The use of satellite imagery in supporting non-military operations: a geospatial intelligence perspective

Authors

  • Rika Kariani Sensing Technology, Faculty of Science and Technology, Republic of Indonesia Defense University, Indonesia
  • Asep Adang Supriyadi Sensing Technology, Faculty of Science and Technology, Republic of Indonesia Defense University, Indonesia

Keywords:

geospatial intelligence, remote sensing, satellite imagery

Abstract

Background: Satellite imagery technology, initially developed for military purposes, has expanded into a critical tool in non-military applications, including environmental monitoring, disaster mitigation, infrastructure development, and humanitarian aid. This shift highlights the evolving role of satellite technology from military functions to addressing sustainability and global well-being challenges. Methods: A literature review approach was employed to examine the use of satellite imagery in non-military settings. Peer-reviewed articles were identified, selected, and analyzed from databases such as Google Scholar and ScienceDirect. The focus was on articles discussing applications in environmental monitoring, disaster management, infrastructure planning, and humanitarian assistance. Relevant literature was categorized and synthesized to identify emerging trends and implications of satellite imagery technology. Findings: Satellite imagery has proven to be invaluable in providing essential geospatial data for non-military purposes. It facilitates monitoring of environmental changes, supports infrastructure planning and evaluation, enhances disaster mitigation through risk analysis, and improves coordination of humanitarian aid during emergencies. The integration of platforms like Google Earth Engine and artificial intelligence significantly increases its utility, especially in object detection, climate change monitoring, and disaster impact assessments. Conclusion: Satellite imagery has evolved into an indispensable tool for a wide range of non-military applications, offering sustainable and efficient solutions to global challenges. It significantly enhances environmental monitoring, infrastructure development, disaster response, and humanitarian operations. The study emphasizes the need for continued innovation in satellite technology and interdisciplinary collaboration to meet future global sustainability goals. Novelty/Originality of this article: This study provides a comprehensive analysis of satellite imagery's growing role in non-military applications, emphasizing its potential in addressing global challenges. By synthesizing insights across multiple fields, the research highlights the transformative power of satellite technology in supporting sustainable development and disaster resilience.

References

Al-Aizari, AR, Al-Masnay, YA, Aydda, A., Zhang, J., Ullah, K., Islam, ARMT, Habib, T., Kaku, DU, Nizeyimana, JC, Al-Shaibah, B. , Khalil, Y. M., AL-Hameedi, W. M. M., & Liu, X. (2022). Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen. Remote Sensing, 14(16). https://doi.org/10.3390/rs14164050

Abid, SK, Sulaiman, N., Chan, S.W., Nazir, U., Abid, M., Han, H., Ariza-Montes, A., & Vega-Muñoz, A. (2021). Toward an integrated disaster management approach: How artificial intelligence can improve disaster management. Sustainability (Switzerland), 13(22), 1–17. https://doi.org/10.3390/su132212560

Afaq, Y., & Manocha, A. (2021). Happiness Index Determination by Analyzing Satellite Images for Urbanization. Applied Artificial Intelligence, 35(15), 1466–1489. https://doi.org/10.1080/08839514.2021.1982533

Alimbekova, N. A., & Walker, N. (2022). Kyrgyz Open Data Cube of Satellite Images and Environmental Products as a Tool for Pasture Monitoring. International Journal of Geoinformatics, 18(1), 81–85. https://doi.org/10.52939/ijg.v18i1.2113

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). https://doi.org/10.3390/rs14205143

Ara, I., Harrison, M.T., Whitehead, J., Waldner, F., Bridle, K., Gilfedder, L., Marques Da Silva, J., Marques, F., & Rawnsley, R. (2021). Modeling seasonal pasture growth and botanical composition at the paddock scale with satellite imagery. In Silico Plants, 3(1), 1–15. https://doi.org/10.1093/insilicoplants/diaa013

Arya, C. (2021). IoT Based Precision Farming and Agriculture - Aspects and Technologies. Mathematical Statistics and Engineering Applications, 70(2), 1426–1433. https://doi.org/10.17762/msea.v70i2.2335

