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1. Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

   https://doi.org/10.1109/TGRS.2023.3268073  

   Herzfeld, Ute Christina ; Trantow, Thomas M. ; Han, Huilin ; Buckley, Ellen ; Farrell, Sinéad Louise ; Lawson, Matthew ( April 2023 , IEEE Transactions on Geoscience and Remote Sensing)

   As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. National Aeronautics and Space Administration (NASA)’s Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not identified on the ICESat-2 standard sea-ice products, in which only a single surface is determined. The objective of this article is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With ATLAS, ICESat-2 carries the first spaceborne multibeam micropulse photon-counting laser altimeter system, operating at 532-nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an autoadaptive algorithm that solves the problem of pond detection near the 0.7-m nominal along-track spacing of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in the background, apparent surface reflectance, and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 datasets from the 2019 and 2020 melt seasons in the multiyear Arctic sea-ice region. Results are evaluated by comparison with those from a manually forced algorithm. 

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2. Improving ICESat ‐2‐based boreal forest height estimation by a multivariate sample quality control approach

   https://doi.org/10.1111/2041-210X.14112  

   Zhang, Tianqi ; Liu, Desheng ( April 2023 , Methods in Ecology and Evolution)

   Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling.

   We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR).

   The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important.

   The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.

    

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3. Orthomosaic Image and Digital Surface Model for Point Lay (Kali), Alaska, 26 June 2022

   https://doi.org/10.18739/A28K74Z49  

   Jones, Benjamin ( January 2024 , NSF Arctic Data Center)

   The Native Village of Point Lay (Kali) on the North Slope of Alaska has been identified as the second-most permafrost thaw-affected community in the state of Alaska (Denali Commission, 2019). The village has 82 residential units, housing a population of approximately 330. There are several North Slope Borough municipal structures and the Kali School that serve the community. Most of the residential buildings in the village are built on an elevated surface underlain by ice-rich permafrost that is susceptible to thaw and terrain subsidence. This dataset consists of an orthomosaic and digital surface model (DSM) derived from drone surveys on 26 June 2022 in Point Lay, Alaska. 990 digital images were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The mapped area was around 130 hectares (ha). The drone system was flown at 120 meters (m) above ground level (agl) and flight speeds varied from 7–8 meters/second (m/s). The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80 percent (%) and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.7.5) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the orthophoto mosaic and digital surface model at spatial resolutions of 5 and 10 centimeters (cm), respectively. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 5 North in Ellipsoid Heights (meters). Elevation information derived over waterbodies is noisy and does not represent the surface elevation of the feature. 

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4. 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)

   null (Ed.)

   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, 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. 

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5. Orthomosaic Image and Digital Surface Model for the Navigating the New Arctic, Ice-rich Permafrost Systems (IRPS) field sites, Prudhoe Bay Oilfields, northern Alaska, 29 August 2021

   https://doi.org/10.18739/A24T6F50K  

   Jones, Benjamin ( January 2024 , NSF Arctic Data Center)

   Ice-rich permafrost is ground that is frozen all year round for two or more years and contains particularly large amounts of water that will be released upon thawing. This ice is the element of Arctic landscapes most susceptible to climate warming. Nearly 50% of the Arctic has ice-rich permafrost. For example, the upper 4-5 meters of the land along Alaska's northern coast contains an estimated 77% ice. Thawing of ice-rich permafrost affects entire arctic ecosystems and makes the ground unstable to build upon. This dataset consists of an orthomosaic and digital surface model (DSM) derived from drone surveys on 29 August 2021 at the Navigating the New Arctic, Ice-rich Permafrost Systems project field sites, in collaboration with the PermaSense project, in the Prudhoe Bay Oilfields. 2,463 digital images were acquired from a DJI Phantom 4 Real-Time Kinematic (DJI P4RTK) quadcopter with a DJI D-RTK 2 Mobile Base Station. The mapped area was around 232 hectares (ha). The drone system was flown at 100 meters (m) above ground level (agl) and flight speeds varied from 7–8 meters/second (m/s). The orientation of the camera was set to 90 degrees (i.e. looking straight down). The along-track overlap and across-track overlap of the mission were set at 80% and 70%, respectively. All images were processed in the software Pix4D Mapper (v. 4.6.4) using the standard 3D Maps workflow and the accurate geolocation and orientation calibration method to produce the orthophoto mosaic and digital surface model at spatial resolutions of 5 and 10 centimeters (cm), respectively. A Leica Viva differential global positioning system (GPS) provided ground control for the mission and the data were post-processed to WGS84 UTM Zone 6 North in Ellipsoid Heights (meters). Elevation information derived over waterbodies is noisy and does not represent the surface elevation of the feature. 

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