Bibliometric Analysis of the Utilization of Remote Sensing in Post-Disaster Environmental Recovery
Keywords:
Remote Sensing; Disaster; Environmental Damage; Recovery
Abstract
Remote sensing is a strategic technology in spatial data collection that plays an important role in supporting post-disaster mitigation through the provision of fast, accurate, and wide-ranging information without direct interaction with affected areas. As the frequency and impact of natural disasters increase, a comprehensive study is needed to understand the development of research related to the use of remote sensing in environmental damage assessment and post-disaster recovery processes. This study uses bibliometric mapping methods to analyze international scientific publications that discuss the use of remote sensing in environmental damage assessment and post-disaster recovery over a period of 15 years. Data were obtained from reputable international journal databases Scopus and Google Scholar which were analyzed using VOSviewer software to identify patterns of author collaboration, major journals, and keyword co-occurrence. This analysis aims to map the knowledge structure, research trends, and dominant topics in the field. The results of the study show a significant increase in publications related to remote sensing, with a dominant focus on the use of satellite imagery for disaster impact identification, mapping environmental damage, and monitoring long-term environmental changes.
References
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Ali, S., & George, A. (2022). Fostering Disaster Mitigation Through Community Participation-Case of Kochi Residents Following the Kerala Floods of 2018 and 2019. Natural Hazards. https://doi.org/10.1007/s11069-021-05058-0
Bai, Y., Mas, E., & Koshimura, S. (2018). Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami. Remote Sensing. https://doi.org/10.3390/rs10101626
Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G. (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. https://doi.org/10.1109/JSTARS.2020.3005403
Chester, M. V., El Asmar, M., Hayes, S., & Desha, C. (2021). Post-Disaster Infrastructure Delivery for Resilience. Sustainability. https://doi.org/10.3390/su13063458
Choi, Y. H., Yoo, D. G., Kwak, P. J., & Yoon, Y. (2023). Development of Vulnerability Evaluation Technology for Environmental Facilities Focused on the Water Treatment Systems in South Korea. Sustainability. https://doi.org/10.3390/su151310257
Colomina, I., & Molina, P. (2014). Unmanned Aerial Systems for Photogrammetry and Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing. http://dx.doi.org/10.1016/j.isprsjprs.2014.02.013
Derakhshan, S., Cutter, S. L., & Wang, C. (2020). Remote Sensing Derrived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery. Remote Sensing. https://doi.org/10.3390/rs12050895
Efendi, F., Indarwati, R., Aurizki, G. E., Susanti, I. A., & Maulana, A. E. (2022). Poilicymakers’ perspectives on responding to the elderly’s mental health needs in post-disaster situations. Journal of Public Health Research. Vol.11:2386. https://doi.org/10.4081/jphr.2021.2386
Fassnacht, F. E., White, J. C., Wulder, M. A., & Naesset, E. (2023). Remote Sensing in Forestry: Current Challenges, Considerations and Directions. Forestry: An International Journal of Forest Research, Vol.97, 11-37. https://doi.org/10.1093/forestry/cpad024
Gao, F., & Zhang, X. (2021). Mapping Crop Phenology in Nera Real-Time Using Satellite Remote Sensing: Challenges and Opportunities. Journal of Remote Sensing Vol. 2021, Article ID 8379391, 14 pages. https://doi.org/10.34133/2021/8379391
Ghaffarian, S., Roy, D., Filatova, T., & Kerie, N. (2021). Agent-based Modelling of Post-Disaster Recovery with Remote Sensing Data. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2021.102285
