Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression

References

• Abdelkarim, A., Al-Alola, S. S., Alogayell, H. M., Mohamed, S. A., Alkadi, I. I., & Youssef, I. Y. (2020). Mapping of gis-flood hazard using the geomorphometric-hazard model: Case study of the al-shamal train pathway in the city of qurayyat, kingdom of Saudi Arabia. Geosciences (Switzerland), 10(9), 1–20. https://doi.org/10.3390/geosciences10090333
   PubMed Web of Science ®Google Scholar
• Alvares, C. A., Stape, J. L., Sentelhas, P. C., & de Moraes Gonçalves, J. L. (2013). Modeling monthly mean air temperature for Brazil. Theoretical and Applied Climatology, 113(3–4), 407–427. https://doi.org/10.1007/s00704-012-0796-6
   Web of Science ®Google Scholar
• ANA. (2022). Agência Nacional de Águas e Saneamento Básico. Base Hidrográfica Ottocodificada da Bacia do Rio Uruguai. Retrieved March 12, 2022, from https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/3d6a7031-5b18-45ea-ab41-39911975e51a
   Google Scholar
• Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
   Web of Science ®Google Scholar
• Anselin, L., Sridharan, S., & Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: The discovery of interesting patterns. Social Indicators Research, 82(2), 287–309. https://doi.org/10.1007/s11205-006-9034-x
   Web of Science ®Google Scholar
• Bettiol, G. M., Ferreira, M. E., Motta, L. P., Cremon, É. H., & Sano, E. E. (2021). Conformity of the NASADEM_HGT and ALOS AW3D30 dem with the altitude from the Brazilian geodetic reference stations: A case study from Brazilian cerrado. Sensors, 21(9), 2935. https://doi.org/10.3390/s21092935
   PubMed Web of Science ®Google Scholar
• Brasington, J., Langham, J., & Rumsby, B. (2003). Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport. Geomorphology, 53(3–4), 299–316. https://doi.org/10.1016/S0169-555X(02)00320-3
   Web of Science ®Google Scholar
• Brazil. (2006). Caderno da Região Hidrográfica do Uruguai. Ministério do Meio Ambiente, Secretaria de Recursos Hídricos. MMA, 2006
   Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
   Web of Science ®Google Scholar
• Bui, D. T., Tsangaratos, P., Nguyen, V.-T., Liem, N. V., & Trinh, P. T. (2020). Comparing the prediction performance of a Deep learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188, 188. https://doi.org/10.1016/j.catena.2019.104426
   Web of Science ®Google Scholar
• Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361–378. https://doi.org/10.1007/s10346-015-0557-6
   Web of Science ®Google Scholar
• Carlisle, B. H. (2005). Modelling the spatial distribution of DEM error. Transactions in GIS, 9(4), 521–540. https://doi.org/10.1111/j.1467-9671.2005.00233.x
   Google Scholar
• Chang, K.-T., Merghadi, A., Yunus, A. P., Pham, B. T., & Dou, J. (2019). Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Scientific Reports, 9(1), 12296. https://doi.org/10.1038/s41598-019-48773-2
   PubMedGoogle Scholar
• Chen, X., & Chen, W. (2021). GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena, 196, 196. https://doi.org/10.1016/j.catena.2020.104833
   Web of Science ®Google Scholar
• Chen, C., Hu, B., & Li, Y. (2021). Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy. Hydrology and Earth System Sciences, 25(11), 5667–5682. https://doi.org/10.5194/hess-25-5667-2021
