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Publication . Article . 2021

Not all DEMs are equal: An evaluation of six globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico

J. J. Carrera-Hernández;
Open Access
Published: 01 Aug 2021 Journal: Remote Sensing of Environment, volume 261, page 112,474 (issn: 0034-4257, Copyright policy )
Publisher: Elsevier BV

Abstract This work assesses the vertical accuracy of eight Digital Surface Models (DSMs) currently available for Mexico (LiDAR, ALOS AW3D30 V2 and V3, ASTER GDEM V2 and V3, SRTM, NASADEM and Mexico's Continuous Elevation Model (CEM)). The AW3D30, ASTER GDEM, SRTM and NASADEM DSMs cover nearly the entire globe and can be downloaded at no cost, while the LiDAR and CEM DSMs are distributed by Mexico's Institute of Geography and Statistics (INEGI). The accuracy of these DSMs is assessed by considering: 1) benchmarks as reference data at the national level, and 2) LiDAR DSM as reference data on six different zones with variability in slope, vegetation cover and elevation. Using geodetic benchmarks as reference elevation on those areas covered by LiDAR (ALiDAR=370,200 km2, nbench=24,175), it was found that LiDAR has the best vertical accuracy of all DSMs considered (MAELiDAR = 1.96), which is why it was used as reference elevation to develop seven DEMs of Difference (DoDs) with the remainder DSMs. Using ncells = 350 × 106 for the aforementioned comparisons, it was found that the vertical accuracy of AW3D30 V2 and V3 is similar (MAE=2.5 m), followed by NASADEM, SRTM, CEM, ASTER GDEM3 and ASTER GDEM 2, with MAE values of 3.1, 3.8, 4.6, 6.0 and 7.2 m respectively. The previously mentioned values vary according to slope and slope orientation (i.e. aspect): for flat areas (slope≤5∘), the NASADEM exhibits the lowest MAE (with MAE values of 1.6 for slope≤1∘ and MAE = 2.0 m when 1∘

Subjects by Vocabulary

Microsoft Academic Graph classification: Reference data (financial markets) Lidar Elevation Remote sensing Shuttle Radar Topography Mission Digital surface Geodetic datum Orientation (computer vision) Aster (genus) biology.organism_classification biology


Computers in Earth Sciences, Geology, Soil Science

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