Atek, S., Pesaresi, C., Eugeni, M., De Vito, C., Cardinale, V., Mecella, M., Rescio, A., Petronzio, L., Vincenzi, A., Pistillo, P. , Bianchini, F., Giusto, G., Pasquali, G., & Gaudenzi, P. (2022). A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19. Acta Astronautica, 197(March), 323–335. https://doi.org/10.1016/j.actaastro.2022.05.013

Avtar, R., Kouser, A., Kumar, A., Singh, D., Misra, P., Gupta, A., Yunus, A.P., Kumar, P., Johnson, B.A., Dasgupta, R., Sahu, N., & Rimba, AB (2021). Remote sensing for international peace and security: Its role and implications. Remote Sensing, 13(3). https://doi.org/10.3390/rs13030439

Bennett, M.M., Van Den Hoek, J., Zhao, B., & Prishchepov, A.V. (2022a). Improving Satellite Monitoring of Armed Conflicts. Earth's Future, 10(9). https://doi.org/10.1029/2022EF002904

Bennett, M. M., Van Den Hoek, J., Zhao, B., & Prishchepov, A. V. (2022b). Improving Satellite Monitoring of Armed Conflicts. Earth's Future, 10(9). https://doi.org/10.1029/2022EF002904

Bhatt, C.M., Rao, G.S., Diwakar, P.G., & Dadhwal, V.K. (2017). Development of flood inundation extent libraries over a range of potential flood levels: a practical framework for quick flood response. Geomatics, Natural Hazards and Risk, 8(2), 384–401. https://doi.org/10.1080/19475705.2016.1220025

Brun, F., Dumont, M., Wagnon, P., Berthier, E., Azam, M.F., Shea, J.M., Sirguey, P., Rabatel, A., & Ramanathan, A. (2015). Seasonal changes in surface albedo of Himalayan glaciers from MODIS data and links with the annual mass balance. Cryosphere, 9(1), 341–355. https://doi.org/10.5194/tc-9-341-2015

Celis, J., Xiao, X., White, P. M., Cabral, O. M. R., & Freitas, H. C. (2024). Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images. Remote Sensing, 16(1). https://doi.org/10.3390/rs16010046

Chen, Y., Weng, Q., Tang, L., Liu, Q., Zhang, X., & Bilal, M. (2021). Automatic mapping of urban green spaces using a geospatial neural network. GIScience and Remote Sensing, 58(4), 624–642. https://doi.org/10.1080/15481603.2021.1933367

Chollett, F. (2017). Machine learning분야 소개 및 주요 방법론 학습 기본machine learning알고리즘에 대한 이해 및 응용 관련 최신 연구 동향 습득. Machine Learning, 45(13), 40–48. https://books.google.ca/books?id=EoYBngEACAAJ&dq=mitchell+machine+learning+1997&hl=en&sa=X&ved=0ahUKEwiomdqfj8TkAhWGslkKHRCbAtoQ6AEIKjAA

Crego, R.D., Stabach, J.A., & Connette, G. (2022). Implementation of species distribution models in Google Earth Engine. Diversity and Distributions, 28(5), 904–916. https://doi.org/10.1111/ddi.13491

Döllner, J. (2020). Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(1), 15–24. https://doi.org/10.1007/s41064-020-00102-3

Ekeu-wei, I. T., & Blackburn, G. A. (2018). Applications of open-access remotely sensed data for flood modeling and mapping in developing regions. In Hydrology (Vol. 5, Issue 3). https://doi.org/10.3390/hydrology5030039

Gu, Z., & Zeng, M. (2024). The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives. Sustainability (Switzerland), 16(1). https://doi.org/10.3390/su16010274

Habib, M., & Okayli, M. (2023). An Overview of Modern Cartographic Trends Aligned with the ICA's Perspective. Revue Internationale de Géomatique, 1–16. https://doi.org/10.32604/rig.2023.043399

Han, L., Lu, L., Lu, J., Liu, X., Zhang, S., Luo, K., He, D., Wang, P., Guo, H., & Li, Q. (2022). Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach. Remote Sensing, 14(19). https://doi.org/10.3390/rs14194985

Hansen, MC, Potapov, PV, Pickens, A.H., Tyukavina, A., Hernandez-Serna, A., Zalles, V., Turubanova, S., Kommareddy, I., Stehman, SV, Song, XP, & Kommareddy, A. (2022). Global land use extent and dispersion within natural land cover using Landsat data. Environmental Research Letters, 17(3). https://doi.org/10.1088/1748-9326/ac46ec

