WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie

Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).

木本植物侵占（Woody encroachment，或称木本植物入侵）正快速将高草草原转变为以灌木和常绿植物为主的生态系统，其主要驱动因素为火抑制作用。追踪木本植物侵占的速率与范围颇具挑战，因为灌木与小型乔木的尺寸远小于常规遥感影像的粗分辨率（>10平方米）。不过，美国政府正通过美国农业部国家农业影像计划（USDA NAIP）与国家生态观测站网络（NEON）推进高分辨率（<2平方米）遥感技术的部署，二者年均投入经费达数百万美元，且涵盖多种遥感产品。本研究针对这两套可免费获取的遥感航空影像，对比了随机森林（Random Forests）与支持向量机（Support Vector Machines）两种分类方法，旨在探究NEON影像能否提升分类精度、提升幅度如何，并比较哪种机器学习方法的分类精度更优。所有模型的总体分类精度均高于91%，其中NEON影像的分类精度较NAIP影像高出数个百分点。NEON影像的分类显著依赖于冠层高度数据（LiDAR），而NAIP影像的分类中各波段的重要性分布更为均衡。最后，针对北美圆柏（Eastern Red Cedar）的分类精度在NEON影像下可达78%-84%，而使用NAIP影像时的分类精度仅为55%-61%，差距较为显著。