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1. Assessing Scale Dependence on Local Sea Level Retrievals from Laser Altimetry Data over Sea Ice

   https://doi.org/10.3390/rs12223732  

   Tian, Liuxi ; Xie, Hongjie ; Ackley, Stephen F. ; Mestas-Nuñez, Alberto M. ( November 2020 , Remote Sensing)

   The measurement of sea ice elevation above sea level or the “freeboard” depends upon an accurate retrieval of the local sea level. The local sea level has been previously retrieved from altimetry data alone by the lowest elevation method, where the percentage of the lowest elevations over a particular segment length scale was used. Here, we provide an evaluation of the scale dependence on these local sea level retrievals using data from NASA Operation IceBridge (OIB) which took place in the Ross Sea in 2013. This is a unique dataset of laser altimeter measurements over five tracks from the Airborne Topographic Mapper (ATM), with coincidently high-spatial resolution images from the Digital Mapping System (DMS), that allows for an independent sea level validation. The local sea level is first calculated by using the mean elevation of ATM L1B data over leads identified by using the corresponding DMS imagery. The resulting local sea level reference is then used as ground truth to validate the local sea levels retrieved from ATM L2 by using nine different percentages of the lowest elevation (0.1%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, and 4%) at seven different segment length scales (1, 5, 10, 15, 20, 25,more »and 50 km) for each of the five ATM tracks. The closeness to the 1:1 line, R2, and root mean square error (RMSE) is used to quantify the accuracy of the retrievals. It is found that all linear least square fits are statistically significant (p < 0.05) using an F test at every scale for all tested data. In general, the sea level retrievals are farther away from the 1:1 line when the segment length scale increases from 1 or 5 to 50 km. We find that the retrieval accuracy is affected more by the segment length scale than the percentage scale. Based on our results, most retrievals underestimate the local sea level; the longer the segment length (from 1 to 50 km) used, especially at small percentage scales, the larger the error tends to be. The best local sea level based on a higher R2 and smaller RMSE for all the tracks combined is retrieved by using 0.1–2% of the lowest elevations at the 1–5 km segment lengths.« less
2. Potential Future Lake Drainage for the Western Arctic Coastal Plain in northern Alaska from an Interferometric Synthetic Aperture Radar (IFSAR)-Derived Digital Surface Model, 2002-2003

   https://doi.org/10.18739/A2JH3D29N  

   Jones, Benjamin ; Arp, Christopher ; Grosse, Guido ; Nitze, Ingmar ; Lara, Mark ; Whitman, Matthew ; Farquharson, Louise ; Kanevskiy, Mikhail ; Parsekian, Andrew ; Breen, Amy ; et al ( January 2019 )

   Abstract

   Assessment of lakes for their future potential to drain relied on the 2002/03 airborne Interferometric Synthetic Aperture Radar (IFSAR) Digital Surface Model (DSM) data for the western Arctic Coastal Plain in northern Alaska. Lakes were extracted from the IfSAR DSM using a slope derivative and manual correction (Jones et al., 2017). The vertical uncertainty for correctly detecting lake-based drainage gradients with the IfSAR DSM was defined by comparing surface elevation differences of several overlapping DSM tile edges. This comparison showed standard deviations of elevation between overlapping IfSAR tiles ranging from 0.0 to 0.6 meters (m). Thus, we chose a minimum height difference of 0.6 m to represent a detectable elevation gradient adjacent to a lake as being most likely to contribute to a rapid drainage event. This value is also in agreement with field verified estimates of the relative vertical accuracy (~0.5 m) of the DSM dataset around Utqiaġvik (formerly Barrow) (Manley et al., 2005) and the stated vertical RMSE (~1.0 m) of the DSM data (Intermap, 2010). Development of the potential lake drainage dataset involved several processing steps. First, lakes were classified as potential future drainage candidates if the difference between the elevation of the lake surface and the lowest elevation within a 100 m buffer of the lake shoreline exceeded our chosen threshold of 0.6 m. Next, we selected lakes with a minimum size of 10 ha to match the historic lake drainage dataset. We further filtered the dataset by selecting lakes estimated to have low hydrological connectivity based on relations between lake contributing area as determined for specific surficial geology types and presented in Jones et al. (2017). This was added to the future projection workflow to isolate the lake population that likely responds to changes in surface area driven largely by geomorphic change as opposed to differences in surface hydrology. Lakes within a basin with low to no hydrologic connectivity that had an elevation change gradient between the lake surface and surrounding landscape are considered likely locations to assess for future drainage potential. Further, the greater the elevation difference, the greater the drainage potential. This dataset provided a first-order estimate of lakes classified as being prone to future drainage. We further refined our assessment of potential drainage lakes by identifying the location of the point with the lowest elevation within the 100 m buffer of the lake shoreline and manually interpreted lakes to have a high drainage potential based on the location of the likely drainage point to known lake drainage pathways using circa 2002 orthophotography or more recent high resolution satellite imagery available for the Western Coastal Arctic Plain (WACP). Lakes classified as having a high drainage potential typically had the likely drainage location associated with one or more of the following: (1) an adjacent lake, (2) the cutbank of a river, (3) the ocean, (4) were located in an area with dense ice-wedge networks, (5) appeared to coincide with a potentially headward eroding stream, or (6) were associated with thermokarst lake shoreline processes in the moderate to high ground ice content terrain. We also added information on potential lake drainage pathways to the high potential drainage dataset by manually interpreting the landform associated with the likely drainage site to draw comparisons with the historic lake drainage dataset.
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3. Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