He, C., Liu, Y., Wang, D., Liu, S., Yu, L., & Ren, Y. (2023). Automatic Extraction of Bare Soil Land from High-Resolution Remote Sensing Images Based on Semantic Segmentation with Deep Learning. Remote Sensing, 15(6). https://doi.org/10.3390/rs15061646

Higuchi, A. (2021). Toward more integrated uses of geostationary satellite data for disaster management and risk mitigation. Remote Sensing, 13(8). https://doi.org/10.3390/rs13081553

James, GL, Ansaf, RB, Al Samahi, SS, Parker, RD, Cutler, JM, Gachette, RV, & Ansaf, BI (2023). An Efficient Wildfire Detection System for AI-Embedded Applications Using Satellite Imagery. Fire, 6(4), 1–13. https://doi.org/10.3390/fire6040169

Jenerowicz, M., Wawrzaszek, A., Drzewiecki, W., Krupinski, M., & Aleksandrowicz, S. (2019). Multifractality in Humanitarian Applications: A Case Study of Internally Displaced Persons/Refugee Camps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11), 4438–4445. https://doi.org/10.1109/JSTARS.2019.2950970

Maus, V., Giljum, S., Gutschlhofer, J., da Silva, D.M., Probst, M., Gass, S.L.B., Luckeneder, S., Lieber, M., & McCallum, I. (2020). A global-scale data set of mining areas. Scientific Data, 7(1), 1–13. https://doi.org/10.1038/s41597-020-00624-w

Mueller, H., Groeger, A., Hersh, J., Matranga, A., & Serrat, J. (2021). Monitoring war destruction from space using machine learning. Proceedings of the National Academy of Sciences of the United States of America, 118(23). https://doi.org/10.1073/pnas.2025400118

Mujetahid, A., Nursaputra, M., & Soma, AS (2023). Monitoring Illegal Logging Using Google Earth Engine in South Sulawesi Tropical Forest, Indonesia. Forests, 14(3). https://doi.org/10.3390/f14030652

Mukonza, S.S., & Chiang, J.-L. (2022). Satellite sensors as an emerging technique for monitoring macro- and microplastics in aquatic ecosystems. Water Emerging Contaminants & Nanoplastics, 1(4), 17. https://doi.org/10.20517/wecn.2022.12

Nelin, Ye., Kasianov, V., & Shterndok, E. (2023). Research on the Directions of Monitoring the Use of Real Estate in Settlements. Municipal Economy of Cities, 6(180), 118–122. https://doi.org/10.33042/2522-1809-2023-6-180-118-122

Nishijima, S., Eckroad, S., Marian, A., Choi, K., Kim, W.S., Terai, M., Deng, Z., Zheng, J., Wang, J., Umemoto, K., Du, J., Febvre, P., Keenan, S., Mukhanov, O., Cooley, L.D., Foley, C.P., Hassenzahl, W.V., & Izumi, M. (2013). Superconductivity and the environment: A Roadmap. Superconductor Science and Technology, 26(11). https://doi.org/10.1088/0953-2048/26/11/113001

Peng, B., Meng, Z., Huang, Q., & Wang, C. (2019). Patch similarity convolutional neural network for urban flood extent mapping using bi-temporal satellite multispectral imagery. Remote Sensing, 11(21). https://doi.org/10.3390/rs11212492

Polpanich, O.U., Bhatpuria, D., Santos Santos, T.F., & Krittasudthacheewa, C. (2022). Leveraging Multi-Source Data and Digital Technology to Support the Monitoring of Localized Water Changes in the Mekong Region. Sustainability (Switzerland), 14(3). https://doi.org/10.3390/su14031739

Pyngrope, O.R., Kumar, M., Pebam, R., Singh, S.K., Kundu, A., & Lal, D. (2021). Investigating forest fragmentation through earth observation datasets and metric analysis in the tropical rainforest area. SN Applied Sciences, 3(7). https://doi.org/10.1007/s42452-021-04683-5

