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1. Understanding the climate impacts on decadal vegetation change in northern Alaska

   https://doi.org/10.1139/as-2020-0050  

   Harris, Jacob A.; Hollister, Robert D.; Botting, Timothy F.; Tweedie, Craig E.; Betway, Katlyn R.; May, Jeremy L.; Barrett, Robert T.S.; Leibig, Jenny A.; Christoffersen, Hana L.; Vargas, Sergio A.; et al (September 2022, Arctic Science)

   The Arctic is experiencing rapid climate change. This research documents changes to tundra vegetation near Atqasuk and Utqiaġvik, Alaska. At each location, 30 plots were sampled annually from 2010 to 2019 using a point frame. For every encounter, we recorded the height and classified it into eight groupings (deciduous shrubs, evergreen shrubs, forbs, graminoids, bryophytes, lichens, litter, and standing dead vegetation); for vascular plants we also identified the species. We found an increase in plant stature and cover over time, consistent with regional warming. Graminoid cover and height increased at both sites, with a 5-fold increase in cover in Atqasuk. At Atqasuk, the cover and height of shrubs and forbs increased. Species diversity decreased at both the sites. Year was generally the strongest predictor of vegetation change, suggesting a cumulative change over time; however, soil moisture and soil temperature were also predictors of vegetation change. We anticipate that plants in the region will continue to grow taller as the region warms, resulting in greater plant cover, especially of graminoids and shrubs. The increase in plant cover and accumulation of litter may negatively impact non-vascular plants. Continued changes in community structure will impact energy balance and carbon cycling and may have regional and global consequences. 

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2. Data from: Using handheld mobile laser scanning to quantify fine-scale surface fuels and detect changes post-disturbance in Northern California forests

   https://doi.org/10.5061/dryad.sxksn038g  

   Post, Alanna; Forbes, Brieanne; Cooper, Zane; Faro, Kristi; Seel, Catherine; Clark, Matthew; Disney, Mathias; Bentley, Lisa (January 2025, Dryad)

   The understory plays a critical role in the disturbance dynamics of forest ecosystems, as it can influence wildfire behavior. Unfortunately, the 3D structure of understory fuels is often difficult to quantify and model due to vegetation and substrate heterogeneity. LiDAR remote sensing can measure changes in 3D forest structure more rapidly, comprehensively, and accurately than manual approaches, but a remote sensing approach is more frequently applied to the overstory compared to the understory. Here we evaluated the use of handheld mobile laser scanning (HMLS) to measure and detect changes in fine-scale surface fuels following wildfire and timber harvest in Northern Californian forests, USA. First, the ability of HMLS to quantify surface fuels was validated by destructively sampling vegetation below 1 m with a known occupied volume within a 3D frame and comparing destructive-based volumes with HMLS-based occupied volume estimates. There was a positive linear relationship (R2 = 0.72) between volume estimates, and occupied volume estimated from 1-cm voxels had the best relationship with measured biomass (R2 = 0.46, RMSE = 50.76 g, p < .0001) compared to larger voxel sizes. Next, HMLS was used to scan forest plots where wildfire or timber harvest had occurred, producing bi-temporal (pre and post) structural measurements. Plot scans were voxelized and the volume occupied by surface fuels was extracted and quantified. Changes in plot-level HMLS estimates of surface fuels were compared to data collected with a standardized manual field protocol to quantify plot-level dead and uprooted vegetation (Brown’s transects). Both HMLS and Brown’s transects detected a similar decrease in surface fuels post-wildfire. However, removal of ground voxels for the HMLS analysis revealed the regrowth of live vegetation one-year post-fire that was not captured by Brown’s transects. Neither remote sensing nor field approaches detected any changes in fine-scale surface fuels post-logging. HMLS can be a valuable tool for land stewards to rapidly quantify understory vegetation, especially following disturbance. An accurate assessment of understory vegetation is crucial for management plans to reduce wildfire risk and both live and dead fuels might not be captured fully post-wildfire using non-remote sensing approaches. Data were collected in a few different ways. 3D frame data were collected by scanning a 3D frame with a handheld mobile laser scanner (HMLS) and then destructively sampling of the vegetation inside. The scans were processed by the scanner's software (GeoSLAM, SLAM algorithm), and the vegetation samples were oven dried to get dry mass measurements. Plot-level data were collected at 11.3 m radius circle plots at 2 locations across 3 time periods, lidar scans were taken with the HMLS and Brown's data were collected using the standard Brown's transect protocol. Brown's data were processed to extract estimates of fuel mass per area for each plot. All of the lidar scans taken with the HMLS (both frame and plot scans) were further processed in Lidar360, CloudCompare, and R with the lidR package to clip scans to the frame/plot boundary, height normalize, and voxelize the scans. Frame scans were voxelized at 4 different voxel sizes (1, 5, 10, and 25 cm), while plot scans were all voxelized at 1 cm voxel size. # Data from: Using handheld mobile laser scanning to quantify fine-scale surface fuels and detect changes post-disturbance in Northern California forests [https://doi.org/10.5061/dryad.sxksn038g](https://doi.org/10.5061/dryad.sxksn038g) The dataset includes processed handheld lidar data and dry mass, from 3D frame and plot sampling. The lidar system used is a handheld mobile laser scanner (GeoSLAM's Zeb-REVO). ## Description of the data and file structure Sheets within the Excel file are separated based on manuscript sections. '3D Frame' includes the data collected from lidar scans and destructive sampling which was collected to validate the use of handheld lidar for vegetation monitoring. 'Plot-level' contains the total occupied voxels from the processed plot scans taken in each survey/campaign. 'Brown's' is the mass per area calculated from Brown's transects collected at the plots and the predicted mass in grams as calculated from the voxelized plot scans. 'Point Density' contains all of the scan point densities and was used to compare point densities across the various surveys/campaigns/plots. Metadata has all of the variable names and their respective meanings, including units. Sheets are designed to be used separately and are optimized for data manipulation in R. For those wishing to use the plot-level and Brown's data, keep in mind that there are 2 study sites, one with 3 survey campaigns called Saddle Mountain ('c6', 'c7', 'c10') and the other with 2 called Jackson ('c3', 'c9'). The labeling came from a larger data collection effort by the Bentley Lab at Sonoma State University, hence the non-consecutive numbers. 

