quality land cover products to fill an observational gap in up to date land cover data at national scale for Liberia and Gabon.

利比里亚与加蓬已加入2012年出台的《非洲可持续性哈博罗内宣言》（Gaborone Declaration for Sustainability in Africa, GDSA），其核心目标为依托自然资本核算框架，评估生态系统提供的多元服务，从而将自然价值纳入国家决策体系。本研究借助谷歌地球引擎（Google Earth Engine, GEE）云平台，为利比里亚和加蓬制作了2015年时相的30米分辨率10类土地覆盖图，以支撑两国当前推进的自然资本核算工作。我们提出了一种集成化的基于像元分类方法，即结合Landsat 8遥感数据、随机森林（Random Forest, RF）分类器与辅助数据，生成可适配包括自然资本核算在内的多类应用场景的高质量土地覆盖产品。该方法重点依托光谱特征与辅助数据开展分类前滤波（掩膜阶段）操作，以降低易出现误分类的像元占比，进而提升最终产品的质量。本方法在利比里亚与加蓬的总体分类精度分别达到83%与81%，在主题内容与精度表现上均优于两国此前发布的各类土地覆盖产品。尽管该方法相对简易且可复现性强，却成功生成了高质量土地覆盖产品，填补了利比里亚与加蓬国家级最新土地覆盖数据的观测空白。