Mapping Research Trends in Remote for Post Disaster Environmental Damage Assessment : A Bibliometric Analysis
Keywords:
Remote Sensing, Bibliometric Analysis, Post Disaster assessment, Environmental Damage, Research Trends
Abstract
Remote sensing has become a key technology for assessing environmental damage following disasters due to its ability to provide timely, spatially explicit information. Despite its growing application, existing studies remain fragmented across disciplines and lack a structured understanding of research development. This study aims to systematically map the scientific landscape of remote sensing applications in post disaster environmental damage assessment using a bibliometric approach. A total of 200 publications indexed in scopus from 2015 to 2024 were analysed, comprising 48.195 citations and h-index of 142. Bibliometric analysis was conducted using VOSviewer to examinekeyword co-occurrence and to generate network, overlay, and density visualizations. The result identify four major research cluster : damage detection and assessment, environmental impact analysis, remote sensing applications, and disaster management. Network analysis indicates that remote sensing and damage assessment function as central nodes linkinginterdisciplinary research themes. Overlay visualization reveals a clear transition from conventional monitoring approaches toward data driven methods, particularly machine learning, predictive modeling, and GIS based analysis. Density analysis shows that research remains concentrated on monitoring and environmental assessment, while emerging topics such as artificial intelligence and UAV based sending remain underexplored. This study provides a structured and quantitative synthesis of research development and highlights the increasing role of remote sensing as a data driven decision support system. The findings offer new insights into research trends and identify under explored areas for future investigation.
References
Abdul Kadir, E., Listia Rosa, S., Syukur, A., Othman, M., & Daud, H. (2022). Forest fire spreading and carbon concentration identification in tropical region Indonesia. Alexandria Engineering Journal, 61(2). https://doi.org/10.1016/j.aej.2021.06.064
Agarwal, P., Sahu, D., Parida, Y., Ranjan Paltasingh, K., & Roy Chowdhury, J. (2023). Land Use changes and natural disaster fatalities: Empirical analysis for India. Ecological Indicators, 154. https://doi.org/10.1016/j.ecolind.2023.110525
The National Disaster Management Agency (BNPB) on the definition of Disaster. Retrieved September 17, 2024 from https://bnpb.go.id/definisi-bencana
Cameron, L., & Shah, M. (2015). Risk-taking behaviour in the wake of natural disasters. Journal of Human Resources, 50(2). https://doi.org/10.3368/jhr.50.2.484
Chandrasekar, V., Chen, H., & Philips, B. (2018). Principles of high-resolution radar network for hazard mitigation and disaster management in an urban environment. Journal of the Meteorological Society of Japan, 96A. https://doi.org/10.2151/JMSJ.2018-015
Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., & Bian, L. (2017). Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188. https://doi.org/10.1016/j.ijpe.2017.03.024
Duan, C., Zheng, X., Jin, L., Chen, Y., Li, R., & Yang, Y. (2022). Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas. Water (Switzerland), 14(14). https://doi.org/10.3390/w14142165
Efendi, F., Indarwati, R., Aurizki, G. E., Susanti, I. A., & Maulana, A. E. F. (2022). Policymakers' perspectives on responding to the elderly's mental health needs in post-disaster situations. Journal of Public Health Research, 11(1). https://doi.org/10.4081/jphr.2021.2386
Ghaffarian, S., Roy, D., Filatova, T., & Kerle, N. (2021). Agent-based modeling of post-disaster recovery with remote sensing data. International Journal of Disaster Risk Reduction, 60. https://doi.org/10.1016/j.ijdrr.2021.102285
Guo, G., & Duan, R. (2022). A new method to determine the scale range of environmental damage caused by water pollution accidents. Water Supply, 22(4). https://doi.org/10.2166/ws.2022.027
Hsiang, S., Oliva, P., & Walker, R. (2019). The Distribution of Environmental Damages. In Review of Environmental Economics and Policy (Vol. 13, Issue 1). https://doi.org/10.1093/reep/rey024
Huang, Y., CHEN, Z. Xin, YU, T., HUANG, X. Zhi, & GU, X. fa. (2018). Agricultural remote sensing big data: Management and applications. In Journal of Integrative Agriculture (Vol. 17, Issue 9). https://doi.org/10.1016/S2095-3119(17)61859-8
Jalil, A., Fathani, T. F., Satyarno, I., & Wilopo, W. (2021). Liquefaction in Palu: the cause of massive mudflows. Geoenvironmental Disasters, 8(1). https://doi.org/10.1186/s40677-021-00194-y
Joakim, E. P., & Wismer, S. K. (2015). Livelihood recovery after disaster. Development in Practice, 25(3). https://doi.org/10.1080/09614524.2015.1020764
Jodhani, K. H., Patel, H., Soni, U., Patel, R., Vadodara, B., Gupta, N., Patel, A., & Omar, P. Jee. (2024). Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India. Fire Ecology, 20(1). https://doi.org/10.1186/s42408-024-00254-2
Kaku, K. (2019). Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. In International Journal of Disaster Risk Reduction (Vol. 33). https://doi.org/10.1016/j.ijdrr.2018.09.015
Kerle, N., & Oppenheimer, C. (2002). Satellite remote sensing as a tool in lahar disaster management. Disasters, 26(2). https://doi.org/10.1111/1467-7717.00197
