ABoVE: Hyperspectral Imagery from AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019

Miller, Christine; Green, R.O.; Thompson, D. R.; Thorpe, A. K.; Eastwood, Michael L.; McCubbin, Ian B.; Olson-Duvall, Winston; Bernas, M.; Sarture, Charles M.; Nolte, Scott H.; Rios, L.M.; Hernández,; Bue, Brian D.; Lundeen, S.

doi:10.3334/ornldaac/1569

Cited by 7 publications

(5 citation statements)

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“…This study integrates an inventory of lake catchment change derived from multiscale remote sensing with in situ measurements of lake water level and a 13-year record of lake hydrology and water chemistry. Surface water area fluctuations and vegetation properties were assessed using high- to-medium resolution data sets that include optical data acquired by a remotely piloted aircraft (drone) system, the Airborne Visible/Infrared Imaging Spectrometer (Next Generation; AVIRIS-NG; Miller et al 2019aMiller et al , 2019b, and spaceborne Landsat and Sentinel-2 satellites. Drone-acquired 3.5 cm resolution orthomosaics and 13.4 cm multispectral data were useful for providing ground truth data to identify general land cover type and shoreline locations.…”

Section: Methodsmentioning

confidence: 99%

“…An inventory of land cover change (i.e., willow shrub vegetation growth) within the former lakebed was required to evaluate potential drivers of hydroecological change. We accomplished this using AVIRIS-NG data (Miller et al 2019a(Miller et al , 2019b; Appendix, Table A5), which were acquired during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Airborne Campaign 17 July 2017. AVIRIS-NG data has a 5-nm spectral resolution spanning 380-2510 nm (427 bands; Miller et al 2019b).…”

Section: Land Cover Classification Using Aviris-ng Datamentioning

confidence: 99%

“…The detected Arctic Science Downloaded from cdnsciencepub.com by 44.224.250.200 on 09/04/22 For personal use only. (Miller et al 2019a(Miller et al , 2019b, which covers most of the catchment, was combined with Landsat 8 (OLI) data acquired during 19 July 2017 (USGS 2020a) to cover the remaining area. The red line identifies the Landsat 8-derived surface water area extent for 19 July 2017.…”

Section: Land Cover Change and Influence On Snowpackmentioning

confidence: 99%

“…Maximum likelihood land cover classification within the former predrainage lakebed of Zelma Lake. AVIRIS-NG data acquired on 11 July 2017(Miller et al 2019a(Miller et al , 2019b, which covers most of the catchment, was combined with Landsat 8 (OLI) data acquired during 19 July 2017 (USGS 2020a) to cover the remaining area. The red line identifies the Landsat 8-derived surface water area extent for 19 July 2017.…”

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confidence: 99%

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Monitoring 13 years of drastic catchment change and the hydroecological responses of a drained thermokarst lake

2022

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Catastrophic drainage of thermokarst lakes transform portions of former lakebed to terrestrial settings, which have largely unknown consequences for the remaining aquatic habitat. Old Crow Flats, northern Yukon (Canada), is a lake-rich area that has recently experienced a climate-driven increase in lake drainage frequency. A notable example occurred during June 2007 when Zelma Lake (originally 12 km2) lost over 80% of its volume. Here we integrate remote sensing techniques with in-situ hydrological and limnological measurements over 13 years following drainage to 1) monitor water surface area and terrestrial land cover change and 2) identify associated effects on aquatic conditions. An airborne drone system was used to provide training data for land cover classification of AVIRIS-NG data, which indicated that tall willow shrubs covered 30.8% of the former lake area by 2017. Lake water isotope-derived deuterium-excess increased during the 13-year record indicating that hydrological input increased with greater snowpack accumulation within encroaching vegetation. Limnological conditions were highly variable and eutrophic during the first few years following drainage but became more stable as vegetation colonized the former lakebed. This long-term study provides insight of aquatic responses to thermokarst lake drainage and shrub vegetation proliferation, which are increasing in Arctic and subarctic regions.

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Section: Methodsmentioning

confidence: 99%

Section: Land Cover Classification Using Aviris-ng Datamentioning

confidence: 99%

Section: Land Cover Change and Influence On Snowpackmentioning

confidence: 99%

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Monitoring 13 years of drastic catchment change and the hydroecological responses of a drained thermokarst lake

2022

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“…This software is also available via the DOI https://doi.org/10.5281/zenodo.4614338. Data for Figure2are cited in the text with the details from the following reference citations: (a) AVIRIS-NG Data Reference:Miller, Green, et al (2019) and (b) ISOFIT Reference:Thompson et al (2018), https://doi.org/10.1016/j.rse.2018.07.003. Reflectance spectra for Figure3are available on GitHub (https://github.com/nelsopet/lecospec) and are archived at the ORNL DAAC(Nelson & Smith 2022, https://doi.org/10.3334/ORNLDAAC/1980).…”

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Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

et al. 2022

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Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including NASA's Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low-stature plants and very fine-scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long-term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi-scale spectroscopy, from lab studies to satellites that enable frequent and continuous long-term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

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

Douglas

Zhang

2021

Environ. Res. Lett.

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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.

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ABoVE: Hyperspectral Imagery from AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019

Cited by 7 publications

References 0 publications

Monitoring 13 years of drastic catchment change and the hydroecological responses of a drained thermokarst lake

Monitoring 13 years of drastic catchment change and the hydroecological responses of a drained thermokarst lake

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

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

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