Plant communities and Uncrewed Aerial Systems data in Northern Alaska (2019-2022)

Multispectral imagery (MS), Light Detection and Ranging (LiDAR) data, and plot-level species abundance observations were used to map plant community heterogeneity across four drained lake basins on the North Slope of Alaska. Species abundance was measured at 149 plots (Species.abundance.csv), which were delineated using dGPS (differential Global Positioning System) coordinates. Of these, 97 plots were used for vegetation classification and 49 were reserved for accuracy assessment (GPS_data.csv). DLB_training.zip and DLB_accuracy.zip describe the delineated training and accuracy plots. Aboveground biomass was estimated for each plant community type (Biomass.csv). Environmental variables including active layer depth, organic layer depth, and water table depth were measured at each plot (Environmental.Data.csv). A positive water table depth indicates standing water. MS and LiDAR data were collected with uncrewed aerial systems (UAS) over the four drained lake basins, as described in MSLiDAR.zip. Historic delineations summarizing vegetation and water extent from 1949–2022 were also created, described in Historic_Outlines.zip.

本数据集利用多光谱影像（Multispectral Imagery, MS）、激光雷达（Light Detection and Ranging, LiDAR）数据以及样方尺度的物种丰度观测数据，对阿拉斯加北坡四个疏干湖盆的植物群落异质性进行制图。共在149个样方内测定物种丰度，相关数据存储于Species.abundance.csv；样方通过差分全球定位系统（differential Global Positioning System, dGPS）坐标完成勾绘。其中97个样方用于植被分类，剩余49个样方留作精度评估，相关GPS数据存储于GPS_data.csv。DLB_training.zip与DLB_accuracy.zip对已勾绘的训练样方与精度评估样方进行了详细说明。针对各植物群落类型估算了地上生物量，相关数据存储于Biomass.csv。在每个样方中测定了包括活动层厚度、有机层厚度与地下水位深度在内的环境变量，相关数据存储于Environmental.Data.csv；其中地下水位深度为正值时代表存在积水。多光谱影像与激光雷达数据通过无人航空系统（Uncrewed Aerial Systems, UAS）在四个疏干湖盆上空采集，采集细节详见MSLiDAR.zip。此外还构建了1949—2022年植被与水体范围的历史勾绘汇总数据集，相关说明详见Historic_Outlines.zip。