Geology topics

David J. Selkowitz

Publications and source records attributed to David J. Selkowitz.

10 recordsLinked to original sources

USGS2025

Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km 2 in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less).

Colorado↗

USGS2021

Integrated hydrology and operations modeling to evaluate climate change impacts in an agricultural valley irrigated with snowmelt runoff

Applying models to developed agricultural regions remains a difficult problem because there are no existing modeling codes that represent both the complex physics of the hydrology and anthropogenic manipulations to water distribution and consumption. We apply an integrated groundwater – surface water and hydrologic river operations model to an irrigated river valley in northwestern Nevada/northern California, United States to evaluate the impacts of climate change on snow-fed agricultural systems that use surface water and groundwater conjunctively. We explicitly represent individual surface water rights within the hydrologic model and allow the integrated code to change river diversions in response to earlier snowmelt runoff and water availability. Historically under-used supplemental groundwater rights are dynamically activated within the model to offset diminished surface water deliveries. The model accounts for feedbacks between the natural hydrology and anthropogenic stresses, which is a first-of-its-kind assessment of the impacts of climate change on individual water rights, and more broadly on river basin operations. Earlier snowmelt decreases annual surface water deliveries to all water rights, not just the junior water rights, owing to a lack of surface water storage in the upper river basin capable of capturing earlier runoff. Conversely, downstream irrigators with access to reservoir storage benefit from earlier runoff flowing past upstream points of diversion prior to the start of the irrigation season. Despite regional shifts toward greater reliance on groundwater for irrigation, crop consumption (a common surrogate for crop yield) decreases due to spatiotemporal changes in water supply that preferentially impact a subset of growers in the region.

California↗

USGS2016

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2015

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.

Remote Sensing↗

USGS2014

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

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.

Remote Sensing↗

USGS2012

A multi-sensor lidar, multi-spectral and multi-angular approach for mapping canopy height in boreal forest regions

Spatially explicit representations of vegetation canopy height over large regions are necessary for a wide variety of inventory, monitoring, and modeling activities. Although airborne lidar data has been successfully used to develop vegetation canopy height maps in many regions, for vast, sparsely populated regions such as the boreal forest biome, airborne lidar is not widely available. An alternative approach to canopy height mapping in areas where airborne lidar data is limited is to use spaceborne lidar measurements in combination with multi-angular and multi-spectral remote sensing data to produce comprehensive canopy height maps for the entire region. This study uses spaceborne lidar data from the Geosciences Laser Altimeter System (GLAS) as training data for regression tree models that incorporate multi-angular and multi-spectral data from the Multi-Angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging SpectroRadiometer (MODIS) to map vegetation canopy height across a 1,300,000 km 2 swath of boreal forest in Interior Alaska. Results are compared to in situ height measurements as well as airborne lidar data. Although many of the GLAS-derived canopy height estimates are inaccurate, applying a series of filters incorporating both data associated with the GLAS shots as well as ancillary data such as land cover can identify the majority of height estimates with significant errors, resulting in a filtered dataset with much higher accuracy. Results from the regression tree models indicate that late winter MISR imagery acquired under snow-covered conditions is effective for mapping canopy heights ranging from 5 to 15 m, which includes the vast majority of forests in the region. It appears that neither MISR nor MODIS imagery acquired during the growing season is effective for canopy height mapping, although including summer multi-spectral MODIS data along with winter MISR imagery does appear to provide a slight increase in the accuracy of resulting height maps. The finding that winter, snow-covered MISR imagery can be used to map canopy height is important because clear sky days are nearly three times as common during the late winter period as during the growing season. The increased odds of acquiring cloud-free imagery during the target acquisition period make regularly updated forest height inventories for Interior Alaska much more feasible. A major advantage of the GLAS–MISR–MODIS canopy height mapping methodology described here is that this approach uses only data that is freely available worldwide, making the approach potentially applicable across the entire circumpolar boreal forest region.

Remote Sensing of Environment↗

USGS2011

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft , as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches.

Alaska↗

USGS2011

Exploring the potential for a fused Landsat-MODIS snow covered area product

Results from nine 3 x 3 km study areas in the Rocky Mountains of Colorado, USA demonstrate there is potential for using sporadically acquired Landsat images in combination with daily coarse resolution fractional snow covered area (SCA) images to produce daily high resolution binary SCA images. The results also highlight several challenges to implementing this type of approach. The approach described here consistently yields accurate results in locations with persistent winter and spring snow cover where ten or more partially snow covered images are available to populate the image database, but is less successful in areas with shallower or more ephemeral snow covers or when fewer images are available to populate the image database. This work represents a first step towards developing an algorithm to combine Landsat and MODIS data to produce daily 30 m resolution binary SCA images. Further research should focus on testing the accuracy of this approach across a range of landscape types and snow cover regimes, developing methods to improve prediction accuracy when snow cover is nearly complete or nearly absent, and developing methods to compensate for the effects of canopy cover on SCA retrievals.

Conference Paper↗

USGS2010

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km 2 study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000–2009.

Alaska↗

USGS2002

Modeling and measuring snow for assessing climate change impacts in Glacier National Park, Montana

A 12-year program of global change research at Glacier National Park by the U.S. Geological Survey and numerous collaborators has made progress in quantifying the role of snow as a driver of mountain ecosystem processes. Spatially extensive snow surveys during the annual accumulation/ablation cycle covered two mountain watersheds and approximately 1,000 km2 . Over 7,000 snow depth and snow water equivalent (SWE) measurements have been made through spring 2002. These augment two SNOTEL sites, 9 NRCS snow courses, and approximately 150 snow pit analyses. Snow data were used to establish spatially-explicit interannual variability in snowpack SWE. East of the Continental Divide, snowpack SWE was lower but also less variable than west of the Divide. Analysis of snowpacks suggest downward trends in SWE, a reduction in snow cover duration, and earlier melt-out dates during the past 52 years. Concurrently, high elevation forests and treelines have responded with increased growth. However, the 80 year record of snow from 3 NRCS snow courses reflects a strong influence from the Pacific Decadal Oscillation, resulting in 20-30 year phases of greater or lesser mean SWE. Coupled with the fine-resolution spatial snow data from the two watersheds, the ecological consequences of changes in snowpack can be empirically assessed at a habitat patch scale. This will be required because snow distribution models have had varied success in simulating snowpack accumulation/ablation dynamics in these mountain watersheds, ranging from R2=0.38 for individual south-facing forested snow survey routes to R2=0.95 when aggregated to the watershed scale. Key ecological responses to snowpack changes occur below the watershed scale, such as snow-mediated expansion of forest into subalpine meadows, making continued spatially-explicit snow surveys a necessity.

Montana↗