Pan-Arctic Vegetation Cover (PAVC) Gridded v1.0 --- High resolution fractional coverage maps of plant functional types at 20-meter spatial resolution

The PAVC-Gridded datasets were created to provide detailed fractional cover information for typical tundra plant functional types (PFTs) across Arctic Alaska, which will be embedded in terrestrial ecosystem models for improving carbon flux estimates. The PFT-level fractional cover also helps characterize the vegetation composition at sub-pixel level for understanding the tundra response to warming climate. This dataset includes 8 Tiff files containing fractional cover for 7 PFTs in the Arctic region of Alaska, USA. There are Tiffs for (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class (8) litter. Each pixel in the Tiff file contains the cover (expressed as a fraction of total ground cover) that was predicted by a random-forest regression model. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

PAVC网格化数据集（PAVC-Gridded datasets）专为美国阿拉斯加北极地区的典型苔原植物功能型（plant functional types, PFTs）提供精细化盖度分数信息，旨在将其嵌入陆地生态系统模型以优化碳通量估算精度。植物功能型层面的盖度分数还有助于刻画亚像元尺度的植被组成，助力解析苔原生态系统对气候变暖的响应机制。本数据集包含8个Tiff文件，涵盖美国阿拉斯加北极区域7类植物功能型的盖度分数，另设1类非植物功能型类别。具体类别如下：(1) 苔藓植物（bryophytes）；(2) 地衣（lichens）；(3) 非维管植物，即地衣与苔藓植物的盖度总和；(4) 落叶灌丛；(5) 常绿灌丛；(6) 杂类草（forbs）；(7) 禾本科草本植物（graminoids），以及(8) 枯落物（非植物功能型类别）。每个Tiff文件的像素值均代表基于随机森林回归模型预测得到的盖度分数，以总地面盖度的占比形式表示。该随机森林模型以2010至2021年间在978个样地采集的盖度数据作为训练集，相关数据已归档于泛北极植被盖度（Pan-Arctic Vegetation Cover, PAVC）数据库（https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557）。样地盖度数据与20米空间分辨率的卫星遥感预测变量进行关联，这些变量包含2019年生长季平均的Sentinel-2光谱数据、Sentinel-1极化数据，以及基于ArcticDEM提取的地形特征。随后，研究团队通过库克距离（Cook’s distance）异常值检测方法，剔除了对回归结果造成较大偏差的时空异常样地数据，并基于高质量样地及其对应的卫星遥感解释变量，为每一类植物功能型重新构建随机森林模型。所有植物功能型的样地实测盖度分数与卫星遥感反演盖度分数之间均呈现良好相关性（决定系数R²为0.69~0.95，枯落物类别R²为0.5），且均方根误差偏差较低（0.02~0.11）。本研究作为北极地下生态系统与全球变化（NGEE Arctic）项目的一部分开展。NGEE Arctic项目旨在通过深化对富碳北极生态系统的预测性认知及其对气候的反馈机制，降低地球系统模型的模拟不确定性，该项目由美国能源部（Department of Energy）生物与环境研究办公室资助。