Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada

Raleigh, M. S.; Rittger, Karl; Moore, Courtney E.; Henn, Brian; Lutz, James A.; Lundquist, Jessica D.

doi:10.1016/j.rse.2012.09.016

Cited by 112 publications

(145 citation statements)

References 63 publications

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“…While integration between flux measurements and core tree census data remains limited, these co-located measurements represent an important opportunity to link the growth and water use of individual trees to whole-ecosystem carbon cycling and evapotranspiration. 24 In addition to meteorological data, some sites monitor soil temperature, moisture, and/or snow presence (e.g., Raleigh et al, 2013). 25 18 39 † Codes are as follows: F-farming; P-pasture; W-wood harvesting; CC-clear cut/ complete clearing; B-burn; H-hunting; E-extraction of NTFP (non-timber forest products); I-invasive species; '-' no significant disturbances.…”

Section: Bio-micrometeorologymentioning

confidence: 99%

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

Anderson‐Teixeira

Davies

Bennett

et al. 2014

Global Change Biology

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506

439

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Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25°S-61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61°C), changes in precipitation (up to AE30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m À2 yr À1 and 3.1 g S m À2 yr À1), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFSForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

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Section: Bio-micrometeorologymentioning

confidence: 99%

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

Anderson‐Teixeira

Davies

Bennett

et al. 2014

Global Change Biology

Self Cite

506

439

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“…At one site where the pixel footprint was dominated by a rocky talus slope, some data loggers were placed below rocks near the surface of the talus pile. Our placement of data loggers varied from the approach used by Raleigh et al [54] in the spatial resolution of the footprint monitored (60 × 60 m in our study vs. 500 × 500 m in Raleigh et al [54]), the number of sensors deployed, and the placement of sensors within the footprint. While Raleigh et al [54] deployed between 37 and 89 sensors in several different sensor configurations, including quasi-regular grids and transects, all of which covered 500 × 500 m or larger areas, we used regular 4 × 4 grids with 20 m spacing at each site, for a total of 16 sensors covering each 60 × 60 m footprint ( Figure 5).…”

Section: In Situ Snow Cover Monitoring Approachmentioning

confidence: 99%

“…We used the algorithm introduced by Raleigh et al [54] to convert the 1.5-hourly temperature time series from individual sensors to a daily snow cover fraction value for each 60 m grid cell footprint. Snow cover located above a sensor insulates the uppermost layer of the soil, resulting in a substantial reduction of the range in temperature variability experienced near the soil surface [7].…”

Section: In Situ Snow Cover Monitoring Approachmentioning

confidence: 99%

Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments

2014

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Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013-August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%-93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow OPEN ACCESSRemote Sens. 2014, 6 12479 cover mapping, mixed pixels accounted for 67%-100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%-98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17-56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

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“…The MODSCAG model estimates the fraction of each pixel that is covered by snow [33]. A threshold value of >0.15 to flag each daily 500-m pixel as snow covered has been used to accurately map snow extent for treeless areas in the Colorado Rocky Mountains, upper Rio Grande, Himalayas in Nepal [35] and the Sierra Nevada Mountains in California [36]. Based on these results, we used a fraction threshold value of >0.15 to flag each daily 500-m pixel as snow covered.…”

Section: Remotely Sensed Snow Fractionmentioning

confidence: 99%

Remote Sensing of 2000–2016 Alpine Spring Snowline Elevation in Dall Sheep Mountain Ranges of Alaska and Western Canada

et al. 2017

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Abstract:The lowest elevation of spring snow ("snowline") is an important factor influencing recruitment and survival of wildlife in alpine areas. In this study, we assessed the spatial and temporal variability of alpine spring snowline across major Dall sheep mountain areas in Alaska and northwestern Canada. We used a daily MODIS snow fraction product to estimate the last day of 2000-2016 spring snow for each 500-m pixel within 28 mountain areas. We then developed annual (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) regression models predicting the elevation of alpine snowline during mid-May for each mountain area. MODIS-based regression estimates were compared with estimates derived using a Normalized Difference Snow Index from Landsat-8 Operational Land Imager (OLI) surface reflectance data. We also used 2000-2009 decadal climate grids to estimate total winter precipitation and mean May temperature for each of the 28 mountain areas. Based on our MODIS regression models, the 2000-2016 mean 15 May snowline elevation ranged from 339 m in the cold arctic class to 1145 m in the interior mountain class. Spring snowline estimates from MODIS and Landsat OLI were similar, with a mean absolute error of 106 m. Spring snowline elevation was significantly related to mean May temperature and total winter precipitation. The late spring of 2013 may have impacted some sheep populations, especially in the cold arctic mountain areas which were snow-covered in mid-May, while some interior mountain areas had mid-May snowlines exceeding 1000 m elevation. We found this regional (>500,000 km 2 ) remote sensing application useful for determining the inter-annual and regional variability of spring alpine snowline among 28 mountain areas.

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Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada

Cited by 112 publications

References 63 publications

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

CTFS‐ForestGEO: a worldwide network monitoring forests in an era of global change

Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments

Remote Sensing of 2000–2016 Alpine Spring Snowline Elevation in Dall Sheep Mountain Ranges of Alaska and Western Canada

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