Study of the implementation of geoint and remote sensing in climate change

Authors

  • Hesti Heningtiyas 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:

climate change, geospatial intelligence, remote sensing

Abstract

Background: This research highlights the critical role of geospatial technologies, including remote sensing and Geospatial Intelligence (GEOINT), in addressing climate change and its impacts. These technologies extend beyond defense and security applications, proving valuable across sectors such as health, social, economic, and environmental fields. By providing real-time data, they enhance the understanding and mitigation of climate change-related issues. Methods: A systematic literature review was conducted by searching databases using relevant keywords. Peer-reviewed articles from the past 10 years were selected. Data were collected through a data extraction form, and the articles were categorized based on themes including geospatial technology applications, benefits, challenges, and recommendations. Findings: The study found that geospatial technologies significantly enhance the understanding of regional environmental conditions, aid in natural disaster mitigation, and support environmental conservation efforts through real-time monitoring of weather and climate change. Despite the high costs and data format challenges, these technologies offer indispensable tools for analyzing climate impacts and formulating effective mitigation strategies. Conclusion: The benefits of geospatial technologies in climate change mitigation are clear, though challenges such as implementation costs and data compatibility remain. These technologies provide policymakers with essential insights for crafting more informed and effective decisions in combating climate change. Novelty/Originality of this article: This study offers a comprehensive review of the diverse applications of geospatial technologies in the context of climate change. It uniquely integrates insights from multiple sectors, showcasing the broader potential of these technologies beyond traditional fields, and provides recommendations for improving data processing and analysis for climate-related decision-making.

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

Alrige, M., Bitar, H., Meccawy, M., & Mullachery, B. (2022). Utilizing geospatial intelligence and user modeling to allow for a customized health awareness campaign during the pandemic: The case of COVID-19 in Saudi Arabia. Journal of Infection and Public Health, 15(10), 1124–1133. https://doi.org/10.1016/j.jiph.2022.08.018

Al-Yadumi, S., Xion, T. E., Wei, S. G. W., & Boursier, P. (2021). Review on Integrating Geospatial Big Datasets and Open Research Issues. IEEE Access, 9, 10604–10620. https://doi.org/10.1109/ACCESS.2021.3051084

Bera, A., Kumar, A., Meraj, G., Kanga, S., Kumar, S., Bojan, Đ., & Anand, S. (2021). Climate vulnerability and economic determinants: Linkages and risk reduction in Sagar Island, India; A geospatial approach. 4. https://doi.org/10.1016/j.qsa.2021.100038

Caldecott, B., McCarten, M., Christiaen, C., & Hickey, C. (2022). Spatial finance: practical and theoretical contributions to financial analysis. Journal of Sustainable Finance and Investment, 1–17. https://doi.org/10.1080/20430795.2022.2153007

Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G.S. (2020). Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3735–3756. https://doi.org/10.1109/JSTARS.2020.3005403

Crăciun, A., Costache, R., Bărbulescu, A., Pal, S.C., Costache, I., & Dumitriu, C. Ștefan. (2022). Modern Techniques for Flood Susceptibility Estimation across the Deltaic Region (Danube Delta) from the Black Sea's Romanian Sector. Journal of Marine Science and Engineering, 10(8). https://doi.org/10.3390/jmse10081149

Cui, Y., Eccles, K.M., Kwok, R.K., Joubert, B.R., Messier, K.P., & Balshaw, D.M. (2022). Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities. Toxics, 10(7), 1–10. https://doi.org/10.3390/toxics10070403

Dias, C., Rahman, N.A., & Zaiter, A. (2021). Evacuation under flooded conditions: Experimental investigation of the influence of water depth on walking behaviors. International Journal of Disaster Risk Reduction, 58(November 2020), 102192. https://doi.org/10.1016/j.ijdrr.2021.102192

Flores, C. C., & Crompvoets, J. (2020). Assessing the governance context support for creating a pluvial flood risk map with climate change scenarios: The Flemish subnational case. ISPRS International Journal of Geo-Information, 9(7). https://doi.org/10.3390/ijgi9070460

Garg, V., Thakur, P.K., Rajak, D.R., Aggarwal, S.P., & Kumar, P. (2022). Spatio-temporal changes in radar zones and ELA estimation of glaciers in NyÅlesund using Sentinel-1 SAR. Polar Science, 31(November 2020), 100786. https://doi.org/10.1016/j.polar.2021.100786

Gehlen, M., Nicola, MRC, Costa, ERD, Cabral, VK, de Quadros, ELL, Chaves, CO, Lahm, RA, Nicolella, ADR, Rossetti, MLR, & Silva, DR (2019). Geospatial intelligence and health analytics: Its application and utility in a city with high tuberculosis incidence in Brazil. Journal of Infection and Public Health, 12(5), 681–689. https://doi.org/10.1016/j.jiph.2019.03.012

Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021). Fairness and accountability of AI in disaster risk management: Opportunities and challenges. Patterns, 2(11), 100363. https://doi.org/10.1016/j.patter.2021.100363

