Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data (<10 cm pixels) is often necessary to resolve patches of many Arctic and boreal plant groups, such as bryophytes and lichens, which are significant components of terrestrial vegetation cover. Separation of co-occurring small vegetation patches in images also requires high spectral resolution. Our goal here was to test the capabilities of UAS-based imaging spectroscopy for mapping plant functional types (PFT) using high spatial and spectral resolution data over Arctic and boreal vegetation at four sites in central Alaska. We then tested several Machine and Deep learning models of PFT cover using the reflectance spectra. The best models were very simple, balancing both bias (overfitting caused by imbalance sample sizes) and variance (fit to the independent validation data), explaining > 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

中文翻译：

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预测野生植物：利用基于 UAS 的可见光和近红外反射光谱绘制北极和北方植物图


北极和寒带地区的生物物理变化推动了植被的变化，例如土壤变暖导致灌木覆盖增加或火灾导致活垫物种丧失。了解当前和未来对这些因素的反应需要以精细的分类分辨率和景观尺度绘制植被图。植物的大小和光谱特征各不相同，这阻碍了在粗空间分辨率下绘制有意义的功能组。遥感数据的精细空间颗粒（<10 厘米像素）通常对于解析许多北极和北方植物群的斑块至关重要，例如苔藓植物和地衣，它们是陆地植被覆盖的重要组成部分。分离图像中同时出现的小植被斑块也需要高光谱分辨率。我们的目标是测试基于 UAS 的成像光谱仪使用阿拉斯加中部四个地点的北极和北方植被的高空间和光谱分辨率数据绘制植物功能类型 (PFT) 的能力。然后，我们使用反射光谱测试了 PFT 覆盖的几种机器和深度学习模型。最好的模型非常简单，平衡了偏差（样本大小不平衡导致的过度拟合）和方差（适合独立验证数据），解释了独立地被覆盖估计的 > 50 % 和估计验证像素的 > 84 % 准确度。我们通过在分类模型中纳入植被指数和窄带（5 nm）到宽带（50 nm）特征的梯度来探索光谱分辨率对 PFT 映射的影响。 植被指数是对 PFT 进行分类的最重要的预测因子，同时包括带状特征改进的模型，窄带宽和宽带宽具有相似的重要性，但宽带宽的模型表现稍好。我们得出的结论是，北极和北方 PFT 反射率可以跨站点汇集，以相对较少的标记像素进行绘图。欠拟合的简单算法在通过平衡偏差和方差对 PFT 进行分类方面优于深度学习，至少在样本量较小的情况下是如此。未来的工作应该致力于增加标记像素的数量和标签的细节，以进一步提高映射分类精度。