Leveraging machine learning and remote sensing to improve grassland inventory in British Columbia

Machine learning algorithms have been widely adopted in the monitoring ecosystem. British Columbia suffers from grassland degradation but the province does not have an accurate spatial database for effective grassland management. Moreover, computational power and storage space remain two of the limiting factors in developing the database. In this study, we leverage supervised machine learning algorithms using the Google Earth Engine to better annual grassland inventory through an automated process.
The pilot study was conducted over the Rocky Mountain district. We compared two different classification algorithms: the Random forest, and the Support vector machine. Training data was sampled through stratified and grided sampling. 19 predictor variables were chosen from Sentinel-1 and Sentinel-2 imageries and relevant topological derivatives, spectral indices, and textural indices using a wrapper-based feature selection method. The resultant map was post-processed to remove land features that were confounded with grasslands.
Random forest was chosen as the prototype because the algorithm predicted features relevant to the project’s scope at relatively higher accuracy (67% - 86%) than its counterparts (50% - 76%). The prototype was good at delineating the boundaries between treed and non-treed areas and ferreting out opened patches among closed forests. These opened patches are usually disregarded by the VRI but they are deemed essential to grassland stewardship and wildlife ecologists. The prototype demonstrated the feasibility of automating grassland delineation by a Random forest classifier using the Google Earth Engine. Furthermore, grassland stewards can use the product to identify monitoring and restoration areas strategically in the future.

机器学习算法已在生态监测领域得到广泛应用。不列颠哥伦比亚省面临草原退化问题，但该省尚无用于高效草原管理的精准空间数据库。此外，计算能力与存储空间仍是该数据库开发过程中的两大制约因素。本研究借助谷歌地球引擎（Google Earth Engine）平台上的监督式机器学习算法，通过自动化流程优化年度草原清查工作。
本试点研究在落基山区域开展。本研究对比了两种不同的分类算法：随机森林（Random Forest）与支持向量机（Support Vector Machine）。训练数据通过分层采样与网格采样的方式采集。本研究采用基于包装器的特征选择方法，从哨兵一号（Sentinel-1）、哨兵二号（Sentinel-2）影像以及相关拓扑导数、光谱指数与纹理指数中筛选出19个预测变量。对最终生成的草原分布图进行后处理，以剔除与草原特征混淆的地物。
最终选择随机森林作为原型算法，因其在匹配本研究项目范围的特征预测上，准确率区间为67%~86%，显著优于支持向量机的50%~76%准确率区间。该原型算法能够精准区分林木区与非林木区的边界，并能识别封闭森林中的开阔斑块。这类开阔斑块通常未被VRI纳入考量，但草原管理者与野生动物生态学家认为其至关重要。该原型验证了借助谷歌地球引擎平台，通过随机森林分类器自动化实现草原边界勾画的可行性。此外，草原管理者未来可依托该产品，战略性地确定监测与修复区域。