   Web of Science ®Google Scholar
• Das, S., Patel, P. P., & Sengupta, S. (2016). Evaluation of different digital elevation models for analyzing drainage morphometric parameters in a mountainous terrain: A case study of the supin–upper tons basin, Indian Himalayas. SpringerPlus, 5(1), 1–38. https://doi.org/10.1186/s40064-016-3207-0
   PubMedGoogle Scholar
• DeWitt, J., Warner, T., & Conley, J. (2015). Comparison of DEMS derived from USGS DLG, SRTM, a statewide photogrammetry program, ASTER GDEM and LiDAR: Implications for change detection. GIScience & Remote Sensing, 52(2), 179–197. https://doi.org/10.1080/15481603.2015.1019708
   Web of Science ®Google Scholar
• Dong, Y., & Shortridge, A. M. (2019). A regional ASTER GDEM error model for the Chinese Loess Plateau. International Journal of Remote Sensing, 40(3), 1048–1065. https://doi.org/10.1080/01431161.2018.1524171
   Web of Science ®Google Scholar
• Drăguţ, L., & Eisank, C. (2011). Object representations at multiple scales from digital elevation models. Geomorphology, 129(3–4), 183–189. https://doi.org/10.1016/j.geomorph.2011.03.003
   PubMed Web of Science ®Google Scholar
• Elkhrachy, I. (2018). Vertical accuracy assessment for SRTM and ASTER digital elevation models: A case study of Najran city, Saudi Arabia. Ain Shams Engineering Journal, 9(4), 1807–1817. https://doi.org/10.1016/j.asej.2017.01.007
   Web of Science ®Google Scholar
• Erdoğan, S. (2010). Modelling the spatial distribution of DEM error with geographically weighted regression: An experimental study. Computers & Geosciences, 36(1), 34–43. https://doi.org/10.1016/j.cageo.2009.06.005
   Web of Science ®Google Scholar
• Esri. (2022). Geographic Information System Company. Calculate Distance Band from Neighbor Count (Spatial Statistics). https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/calculate-distance-band-from-neighbor-count.htm
   Google Scholar
• Ferreira, Z. A., & Cabral, P. (2022). A comparative study about vertical accuracy of four freely available digital elevation models: A case study in the Balsas river watershed, Brazil. ISPRS International Journal of Geo-Information, 11(2), 106. https://doi.org/10.3390/ijgi11020106
   Web of Science ®Google Scholar
• Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons.
   Google Scholar
• Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480
   Web of Science ®Google Scholar
• Fotheringham, A. S., Yue, H., & Li, Z. (2019). Examining the influences of air quality in China’s cities using multi-scale geographically weighted regression. Transactions in GIS, 23(6), 1444–1464. https://doi.org/10.1111/tgis.12580
   Google Scholar
• Gallay, M., Lloyd, C., & McKinley, J. (2010, July, 20-23). Using geographically weighted regression for analysing elevation error of high-resolution DEMs. (Ed.),^(eds.) Proceedings of the Ninth International Accuracy Symposium, Leicester, UK.
   Google Scholar
• Garofalo, D. F. T., & Liesenberg, V. (2015). Análise comparativa da informação altimétrica presente nos modelos digitais de elevação ASTER GDEM 1 e 2, SRTM e TOPODATA. Anais XVII Simpósio Brasileiro de Sensoriamento Remoto-SBSR. João Pessoa-PB:[sn], 2867–2875.