Halder, B., & Bandyopadhyay, J. (2021). Evaluating the impact of climate change on urban environment using geospatial technologies in the planning area of Bilaspur, India. Environmental Challenges, 5(May), 100286. https://doi.org/10.1016/j.envc.2021.100286

Hatta Antah, F., Khoiry, MA, Abdul Maulud, KN, & Ibrahim, ANH (2022). Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia. Sustainability (Switzerland), 14(15), 1–19. https://doi.org/10.3390/su14158977

Inwood, J. F. J., & Alderman, D. H. (2020). “The Care and Feeding of Power Structures”: Reconceptualizing Geospatial Intelligence through the Countermapping Efforts of the Student Nonviolent Coordinating Committee. Annals of the American Association of Geographers, 110(3), 705–723. https://doi.org/10.1080/24694452.2019.1631747

Islam, M., & Azizul, SNM (2020). Flood monitoring and forecasting using synthetic aperture radar (SAR) and meteorological data: A case study. Malaysian Journal of Fundamental and Applied Sciences, 16(3), 300–306.

Jesús Pinto Hidalgo, J., & Antonio Silva Centeno, J. (2023). Environmental scanning of cocaine trafficking in Brazil: Evidence from geospatial intelligence and natural language processing methods. Science and Justice, 63(6), 689–723. https://doi.org/10.1016/j.scijus.2023.09.002

Jones, A., Koehler, S., Jerge, M., Graves, M., King, B., Dalrymple, R., Freese, C., & Von Albade, J. (2023). BATMAN: A Brain-like Approach for Tracking Maritime Activity and Nuance. Censorship, 23(5). https://doi.org/10.3390/s23052424

Jones, A., Kuehnert, J., Fraccaro, P., Meuriot, O., Ishikawa, T., Edwards, B., Stoyanov, N., Remy, S.L., Weldemariam, K., & Assefa, S. ( 2023). AI for climate impacts: applications in flood risk. Npj Climate and Atmospheric Science, 6(1). https://doi.org/10.1038/s41612-023-00388-1

Kaur, R., & Gupta, K. (2022). Blue-Green Infrastructure (BGI) network in urban areas for sustainable storm water management: A geospatial approach. City and Environment Interactions, 16(September), 100087. https://doi.org/10.1016/j.cacint.2022.100087

Krassakis, P., Karavias, A., Nomikou, P., Karantzalos, K., Koukouzas, N., Kazana, S., & Parcharidis, I. (2022). Geospatial Intelligence and Machine Learning Technique for Urban Mapping in Coastal Regions of South Aegean Volcanic Arc Islands. Geomatics, 2(3), 297–322. https://doi.org/10.3390/geomatics2030017

Kumar, A., Chandra, G., Singh, D., Bisht, H., Mehta, P., Sharma, M., Mahajan, S., Roy, S., Kumar, A., & Ali, S. (2021). Spatio-temporal changes in the Machoi glacier Zanskar Himalaya India using geospatial technology. Quaternary Science Advances, 4(February), 100031. https://doi.org/10.1016/j.qsa.2021.100031

Malhotra, R., Kantor, C., & Vlahovic, G. (2018). Geospatial intelligence workforce development in a changing world – An HBCU focus. Southeastern Geographer, 58(1), 125–135. https://doi.org/10.1353/sgo.2018.0008

Meester, M.J., & Baslamisli, AS (2022). SAR image edge detection: review and benchmark experiments. International Journal of Remote Sensing, 43(14), 5372–5438. https://doi.org/10.1080/01431161.2022.2131480

Park, J., & Yang, B. (2020). GIS-enabled digital twin system for sustainable evaluation of carbon emissions: A case study of Jeonju city, south Korea. Sustainability (Switzerland), 12(21), 1–21. https://doi.org/10.3390/su12219186

Park, Y.M., Sousan, S., Streuber, D., & Zhao, K. (2021). Science Research and Geospatial Assessments of Personal Exposure. MDPI, 10, 14.

Pinto Hidalgo, J. J., & Silva Centeno, J. A. (2023). Geospatial Intelligence and Artificial Intelligence for Detecting Potential Coca Paste Production Infrastructure in the Border Region of Venezuela and Colombia. Journal of Applied Security Research, 18(4), 1000–1050. https://doi.org/10.1080/19361610.2022.2111184

Randazzo, G., Italiano, F., Micallef, A., Tomasello, A., Cassetti, F.P., Zammit, A., D'amico, S., Lintasa, O., Cascio, M., Cavallaro, F. , Crupi, A., Fontana, M., Gregorio, F., Lanza, S., Colica, E., & Muzirafuti, A. (2021). WebGIS implementation for dynamic mapping and visualization of coastal geospatial data: A case study of BESS project. Applied Sciences (Switzerland), 11(17). https://doi.org/10.3390/app11178233

Raubal, M. (2020). Spatial data science for sustainable mobility. Journal of Spatial Information Science, 20(20), 109–114. https://doi.org/10.5311/JOSIS.2020.20.651

Safari Bazargani, J., Zafari, M., Sadeghi-Niaraki, A., & Choi, S. M. (2022). A Survey of GIS and AR Integration: Applications. Sustainability (Switzerland), 14(16), 1–14. https://doi.org/10.3390/su141610134

