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Delineations of Retrogressive Thaw Slump (RTS) expansion and light detection and ranging (LiDAR) datasets (LAS files) of RTS sites were used to model how rates of RTS change are influenced by topographic and climatic characteristics across northern Alaska. LiDAR data were collected at ten sites, where five were collected from an uncrewed aerial system (UAS) and five were collected from a terrestrial LiDAR systems (TLS). LiDAR datasets were used to bias correct the open-source ArcticDEM (2 meter-resolution) for calculating annual rates of RTS volumetric losses across all sites. RTS Delineations were used to calculate annual rates of RTS areal expansion and summarize topographic characteristics calculated from the corrected ArcticDEM. Two shapefiles were created from historic satellite and aerial imagery (1949-2021) to summarize RTS areal change across 44 slumps: AK_RTS_ExansionDelineations.shp summarizes the area of RTS expansion between two time periods and AK_RTS_Delineations.shp summarizes the total RTS outline in each year where RTS expansion occurs. LiDAR UAS and TLS data are provided as LAS files from 12 slumps (five sites) near Toolik Lake and 9 slumps (5 sites) within the Noatak National Preserve. 

Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers. 

Peatlands cover 3% of the global land surface, yet store 25% of the world’s soil organic carbon. These organic-rich soils are widespread across permafrost regions, representing nearly 18% of land surface and storing between 500 and 600 petagrams of carbon (PgC). Peat (i.e., partially decomposed thick organic layers) accumulates due to the imbalance between plant production and decomposition often within saturated, nutrient deficient, and acidic soils, which limit decomposition. As warmer and drier conditions become more prevalent across northern ecosystems, the vulnerability of peatland soils may increase with the susceptibility of peat-fire ignitions, yet the distribution of peatlands across Alaska remains uncertain. Here we develop a new high-resolution (20 meter (m) resolution) wall-to-wall ~1.5 million square kilometer (km2) peatland map of Alaska, using a combination of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM). Machine learning classifiers were trained and tested using peat cores, ground observations, and sub-meter resolution image interpretation, which was spatially constrained by a peatland suitability model that described the extent of terrain suitable for peat accumulation. This product identifies peatlands in Polar, Boreal, and Maritime ecoregions in Alaska to cover 26,842 (4.6%), 69,783 (10.4%), and 13,506 (5.3%) km2, respectively.