   Google Scholar
• Gdulová, K., Marešová, J., & Moudrý, V. (2020). Accuracy assessment of the global TanDEM-X digital elevation model in a mountain environment. Remote Sensing of Environment, 241, 111724. https://doi.org/10.1016/j.rse.2020.111724
   Web of Science ®Google Scholar
• Getis, A., & Ord, J. K. (1992). The analysis of spatial association by the use of distance statistics. Geographical Analysis, 24(2), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
   Google Scholar
• Gonzalez-Moradas, M. D. R., & Viveen, W. (2020). Evaluation of ASTER GDEM2, SRTMv3. 0, ALOS AW3D30 and TanDEM-X DEMs for the Peruvian Andes against highly accurate GNSS ground control points and geomorphological-hydrological metrics. Remote Sensing of Environment, 237, 111509. https://doi.org/10.1016/j.rse.2019.111509
   Web of Science ®Google Scholar
• González-Moradas, M. D. R., & Viveen, W. (2020). Evaluation of ASTER GDEM2, SRTMv3. 0, ALOS AW3D30 and TanDEM-X DEMs for the Peruvian Andes against highly accurate GNSS ground control points and geomorphological-hydrological metrics. Remote Sensing of Environment, 237, 111509. https://doi.org/10.1016/j.rse.2019.111509
   Web of Science ®Google Scholar
• Gorini, M. A. V., & Mota, G. L. A. (2016). Dealing with double vagueness in DEM morphometric analysis. International Journal of Geographical Information Science, 30(8), 1644–1666. https://doi.org/10.1080/13658816.2016.1150484
   Web of Science ®Google Scholar
• Gorokhovich, Y., & Voustianiouk, A. (2006). Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics. Remote Sensing of Environment, 104(4), 409–415. https://doi.org/10.1016/j.rse.2006.05.012
   Web of Science ®Google Scholar
• Habib, M. (2021). Evaluation of DEM interpolation techniques for characterizing terrain roughness. Catena, 198, 105072. https://doi.org/10.1016/j.catena.2020.105072
   Web of Science ®Google Scholar
• Hawker, L., Neal, J., & Bates, P. (2019). Accuracy assessment of the TanDEM-X 90 digital elevation model for selected floodplain sites. Remote Sensing of Environment, 232, 111319. https://doi.org/10.1016/j.rse.2019.111319
   Web of Science ®Google Scholar
• Hirano, A., Welch, R., & Lang, H. (2003). Mapping from ASTER stereo image data: DEM validation and accuracy assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 57(5), 356–370. https://doi.org/10.1016/S0924-2716(02)00164-8
   Web of Science ®Google Scholar
• Hladik, C., & Alber, M. (2012). Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model. Remote Sensing of Environment, 121, 224–235. https://doi.org/10.1016/j.rse.2012.01.018
   Web of Science ®Google Scholar
• Holmes, K., Chadwick, O., & Kyriakidis, P. C. (2000). Error in a USGS 30-meter digital elevation model and its impact on terrain modeling. Journal of Hydrology, 233(1–4), 154–173. https://doi.org/10.1016/S0022-1694(00)00229-8
   Web of Science ®Google Scholar
• Hutcheson, G. D., & Hutcheson, G. (2011). Ordinary least-squares regression. In L. Moutinho & G. D. Hutcheson (Eds.), The SAGE dictionary of quantitative management research (pp. 225–228). https://doi.org/10.4135/9781446251119.n67
   Google Scholar
• IBGE. (2017). Instituto Brasileiro de Geografia e Estatística. Monitoramento da cobertura e uso da terra do Brasil 2000 - 2010 - 2012 - 2014. Retrieved June 17, 2023, from https://biblioteca.ibge.gov.br/visualizacao/livros/liv101469.pdf
   Google Scholar
• IBGE. (2019). Instituto Brasileiro de Geografia e Estatística. Reajustamento da Rede Altimétrica com Números Geopotenciais (2nd ed.). Instituto Brasileiro de Geografia e Estatística - Coordenação de Geodésia. Retrieved July 14, 2020, from. https://biblioteca.ibge.gov.br/visualizacao/livros/liv101666.pdf
   Google Scholar
• IBGE. (2022a). Instituto Brasileiro de Geografia e Estatística. Banco de dados geodésicos. Retrieved March 19, 2022, from https://www.ibge.gov.br/geociencias/informacoes-sobre-posicionamento-geodesico/rede-geodesica/16327-banco-de-dados-geodesicos.html?=&t=o-que-e
   Google Scholar
• IBGE. (2022b). Instituto Brasileiro de Geografia e Estatística. Monitoramento da Cobertura e Uso da Terra. Retrieved March 26, 2022, from https://www.ibge.gov.br/geociencias/informacoes-ambientais/cobertura-e-uso-da-terra/15831-cobertura-e-uso-da-terra-do-brasil.html?=&t=notas-tecnicas
   Google Scholar
• INPE. (2008). Insitituto Nacional de Pesquisas Espaciais. TOPODATA - Banco de Dados Geomorfométricos do Brasil. Retrieved October 12, 2022, from http://www.dsr.inpe.br/topodata/index.php
