Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

• Published in: Environmental Research Letters
• Main Authors: Thomas A Douglas, Caiyun Zhang
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2021
• Subjects: boreal forest; snow; machine learning; permafrost; Environmental technology. Sanitary engineering; TD1-1066; Environmental sciences; GE1-350; Science; Q; Physics; QC1-999; Arctic; Alaska
• Online Access: https://doi.org/10.1088/1748-9326/ac04d8; https://doaj.org/article/ac102daffac646d2b1ca4daf55a03747

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be markedly different from one season to another there are strong repeated relationships between ecotype and snowpack depth. In the diverse vegetative cover of the boreal forest of Interior Alaska, a warming climate has shortened the winter season. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. More than 26 000 snow depth measurements were collected between 2014 and 2019 at three field sites representing common boreal ecoregion land cover types. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. Of the three modeling approaches we used, support vector machine yields slightly stronger statistical correlations between snowpack depth and ecotype for most winters. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.