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7. LIDAR-DERIVED ELEVATION DATA FOR THANE ROAD, SOUTHEAST ALASKA, COLLECTED SEPTEMBER 6, 2019.

Author

Wikstrom Jones, Katreen M. and Wolken, Gabriel J.

Subjects
*GLOBAL Positioning System, *ALTITUDES
Abstract

The document titled "LIDAR-DERIVED ELEVATION DATA FOR THANE ROAD, SOUTHEAST ALASKA, COLLECTED SEPTEMBER 6, 2019" provides information on a survey conducted by the Alaska Division of Geological & Geophysical Surveys (DGGS) using aerial LIDAR technology. The survey collected data on Mount Roberts and Gastineau Peak along Thane Road in Southeast Alaska. The data includes classified point cloud, digital surface model (DSM), digital terrain model (DTM), and intensity model. The survey area covers approximately 33.7 km2, and the data is available for download on the DGGS website. The document also includes details on the mission plan, weather conditions, data processing, and accuracy assessment. The survey was funded by the U.S. Geological Survey, Alaska Electric Light & Power Company, and the State of Alaska. [Extracted from the article]

Published
2024
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31. LIDAR-DERIVED ELEVATION DATA FOR NAPAKIAK, SOUTHWEST ALASKA, COLLECTED AUGUST 20, 2021.

Author

Zechmann, Jenna M., Herbst, Andrew M., Buzard, Richard M., Wikstrom Jones, Katreen M., and Wolken, Gabriel J.

Subjects
GLOBAL Positioning System, BEACH erosion, STANDARD deviations, GEOPHYSICAL surveys, POINT cloud
Abstract

This document provides lidar-derived elevation data for Napakiak, Southwest Alaska, collected on August 20, 2021. The data includes a classified point cloud, digital surface model (DSM), digital terrain model (DTM), and an intensity model. The survey was conducted to assess flooding hazards and coastal erosion. The data is available for download on the Alaska Division of Geological & Geophysical Surveys website. The document also includes information on the mission plan, processing report, survey report, and acknowledgments. The document provides detailed information on elevation differences at various points, including coordinates and measurements. It also includes information on non-vegetated and vegetated check points, with corrected point cloud elevations and the differences between the corrected point cloud elevations and the checkpoint elevations. The average elevation difference for the non-vegetated check points is -0.007 meters, with a minimum difference of -0.202 meters and a maximum difference of 0.115 meters. The average elevation difference for the vegetated check points is 0.184 meters, with a minimum difference of 0.007 meters and a maximum difference of 0.494 meters. [Extracted from the article]

Published
2024

33. POST-LANDSLIDE ELEVATION CHANGES DETECTED FROM MULTI-TEMPORAL LIDAR SURVEYS OF THE NOVEMBER 2023WRANGELL, ALASKA, LANDSLIDES.

Author

Nicolazzo, Jillian A., Wikstrom Jones, Katreen M., Salisbury, J. Barrett, and Horen, Keith C.

Subjects
*LANDSLIDES, *LIDAR, *DEBRIS avalanches, *EMERGENCY management, *OPTICAL radar, *SLASH (Logging)
Abstract

This article provides an overview of the landslides that occurred in Wrangell, Alaska in November 2023. The landslides resulted in the destruction of homes and loss of life. Geologists conducted field visits and collected aerial lidar data to assess the extent and volume of the landslides. The article also discusses the weather conditions leading up to the landslides, including heavy rainfall and wind gusts. The text highlights the mapping of landslides in the Tongass National Forest and the use of LIDAR datasets for remote sensing. Additionally, the document describes the collection of lidar data for Wrangell Island and the identification of several landslides in the area. The investigation also found no recent mass movement near water reservoirs. [Extracted from the article]

Published
2024

42. Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations.

Author

Crumley, Ryan L., Hill, David F., Wikstrom Jones, Katreen, Wolken, Gabriel J., Arendt, Anthony A., Aragon, Christina M., Cosgrove, Christopher, and Community Snow Observations Participants

Subjects
CITIZEN science, SNOW accumulation, DATA modeling, BATHYMETRY, WATER management, KALMAN filtering
Abstract

A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/ , last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling. [ABSTRACT FROM AUTHOR]

Published
2021
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