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Abstract <p>With the accelerating pace of global warming, the Arctic glacier region is facing significant pressure from melting. Traditional methods for monitoring glacier surface elevation have limitations. In this study, spaceborne LiDAR was used to micro-monitor the study area. We propose an Elevation-Sampling Point-Slope Aspect Segmented Weighted Analysis (ESSWA) method to analyze the trend of elevation change on the surface of the Kangerlussuaq Glacier in Greenland over the past 20 years. The glacier surface elevation data from 2003 to 2009 provided by ICESat and from 2019 to 2023 provided by ICESat-2 were used, and the data were denoised based on elevation frequency histograms and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. The results show an overall decreasing trend in Arctic glacier surface elevation from 2003 to 2023. Additionally, glacier elevation changes exhibit a clear seasonal pattern, although the long-term trend indicates continued decline. Using ERA5 data, we analyzed local temperature and precipitation changes, revealing a very close relationship between glacier elevation changes area and climate change.</p> Research highlights <p>In this paper, the original elevation box method is improved, and the "Elevation-Sampling Point-Slope Aspect Segmented Weighted Analysis (ESSWA) method" is proposed to calculate the elevation difference. This new approach incorporates two key factors: slope orientation and orbital data point density. Slope orientation affects the reflection and absorption of solar radiation, which in turn influences glacier elevation. Additionally, the method accounts for the impact of orbital data point density on elevation changes. Elevation boxes with higher data point density and greater weight have a larger impact on the average elevation change. By integrating these factors, the ESSWA method enhances the reliability of analytical results.</p>

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Study and analysis of surface elevation changes of the Kangerlussuaq Glacier in Greenland based on ICESat/ICESat-2 Satellite LiDAR data

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Xuefu Dan,
  • Jinye Zhang,
  • Peng Xu,
  • Ruibei Liu,
  • Weihui Zhu,
  • Zhixuan He

摘要

Abstract

With the accelerating pace of global warming, the Arctic glacier region is facing significant pressure from melting. Traditional methods for monitoring glacier surface elevation have limitations. In this study, spaceborne LiDAR was used to micro-monitor the study area. We propose an Elevation-Sampling Point-Slope Aspect Segmented Weighted Analysis (ESSWA) method to analyze the trend of elevation change on the surface of the Kangerlussuaq Glacier in Greenland over the past 20 years. The glacier surface elevation data from 2003 to 2009 provided by ICESat and from 2019 to 2023 provided by ICESat-2 were used, and the data were denoised based on elevation frequency histograms and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. The results show an overall decreasing trend in Arctic glacier surface elevation from 2003 to 2023. Additionally, glacier elevation changes exhibit a clear seasonal pattern, although the long-term trend indicates continued decline. Using ERA5 data, we analyzed local temperature and precipitation changes, revealing a very close relationship between glacier elevation changes area and climate change.

Research highlights

In this paper, the original elevation box method is improved, and the "Elevation-Sampling Point-Slope Aspect Segmented Weighted Analysis (ESSWA) method" is proposed to calculate the elevation difference. This new approach incorporates two key factors: slope orientation and orbital data point density. Slope orientation affects the reflection and absorption of solar radiation, which in turn influences glacier elevation. Additionally, the method accounts for the impact of orbital data point density on elevation changes. Elevation boxes with higher data point density and greater weight have a larger impact on the average elevation change. By integrating these factors, the ESSWA method enhances the reliability of analytical results.