Application of Lidar Altimetry and Hyperspectral Imaging to Ice Sheet and Snow Monitoring

The Greenland Ice Sheet (GrIS) is of tremendous importance for climate change projections. The GrIS has contributed an estimated 10.8 mm to sea level rise since 1992, and that contribution is expected to increase in the coming decades. It is therefore essential to make routine measurements of ice, meltwater, and snow over the GrIS using satellite and airborne observations. Two prominent methods for ice sheet monitoring include lidar altimetry and hyperspectral imaging. Lidar altimetry is typically used to make fine-scale estimates of ice sheet surface height, whereas hyperspectral imaging is commonly utilized to infer snow or ice surface composition. In this dissertation, I use data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to examine light transmittance over the Greenland Ice Sheet. I first utilize ICESat-2 photon-counting data for the development of a retrieval algorithm for supraglacial lake depth, with validation from the Operation IceBridge airborne mission. This work was performed to support other depth retrieval efforts that struggle with attenuation in deep water. I then use hyperspectral radiative transfer models to perform a sensitivity analysis on snow grain size retrievals. Snow grain size is an important metric for snowpack evolution, but there are limited efforts to quantify potential errors in an existing inversion algorithm. Lastly, I used a combination of Operation IceBridge altimetry and AVIRIS-NG hyperspectral data to assess the impacts of snow grain size on surface heights derived from lidar altimetry. Results from the three studies indicate that lidar signals and ice reflectance in the near-infrared are highly sensitive to changes in surface media. Because it operates at 532 nm, the ICESat-2 laser penetrates through liquid water with minimal signal loss, but volumetric scattering within a snowpack may induce significant errors in surface heights derived from Operation IceBridge, especially at large snow grain sizes. The ICESat-2 laser is susceptible to noise from clouds and rough surface topography, so additional work is needed to accurately identify supraglacial lake beds and volumetric scattering caused by snow. Also, the near-infrared spectrum of snow is highly sensitive to changes in solar geometry and to the presence of dust, therefore increasing uncertainties in snow grain size retrievals. Co-dependencies between snowpack perturbations were not considered, but I speculate that snow particle shape and snow impurities will impact the angular distribution of radiation reflected from a snowpack. I expect that the research presented here will motivate the development of improved algorithms for supraglacial lake depth, snow grain size, and lidar altimetry bias.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169872/1/zhfair_1.pd

Practice characteristics of Emergency Department extracorporeal cardiopulmonary resuscitation (eCPR) programs in the United States: The current state of the art of Emergency Department extracorporeal membrane oxygenation (ED ECMO).

PURPOSE: To characterize the current scope and practices of centers performing extracorporeal cardiopulmonary resuscitation (eCPR) on the undifferentiated patient with cardiac arrest in the emergency department. METHODS: We contacted all US centers in January 2016 that had submitted adult eCPR cases to the Extracorporeal Life Support Organization (ELSO) registry and surveyed them, querying for programs that had performed eCPR in the Emergency Department (ED ECMO). Our objective was to characterize the following domains of ED ECMO practice: program characteristics, patient selection, devices and techniques, and personnel. RESULTS: Among 99 centers queried, 70 responded. Among these, 36 centers performed ED ECMO. Nearly 93% of programs are based at academic/teaching hospitals. 65% of programs are less than 5 years old, and 60% of programs perform ≤3 cases per year. Most programs (90%) had inpatient eCPR or salvage ECMO programs prior to starting ED ECMO programs. The majority of programs do not have formal inclusion and exclusion criteria. Most programs preferentially obtain vascular access via the percutaneous route (70%) and many (40%) use mechanical CPR during cannulation. The most commonly used console is the Maquet Rotaflow(®). Cannulation is most often performed by cardiothoracic (CT) surgery, and nearly all programs (\u3e85%) involve CT surgeons, perfusionists, and pharmacists. CONCLUSIONS: Over a third of centers that submitted adult eCPR cases to ELSO have performed ED ECMO. These programs are largely based at academic hospitals, new, and have low volumes. They do not have many formal inclusion or exclusion criteria, and devices and techniques are variable

