Evaluation of the Applications of using Global free Digital Elevation Models and GNSS-RTK data for Agricultural purposes in Egypt using Machine Learning

abdallah, Ashraf; Al-MISTAREHI, Bara; SHTAYAT, Amir

doi:10.21608/jesaun.2024.326709.1374

	Evaluation of the Applications of using Global free Digital Elevation Models and GNSS-RTK data for Agricultural purposes in Egypt using Machine Learning
JES. Journal of Engineering Sciences
Article 1, Volume 53, Issue 1, January and February 2025, Pages 1-22 PDF (1.56 M)
Document Type: Research Paper
DOI: 10.21608/jesaun.2024.326709.1374
Authors
Ashraf abdallah^* ¹; Bara Al-MISTAREHI²; Amir SHTAYAT³
¹Faculty of Engineering, Aswan University, Aswan, Egypt
²Department of Civil Engineering, Jordan University of Science and Technology, Irbid, Jordan
³Department of City Planning and Design, Jordan University of Science and Technology, Irbid, Jordan
Abstract
Agriculture is a vital component of Egypt's economy; therefore, using Digital Elevation Models (DEMs) in agricultural planning in Egypt has significant benefits regarding water management, site appropriateness assessment, flood risk mitigation, and infrastructure construction. It is also essential for planners to make more informed decisions, optimize resource allocation, and support sustainable farming practices. This research paper investigates the accuracy of obtaining DEM data from four free global models (STRM30, ALOS30, COP30, and TanDEM-X90). The global DEM data has been compared to an actual GNSS-RTK DEM data surveyed onsite for two agricultural block areas in Aswan, the southern Government of Egypt. The two blocks are a part of a national project. For Block I and II, the RMSE of the Model STRM30 was 2.92 m and 3.59 m, respectively, indicating a poorer solution. Regarding accuracy, the ALOS30 model ranks third, reporting an RMSE of 2.58 m for block II and 3.30 m for block I. COP30 has an RMSE value of 1.06 m for blocks I and II and.91 m overall. TanDEM-X90 is the most accurate model in this investigation; block I provided an RMSE of 0.90 m with an SD of 0.58 m (SD95% = 0.38 m). After removing the anomalies, the model's stated RMSE for block II was 0.34 m, with an SD value of 0.62 m and 1.03 m. According to the classification using machine learning algorithms, with an accuracy of 84.7% for block I and 85% for block II, TanDEM-X90 is the best solution.
Keywords
DEM; GNSS-RTK; Agricultural; Machine Learning; Egypt

References
[1] Rahmati, O., Yousefi, S., Kalantari, Z., Uuemaa, E., Teimurian, T., Keesstra, S., Pham, T. D. and Tien Bui, D. (2019). Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study From Iran. Remote Sensing, 11, 1943. [2] Scown, M. W., Thoms, M. C. and De Jager, N. R. (2015). Floodplain Complexity and Surface Metrics: Influences Of Scale And Geomorphology. Geomorphology, 245, 102-116. [3] Bonilla-Sierra, V., Scholtes, L., Donzé, F. & Elmouttie, M. (2015). Rock Slope Stability Analysis Using Photogrammetric Data And DFN–Dem Modelling. Acta Geotechnica, 10, 497-511. [4] Fenta, A. A., Kifle, A., Gebreyohannes, T. and Hailu, G. (2015). Spatial Analysis Of Groundwater Potential Using Remote Sensing And GIS-Based Multi-Criteria Evaluation in Raya Valley, Northern Ethiopia. Hydrogeology Journal, 23, 195. [5] He, Y., Song, Z. and Liu, Z. (2017). Updating Highway Asset Inventory Using Airborne Lidar. Measurement, 104, 132-141. [6] Zhang, K., Gann, D., Ross, M., Robertson, Q., Sarmiento, J., Santana, S., Rhome, J. and Fritz, C. (2019). Accuracy Assessment Of ASTER, SRTM, ALOS, AND TDX DEMS For Hispaniola and Implications for Mapping Vulnerability to Coastal Flooding. Remote Sensing of Environment, 225, 290-306. [7] Li, L., Nearing, M. A., Nichols, M. H., Polyakov, V. O., Guertin, D. P. and