Keywords
GIS
Altimetric accuracy
Digital Elevation Models (DEMs)
How to Cite
Abstract
This study evaluates the altimetric accuracy of the TOPODATA Digital Elevation Model (DEM), derived from the Shuttle Radar Topography Mission (SRTM), through comparison with GNSS-RTK data in an area of high topographic variability. The analysis was conducted in Cachoeira do Lepa, located in the municipality of Canguçu, Rio Grande do Sul (Brazil), a region characterized by pronounced altimetric breaks over short distances. A total of 306 georeferenced points were used, and spatial statistical analyses were applied. The results revealed systematic discrepancies, with a tendency of the TOPODATA to underestimate elevations, showing a mean bias error (MBE) of 3.15 m, mean absolute error (MAE) of 3.57 m, and root mean square error (RMSE) of 4.33 m. Spatial autocorrelation was significant (Moran’s I = 0.771; p < 0.001), reducing the effective degrees of freedom (Dutilleul: 120.4) and requiring robust tests (Brunner–Munzel: p < 0.0001; Cliff’s Delta = 0.60). Accuracy varied with elevation: low-lying areas presented an MBE of 0.75 m, while higher terrains reached 6.02 m (Kruskal–Wallis: p < 0.0001). Spatial cross-validation indicated an RMSE of 5.18 m (95% CI: 3.68–6.30 m), and Spearman’s correlation was weak (ρ = –0.077; p = 0.125). It is concluded that TOPODATA tends to underestimate elevations, with larger errors in higher terrains, limiting its reliability for micro-scale applications. The study highlights the methodological risks of using generalized DEMs in morphologically complex regions and suggests hybrid approaches supported by field data as an alternative. The findings align with the United Nations Sustainable Development Goals (SDGs), particularly in the context of precision agriculture, sustainable urban planning, and climate action.
References
AKTURK, E.; ALTUNEL, A. O. Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain. Measurement, v. 136, p. 382–386, 2019. https://doi.org/10.1016/j.measurement.2018.12.101
ANSELIN, L. Local Indicators of Spatial Association—LISA. Geographical Analysis, v. 27, n. 2, p. 93–115, 1995. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
BIELSKI, C.; LÓPEZ-VÁZQUEZ, C.; GROHMANN, C. H.; GUTH, P. L.; HAWKER, L.; GESCH, D.; TREVISANI, S. Novel Approach for Ranking DEMs: Copernicus DEM Improves One Arc Second Open Global Topography. IEEE Transactions on Geoscience and Remote Sensing, v. 62, p. 1–22, 2024. https://doi.org/10.1109/TGRS.2024.3368015
BORMANN, H. Impact of spatial data resolution on simulated catchment water balances and model performance of the multi-scale TOPLATS model. Hydrology and Earth System Sciences, 2006. https://doi.org/10.5194/hess-10-165-2006
Cao, X.; Liu, Z.; Hu, C.; Song, X.; Quaye, J.; Lu, N. Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis. Minerals, 2024. https://doi.org/10.3390/min14070686CHANG
CHANG, K; TSAI, B. The Effect of DEM Resolution on Slope and Aspect Mapping. Cartography and Geographic Information Systems, [s. l.], v. 18, n. 1, p. 69–77, 1991. https://doi.org/10.1559/152304091783805626.
CLIFF, N. Dominance Statistics: Ordinal Analyses to Answer Ordinal Questions. Psychological Bulletin, p. 494–509, 1993. https://doi.org/10.1037/0033-2909.114.3.494
CRUZ, J. P. Bioestatística. 2019. Disponível em: https://sweet.ua.pt/pedrocruz/bioestatistica/an-ks.html#gsc.tab=0. Acesso em: 15 jan. 2025.
CRUZ, J.; BUDAY-BÓDI, E.; NAGY, A.; FEHÉR, Z. Z.; TAMÁS, A.; VIRÁG, I. C.; BOJTOR, C.; FORGÁCS, F.; VAD, A. M.; KUTASY, E. Multispectral Analysis of Small Plots Based on Field and Remote Sensing Surveys—A Comparative Evaluation. Sustainability, v. 14, n. 6, p. 3339, 2022. https://doi.org/10.3390/su14063339
CSAJBÓK, J.; BUDAY-BÓDI, E.; NAGY, A.; FEHÉR, Z. Z.; TAMÁS, A.; VIRÁG, I. C.; BOJTOR, C.; FORGÁCS, F.; VAD, A. M.; KUTASY, E., 2022. Multispectral Analysis of Small Plots Based on Field and Remote Sensing Surveys—A Comparative Evaluation. Sustainability, v. 14, n. 3339. https://doi.org/10.3390/su14063339
DALPOSSO, G. H. Método Bootstrap na agricultura de precisão. 2017. 90 f. Tese (Doutorado em Engenharia Agrícola) – Universidade Estadual do Oeste do Paraná, Cascavel, 2017. Disponível em: https://tede.unioeste.br/handle/tede/3075. Acesso em: 17 jul. 2025.
