Comparison of Gridded DEMs by Buffering
Comparing two digital elevation models (DEMs), S1 (reference) and S2 (product), in order to get the S2 quality, has usually been performed on sampled points. However, it seems more natural, as we propose, comparing both DEMs using 2.5D surfaces: applying a buffer to S1 (single buffer method, SBM) or...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-07-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/15/3002 |
id |
doaj-a3fdd418e02a437eb121db8ea2425fc3 |
---|---|
record_format |
Article |
spelling |
doaj-a3fdd418e02a437eb121db8ea2425fc32021-08-06T15:30:46ZengMDPI AGRemote Sensing2072-42922021-07-01133002300210.3390/rs13153002Comparison of Gridded DEMs by BufferingFrancisco Javier Ariza-López0Juan Francisco Reinoso-Gordo1Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría, Universidad de Jaén, 23071 Jaén, SpainDepartamento de Expresión Gráfica, Arquitectónica y en la Ingeniería; Universidad de Granada, 18071 Granda, SpainComparing two digital elevation models (DEMs), S1 (reference) and S2 (product), in order to get the S2 quality, has usually been performed on sampled points. However, it seems more natural, as we propose, comparing both DEMs using 2.5D surfaces: applying a buffer to S1 (single buffer method, SBM) or to both S1 and S2 (double buffer method, DBM). The SBM and DBM approaches have been used in lines accuracy assessment and, in this paper, we generalize them to a DEM surface, so that more area of the S2 surface (in the case of the SBM), or the area and volume (in the case of the DBM) that are involved, more similarly are S1 and S2. The results obtained show that across both methods, SBM recognizes the presence of outliers and vertical bias while DBM allows a richer and more complex analysis based on voxel intersection. Both methods facilitate creating observed distribution functions that eliminate the need for the hypothesis of normality on discrepancies and allow the application of quality control techniques based on proportions. We consider that the SBM is more suitable when the S1 accuracy is much greater than that of S2 and DBM is preferred when the accuracy of S1 and S2 are approximately equal.https://www.mdpi.com/2072-4292/13/15/3002grid DEMbuffering surfacequality assessmentaccuracydistribution function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Francisco Javier Ariza-López Juan Francisco Reinoso-Gordo |
spellingShingle |
Francisco Javier Ariza-López Juan Francisco Reinoso-Gordo Comparison of Gridded DEMs by Buffering Remote Sensing grid DEM buffering surface quality assessment accuracy distribution function |
author_facet |
Francisco Javier Ariza-López Juan Francisco Reinoso-Gordo |
author_sort |
Francisco Javier Ariza-López |
title |
Comparison of Gridded DEMs by Buffering |
title_short |
Comparison of Gridded DEMs by Buffering |
title_full |
Comparison of Gridded DEMs by Buffering |
title_fullStr |
Comparison of Gridded DEMs by Buffering |
title_full_unstemmed |
Comparison of Gridded DEMs by Buffering |
title_sort |
comparison of gridded dems by buffering |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-07-01 |
description |
Comparing two digital elevation models (DEMs), S1 (reference) and S2 (product), in order to get the S2 quality, has usually been performed on sampled points. However, it seems more natural, as we propose, comparing both DEMs using 2.5D surfaces: applying a buffer to S1 (single buffer method, SBM) or to both S1 and S2 (double buffer method, DBM). The SBM and DBM approaches have been used in lines accuracy assessment and, in this paper, we generalize them to a DEM surface, so that more area of the S2 surface (in the case of the SBM), or the area and volume (in the case of the DBM) that are involved, more similarly are S1 and S2. The results obtained show that across both methods, SBM recognizes the presence of outliers and vertical bias while DBM allows a richer and more complex analysis based on voxel intersection. Both methods facilitate creating observed distribution functions that eliminate the need for the hypothesis of normality on discrepancies and allow the application of quality control techniques based on proportions. We consider that the SBM is more suitable when the S1 accuracy is much greater than that of S2 and DBM is preferred when the accuracy of S1 and S2 are approximately equal. |
topic |
grid DEM buffering surface quality assessment accuracy distribution function |
url |
https://www.mdpi.com/2072-4292/13/15/3002 |
work_keys_str_mv |
AT franciscojavierarizalopez comparisonofgriddeddemsbybuffering AT juanfranciscoreinosogordo comparisonofgriddeddemsbybuffering |
_version_ |
1721217714643533824 |