Cavanaugh, M. L. (2020). The Effects Of Dem Interpolation on Quantifying Soil Surface Roughness Using Terrestrial Lidar. Soil And Tillage Research, 198, 104520. [8] Heo, J., Jung, J., Kim, B. and Han, S. (2020). Digital Elevation Model-Based Convolutional Neural Network Modeling for Searching Of High Solar Energy Regions. Applied Energy, 262, 114588. [9] Bhatta, B., Shrestha, S., Shrestha, P. K. and Talchabhadel, R. (2019). Evaluation and Application of A SWAT Model To Assess The Climate Change Impact On The Hydrology Of The Himalayan River Basin. Catena, 181, 104082. [10] Amirkolaee, H. A., Arefi, H., Ahmadlou, M. and Raikwar, V. (2022). Dtm Extraction from DSM Using A Multi-Scale Dtm Fusion Strategy Based On Deep Learning. Remote Sensing Of Environment, 274, 113014. [11] Maune, D. F., Kopp, S. and Zerdas, C. (2007). Digital Elevation Model Technologies And Applications. The Dem Users Manual. [12] Pavlis, N. K., Holmes, S. A., Kenyon, S. C. & Factor, J. K. (2012). The Development And Evaluation of The Earth Gravitational Model 2008 (Egm2008). Journal Of Geophysical Research: Solid Earth, 117. [13] Milbert, D. G. and Smith, D. A. Converting GPS Height into NAVD88 Elevation with the GEOID96 Geoid Height Model. (1996). GIS LIS-International Conference, 681-692. [14] Leick, A., Rapoport, L. and Tatarnikov, D. (2015). GPS Satellite Surveying, John Wiley & Sons. [15] Farah, A., Talaat, A. and Farrag, F. (2008). Accuracy Assessment Of Digital Elevation Models Using GPS. Artificial Satellites, 43, 151-161. [16] Abdallah, A., Saifeldin, A., Abomariam, A. and Ali, R. (2020). Efficiency of Using GNSS-PPP for Digital Elevation Model (DEM) Production. Artificial Satellites, 55, 17-28. [17] Abdallah, A. (2016). Precise Point Positioning For Kinematic Applications to Improve Hydrographic Survey. ” Ph.D. dissertation, Univ. of Stuttgart, Stuttgart, Germany. [18] Hofmann-Wellenhof, B., Lichtenegger, H. and Wasle, E. (2007). GNSS–Global Navigation Satellite Systems: GPS, GLONASS, GALILEO, and More, Springer Science & Business Media. [19] Li, J., Chapman, M. and Sun, X. (2006). Validation of Satellite-Derived Digital Elevation Models From In-Track Ikonos Stereo Imagery. Ontario Ministry Of Transport, Toronto. [20] Fisher, P. F. and Tate, N. J. (2006). Causes And Consequences of Error in Digital Elevation Models. Progress In Physical Geography, 30, 467-489. [21] Hebeler, F. and Purves, R. S. (2009). The Influence Of Elevation Uncertainty on Derivation of Topographic Indices. Geomorphology, 111, 4-16. [22] Van Zyl, J. J. (2001). The Shuttle Radar Topography Mission (SRTM): A Breakthrough In Remote Sensing of Topography. Acta Astronautica, 48, 559-565. [23] Werner, M. (2001). Shuttle Radar Topography Mission (SRTM) Mission Overview. Frequenz, 55, 75-79. [24] Takaku, J., Tadono, T., Doutsu, M., Ohgushi, F. and Kai, H. (2020). Updates Of ‘Aw3d30’alos Global Digital Surface Model with Other Open Access Datasets. The International Archives Of The Photogrammetry, Remote Sensing And Spatial Information Sciences, 43, 183-189. [25] Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Tridon, D. B., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T. and Huber, M. (2017). Generation and Performance Assessment of the Global TANDEM-X Digital Elevation Model. ISPRS Journal Of Photogrammetry And Remote Sensing, 132, 119-139. [26] Uuemaa, E., Ahi, S., Montibeller, B., Muru, M. and Kmoch, A. (2020). Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TANDEM-X, SRTM, And NASADEM). Remote Sensing, 12, 3482. [27] Pakoksung, K. and Takagi, M. (2021). 