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1. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles, 2018-2019

   https://doi.org/10.18739/A2R785Q5B  

   Orndahl, Kathleen (January 2022, Arctic Data Center)

   Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data. 

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2. Gridded 30-meter resolution estimates of aboveground plant biomass, woody plant biomass and woody plant dominance across the Arctic tundra biome (2020)

   https://doi.org/10.18739/a2ns0m06b  

   Orndahl, Kathleen M; Berner, Logan T; Macander, Matthew J; Arndal, Marie F; Alexander, Heather D; Humphreys, Elyn R; Loranty, Michael M; Ludwig, Sarah M; Nyman, Johanna; Juutinen, Sari; et al (January 2024, NSF Arctic Data Center)

   This dataset provides estimates of live, oven-dried aboveground biomass of all plants (tree, shrub, graminoid, forb, bryophyte) and all woody plants (tree, shrub) at 30-meter resolution across the Arctic tundra biome. Estimates of woody plant dominance are also provided as: (woody plant biomass / plant biomass) * 100. Plant biomass and woody plant biomass were estimated for each pixel (grams per square meter [g / m2]) using field harvest data for calibration/validation along with modeled seasonal surface reflectance data derived using Landsat satellite imagery and the Continuous Change Detection and Classification algorithm, and other supplementary predictors related to topography, region (e.g. bioclimate zone, ecosystem type), land cover, and derivative spectral products. Modeling was performed in a two-stage process using random forest models. First, biomass presence/absence was predicted using probability forests. Then, biomass quantity was predicted using regression forests. The model outputs were combined to produce final biomass estimates. Pixel uncertainty was assessed using Monte Carlo iterations. Field and remote sensing data were permuted during each iteration and the median (50th percentile, p500) predictions for each pixel were considered best estimates. In addition, this dataset provides the lower (2.5th percentile, p025) and upper (97.5th percentile, p975) bounds of a 95% uncertainty interval. Estimates of woody plant dominance are not modeled directly, but rather derived from plant biomass and woody plant biomass best estimates. The Pan Arctic domain includes both the Polar Arctic, defined using bioclimate zone data from the Circumpolar Arctic Vegetation Mapping Project (CAVM; Walker et al., 2005), and the Oro Arctic (treeless alpine tundra at high latitudes outside the Polar Arctic), defined using tundra ecoregions from the RESOLVE ecoregions dataset (Dinerstein et al., 2017) and treeline data from CAVM (CAVM Team, 2003). The mapped products focus on Arctic tundra vegetation biomass, but the coarse delineation of this biome meant some forested areas were included within the study domain. Therefore, this dataset also provides a tree mask product that can be used to mask out areas with canopy height ≥ 5 meters. This mask helps reduce, but does not eliminate entirely, areas of dense tree cover within the domain. Users should be cautious of predictions in forested areas as the models used to predict biomass were not well constrained in these areas. This dataset includes 132 files: 128 cloud-optimized GeoTIFFs, 2 tables in comma-separated values (CSV) format, 1 vector polygon in Shapefile format, and one figure in JPEG format. Raster data is provided in the WGS 84 / North Pole LAEA Bering Sea projection (EPSG:3571) at 30 meter (m) resolution. Raster data are tiled with letters representing rows and numbers representing columns, but note that some tiles do not contain unmasked pixels. We included all tiles nonetheless to maintain consistency. Tiling information can be found in the ‘metadata’ directory as a figure (JPEG) or shapefile. 

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3. Monitoring ecological characteristics of a tallgrass prairie using an unmanned aerial vehicle

   https://doi.org/10.1111/rec.13339  

   Blackburn, Ryan C.; Barber, Nicholas A.; Farrell, Anna K.; Buscaglia, Robert; Jones, Holly P. (February 2021, Restoration Ecology)

   Site‐specific conditions, climate, and management decisions all dictate the establishment and composition of desired plant communities within grassland restorations. The uncertainty, complexity, and large size of grassland restorations necessitate monitoring plant communities across spatial and temporal scales. Remote sensing with unmanned aerial vehicles (UAVs) may provide a tool to monitor restored plant communities at various scales, but many potential applications are still unknown. In a tallgrass prairie restoration located in Franklin Grove, IL, we used UAV‐based multispectral imagery to assess the ability of spectral indices to predict ecological characteristics (plant community, plant traits, soil properties) in the summer of 2017. Using 19 sites, we calculated the moments of 26 vegetation indices and four spectral bands (green, red, red edge, near infrared). Models based on each moment and a model with all moments were estimated using ridge regression with model training based on a subset of 15 sites. Each tested for significant error reduction against a null model. We predicted mean graminoid cover, mean dead aboveground biomass, mean dry mass, and mean soil K with significant reductions in cross‐validated root mean square error. Averaged coefficients determined from cross‐validation of ridge regression models were used to develop a final predictive model of the four successfully predicted ecological characteristics. Graminoid cover and soil potassium were successfully predicted in one of the sites while the other two were not successfully predicted in any site. This study provides a path toward a new level of ease and precision in monitoring community dynamics of restored grasslands. 

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4. Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

   https://doi.org/10.1002/rse2.228  

   Cunliffe, Andrew M.; Anderson, Karen; Boschetti, Fabio; Brazier, Richard E.; Graham, Hugh A.; Myers‐Smith, Isla H.; Astor, Thomas; Boer, Matthias M.; Calvo, Leonor G.; Clark, Patrick E.; et al (July 2021, Remote Sensing in Ecology and Conservation)

   Abstract Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjustedR²of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similarwithinbut differentamong,plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha⁻¹. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe. 

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5. Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

   https://doi.org/10.3390/rs12182970  

   Talucci, Anna C.; Forbath, Elena; Kropp, Heather; Alexander, Heather D.; DeMarco, Jennie; Paulson, Alison K.; Zimov, Nikita S.; Zimov, Sergei; Loranty, Michael M. (September 2020, Remote Sensing)

   null (Ed.)

   The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests. 

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