Qi, O., Zhang, L., Shi, W., & Wang, Y. (2020). Analysis of the Survivability of Equipment Support System of Non-War Military Operations Based on Operational Efficiency. Journal of Physics: Conference Series, 1649(1). https://doi.org/10.1088/1742-6596/1649/1/012040

Raimondi, G., Maucieri, C., Borin, M., Pancorbo, J. L., Cabrera, M., & Quemada, M. (2023). Satellite imagery and modeling contribute to understanding cover crop effects on nitrogen dynamics and water availability. Agronomy for Sustainable Development, 43(5), 1–19. https://doi.org/10.1007/s13593-023-00922-8

Shi, G., & Zuo, B. (2022). CloudRCNN: A Framework Based on Deep Neural Networks for Semantic Segmentation of Satellite Cloud Images. Applied Sciences (Switzerland), 12(11). https://doi.org/10.3390/app12115370

Spiller, D., Carbone, A., Amici, S., Thangavel, K., Sabatini, R., & Laneve, G. (2023). Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis. Remote Sensing, 15(19), 1–22. https://doi.org/10.3390/rs15194855

Sticher, V., Wegner, J.D., & Pfeifle, B. (2023a). Toward the remote monitoring of armed conflicts. PNAS Nexus, 2(6), 1–12. https://doi.org/10.1093/pnasnexus/pgad181

Sticher, V., Wegner, J.D., & Pfeifle, B. (2023b). Toward the remote monitoring of armed conflicts. PNAS Nexus, 2(6), 1–12. https://doi.org/10.1093/pnasnexus/pgad181

Tehsin, S., Kausar, S., Jameel, A., Humayun, M., & Almofarreh, D. K. (2023). Satellite Image Categorization Using Scalable Deep Learning. Applied Sciences (Switzerland), 13(8). https://doi.org/10.3390/app13085108

Tian, J., Zhu, X., Wu, J., Shen, M., & Chen, J. (2020). Coarse-resolution satellite images overestimate urbanization effects on spring vegetation phenology. Remote Sensing, 12(1). https://doi.org/10.3390/RS12010117

Tingzon, I., Orden, A., Go, K.T., Sy, S., Sekara, V., Weber, I., Fatehkia, M., García-Herranz, M., & Kim, D. (2019). MAPPING POVERTY in the PHILIPPINES USING MACHINE LEARNING, SATELLITE IMAGERY, and CROWD-SOURCED GEOSPATIAL INFORMATION. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(4/W19), 425–431. https://doi.org/10.5194/isprs-archives-XLII-4-W19-425-2019

Vidal, O., López-García, J., & Rendón-Salinas, E. (2014). Trends in Deforestation and Forest Degradation after a Decade of Monitoring in the Monarch Butterfly Biosphere Reserve in Mexico. Conservation Biology, 28(1), 177–186. https://doi.org/10.1111/cobi.12138

Villate Daza, D.A., Moreno, H.S., Portz, L., Manzolli, R.P., Bolívar-Anillo, H.J., & Anfuso, G. (2020). Mangrove forests evolution and threats in the Caribbean Sea of Colombia. Water (Switzerland), 12(4). https://doi.org/10.3390/W12041113

Voigt, S., Schoepfer, E., Fourie, C., & Mager, A. (2014). Towards semi-automated satellite mapping for humanitarian situational awareness. Proceedings of the 4th IEEE Global Humanitarian Technology Conference, GHTC 2014, 412–416. https://doi.org/10.1109/GHTC.2014.6970315

Yi, T. J., & bin Ahmad, A. (2023). Quality Assessments of Unmanned Aerial Vehicle (Uav) and Terrestrial Laser Scanning (Tls) Methods in Road Cracks Mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48(4/W6-2022), 183–193. https://doi.org/10.5194/isprs-archives-XLVIII-4-W6-2022-183-2023

Zeng, T., Shi, L., Huang, L., Zhang, Y., Zhu, H., & Yang, X. (2023). A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica. Remote Sensing, 15(18), 1–20. https://doi.org/10.3390/rs15184399

Zhong, L., Hawkins, T., Holland, K., Gong, P., & Biging, G. (2009). Satellite imagery can support water planning in the Central Valley. California Agriculture, 63(4), 220–224. https://doi.org/10.3733/ca.v063n04p220

Downloads

Published

2024-08-31

Issue

Section

Articles

Citation Check