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, 7, 465-484. [28] Hawker, L., Neal, J. and Bates, P. (2019). Accuracy Assessment of the TANDEM-X 90 Digital Elevation Model for Selected Floodplain Sites. Remote Sensing Of Environment, 232, 111319. [29] Preety, K., Prasad, A. K., Varma, A. K. and El-Askary, H. (2022). Accuracy Assessment, Comparative Performance, And Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CARTOSAT 30 m, SRTM 90 m, MERIT 90 m, and TANDEM-X 90 m) using DGPS. Remote Sensing, 14, 1334. [30] Jain, A. O., Thaker, T., Chaurasia, A., Patel, P. and Singh, A. K. (2018). Vertical Accuracy Evaluation Of SRTM-Gl1, GDEM-V2, AW3D30 And CARTODEM-V3. 1 of 30-m Resolution with Dual Frequency GNSS For Lower Tapi Basin India. Geocarto International, 33, 1237-1256. [31] Marešová, J., Gdulová, K., Pracná, P., Moravec, D., Gábor, L., Prošek, J., Barták, V. and Moudrý, V. (2021). Applicability Of Data Acquisition Characteristics to the Identification Of Local Artefacts in Global Digital Elevation Models: Comparison of The COPERNICUS And TANDEM-X Dems. Remote Sensing, 13, 3931. [32] El Ashiry, A. and Elkhalil, O. (2024). Vertical Accuracy Assessment for The Free Digital Elevation Models SRTM and ASTER in Various Sloping Areas. Jes. Journal Of Engineering Sciences, 52, 250-268. [33] Polidori, L. and El Hage, M. (2020). Digital Elevation Model Quality Assessment Methods: A Critical Review. Remote Sensing, 12, 3522. [34] Fazilova, D., Magdiev, K. and Sichugova, L. (2021). Vertical Accuracy Assessment of Open Access Digital Elevation Models Using GPS. International Journal of Geoinformatics, 17, 19-26. [35] Ferreira, Z. A. and 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, 106. [36] Cai, C. and Gao, Y. (2013). Modeling And Assessment of Combined GPS/GLONASS Precise Point Positioning. GPS Solutions, 17, 223-236. [37] Szypuła, B. (2019). Quality Assessment of DEM Derived From Topographic Maps for Geomorphometric Purposes. Open Geosciences, 11, 843-865. [38] Mashimbye, Z. E., De Clercq, W. P. and Van Niekerk, A. (2014). An Evaluation of Digital Elevation Models (DEMS) for Delineating Land Components. Geoderma, 213, 312-319. [39] DAAC, L. (2015). The Shuttle Radar Topography Mission (SRTM) Collection User Guide. NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center: Sioux Falls, Sd, Usa. [40] Rabah, M., El-Hattab, A. and Abdallah, M. (2017). Assessment Of The Most Recent Satellite Based Digital Elevation Models of Egypt. NRIAG J Astron Geophys. [41] ALOSWORLD3D. (2023). Alos World 3d [Online]. Available: Https://Portal.Opentopography.Org/Raster?Opentopoid=Otalos.112016.4326.2 [Accessed 22.12.2023]. [42] Grohmann, C. H. (2018). Evaluation of Tandem-X Dems on Selected Brazilian Sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30. Remote Sensing of Environment, 212, 121-133. [43] DLR-TANDEM-X. (2023). DLR-TANDEM-X [Online]. Available: Https://Www.Dlr.De/Hr/En/Desktopdefault.Aspx/Tabid-2317/3669_Read-5488/ [Accessed 22-12-2023]. [44] COPERNICUS (2024). Copernicus Global Digital Elevation Models. [45] GARB. (2024). GARB [Online]. Available: [https://garb.gov.eg/] [Accessed 6 2024]. [46] Habib, A., Akdim, N., El Ghandour, F.-E., Labbassi, K., Khoshelham, K. and Menenti, M. (2017) Extraction and Accuracy Assessment of High-Resolution Dem and Derived Orthoimages from ALOS-PRISM Data over Sahel-Doukkala (Morocco). Earth Science Informatics, 10, 197-217. [47] Biswal, S., Sahoo, B., Jha, M. K. and Bhuyan, M. K. (2023). A Hybrid Machine Learning-Based Multi-Dem Ensemble Model Of River Cross-Section Extraction: Implications on Streamflow Routing. Journal of Hydrology, 625, 129951. [48] Ziari, H., Maghrebi, M., Ayoubinejad, J. and Waller, S. T. (2016). Prediction of Pavement Performance: Application Of Support Vector Regression With Different Kernels. Transportation Research Record, 2589, 135-145.
Statistics Article View: 490 PDF Download: 737