Saran, S., Singh, P., Kumar, V., & Chauhan, P. (2020). Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19. Journal of the Indian Society of Remote Sensing, 48(8), 1121–1138. https://doi.org/10.1007/s12524-020-01140-5

Scholze, AR, Delpino, FM, Alves, LS, Alves, JD, Berra, TZ, Ramos, ACV, Fuentealba-Torres, M., Fronteira, I., & Arcêncio, R.A. (2022). Identifying Hotspots of People Diagnosed of Tuberculosis with Addiction to Alcohol, Tobacco, and Other Drugs through a Geospatial Intelligence Application in Communities from Southern Brazil. Tropical Medicine and Infectious Diseases, 7(6). https://doi.org/10.3390/tropicalmed7060082

Schuler, T.V., Kohler, J., Elagina, N., Hagen, JOM, Hodson, AJ, Jania, JA, Kääb, A.M., Luks, B., Małecki, J., Moholdt, G., Pohjola, V.A., Sobota , I., & Van Pelt, W. J. J. (2020). Reconciling Svalbard Glacier Mass Balance. Frontiers in Earth Science, 8(May), 1–16. https://doi.org/10.3389/feart.2020.00156

Senf, C. (2022). Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics. Ecosystems, 25(8), 1719–1737. https://doi.org/10.1007/s10021-022-00777-2

Shafapourtehrany, M., Batur, M., Shabani, F., Pradhan, B., Kalantar, B., & Özener, H. (2023). A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment. Remote Sensing, 15(7). https://doi.org/10.3390/rs15071939

Sishodia, R.P., Ray, R.L., & Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 1–31. https://doi.org/10.3390/rs12193136

Slesinski, J., Wierzbicki, D., & Kedzierski, M. (2023). Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence. Sensors, 23(10). https://doi.org/10.3390/s23104922

Su, P., Liu, J., Li, Y., Liu, W., Wang, Y., Ma, C., & Li, Q. (2021). Changes in glacial lakes in the Poiqu River basin in the central Himalayas. Hydrology and Earth System Sciences, 25(11), 5879–5903. https://doi.org/10.5194/hess-25-5879-2021

Sufi, F., & Alsulami, M. (2022). A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders. Information (Switzerland), 13(3). https://doi.org/10.3390/info13030120

Tripathi, J.N., Sonker, I., Swarnim, Tripathi, S., & Singh, A.K. (2022). Climate change traces on Lhonak Glacier using geospatial tools. Quaternary Science Advances, 8(July), 100065. https://doi.org/10.1016/j.qsa.2022.100065

Tsiakos, C. A. D., & Chalkias, C. (2023). Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature. Applied Sciences (Switzerland), 13(5). https://doi.org/10.3390/app13053268

Ullo, S.L., & Sinha, G.R. (2021). Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications. Remote Sensing, 13(13). https://doi.org/10.3390/rs13132585

Velasquez-Camacho, L., Merontausta, E., Etxegarai, M., & de-Miguel, S. (2024). Assessing urban forest biodiversity through automatic taxonomic identification of street trees from citizen science applications and remote-sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 128(November 2023), 103735. https://doi.org/10.1016/j.jag.2024.103735

Wang, X., Wang, A., Yi, J., Song, Y., & Chehri, A. (2023). Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review. Remote Sensing, 15(13), 1–29. https://doi.org/10.3390/rs15133265

Weday, M.A., Tabor, K.W., & Gemeda, D.O. (2023). Flood hazards and risk mapping using geospatial technologies in Jimma City, southwestern Ethiopia. Heliyon, 9(4), e14617. https://doi.org/10.1016/j.heliyon.2023.e14617

Willockx, B., Lavaert, C., & Cappelle, J. (2022). Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level. Energy Reports, 8, 8736–8751. https://doi.org/10.1016/j.egyr.2022.06.076

Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771

Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C.D. (2022). Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sensing, 14(14). https://doi.org/10.3390/rs14143253

Yu, H., Liu, X., Kong, B., Li, R., & Wang, G. (2019). Landscape ecology development supported by geospatial technologies: A review. Ecological Informatics, 51(March), 185–192. https://doi.org/10.1016/j.ecoinf.2019.03.006

Zhang, B., Wu, Y., Zhao, B., Chanussot, J., Hong, D., Yao, J., & Gao, L. (2022). Progress and Challenges in Intelligent Remote Sensing Satellite Systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1814–1822. https://doi.org/10.1109/JSTARS.2022.3148139

Zhang, X., Zhou, Y., & Luo, J. (2022). Deep learning for processing and analysis of remote sensing big data: a technical review. Big Earth Data, 6(4), 527–560. https://doi.org/10.1080/20964471.2021.1964879

Zhong, B., Wu, S., Sun, G., & Wu, N. (2022). Farmers' Strategies to Climate Change and Urbanization: Potential of Ecosystem-Based Adaptation in Rural Chengdu, Southwest China. International Journal of Environmental Research and Public Health, 19(2). https://doi.org/10.3390/ijerph19020952

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2024-08-31

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