   https://doi.org/10.3390/rs13163277  

   Koo, YoungHyun ; Xie, Hongjie ; Kurtz, Nathan T. ; Ackley, Stephen F. ; Mestas-Nuñez, Alberto M. ( August 2021 , Remote Sensing)

   NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistentmore »freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.« less
4. Poor relationships between NEON Airborne Observation Platform data and field‐based vegetation traits at a mesic grassland

   https://doi.org/10.1002/ecy.3590  

   Pau, Stephanie ; Nippert, Jesse B. ; Slapikas, Ryan ; Griffith, Daniel ; Bachle, Seton ; Helliker, Brent R. ; O’Connor, Rory C. ; Riley, William J. ; Still, Christopher J. ; Zaricor, Marissa ( November 2021 , Ecology)

   null (Ed.)

   Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total Biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation 𝑅2 R 2 of more than 0.25. For all vegetation traits, validation 𝑅2 R 2 ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our resultsmore »suggest that currently available AOP-derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.« less
5. A remote field course implementing high-resolution topography acquisition with geomorphic applications

   https://doi.org/10.5194/gc-5-101-2022  

   Bywater-Reyes, Sharon ; Pratt-Sitaula, Beth ( January 2022 , Geoscience Communication)

   Abstract. Here we describe the curriculum and outcomes from a data-intensivegeomorphic analysis course, “Geoscience Field Issues Using High-ResolutionTopography to Understand Earth Surface Processes”, which pivoted to virtualin 2020 due to the COVID-19 pandemic. The curriculum covers technologies formanual and remotely sensed topographic data methods, including (1) GlobalPositioning Systems and Global Navigation Satellite System (GPS/GNSS)surveys, (2) Structure from Motion (SfM) photogrammetry, and (3) ground-based(terrestrial laser scanning, TLS) and airborne lidar. Course content focuseson Earth-surface process applications but could be adapted for othergeoscience disciplines. Many other field courses were canceled in summer2020, so this course served a broad range of undergraduate and graduatestudents in need of a field course as part of degree or researchrequirements. Resulting curricular materials are available freely within theNational Association of Geoscience Teachers' (NAGT's) “Teaching with Online Field Experiences” collection. Theauthors pre-collected GNSS data, uncrewed-aerial-system-derived (UAS-derived) photographs, and ground-based lidar, which students then used in courseassignments. The course was run over a 2-week period and had synchronousand asynchronous components. Students created SfM models that incorporatedpost-processed GNSS ground control points and created derivative SfM and TLSproducts, including classified point clouds and digital elevation models(DEMs). Students were successfully able to (1) evaluate the appropriatenessof a given survey/data approach given site conditions, (2) assess pros andconsmore »of different data collection and post-processing methods in light offield and time constraints and limitations of each, (3) conduct error andgeomorphic change analysis, and (4) propose or implement a protocol to answera geomorphic question. Overall, our analysis indicates the course had asuccessful implementation that met student needs as well as course-specificand NAGT learning outcomes, with 91 % of students receiving an A, B, or Cgrade. Unexpected outcomes of the course included student self-reflectionand redirection and classmate support through a daily reflection anddiscussion post. Challenges included long hours in front of a computer,computing limitations, and burnout because of the condensed nature of thecourse. Recommended implementation improvements include spreading the courseout over a longer period of time or adopting only part of the course andproviding appropriate computers and technical assistance. This paperand published curricular materials should serve as an implementation andassessment guide for the geoscience community to use in virtual or in-personhigh-resolution topographic data courses that can be adapted for individuallabs or for an entire field or data course.« less