DELCHIARO, M.; VERGARI, F.; ESPOSITO, C.; DEL MONTE, M. The influence of anthropogenic topographic changes on geomorphological processes in the city of Rome (Italy): A case study of the Malagrotta area. Earth Surface Processes and Landforms, v. 50, n. 3, p. e70033, 2025. https://doi.org/10.1002%2Fesp.70033
DONG, X.; GARRATT, M. A.; ANAVATTI, S. G.; ABBASS, H. A. Towards Real-Time Monocular Depth Estimation for Robotics: A Survey. IEEE Transactions on Intelligent Transportation Systems, v. 23, n. 10, p. 16940–16961, 2022. https://doi.org/10.48550/arXiv.2111.08600
DUTILLEUL, P.; CLIFFORD, P.; RICHARDSON, S.; HEMON, D. Modifying the t Test for Assessing the Correlation Between Two Spatial Processes. Biometrics, v. 49, n. 1, p. 305, 1993. https://doi.org/10.2307/2532625
DUTRA, D. da S. Caracterização e zoneamento geoambiental do município de Canguçu/RS. 2021. 278 f. Dissertação (Mestrado em Geografia) – Universidade Federal do Rio Grande do Sul, Porto Alegre, 2021.
FERREIRA, Z.; COSTA, A. C.; CABRAL, P. Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression. European Journal of Remote Sensing, v. 56, n. 1, p. 2260092, 2023. https://doi.org/10.1080/22797254.2023.2260092
FRANÇA, L. Decifrando o RINEX: o formato aberto para o posicionamento GNSS. Disponível em: https://geoone.com.br/sobre-rinex/. Acesso em: 30 jun. 2025.
GEORGE, C.; MOHAN, K. Digital Elevation Model (DEM) from Google Earth Pro and Freely Downloadable DEMs – A Case Study. Scholars Journal of Engineering and Technology, v. 12, n. 08, p. 263–266, 2024. https://doi.org/10.36347/sjet.2024.v12i08.002
GOLDSTEIN A; FOTI R; MONTALTO F. Effect of Spatial Resolution in Modeling Stormwater Runoff for an Urban Block. Journal of Hydrologic Engineering, v. 21, n. 11, p. 06016009, 2016. https://doi.org/10.1061/%28ASCE%29HE.1943-5584.0001377
GOMES, F. C. M.; ZAIDAN, R. T.; ROCHA, C. H. B. Análise comparativa entre a aplicação de métodos de interpolação, para a geração de modelos digitais de elevação. Revista Brasileira de Geografia Física, v. 15, no 5, p. 2448–2462, 2022. https://doi.org/10.26848/rbgf.v15.5.p2448-2462
GUTH, P. L.; VAN NIEKERK, A.; GROHMANN, C. H.; MULLER, J. P.; HAWKER, L.; FLORINSKY, I. V.; GESCH, D.; REUTER, H. I.; HERRERA-CRUZ, V.; RIAZANOFF, S.; LÓPEZ-VÁZQUEZ, C.; CARABAJAL, C. C.; ALBINET, C.; STROBL, P. Digital Elevation Models: Terminology and Definitions. Remote Sensing, v. 13, n. 18, p. 3581, 2021. https://doi.org/10.3390/rs13183581
HENKEL, P.; GUNTHER, C. Precise point positioning with multiple Galileo frequencies. 2008 IEEE/ION Position, Location and Navigation Symposium, p. 592–599, 2008. https://doi.org/10.1109/PLANS.2008.4570102
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE). Canguçu - RS: panorama. Cidades@, 2025. Disponível em: https://cidades.ibge.gov.br/brasil/rs/cangucu/panorama. Acesso em: 11 nov. 2025.
JETBRAINS. PyCharm: The only Python IDE you need. 2025. Disponível em: https://www.jetbrains.com/pycharm/. Acesso em: 11 nov. 2025.
KRAMM, T.; HOFFMEISTER, D. Comprehensive vertical accuracy analysis of freely available DEMs for different landscape types of the Rur catchment, Germany. Geocarto International, v. 37, n. 25, p. 7774–7799, 2022. https://doi.org/10.1080/10106049.2021.1984588
KRAMM, T.; HOFFMEISTER, D. A Relief Dependent Evaluation of Digital Elevation Models on Different Scales for Northern Chile. ISPRS International Journal of Geo-Information, v. 8, n. 10, p. 430, 2019. https://doi.org/10.3390/ijgi8100430
LI, D., LI, B.; FENG, H. Q.; KANH, S.; WANG, J.; WEI, Z. Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles - beyond crop row detection. ISPRS Journal of Photogrammetry and Remote Sensing, 2024. Disponível em: https://api.semanticscholar.org/CorpusID:268307684. Acesso em: 30 jun. 2025.