   Google Scholar
• Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Morphometric analysis of the River thamirabarani sub-basin in Kanyakumari District, South west coast of Tamil Nadu, India, using remote sensing and GIS. Environmental Earth Sciences, 73(11), 7375–7401. https://doi.org/10.1007/s12665-014-3914-1
   Web of Science ®Google Scholar
• Kastrisios, C., & Tsoulos, L. (2018). Voronoi tessellation on the ellipsoidal earth for vector data. International Journal of Geographical Information Science, 32(8), 1541–1557. https://doi.org/10.1080/13658816.2018.1434890
   Web of Science ®Google Scholar
• Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847–855. https://doi.org/10.1007/s00254-006-0256-7
   Web of Science ®Google Scholar
• Lei, X., Chen, X., Yang, Z., He, S., Zhu, L., & Liang, H. (2022). A simple and robust MPM framework for modelling granular flows over complex terrains. Computers and Geotechnics, 149, 104867. https://doi.org/10.1016/j.compgeo.2022.104867
   Web of Science ®Google Scholar
• Leon, J. X., Heuvelink, G. B., Phinn, S. R., & Schumann, G. J. P. (2014). Incorporating DEM uncertainty in coastal inundation mapping. PLoS One, 9(9), e108727. https://doi.org/10.1371/journal.pone.0108727
   PubMed Web of Science ®Google Scholar
• Lindsay, J. B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers and Geosciences, 95, 75–84. https://doi.org/10.1016/j.cageo.2016.07.003
   Web of Science ®Google Scholar
• Li, L., Nearing, M. A., Nichols, M. H., Polyakov, V. O., Guertin, D. P., & Cavanaugh, M. L. (2020). The effects of DEM interpolation on quantifying soil surface roughness using terrestrial LiDAR. Soil and Tillage Research, 198, 104520. https://doi.org/10.1016/j.still.2019.104520
   Web of Science ®Google Scholar
• Liu, Z., Zhu, J., Fu, H., Zhou, C., & Zuo, T. (2020). Evaluation of the vertical accuracy of open global DEMs over steep terrain regions using ICESat data: A case study over Hunan Province, China. Sensors, 20(17), 4865. https://doi.org/10.3390/s20174865
   PubMed Web of Science ®Google Scholar
• Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 207. https://doi.org/10.1016/j.earscirev.2020.103225
   Web of Science ®Google Scholar
• Mesa-Mingorance, J. L., & Ariza-López, F. J. (2020). Accuracy assessment of digital elevation models (DEMs): A critical review of practices of the past three decades. Remote Sensing, 12(16), 2630. https://doi.org/10.3390/rs12162630
   Web of Science ®Google Scholar
• Miceli, B. S., Dias, F. D. M., Seabra, F. M., Santos, P., & Fernandes, M. D. C. (2011). Avaliação vertical de modelos digitais de elevação (MDEs) em diferentes configurações topográficas para médias e pequenas escalas. Revista Brasileira de Cartografia, 63(1), 191–201. https://doi.org/10.14393/rbcv63n1-43731
   Google Scholar
• Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. https://doi.org/10.1093/biomet/37.1-2.17
   PubMed Web of Science ®Google Scholar
• Moura, L. Z., Bias, E. D. S., & Brites, R. (2014). Avaliação da acurácia vertical de modelos digitais de elevação (MDEs) nas bacias do Paranoá e São Bartolomeu. Revista Brasileira de Cartografia, 66(1). https://doi.org/10.14393/rbcv66n1-43892
   Google Scholar
• Mukherjee, S., Joshi, P. K., Mukherjee, S., Ghosh, A., Garg, R. D., & Mukhopadhyay, A. (2013). Evaluation of vertical accuracy of open source digital elevation model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21, 205–217. https://doi.org/10.1016/j.jag.2012.09.004
   Web of Science ®Google Scholar
• NASA. (2013). National Aeronautics and space Administration. Shuttle Radar Topography Mission global 1 arc second. Version 3. In NASA EOSDIS land processes DAAC. USGS Earth Resources Observation and Science (EROS) Center. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003
   Google Scholar
• NASA. (2022). National Aeronautics and Space Administration. Shuttle Radar Topography Mission. Shuttle Radar Topography Mission. Retrieved July 15, 2022, from https://www2.jpl.nasa.gov/srtm/index.html
   Google Scholar
• Nene, S. A., & Nayar, S. K. (1997). A simple algorithm for nearest neighbor search in high dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(9), 989–1003. https://doi.org/10.1109/34.615448
   Web of Science ®Google Scholar
• Orlando, F. C. (2019). Análise da potencialidade do uso de MDE global em trabalho de grande escala ao longo de todo o território brasileiro.
