Landsat-based snow persistence map for northwest Alaska

Macander, M. J.; Swingley, C. S.; Joly, Kyle; Raynolds, Martha K.

doi:10.1016/j.rse.2015.02.028

Cited by 71 publications

(76 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These data match well with the numbers extracted from our spatially explicit maps (Appendix, Figure A2), a finding that acts as an independent validation. This is in line with the accuracy of other similar approaches (Macander et al 2015). As covariates we used maximum snow depth recorded at the weather station, May-July average temperature, and temperature sums (defined as the sum of daily mean temperatures above zero during the first 200 days of the year).…”

Section: Methodsmentioning

confidence: 82%

Spatiotemporal snowmelt patterns within a high Arctic landscape, with implications for flora and fauna

Kankaanpää

Skov

Abrego

et al. 2018

Arctic, Antarctic, and Alpine Research

View full text Add to dashboard Cite

Snow conditions are important drivers of the distribution and phenology of Arctic flora and fauna, but the extent and effects of local variation in snowmelt are still inadequately studied. We analyze snowmelt patterns within the Zackenberg valley in northeast Greenland. Drawing on landscapelevel snowmelt dates and meteorological data from a central climate station, we model snowmelt trends during 1998-2014. We then use time-lapse photographs to examine consistency in spatiotemporal snowmelt patterns during 2006-2014. Finally, we use monitoring data on arthropods and plants for 1998-2014 to investigate how snowmelt date affects the phenology of Arctic organisms. Despite large interannual variation in snowmelt timing, we find consistency in the relative order of snowmelt among sites within the landscape. With a slight overall advancement in snowmelt during the study period, early melting locations have advanced more than late-melting ones. Individual organism groups differ greatly in how their phenology shifts with snowmelt, with much variance attributable to variation in life history and diet. Overall, we note that local variation in snowmelt patterns may drive important ecological processes, and that more attention should be paid to variability within landscapes. Areas optimal for a given taxon vary between years, thereby creating spatial structure in a seemingly uniform landscape. ARTICLE HISTORY

show abstract

Section: Methodsmentioning

confidence: 82%

Spatiotemporal snowmelt patterns within a high Arctic landscape, with implications for flora and fauna

Kankaanpää

Skov

Abrego

et al. 2018

Arctic, Antarctic, and Alpine Research

View full text Add to dashboard Cite

show abstract

“…Snow melt dates for the end of the continuous snow season (LCLD) were 3.4 days earlier, on average, than snow-free dates derived from Landsat and mean absolute difference between the two data sets was 4.2 days [53].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Deriving Snow Cover Metrics for Alaska from MODIS

Lindsay

Zhu²,

Miller³

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1) and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel's position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence) for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179-311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered OPEN ACCESSRemote Sens. 2015, 7 12962 periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/.

show abstract

“…There was, however, no systematic bias for any of the three periods toward early or late acquisition dates, so the overall trends of increasing thaw pit extent at eastern sites and decreasing extent at Chukchi sites are not readily explained by bias in seasonal timing of imagery. Macander et al (2015) [44] produced a snow persistence map for northwest Alaska that provides median spring snow-off dates for each study area at a 30-m resolution using Landsat TM/ETM+ from 1985-2011. The median snowmelt dates for the eleven sites all fall within a 14-day period (24 May-7 June) and provide confidence that the acquisition dates of imagery used in our analysis are appropriate for comparisons both within, and among sites.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

Regional Patterns and Asynchronous Onset of Ice-Wedge Degradation since the Mid-20th Century in Arctic Alaska

Frost

Christopherson

Jorgenson³

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Ice-wedge polygons are widespread and conspicuous surficial expressions of ground-ice in permafrost landscapes. Thawing of ice wedges triggers differential ground subsidence, local ponding, and persistent changes to vegetation and hydrologic connectivity across the landscape. Here we characterize spatio-temporal patterns of ice-wedge degradation since circa 1950 across environmental gradients on Alaska's North Slope. We used a spectral thresholding approach validated by field observations to map flooded thaw pits in high-resolution images from circa 1950, 1982, and 2012 for 11 study areas (1577-4460 ha). The total area of flooded pits increased since 1950 at 8 of 11 study areas (median change +3.6 ha; 130.3%). There were strong regional differences in the timing and extent of degradation; flooded pits were already extensive by 1950 on the Chukchi coastal plain (alluvial-marine deposits) and subsequent changes there indicate pit stabilization. Degradation began more recently on the central Beaufort coastal plain (eolian sand) and Arctic foothills (yedoma). Our results indicate that ice-wedge degradation in northern Alaska cannot be explained by late-20th century warmth alone. Likely mechanisms for asynchronous onset include landscape-scale differences in surficial materials and ground-ice content, regional climate gradients from west (maritime) to east (continental), and regional differences in the timing and magnitude of extreme warm summers after the Little Ice Age.

show abstract

Landsat-based snow persistence map for northwest Alaska

Cited by 71 publications

References 23 publications

Spatiotemporal snowmelt patterns within a high Arctic landscape, with implications for flora and fauna

Spatiotemporal snowmelt patterns within a high Arctic landscape, with implications for flora and fauna

Deriving Snow Cover Metrics for Alaska from MODIS

Regional Patterns and Asynchronous Onset of Ice-Wedge Degradation since the Mid-20th Century in Arctic Alaska

Contact Info

Product

Resources

About