Lichen communities are known to be most resistant and adapted organisms to the extreme environments; however, their abundance is not well mapped. Extensive lichen surveys were conducted as part of the 39th Indian scientific expedition and in-situ spectra (350 nm–2500 nm) of lichens were collected in the Larsemann Hills, East Antarctica during austral summer of 2020. Lichen abundance mapping was carried out with the help of Sentinel-2 MSI L2 data and surveyed records along with in-situ spectra. We generated feature collections for lichen, snow, water, bare surface and trained a random forest (RF) classification algorithms implemented in GEE and generated multi-class outputs. We finally merged all non-lichen classes and produced binary pixels with a confidence value (between 0 and 100) depicting similarity of its spectral response to that of a lichen pixel. Total 92 lichen points, 20 bare rock points, 26 points of water and 74 snow points were used to generate the probabilistic lichen abundance map. Resubstitution accuracy of 97.31% was obtained with 10 number of RF trees. Validation was done with geotagged ground photographs having 232 lichens, 20 bare rocks, 22 water and 69 snow points and achieved test accuracy of 82.44%.

地衣群落被认为是对极端环境最具抵抗力和适应性的生物体；然而，它们的丰度尚未得到很好的绘制。作为第 39 次印度科考的一部分，进行了广泛的地衣调查，并于 2020 年夏季在南极洲东部的拉斯曼山收集了地衣的原位光谱（350 nm-2500 nm）。地衣丰度测绘是利用Sentinel-2 MSI L2 数据和测量记录以及原位光谱的帮助。我们生成了地衣、雪、水、裸露表面的特征集合，并训练了在 GEE 中实现的随机森林 (RF) 分类算法，并生成了多类输出。我们最终合并了所有非地衣类，并生成了具有置信值（0 到 100 之间）的二进制像素，描述了其光谱响应与地衣像素的光谱响应的相似性。总共 92 个地衣点、20 个裸露岩石点、26 个水点和 74 个雪点用于生成概率地衣丰度图。使用 10 个 RF 树获得了 97.31% 的重代精度。使用具有 232 个地衣、20 个裸露岩石、22 个水点和 69 个雪点的地理标记地面照片进行验证，测试准确率达到 82.44%。