   Google Scholar
• Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). MGWR: A python implementation of Multiscale Geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269. https://doi.org/10.3390/ijgi8060269
   Web of Science ®Google Scholar
• Pandey, P., Manickam, S., Bhattacharya, A., Ramanathan, A., Singh, G., & Venkataraman, G. (2017). Qualitative and quantitative assessment of TanDEM-X DEM over western Himalayan glaciated terrain. Geocarto International, 32(4), 442–454. https://doi.org/10.1080/10106049.2016.1155655
   Web of Science ®Google Scholar
• Pipaud, I., Loibl, D., & Lehmkuhl, F. (2015). Evaluation of TanDEM-X elevation data for geomorphological mapping and interpretation in high mountain environments - a case study from SE Tibet, China. Geomorphology, 246, 232–254. https://doi.org/10.1016/j.geomorph.2015.06.025
   Web of Science ®Google Scholar
• Polidori, L., & El Hage, M. (2020). Digital elevation model quality assessment methods: A critical review. Remote Sensing, 12(21), 3522. https://doi.org/10.3390/rs12213522
   Web of Science ®Google Scholar
• Prasannakumar, V., Vijith, H., Charutha, R., & Geetha, N. (2011). Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia-Social and Behavioral Sciences, 21, 317–325. https://doi.org/10.1016/j.sbspro.2011.07.020
   Google Scholar
• Rawat, K. S., Singh, S. K., Singh, M. I., & Garg, B. (2019). Comparative evaluation of vertical accuracy of elevated points with ground control points from ASTERDEM and SRTMDEM with respect to CARTOSAT-1DEM. Remote Sensing Applications: Society & Environment, 13, 289–297. https://doi.org/10.1016/j.rsase.2018.11.005
   Google Scholar
• Rodrigues, T. G., Paradella, W. R., & Oliveira, C. G. (2011). Evaluation of the altimetry from SRTM-3 and planimetry from high-resolution PALSAR FBD data for semi-detailed topographic mapping in the Amazon region. Anais da Academia Brasileira de Ciências, 83(3), 953–966. https://doi.org/10.1590/S0001-37652011000300014
   PubMed Web of Science ®Google Scholar
• Safar, M. (2005). K nearest neighbor search in navigation systems. Mobile Information Systems, 1(3), 207–224. https://doi.org/10.1155/2005/692568
   Web of Science ®Google Scholar
• Saleem, N., Huq, M., Twumasi, N. Y. D., Javed, A., & Sajjad, A. (2019). Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility mapping and landslide risk assessment: A review. ISPRS International Journal of Geo-Information, 8(12), 545. https://doi.org/10.3390/ijgi8120545
   Web of Science ®Google Scholar
• Sánchez-Guillamón, O., Fernández-Salas, L. M., Vázquez, J. T., Palomino, D., Medialdea, T., López-González, N., Somoza, L., & León, R. (2018). Shape and size complexity of deep seafloor mounds on the canary basin (west to canary islands, Eastern Atlantic): A DEM-based geomorphometric analysis of domes and volcanoes. Geosciences (Switzerland), 8(2), 37. https://doi.org/10.3390/geosciences8020037
   Web of Science ®Google Scholar
• Sarma, C. P., Dey, A., & Krishna, A. M. (2020). Influence of digital elevation models on the simulation of rainfall-induced landslides in the hillslopes of Guwahati, India. Engineering Geology, 268, 105523. https://doi.org/10.1016/j.enggeo.2020.105523
   Web of Science ®Google Scholar