Hardin, P. J., Lulla, V., Jensen, R. R., & Jensen, J. R. (2018). Small Unmanned Aerial Systems (sUAS) for Environmental Remote Sensing: Challenges and Opportunities Revisited. GIScience and Remote Sensing. https://doi.org/10.1080/15481603.2018.1510088
Helmi, H., Basri, H., & Sufardi. (2019). Flood Vulnerability Level Analysis as a Hydrological Disaster Mitigation Effort in Krueng Jreue Sub-Watershed, Aceh Besar, Indonesia. Journal of Disaster Risk Studies. https://doi.org/10.4102/jamba.v11i1.737
Helmrich, A., Chester, M. V., Hayes, S., Markolf, S., Desha, C., & Grimm, N. B. (2020). Using biomimicry to support resilient infrastructure design. Earth’s Future. https://doi.org/10.1029/2020EF001653
Higuchi, A. (2021). Toward More Integrated Utilizations of Geostationary Satellite Data for Disaster Management and Risk Mitigation. Remote Sensing. https://doi.org/10.3390/rs13081553
Hsiang, S., Oliva, P., & Walker, R. (2019). THe Distribution of Environmental Damages.Review of Environmental Economics and Policy. https://doi.org/10.1093/reep/rey024
Huang, Y., ZhongXin, C., Tao, Y., XiangZhi, H., & XingFa, G. (2018). Agricultural Remote Sensing Big Data: Management and Applications. Journal of Integrative Agriculture. https://doi.org/10.1016/S2095-3119(17)61859-8
Joakim, E. P., & Wismer, S. K. (2015). Livelihood Recovery after Disaster. Development in Practice Vol.25, No.3, 401-418. https://doi.org/10.1080/09614524.2015.1020764
Junjie, L., Jin, Y., Zhao, R., & Han, Y. (2023). Using the EEG+VR+LEC Evaluation Method to Explore the Combined Influence of Temperature and Spatial Openness on the Physiological Recovery of Post-Disaster Populations. Building and Environment. https://doi.org/10.1016/j.buildenv.2023.110637
Kaku, K. (2018). Satellite Remote Sensing for Disaster Management Support: A Holistic and Staged Approach Based on Case Studies in Sentinel Asia. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2018.09.015
Kadir, E. A., Rosa, S. L., Syukur, A., Othman, M., & Daud, H. (2021). Forest Fire Spreading and Carbon Concentration Identification in Tropical Region Indonesia. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2021.06.064
Kapian, G., Jovanovska, T., Gasparovic, M., & Pietrelli, A. (2022). Monitoring War-Generated Environmental Security Using Remote Sensing: A-Review. Land Degradation and Development. https://doi.org/10.1002/ldr.4249
Koshimura, S., Moya, L., Mas, E., & Yanbing, B. (2020). Tsunami Damage Detection with Remote Sensing: A Review. Geosciences. https://doi.org/10.3390/geosciences10050177
Kulk, G., Sathyaendranath, S., Platt, T., George, G., Suresan, A. K., Menon, N., King, H. E., & Abdulaziz, A. (2023). Using Multi-Spectral Remote Sensing for Flood Mapping: A Case Study in Lake Vembanad, India. Remote Sensing. https://doi.org/10.3390/rs15215139
Kuswandi, Y., Erwindi, J., Hadian, M. S. D., & Muslim, D. (2020). Disaster Mitigation for Palu City Residents in Dealing with Liquefaction Disasters in Accordance of Spatial Patterns of Palu City, Central Sulawesi Province, Indonesia. Journal of Geoscience, Engineering, Environment, and Technology. https://doi.org/10.25299/jgeet.2020.5.4.5653
Li, F., Wang, Q., Hu, W., Liu, J., & Zhang, X. (2022). Rapid Assessment of Disaster Damage and Economic Resilience on Relation to the Flooding in Zhengzhou, China in 2021. Remote Sensing Letters. https://doi.org/10.1080/2150704X.2022.2068987
Lopes, L. F., Dias, F. S., Fernandes, P. M., & Acacio, V. (2024). A Remote Sensing Assessment of Oak Forest Recovery After Post Fire Restoration. European Journal of Forest Research. https://doi.org/10.1007/s10342-024-01667-z
Low, F., Stieglitz, K., & Diemar, O. (2021). Terrestrial Oil Spill Mapping Using Satellite Earth Observation and Machine Learning: A Case Study in South Sudan. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2021.113424
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep Learning in Remote Sensing Applications: A Meta-analysis and Review. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2019.04.015