LI, M.; YIN, X.; TANG, B.; YANG, M. Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China. Remote Sensing, v. 15, n. 7, p. 1952, 2023. https://doi.org/10.3390/rs15071952
LIU, S.; YANG, L. T.; TU, X.; LI, R.; XU, C. Lightweight Monocular Depth Estimation on Edge Devices. IEEE Internet of Things Journal, v. 9, n. 17, p. 16168–16180, 2022. https://doi.org/10.1109/JIOT.2022.3151374
MONGRÉDIEN, C.; DOYEN, J. P.; STROM, M.; AMMANN, D. Centimeter-Level Positioning for UAVs and Other Mass-Market Applications. Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Proceedings […], Portland, Oregon, September 2016, p. 1441-1454. https://doi.org/10.33012/2016.14619
MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; CATEN, A. T.; RUIZ, L. F. C.; RAMOS, P. V.; DOTTO, A. C. Assessment of Digital Elevation Model for Digital Soil Mapping in a Watershed with Gently Undulating Topography. Revista Brasileira de Ciência do Solo, v. 40, n. 0, 2016. Disponível em: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832016000100304&lng=en&tlng=en. Acesso em: 11 nov. 2025.
MUÑOZ, V. A.; VALERIANO, M. de M. Estimativa da amplitude topográfica por geoprocessamento de dados SRTM para modelagem do relevo. [S. l.], 2011. Disponível em: https://www.periodicos.rc.biblioteca.unesp.br/index.php/ageteo/article/download/4908/5137/3824. Acesso em: 01 ago. 2025.
MOYA, C. R. Como escolher o teste estatístico: um guia para o pesquisador iniciante. São Paulo, SP: Claudia Moya, 2021. Disponível em: https://www.sbquadril.org.br/app/uploads/2021/10/Como-escolher-o-teste-estati%CC%81stico-Um-guia-para-o-pesquisador-iniciante.pdf. Acesso em: 08 jun. 2025.
ORGANIZAÇÃO DAS NAÇÕES UNIDAS. Transformando Nosso Mundo: A Agenda 2030 para o Desenvolvimento Sustentável. Nova York: ONU, 2015. Disponível em: https://sustainabledevelopment.un.org/post2015/transformingourworld. Acesso em: 28 jan. 2025.
PAKOKSUNG, K.; TAKAGI, M. Assessment and comparison of Digital Elevation Model (DEM) products in varying topographic, land cover regions and its attribute: a case study in Shikoku Island Japan. Modeling Earth Systems and Environment, v. 7, n. 1, p. 465–484, 2021. https://doi.org/10.1007/s40808-020-00891-x
PANAGIOTOU, E.; CHOCHLAKIS, G.; GRAMMATIKOPOULOS, L.; CHAROU, E. Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning. Remote Sensing, v. 12, n. 12, p. 2002, 2020. https://doi.org/10.3390/rs12122002
POLIDORI, L.; EL HAGE, M. Digital Elevation Model Quality Assessment Methods: A Critical Review. Remote Sensing, v. 12, n. 21, p. 3522, 2020. https://doi.org/10.3390/rs12213522
PREFEITURA DE CACHOEIRA DO SUL. Cachoeira tem 2.780 estabelecimentos rurais produtivos. 2018. Disponível em: https://www.cachoeiradosul.rs.gov.br/portal/noticias/0/3/1932/cachoeira-tem-2780-estabelecimentos-rurais-produtivos. Acesso em: 15 de jan. 2025.
QGIS DEVELOPMENT TEAM. QGIS Geographic Information System. Version 3.34. Beaverton, OR: OSGeo, 2025. Software. Disponível em: https://qgis.org/. Acesso em: 20 jul. 2025.
RAHRIG, R. R. ANOVA F-test and Kruskal-Wallis test performance comparison under varying distributions, variance heterogeneity, sample sizes, and noncentrality structures. Journal of Statistics and Management Systems, 2024. https://doi.org/10.47974/JSMS-1116
ROBERTS, D. R.; BAHN, V.; CIUTI, S.; BOYCE, M. S.; ELITE, J.; GUILLERA-ARROITA, G.; HAUENSTEIN, S.; LAHOZ-MONFORT, J. J.; SCHRODER, B.; THUILLER, W.; WARTON, D. I.; WINTLE, B. A.; HARTIG, F.; DORMANN, C. F. Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, v. 40, n. 8, p. 913–929, 2017. https://doi.org/10.1111/ecog.02881
SAMPAIO, N. A. D. S.; ASSUMPÇÃO, A. R. P. D.; FONSECA, B. B. D. Estatística Inferencial. 2018. Disponível em: http://www.poisson.com.br/livros/estatistica/volume2/Estatistica_Inferencial.pdf. Acesso em: 04 jan. 2025.