• Satgé, F., Bonnet, M.-P., Timouk, F., Calmant, S., Pillco, R., Molina, J., Lavado-Casimiro, W., Arsen, A., Crétaux, J., & Garnier, J. (2015). Accuracy assessment of SRTM v4 and ASTER GDEM v2 over the altiplano watershed using ICESat/GLAS data. International Journal of Remote Sensing, 36(2), 465–488. https://doi.org/10.1080/01431161.2014.999166
   Web of Science ®Google Scholar
• Satge, F., Denezine, M., Pillco, R., Timouk, F., Pinel, S., Molina, J., Garnier, J., Seyler, F., & Bonnet, M.-P. (2016). Absolute and relative height-pixel accuracy of SRTM-GL1 over the South American Andean Plateau. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 157–166. https://doi.org/10.1016/j.isprsjprs.2016.09.003
   Web of Science ®Google Scholar
• Schwendel, A. C., & Milan, D. J. (2020). Terrestrial structure-from-motion: Spatial error analysis of roughness and morphology. Geomorphology, 350, 106883. https://doi.org/10.1016/j.geomorph.2019.106883
   Web of Science ®Google Scholar
• Sharma, M., Garg, R. D., Badenko, V., Fedotov, A., Min, L., & Yao, A. (2021). Potential of airborne LiDAR data for terrain parameters extraction. Quaternary International, 575, 317–327. https://doi.org/10.1016/j.quaint.2020.07.039
   Web of Science ®Google Scholar
• Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R., Porter, C., & Morin, P. (2016). 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, 116, 101–117. https://doi.org/10.1016/j.isprsjprs.2016.03.012
   Web of Science ®Google Scholar
• Silva, J. F. D., Queiroga Miranda, R. D., & Candeias, A. L. B. (2022). Análise dos Modelos Digitais de Elevação (PE3D, SRTM-30, SRTM-90, ASTER GDEM, TOPODATA, TANDEM-X, ALOS PALSAR e ALOS AW3D30) e a necessidade da produção de dados altimétricos em excelência no Brasil. Revista Brasileira de Geografia Física, 15(3), 1543–1555. https://doi.org/10.26848/rbgf.v15.3.p1543-1555
   Google Scholar
• Suliman, A. H. A., Gumindoga, W., Awchi, T. A., & Katimon, A. (2021). DEM resolution influences on peak flow prediction: A comparison of two different based DEMs through various rescaling techniques. Geocarto International, 36(7), 803–819. https://doi.org/10.1080/10106049.2019.1622599
   Web of Science ®Google Scholar
• Szypuła, B. (2019). Quality assessment of DEM derived from topographic maps for geomorphometric purposes. Open Geosciences, 11(1), 843–865. https://doi.org/10.1515/geo-2019-0066
   Web of Science ®Google Scholar
• Taheri, M., Dolatabadi, N., Nasseri, M., Zahraie, B., Amini, Y., & Schoups, G. (2020). Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data. Theoretical and Applied Climatology, 142(1–2), 623–641. https://doi.org/10.1007/s00704-020-03320-2
   Web of Science ®Google Scholar
• Temme, A., Heuvelink, G., Schoorl, J., & Claessens, L. (2009). Geostatistical simulation and error propagation in geomorphometry. Developments in Soil Science, 33, 121–140. https://doi.org/10.1016/S0166-2481(08)00005-6
   Google Scholar
• UNAVCO. (2022). EarthScope consortium. Geoid height calculator. Retrieved March 28, 2022, from https://www.unavco.org/software/geodetic-utilities/geoid-height-calculator/geoid-height-calculator.html
   Google Scholar
• Uysal, M., Toprak, A. S., & Polat, N. (2015). DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill. Measurement, 73, 539–543. https://doi.org/10.1016/j.measurement.2015.06.010
   Web of Science ®Google Scholar