Ma, W., Feng, Z., Cheng, Z., Chen, S., & Wang, F. (2020). Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm. Forests. https://doi.org/10.3390/f11050507
Marlier, M. E., Resetar, S. S., Lachman, B. E., Anania, K., & Adams, K. (2022). Remote Sensing for Natural Disaster Recovery: Lessons Learned from Hurricanes Irma and Maria in Puerto Rico. Environmental Science and Policy. https://doi.org/10.1016/j.envsci.2022.02.023
Meng, G., Li, J., Sheng, C. L., & Xu, J. (2017). A Review of Wetland Remote Sensing. Sensors. https://doi.org/10.3390/s17040777
Menya, A. A., & K’Akumu, O. A. (2016). Inter-Agency Collaboration for Fire Disaster Management in Nairobi City. Journal of Urban Management. https://doi.org/10.1016/j.jum.2016.08.001
Mihardja, E. J., Alisjahbana, S., Agustini, P. M., Sari, D. E. P., & Pardede, T. S. (2023). Forest Wellness Tourism Destination Branding for Supporting Disaster Mitigation: A Case of Batur UNESCO Global Geopark, Bali. International Journal of Geoheritage and Parks. https://doi.org/10.1016/j.ijgeop.2023.01.003
Mohajane, M., Costache, R., Karimi, F., Pham, Q. B., Essahlaoui, A., Nguyen, H., Laneve, G., & Oudija, F. (2021). Application of Remote Sensing and Machine Learning Algorithms for forest fire mapping in a Mediterranean Area. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2021.107869
Monteil, C., Barclay, J., & Hicks, A. (2020). Remembering, Forgetting, and Absencing Disaster in the Post-disaster Recovery Process. International Journal of Disaster Risk. https://doi.org/10.1007/s13753-020-00277-8
Munawar, H. S., Hammad, A. W. A., & Waller, S.T. (2022). Disaster Region Coverage Using Drones: Maximum Area Coverage and Minimum Resource Utilisation. Drones. https://doi.org/10.3390/drones6040096
Osco, L. P., Junior, J. M., Ramos, A. P. M., Jorge, L. A. C., Fatholahi, S. N., Silva, J. A., Matsubara, E. T., Pistori, H., Gancalves, W. N., & Li, J. (2021). A Review on Deep Learning in UAV Remote Sensing. International Journal of Applied Earth Observations and Geoinformation. https://doi.org/10.1016/j.jag.2021.102456
Priyatna, M., Khomarudin, R., Wijaya, S. K., & Yulianto, F. (2023). Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 in the Barito Watershed, South Kalimantan Province, Indonesia. Journal of Engineering and Technological Sciences, Vol.55, No.1, 98-107. https://doi.org/10.5614/j.eng.technol.sci.2023.55.1.10
Rahman, S., & Liping, D. (2020). A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment. Agriculture. https://doi.org/10.3390/agriculture10040131
Rokhmatuloh., Ardiansyah., Indratmoko, S., Riyanto, I., Margatama, L., & Arief Rahmat. (2022). Burnt-Area Quick Mapping Method with Synthetic Aperture Radar. Applied Science. https://doi.org/10.3390/app122311922
Roy, P. S., Behera, M. D., & Srivastav, S. K. (2017). Satellite Remote Sensing: Sensors, Applications and Techniques. https://doi.org/10.1007/s40010-017-0428-8
Samuele, D. P., Filippo, S., & Borgogno, M. E. (2020). Multi-Temporal Mapping of Flood Damage to Crops Using Sentinel-1 Imagery: a Case Study of the Sesia River. Remote Sensing Letters. https://doi.org/10.1080/2150704X.2021.1890262
Senf, C. (2022). Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics. Ecosystems. https://doi.org/10.1007/s10021-022-00777-2
Wahyuningtyas, N., Tanjung, A., Idris., & Dewi, K. (2019). Disaster Mitigation on Cultural Tourism in Lombok Indonesia. Geojournal of Tourism and Geosites. https://doi.org/10.30892/gtg.27409-428
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Published
2026-02-04
How to Cite
Sekar Dwianti, F. A., Trismadi, Syachrul Arief, & Asep Adang Supriyadi. (2026). Bibliometric Analysis of the Utilization of Remote Sensing in Post-Disaster Environmental Recovery. International Journal of Education, Vocational and Social Science , 5(01), 573-592. https://doi.org/10.63922/ijevss.v5i01.2746
Contact Person +6282139474255
Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
