Military training area design with GIS analysis
DOI:
https://doi.org/10.3846/da.2026.2532Language:
EnglishAbstract
The article analyses the design of military training areas using geographic information systems (GIS). It systematises the main stages of range planning – strategic planning, site selection, master planning, detailed design, safety analysis, environmental and noise assessment, and operational planning – and examines how these stages can be implemented in a GIS environment. Special attention is given to the modelling of Surface Danger Zones (SDZ) and the role of high-resolution spatial data obtained from remote sensing, including digital elevation and surface models, LiDAR point clouds and UAV photogrammetry. These datasets enable accurate assessment of terrain, land cover and built-up areas, improving the positioning of firing sectors and reducing risks to soldiers and civilians. The paper also discusses key challenges – data accuracy and availability, inconsistencies between different spatial datasets, regulatory constraints, and conflicts with civil infrastructure, recreation and residential areas. By integrating GIS-based analysis with environmental impact assessment procedures and stakeholder interests, military training areas can be planned in a way that enhances safety, increases planning efficiency and supports more socially acceptable and environmentally responsible military activities.
Article in Lithuanian
Keywords:
military training area, GIS, remote sensing, firing safety zoneAbdullah, Q. (2023). The ASPRS Positional Accuracy Standards, edition 2: The geospatial mapping industry guide to best practices. Photogrammetric Engineering and Remote Sensing, 89(10), 581–588. https://doi.org/10.14358/PERS.89.10.581
Almalki, R., Khaki, M., Saco, P. M., & Rodriguez, J. F. (2022). Monitoring and mapping vegetation cover changes in arid and semi-arid areas using remote sensing technology: A review. Remote Sensing, 14(20), Article 5143. https://doi.org/10.3390/rs14205143
Baek, J. W., Jang, C. S., & Park, Y. J. (2020). A methodology for the zoning danger region in small arm firing range using aerial photogrammetry with drone. Advances in Civil Engineering, 2020, Article 8864409. https://doi.org/10.1155/2020/8864409
Barker, A. J., Clausen, J. L., Douglas, T. A., Bednar, A. J., Griggs, C. S., & Martin, W. A. (2021). Environmental impact of metals resulting from military training activities: A review. Chemosphere, 265, Article 129110. https://doi.org/10.1016/j.chemosphere.2020.129110
Chipatiso, E. (2023). Surveying and geographic information systems (GIS): Exploring the capabilities of unmanned aerial vehicles (UAV)/drones. Preprints. https://doi.org/10.20944/preprints202309.1431.v1
Drinnan, C. H. (1985). Military base planning using geographic information systems technology: Biographical sketch. In Proceedings of the Digital Representations of Spatial Knowledge (pp. 162–171). https://cartogis.org/docs/proceedings/archive/auto-carto-7/index.html
Fernandez-Diaz, J. C., Carter, W. E., Shrestha, R. L., & Glennie, C. L. (2014). Now you see it... Now you don’t: Understanding airborne mapping LiDAR collection and data product generation for archaeological research in Mesoamerica. Remote Sensing, 6(10), 9951–10001. https://doi.org/10.3390/rs6109951
Grčar, S., Sotlar, A., & Eman, K. (2025). Attitudes of local population and armed forces in the Republic of Slovenia on the environmental impacts of peacetime military activities: Unaddressed perspective in civil-military relations. Discover Sustainability, 6(1), Article 200. https://doi.org/10.1007/s43621-025-01011-4
Greene, R., Devillers, R., Luther, J. E., & Eddy, B. G. (2011). GIS-based multiple-criteria decision analysis. Geography Compass, 5(6), 412–432. https://doi.org/10.1111/j.1749-8198.2011.00431.x
Hoxha, S., & Vazquez, E. B. (1995). Surface danger zone (sdz) methodology study, probability based surface danger zones (Special Publication ARPAD-SP-94001).
