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Lookup NU author(s): Chukwuma Okolie, Professor Jon MillsORCiD, Dr Maria-Valasia PeppaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.
Author(s): Okolie CJ, Mills JP, Adeleke AK, Smit JL, Peppa MV, Altunel AO, Arungwa ID
Publication type: Article
Publication status: Published
Journal: Geo-Spatial Information Science
Year: 2024
Volume: 27
Issue: 4
Pages: 1362-1390
Online publication date: 01/02/2024
Acceptance date: 11/12/2023
Date deposited: 20/02/2024
ISSN (print): 1009-5020
ISSN (electronic): 1993-5153
Publisher: Taylor & Francis
URL: https://doi.org/10.1080/10095020.2023.2296010
DOI: 10.1080/10095020.2023.2296010
Data Access Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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