David J. Selkowitz

A spatially stratified, multi-stage cluster sampling design for assessing accuracy of the Alaska (USA) National Land Cover Database (NLCD)

Assessing the accuracy of a land-cover map is typically expensive, and at the planning stage it is often uncertain what final sample size will be affordable. The aim of this study is to develop an accuracy assessment sampling design that accommodates an ‘in progress’ change in target sample size without sacrificing other desirable design criteria. The sampling design constructed to assess the accuracy of the National Land Cover Database (NLCD) for Alaska achieves these desirable criteria. Spatial stratification provides the flexibility to accommodate a change in sample size and cluster sampling contributes to the cost-effectiveness of the design. We describe the advantages of these design features when the difficulty of accessing remote, large areas is a primary driver of the choice of a sampling design for accuracy assessment. Estimators for overall, users, and producers accuracies along with approximate standard errors are provided for the stratified, multi-stage cluster sampling design proposed.

An Automated Approach for Mapping Persistent Ice and Snow Cover over High Latitude Regions

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.

Radar imaging of winter seismic survey activity in the National Petroleum Reserve-Alaska

During the spring of 2006, Radarsat-1 synthetic aperture radar (SAR) imagery was acquired on a continual basis for the Teshekpuk Lake Special Area (TLSA), in the northeast portion of the National Petroleum Reserve, Alaska (NPR-A) in order to monitor lake ice melting processes. During data processing, it was discovered that the Radarsat-1 imagery detected features associated with winter seismic survey activity. Focused analysis of the image time series revealed various aspects of the exploration process such as the grid profile associated with the seismic line surveys as well as trails and campsites associated with the mobile survey crews. Due to the high temporal resolution of the dataset it was possible to track the progress of activities over a one month period. Spaceborne SAR imagery can provide information on the location of winter seismic activity and could be used as a monitoring tool for land and resource managers as increased petroleum-based activity occurs in the TLSA and NPR-A.

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

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 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.