ICESat-2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat-2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat-2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat-2 and two from coincident Landsat-8 and Sentinel-2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat-2 produced the most accurate depths; the image-based algorithms underestimated depths (by 30%–70%). This implies that ICESat-2 depths can be used to tune image-based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.</p

ICESat-2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat-2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat-2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat-2 and two from coincident Landsat-8 and Sentinel-2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat-2 produced the most accurate depths; the image-based algorithms underestimated depths (by 30%–70%). This implies that ICESat-2 depths can be used to tune image-based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.Physical and Space Geodes

ICESat‐2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA’s ICESat‐2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat‐2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat‐2 and two from coincident Landsat‐8 and Sentinel‐2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat‐2 produced the most accurate depths; the image‐based algorithms underestimated depths (by 30%–70%). This implies that ICESat‐2 depths can be used to tune image‐based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.Plain Language SummarySummer surface melting on Antarctica’s ice shelves is a small component of overall ice sheet mass loss but can be important for individual ice shelves and may increase as the climate warms. However, the volume of meltwater has been difficult to monitor because depth estimates are challenging. NASA’s ICESat‐2 laser altimetry mission brings a new capability to this problem. ICESat‐2 532 nm photons (green light) are able to pass through water and reflect from both the water surface and the underlying ice surface; the difference in elevation provides meltwater depth estimates. In this pilot study, we compared depths from eight algorithms (six ICESat‐2 and two image based) over four Amery Ice Shelf meltwater lakes for an ICESat‐2 pass in early January 2019. The ICESat‐2 algorithms all produced more reliable depth estimates, and the image‐based algorithms underestimated the depths. This implies that ICESat‐2 water depths can be used to tune image‐based depth retrieval algorithms, enabling improved performance and allowing us to estimate more accurately how much surface melt is stored in melt ponds on the ice sheets each summer.Key PointsICESat‐2 photons penetrate surface melt lakes and reflect from both the water surface and the underlying ice, providing depth estimatesWe compared depths from eight algorithms (six ICESat‐2 and two image‐based) for four lakes present on Amery Ice Shelf in January 2019Depths from ICESat‐2 were more accurate than from imagery (30%–70% too low); merging these data will improve estimates ice‐sheet widePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167549/1/grl61701_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167549/2/grl61701.pd

ICESat‐2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA’s ICESat‐2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat‐2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat‐2 and two from coincident Landsat‐8 and Sentinel‐2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat‐2 produced the most accurate depths; the image‐based algorithms underestimated depths (by 30%–70%). This implies that ICESat‐2 depths can be used to tune image‐based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.Plain Language SummarySummer surface melting on Antarctica’s ice shelves is a small component of overall ice sheet mass loss but can be important for individual ice shelves and may increase as the climate warms. However, the volume of meltwater has been difficult to monitor because depth estimates are challenging. NASA’s ICESat‐2 laser altimetry mission brings a new capability to this problem. ICESat‐2 532 nm photons (green light) are able to pass through water and reflect from both the water surface and the underlying ice surface; the difference in elevation provides meltwater depth estimates. In this pilot study, we compared depths from eight algorithms (six ICESat‐2 and two image based) over four Amery Ice Shelf meltwater lakes for an ICESat‐2 pass in early January 2019. The ICESat‐2 algorithms all produced more reliable depth estimates, and the image‐based algorithms underestimated the depths. This implies that ICESat‐2 water depths can be used to tune image‐based depth retrieval algorithms, enabling improved performance and allowing us to estimate more accurately how much surface melt is stored in melt ponds on the ice sheets each summer.Key PointsICESat‐2 photons penetrate surface melt lakes and reflect from both the water surface and the underlying ice, providing depth estimatesWe compared depths from eight algorithms (six ICESat‐2 and two image‐based) for four lakes present on Amery Ice Shelf in January 2019Depths from ICESat‐2 were more accurate than from imagery (30%–70% too low); merging these data will improve estimates ice‐sheet widePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167549/1/grl61701_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167549/2/grl61701.pd