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3. Plant and litter cover estimates derived from overhead microplot photos in the Cross-Scale Interactions Study (CSIS) at Jornada Basin LTER, 2013-ongoing

   https://doi.org/10.6073/pasta/e0f907ca18c738795abf86686123d393  

   Peters, Debra C; Anderson, John (January 2023, Environmental Data Initiative)

   This dataset contains plant and litter cover estimates derived from overhead photos collected from microplots in a long-term experiment (2013-present) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Experimental treatments were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, control without manipulations. Repeat, overhead (downward-looking) photographs of ten "microplots" in each plot were taken for estimation of litter, soil, and vegetation cover in the experimental treatment and control plots over time. Photographs were rotated and then cropped to provide standardized areas, and then were analyzed with USDA SamplePoint software to determine coverage by ~27 plant, litter or other cover classes on a 100 point grid. Raw and corrected cover estimates are provided. This study is ongoing and new data will be added annually. Cover estimates are derived from photos in EDI dataset knb-lter-jrn.210413004. 

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4. Time-lapse camera (phenocam) imagery of sensor network plots, 2017 - ongoing.

   https://doi.org/10.6073/pasta/ef7d0b8ee4e70c3a4475638e2c66e189  

   Carter, Kelsey E; Smith, Jane G; Elmendorf, Sarah C; Willbern, T Austin; LTER, Niwot Ridge (January 2025, Environmental Data Initiative)

   Images from time-lapse cameras were analyzed to track the greenness curves of 16 plots in the Sensor Network at Niwot Ridge. Images were taken every 30 minutes during daylight hours throughout the growing season. Cameras were angled to view 1m^2 vegetation plots located at each sensor node. Pixels in the portion of the image capturing the vegetation plot were used to calculate the green chromatic coordinate (GCC). The change in GCC over the growing season represents the growth and phenology of the plant communities captured. 

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5. Tundra Plant Canopies Gradually Close Over Three Decades While Cryptogams Persist

   https://doi.org/10.1111/gcb.70155  

   Betway‐May, Katlyn R; Gould, William A; Elmendorf, Sarah C; May, Jeremy L; Hollister, Robert D; Oberbauer, Steven F; Breen, Amy; Crain, Benjamin J; Cuervo, Ana_Maria Sanchez; Walker, Marilyn D; et al (April 2025, Global Change Biology)

   ABSTRACT Global climate change phenomena are amplified in Arctic regions, driving rapid changes in the biota. Here, we examine changes in plant community structure over more than 30 years at two sites in arctic Alaska, USA, Imnavait Creek and Toolik Lake, to understand long‐term trends in tundra response to changing climate. Vegetation cover was sampled every 4–7 years on permanent 1 m²plots spanning a 1 km²grid using a point‐frame. The vascular plant canopies progressively closed at both locations. Canopy cover, defined here as an encounter of a vascular plant above the ground surface, increased from 63% to 91% at Imnavait Creek and from 63% to 89% at Toolik Lake. Both sites showed steady increases in maximum canopy height, increasing by approximately 50% (8 cm). While cover and height increased to some extent for all vascular plant growth forms, deciduous shrubs and graminoids changed the most. For example, at Imnavait Creek the cover of graminoids more than tripled (particularly in wet meadow plots), increasing by 237%. At Toolik Lake the cover of deciduous shrubs more than doubled (particularly in moist acidic plots), increasing by 145%. Despite the steady closing of the plant canopy, cryptogams (lichens and mosses) persisted; in fact, the cover of lichens increased. These results call into question the dominant dogma that cryptogams will decline with increases in vascular plant abundance and demonstrate the resilience of these understory plants. In addition to overall cover, the diversity of vascular plants increased at one site (Imnavait Creek). In contrast to much of the Arctic, summer air temperatures in the Toolik Lake region have not significantly increased over the 30+ year sampling period; however, winter temperatures increased substantially. Changes in vegetation community structure at Imnavait Creek and Toolik Lake are likely the result of winter warming. 

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