Not all DEMs are equal: An evaluation of six globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico. Carrera-Hernández, J. Remote Sensing of Environment, 261:112474, August, 2021. ![Not all DEMs are equal: An evaluation of six globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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∘\textlessslope≤5∘), whereas MAEAW3D30V3=1.9 and 2.2 m for the previously mentioned slopes. With the use of radial boxplots developed on slope groups of 5∘, it was found that both MAE and bias are increasingly affected by aspect as slope increases on all the DSMs. In the case of both AW3D30 DSMs, on flat terrain a difference of only 0.1 m in bias (i.e. median of differences with respect to LiDAR) is found between SE and NW slopes; however, this difference increases according to slope: 0.6 m for 5∘\textlessslope≤10∘, 1.2 m for 10∘\textlessslope≤15∘, and 1.9 m for 15∘\textlessslope≤20∘. Through the analyses undertaken, it is shown that slope—and not vegetation cover—is the factor that has the largest impact on the error of DSMs, and that the effect of aspect on error increases as terrain steepens. This work shows that all DSMs present errors and that an adequate accuracy assessment of DSMs needs to consider the spatial distribution of GCPs, Difference of DSMs (DoDs) and derivatives of DSMs (i.e., slope and aspect) as the use of DoDs provide information on DSM errors (i.e. interpolation artefacts) that can not be assessed through the use of geodetic benchmarks and because DSM errors depend on both slope and aspect.
@article{carrera-hernandez_not_2021,
title = {Not all {DEMs} are equal: {An} evaluation of six globally available 30 m resolution {DEMs} with geodetic benchmarks and {LiDAR} in {Mexico}},
volume = {261},
issn = {00344257},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425721001929},
doi = {10.1016/j.rse.2021.112474},
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∘{\textless}slope≤5∘), whereas MAEAW3D30V3=1.9 and 2.2 m for the previously mentioned slopes. With the use of radial boxplots developed on slope groups of 5∘, it was found that both MAE and bias are increasingly affected by aspect as slope increases on all the DSMs. In the case of both AW3D30 DSMs, on flat terrain a difference of only 0.1 m in bias (i.e. median of differences with respect to LiDAR) is found between SE and NW slopes; however, this difference increases according to slope: 0.6 m for 5∘{\textless}slope≤10∘, 1.2 m for 10∘{\textless}slope≤15∘, and 1.9 m for 15∘{\textless}slope≤20∘. Through the analyses undertaken, it is shown that slope—and not vegetation cover—is the factor that has the largest impact on the error of DSMs, and that the effect of aspect on error increases as terrain steepens. This work shows that all DSMs present errors and that an adequate accuracy assessment of DSMs needs to consider the spatial distribution of GCPs, Difference of DSMs (DoDs) and derivatives of DSMs (i.e., slope and aspect) as the use of DoDs provide information on DSM errors (i.e. interpolation artefacts) that can not be assessed through the use of geodetic benchmarks and because DSM errors depend on both slope and aspect.},
journal = {Remote Sensing of Environment},
author = {Carrera-Hernández, J.J.},
month = aug,
year = {2021},
keywords = {NALCMS},
pages = {112474},
}
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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∘\\textlessslope≤5∘), whereas MAEAW3D30V3=1.9 and 2.2 m for the previously mentioned slopes. With the use of radial boxplots developed on slope groups of 5∘, it was found that both MAE and bias are increasingly affected by aspect as slope increases on all the DSMs. In the case of both AW3D30 DSMs, on flat terrain a difference of only 0.1 m in bias (i.e. median of differences with respect to LiDAR) is found between SE and NW slopes; however, this difference increases according to slope: 0.6 m for 5∘\\textlessslope≤10∘, 1.2 m for 10∘\\textlessslope≤15∘, and 1.9 m for 15∘\\textlessslope≤20∘. Through the analyses undertaken, it is shown that slope—and not vegetation cover—is the factor that has the largest impact on the error of DSMs, and that the effect of aspect on error increases as terrain steepens. This work shows that all DSMs present errors and that an adequate accuracy assessment of DSMs needs to consider the spatial distribution of GCPs, Difference of DSMs (DoDs) and derivatives of DSMs (i.e., slope and aspect) as the use of DoDs provide information on DSM errors (i.e. interpolation artefacts) that can not be assessed through the use of geodetic benchmarks and because DSM errors depend on both slope and aspect.","journal":"Remote Sensing of Environment","author":[{"propositions":[],"lastnames":["Carrera-Hernández"],"firstnames":["J.J."],"suffixes":[]}],"month":"August","year":"2021","keywords":"NALCMS","pages":"112474","bibtex":"@article{carrera-hernandez_not_2021,\n\ttitle = {Not all {DEMs} are equal: {An} evaluation of six globally available 30 m resolution {DEMs} with geodetic benchmarks and {LiDAR} in {Mexico}},\n\tvolume = {261},\n\tissn = {00344257},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S0034425721001929},\n\tdoi = {10.1016/j.rse.2021.112474},\n\tabstract = {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)). 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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∘{\\textless}slope≤5∘), whereas MAEAW3D30V3=1.9 and 2.2 m for the previously mentioned slopes. With the use of radial boxplots developed on slope groups of 5∘, it was found that both MAE and bias are increasingly affected by aspect as slope increases on all the DSMs. In the case of both AW3D30 DSMs, on flat terrain a difference of only 0.1 m in bias (i.e. median of differences with respect to LiDAR) is found between SE and NW slopes; however, this difference increases according to slope: 0.6 m for 5∘{\\textless}slope≤10∘, 1.2 m for 10∘{\\textless}slope≤15∘, and 1.9 m for 15∘{\\textless}slope≤20∘. Through the analyses undertaken, it is shown that slope—and not vegetation cover—is the factor that has the largest impact on the error of DSMs, and that the effect of aspect on error increases as terrain steepens. 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