Ye, K., Liang, Y., & Shi, J. (2023). Evaluation and classification of public participation in EIA for transportation infrastructure megaprojects in China. Environmental Impact Assessment Review, 101, Article 107138. https://doi.org/10.1016/j.eiar.2023.107138
Ma, Y., Lu, L., Ma, T., & Qiu, K. (2026). Error correction and evaluation of total station-based column displacement data for steel tubular truss structures. Measurement, 257, Article 118716. https://doi.org/10.1016/J.MEASUREMENT.2025.118716
Mantey, S., & Aduah, M. S. (2022). Assessment of positional accuracies of UAV-based coordinates derived from orthophotos at varying times of the day – a case study. South African Journal of Geomatics, 10(1), 46–59. https://doi.org/10.4314/sajg.v10i1.4
Massachusetts Army National Guard – Environmental & Readiness Center. (2022). Final annual State of the reservation report for training year 2021.
Mozas-Calvache, A. T. (2023). Positional accuracy assessment of Digital Elevation Models and 3D vector datasets using check-surfaces. ISPRS International Journal of Geo-Information, 12(9), Article 348. https://doi.org/10.3390/ijgi12090348
Phojaem, T., Dangbut, A., Wisutwattanasak, P., Janhuaton, T., Champahom, T., Ratanavaraha, V., & Jomnonkwao, S. (2025). Evaluating UAV flight parameters for high-accuracy in road accident scene documentation: A planimetric assessment under simulated roadway conditions. ISPRS International Journal of Geo-Information, 14(9), Article 357. https://doi.org/10.3390/ijgi14090357
Rikalovic, A., Cosic, I., & Lazarevic, D. (2014). GIS based multi-criteria analysis for industrial site selection. Procedia Engineering, 69, 1054–1063. https://doi.org/10.1016/j.proeng.2014.03.090
Romero, K. F., Castillo, Y., Quesada, M., Zumbado, Y., & Jiménez, J. C. (2025). Development of a testing method for the accuracy and precision of GNSS and LiDAR technology. AgriEngineering, 7(9), Article 310. https://doi.org/10.3390/agriengineering7090310
Sabzali, M., & Pilgrim, L. (2025). A comprehensive review of mathematical error characterization and mitigation strategies in terrestrial laser scanning. Remote Sensing, 17(14), Article 2528. https://doi.org/10.3390/rs17142528
Słowiński, M., Lamentowicz, M., Łuców, D., Barabach, J., Brykała, D., Tyszkowski, S., Pieńczewska, A., Śnieszko, Z., Dietze, E., Jażdżewski, K., Obremska, M., Ott, F., Brauer, A., & Marcisz, K. (2019). Paleoecological and historical data as an important tool in ecosystem management. Journal of Environmental Management, 236, 755–768. https://doi.org/10.1016/j.jenvman.2019.02.002
Spencer, R. W., Brokaw, E., Carr, W., Chen, Z. J., Garfield, B. A., Garimella, H. T., Gharahi, H., Iampaglia, J., Lalis, L., Przekwas, A., Skotak, M., Xynidis, M. A., Dominijanni, A., Dias, G., Danley, L., & Gupta, R. K. (2023). Fiscal Year 2018 National Defense Authorization Act, Section 734, weapon systems line of inquiry: Overview and blast overpressure tool—A module for human body blast wave exposure for safer weapons training. Military Medicine, 188, 536–544. https://doi.org/10.1093/milmed/usad225
Tamimi, R., & Toth, C. (2024). Accuracy assessment of UAV LiDAR compared to traditional total station for geospatial data collection in land surveying contexts. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-2–2024, 421–426. https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-421-2024
U.S. Army Corps of Engineers. (2009). Base camp development in the theater of operations (EP 1105-3-1). https://www.publications.usace.army.mil/Portals/76/Publications/EngineerPamphlets/EP_1105-3-1.pdf
Quamar, M. M., Al-Ramadan, B., Khan, K., Shafiullah, M., & El Ferik, S. (2023). Advancements and applications of drone-integrated geographic information system technology—A review. Remote Sensing, 15(20), Article 5039. https://doi.org/10.3390/rs15205039
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