• Valeriano M. D. M.2008. São José dos Campos: Instituto Nacional de Pesquisas Espaciais (INPE) (Report no. INPE-15318-RPQ/818). http://mtc-m16c.sid.inpe.br/col/sid.inpe.br/mtc-m18@80/2008/07.11.19.24/doc/publicacao.pdf
   Google Scholar
• Valeriano M. D. M., and Rossetti D. D. F. 2008. São José dos Campos (Report no. INPE-16701-RPQ/853). INPE. http://mtc-m16d.sid.inpe.br/col/sid.inpe.br/mtc-m19@80/2010/05.10.18.35/doc/publicacao.pdf
   Google Scholar
• Valeriano, M. D. M., & Rossetti, D. D. F. (2012). Topodata: Brazilian full coverage refinement of SRTM data. Applied Geography, 32(2), 300–309. https://doi.org/10.1016/j.apgeog.2011.05.004
   Web of Science ®Google Scholar
• Varga, M., & Bašić, T. (2015). Accuracy validation and comparison of global digital elevation models over Croatia. International Journal of Remote Sensing, 36(1), 170–189. https://doi.org/10.1080/01431161.2014.994720
   Web of Science ®Google Scholar
• Vignesh, K. S., Anandakumar, I., Ranjan, R., & Borah, D. (2021). Flood vulnerability assessment using an integrated approach of multi-criteria decision-making model and geospatial techniques. Modeling Earth Systems and Environment, 7(2), 767–781. https://doi.org/10.1007/s40808-020-00997-2
   Web of Science ®Google Scholar
• Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., & Roth, A. (2018). Accuracy assessment of the global TanDEM-X digital elevation model with GPS data. ISPRS Journal of Photogrammetry and Remote Sensing, 139, 171–182. https://doi.org/10.1016/j.isprsjprs.2018.02.017
   Web of Science ®Google Scholar
• Weydahl, D. J., Sagstuen, J., Dick, O. B., & Ronning, H. (2007). SRTM DEM accuracy assessment over vegetated areas in Norway. International Journal of Remote Sensing, 28(16), 3513–3527. https://doi.org/10.1080/01431160600993447
   Web of Science ®Google Scholar
• Wise, S. (2000). Assessing the quality for hydrological applications of digital elevation models derived from contours. Hydrological Processes, 14(11–12), 1909–1929. https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<1909:AID-HYP45>3.0.CO;2-6
   Web of Science ®Google Scholar
• Wise, S. (2012). Information entropy as a measure of DEM quality. Computers & Geosciences, 48, 102–110. https://doi.org/10.1016/j.cageo.2012.05.011
   Web of Science ®Google Scholar
• Xu, S., Wu, C., Wang, L., Gonsamo, A., Shen, Y., & Niu, Z. (2015). A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics. Remote Sensing of Environment, 162, 119–140. https://doi.org/10.1016/j.rse.2015.02.024
   Web of Science ®Google Scholar
• Xu, Y., Zhu, H., Hu, C., Liu, H., & Cheng, Y. (2021). Deep learning of DEM image texture for landform classification in the Shandong area, China. Frontiers of Earth Science, 16(2), 352–367. https://doi.org/10.1007/s11707-021-0884-y
   Web of Science ®Google Scholar
• Yap, L., Kandé, L. H., Nouayou, R., Kamguia, J., Ngouh, N. A., & Makuate, M. B. (2019). Vertical accuracy evaluation of freely available latest high-resolution (30 m) global digital elevation models over Cameroon (Central Africa) with GPS/leveling ground control points. International Journal of Digital Earth, 12(5), 500–524. https://doi.org/10.1080/17538947.2018.1458163
   Web of Science ®Google Scholar
• Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in Multiscale Geographically Weighted regression. Geographical Analysis, 52(1), 87–106. https://doi.org/10.1111/gean.12189
   Web of Science ®Google Scholar