A New Framework for Vertical Accuracy Assessment and Spatial Error Mapping of Global Digital Elevation Models Using ICESat-2
(2025) In Journal of Geovisualization and Spatial Analysis 10(1).- Abstract
Although Global Digital Elevation Models (GDEMs) are widely used, their spatial error distributions over extensive regions remain insufficiently understood. Using ICESat-2 Reference Control Points (RCPs) (~ 30 points/km²), this paper not only evaluates GDEMs with point-based metrics such as Root Mean Square Error (RMSE) and Mean Error (ME), but also proposes a novel raster-independent Spatial Error Mapping (SEM) approach. The proposed SEM leverages dense RCPs to generate two complementary maps: the Spatial Accuracy Map (SAM) and the Spatial Bias Map (SBM). Results show that FABDEM consistently exhibits the highest vertical accuracy (RMSE = 2.06 m; ME = -1.01 m), followed by AW3D30 (3.47 m; -2.31 m), NASADEM (3.59 m; -1.33 m), SRTM (3.90... (More)
Although Global Digital Elevation Models (GDEMs) are widely used, their spatial error distributions over extensive regions remain insufficiently understood. Using ICESat-2 Reference Control Points (RCPs) (~ 30 points/km²), this paper not only evaluates GDEMs with point-based metrics such as Root Mean Square Error (RMSE) and Mean Error (ME), but also proposes a novel raster-independent Spatial Error Mapping (SEM) approach. The proposed SEM leverages dense RCPs to generate two complementary maps: the Spatial Accuracy Map (SAM) and the Spatial Bias Map (SBM). Results show that FABDEM consistently exhibits the highest vertical accuracy (RMSE = 2.06 m; ME = -1.01 m), followed by AW3D30 (3.47 m; -2.31 m), NASADEM (3.59 m; -1.33 m), SRTM (3.90 m; -1.22 m), and ASTER GDEM (6.58 m; -0.89 m). The results reveal three key patterns: (1) accuracy generally decreases with increasing elevation and slope; (2) most GDEMs exhibit higher errors on north- and northwest-facing slopes; and (3) forested and urban areas show the lowest overall accuracy. The SEM analysis uncovered continuous spatial error patterns in GDEMs such as localized biases and processing- or acquisition-geometry-related artifacts in the SBM and SAM that were previously unreported in the literature and are not captured by traditional point-based metrics.
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- author
- Shahabi, Erfan ; Jafari, Mohsen and Taheri Dehkordi, Alireza LU
- organization
- publishing date
- 2025-12-17
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Global digital elevation model (GDEM), ICESat-2 satellite, Remote sensing, Spatial error map, Vertical accuracy
- in
- Journal of Geovisualization and Spatial Analysis
- volume
- 10
- issue
- 1
- article number
- 2
- publisher
- Springer
- external identifiers
-
- scopus:105025156653
- ISSN
- 2509-8810
- DOI
- 10.1007/s41651-025-00245-0
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2025.
- id
- 83e31cbb-c45c-4bd2-b8b0-7c8541abedff
- date added to LUP
- 2026-03-10 13:47:35
- date last changed
- 2026-03-10 13:48:19
@article{83e31cbb-c45c-4bd2-b8b0-7c8541abedff,
abstract = {{<p>Although Global Digital Elevation Models (GDEMs) are widely used, their spatial error distributions over extensive regions remain insufficiently understood. Using ICESat-2 Reference Control Points (RCPs) (~ 30 points/km²), this paper not only evaluates GDEMs with point-based metrics such as Root Mean Square Error (RMSE) and Mean Error (ME), but also proposes a novel raster-independent Spatial Error Mapping (SEM) approach. The proposed SEM leverages dense RCPs to generate two complementary maps: the Spatial Accuracy Map (SAM) and the Spatial Bias Map (SBM). Results show that FABDEM consistently exhibits the highest vertical accuracy (RMSE = 2.06 m; ME = -1.01 m), followed by AW3D30 (3.47 m; -2.31 m), NASADEM (3.59 m; -1.33 m), SRTM (3.90 m; -1.22 m), and ASTER GDEM (6.58 m; -0.89 m). The results reveal three key patterns: (1) accuracy generally decreases with increasing elevation and slope; (2) most GDEMs exhibit higher errors on north- and northwest-facing slopes; and (3) forested and urban areas show the lowest overall accuracy. The SEM analysis uncovered continuous spatial error patterns in GDEMs such as localized biases and processing- or acquisition-geometry-related artifacts in the SBM and SAM that were previously unreported in the literature and are not captured by traditional point-based metrics.</p>}},
author = {{Shahabi, Erfan and Jafari, Mohsen and Taheri Dehkordi, Alireza}},
issn = {{2509-8810}},
keywords = {{Global digital elevation model (GDEM); ICESat-2 satellite; Remote sensing; Spatial error map; Vertical accuracy}},
language = {{eng}},
month = {{12}},
number = {{1}},
publisher = {{Springer}},
series = {{Journal of Geovisualization and Spatial Analysis}},
title = {{A New Framework for Vertical Accuracy Assessment and Spatial Error Mapping of Global Digital Elevation Models Using ICESat-2}},
url = {{http://dx.doi.org/10.1007/s41651-025-00245-0}},
doi = {{10.1007/s41651-025-00245-0}},
volume = {{10}},
year = {{2025}},
}