SAMEJIMA, K. Estatística não paramétrica - Testes de Aderência. 2018. Disponível em: https://est.ufba.br/sites/est.ufba.br/files/kim/matd49-aula05-aderencia.pdf. Acesso em: 04 jan. 2025.
SHEAN, D. E.; ALEXANDROV, O.; MORATTO, Z. M.; SMITH, B. E.; JOUGHIN, I. R.; PORTER, C.; MORIN, P. An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, v. 116, p. 101–117, 2016. https://doi.org/10.1016/j.isprsjprs.2016.03.012
SHIN, Y.; LEE, C.; KIM, E. Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles. Machines, v. 12, n. 3, p. 202, 2024. https://doi.org/10.3390/machines12030202
SIAVOSHI, M. 10 Python Statistical Functions. 2025. Disponível em: https://www.kdnuggets.com/10-pythonstatisticalfunctions#:~:text=Libraries%20for%20Statistical%20Analysis,a%20range%20of%20mathematical%20functions. Acesso em: 05 jan. 2025
VALERIANO, M. M. Topodata: guia para utilização de dados geomorfológicos locais. São José dos Campos: INPE. 2008. Disponível em: http://mtc-m18.sid.inpe.br/col/sid.inpe.br/mtc-m18@80/2008/07.11.19.24/doc/publicacao.pdf. Acesso em: 05 fev. 2025
VALERIANO, M. de M. Modelo digital de variáveis morfométricas com dados SRTM para o território nacional: o projeto TOPODATA. 2005. Anais [...].2005. Disponível em: http://marte.sid.inpe.br/col/ltid.inpe.br/sbsr/2004/10.29.11.41/doc/3595.pdf. Acesso em: 05 fev. 2025.
VALERIANO, M. M.; MUNOZ, V. V. Estimativa da amplitude topográfica por geoprocessamento de dados SRTM para modelagem do relevo. Geografia – INPE. 2011. Disponível em: http://observatoriodageografia.uepg.br/files/original/d539e3c4eb516c5dba5a51e40f433c678341b860.pdf. Acesso em: 05 fev. 2025.
VAN ECK, N. J.; WALTMAN, L. VOSviewer – Visualizing scientific landscapes. Leiden: Centre for Science and Technology Studies, Leiden University, 2025. Disponível em: https://www.vosviewer.com. Acesso em: 17 jul. 2025.
XU, W.; LI, J.; PENG, D.; YIN, H.; JIANG, J.; XIA, H.; WEN, D. Vertical Accuracy Assessment and Improvement of Five High-Resolution Open-Source Digital Elevation Models Using ICESat-2 Data and Random Forest: Case Study on Chongqing, China. Remote Sensing, v. 16, n. 11, p. 1903, 2024. https://doi.org/10.3390/rs16111903
YANG, L.; ZHU, Z.; SUN, L.; ZHANG, D. Global Attention-Based DEM: A Planet Surface Digital Elevation Model-Generation Method Combined with a Global Attention Mechanism. Aerospace, v. 11, n. 7, p. 529, 2024. https://doi.org/10.3390/aerospace11070529
TIAN, Y.; GUO, P.; LYU, M.R. Comparative Studies on Feature Extraction Methods for Multispectral Remote Sensing Image Classification. In: 2005 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, 2005, Waikoloa, HI, USA. 2005 IEEE International Conference on Systems, Man and Cybernetics. Waikoloa, HI, USA: IEEE, 2005. p. 1275–1279. https://doi.org/10.1109/ICSMC.2005.1571322
YIN, Q.; CHEN, Z.; ZHENG, X.; XU, Y.; LIU, T. Sliding Windows Method Based on Terrain Self-Similarity for Higher DEM Resolution in Flood Simulating Modeling. Remote Sensing, v. 13, n. 18, p. 3604, 2021. https://doi.org/10.3390/rs13183604
ZHANG, Y.; YU, W. Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks. Sensors, v. 22, n. 3, p. 745, 2022. https://doi.org/10.3390/s22030745
ZHU, H.; CHEN, Y. A Study of the Effect of DEM Spatial Resolution on Flood Simulation in Distributed Hydrological Modeling. Remote Sensing, v. 16, n. 16, p. 3105, 2024. https://doi.org/10.3390/rs16163105
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Tássia Parada Sampaio, Luciano Martins Tavares, Tainara Goulart Corrêa, Larissa Aldrighi da Silva, Carolina Moraes de Souza, Lucas Simões dos Santos, Diuliana Leandro, Andréa Souza Castro, Adriano Luis Heck Simon