Kharb, A., Bhandari, S., Moitinho de Almeida, M., Castro Delgado, R., Arcos González, P., & Tubeuf, S. (2022). Valuing Human Impact of Natural Disasters: A Review of Methods. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 18). https://doi.org/10.3390/ijerph191811486
Kurbanov, E., Vorobev, O., Lezhnin, S., Sha, J., Wang, J., Li, X., Cole, J., Dergunov, D., & Wang, Y. (2022). Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review. In Remote Sensing (Vol. 14, Issue 19). https://doi.org/10.3390/rs14194714
Kuswandi, Y., Erwindi, J., Dwi Hadian, Moh. S., & 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, 5(4). https://doi.org/10.25299/jgeet.2020.5.4.5653
Lassa, J., Petal, M., & Surjan, A. (2023). Understanding the impacts of floods on learning quality,
school facilities, and educational recovery in Indonesia. Disasters, 47(2). https://doi.org/10.1111/disa.12543
Li, F., Wang, Q., Hu, W., Liu, J., & Zhang, X. (2022). Rapid assessment of disaster damage and economic resilience about the flooding in Zhengzhou, China in 2021. Remote Sensing Letters, 13(7). https://doi.org/10.1080/2150704X.2022.2068987
Li, J., Jin, Y., Zhao, R., Han, Y., & Habert, G. (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, 243. https://doi.org/10.1016/j.buildenv.2023.110637
Li, X. J., Zheng, J. X., & Lu, H. (2022). A Study on the Impact of Natural Disasters on Farmers'
Relative Poverty. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.908744
Lopes, L. F., Dias, F. S., Fernandes, P. M., & Acácio, V. (2024). A remote sensing assessment of oak forest recovery after postfire restoration. European Journal of Forest Research, 143(3). https://doi.org/10.1007/s10342-024-01667-z
M, Dr. J., & Asio, F. J. M. R. (2022). The Impact of Disasters and Climate Change on Migration and Displacement. Technoarete Transactions on Climate Change and Disaster Management Research, 1(2). https://doi.org/10.36647/ttccdmr/01.02.a004
Ma, H., Chiu, Y. H., Tian, X., Zhang, J., & Guo, Q. (2020). Safety or travel: Which is more important? The impact of disaster events on Tourism. Sustainability (Switzerland), 12(7). https://doi.org/10.3390/su12073038
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, 11(5). https://doi.org/10.3390/F11050507
Marlier, M. E., Resetar, S. A., 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, 132. https://doi.org/10.1016/j.envsci.2022.02.023
McKnight, B., & Linnenluecke, M. K. (2019). Patterns of Firm Responses to Different Types of Natural Disasters. Business and Society, 58(4). https://doi.org/10.1177/0007650317698946
Monteil, C., Barclay, J., & Hicks, A. (2020). Remembering, Forgetting, and Absencing Disasters in the Post-disaster Recovery Process. International Journal of Disaster Risk Science, 11(3). 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, 6(4). https://doi.org/10.3390/drones6040096
Priyatna, M., Khomarudin, M. R., Wijaya, S. K., Yulianto, F., Nugroho, G., Afgatiani, P. M., Rarasati, A., & Hussein, M. A. (2023). Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia. Journal of Engineering and Technological Sciences, 55(1). https://doi.org/10.5614/j.eng.technol.sci.2023.55.1.10
Rahman, M. S., & Di, L. (2020). A systematic review of case studies of remote-sensing-based flood crop loss assessment. In Agriculture (Switzerland) (Vol. 10, Issue 4). https://doi.org/10.3390/agriculture10040131
Rakwatin, P., Sansena, T., Marjang, N., & Rungsipanich, A. (2013). Using multi-temporal remote-
sensing data to estimate the 2011 flood area and volume over the Chao Phraya River basin, Thailand. Remote Sensing Letters, 4(3). https://doi.org/10.1080/2150704X.2012.723833
Rokhmatuloh, Ardiansyah, Indratmoko, S., Riyanto, I., Margatama, L., & Arief, R. (2022). Burnt-
Area Quick Mapping Method with Synthetic Aperture Radar Data. Applied Sciences, 12(23). https://doi.org/10.3390/app122311922
Sakuntala, D., Majid, M. S. A., Aliasuddin, & Suriani, S. (2022). Causality between Green Stock Market with Monetary Policy, Global Uncertainty, and Environmental Damage in Indonesia. International Journal of Energy Economics and Policy, 12(6). https://doi.org/10.32479/ijeep.13348
Samuele, D. P., Filippo, S., & Enrico, B. M. (2021). Multi-temporal mapping of flood damage to crops using sentinel-1 imagery: a case study of the Sesia River (October 2020). Remote Sensing Letters, 12(5). https://doi.org/10.1080/2150704X.2021.1890262
Sato, M. (2015). Near-range radar and its application to near-surface geophysics and disaster mitigation. Journal of Earth Science, 26(6). https://doi.org/10.1007/s12583-015-0595-y
Senf, C. (2022). Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics. Ecosystems, 25(8). https://doi.org/10.1007/s10021-022-00777-2
Siemes, C., van den IJssel, J., & Visser, P. (2024). Uncertainty of thermosphere mass density observations derived from accelerometer and GNSS tracking data. Advances in Space Research, 73(10). https://doi.org/10.1016/j.asr.2024.02.057
Tafti, M. T., & Tomlinson, R. (2015). Best practice post-disaster housing and livelihood recovery
interventions: Winners and losers. International Development Planning Review, 37(2). https://doi.org/10.3828/idpr.2015.14
Towfiqul Islam, A. R. M., Talukdar, S., Mahato, S., Kundu, S., Eibek, K. U., Pham, Q. B., Kuriqi, A., & Linh, N. T. T. (2021). Flood susceptibility modeling using advanced ensemble machine learning models. Geoscience Frontiers, 12(3). https://doi.org/10.1016/j.gsf.2020.09.006
Law no. 32 of 2009 concerning Environmental Protection and Management (PPLH). Retrieved September 19, 2024, from https://jdih.esdm.go.id/peraturan/UU%2032%20Tahun%202009%20%28PPLH%29.pdf
Upomo, T. C., Chang, M., Kusumawardani, R., Prayitno, G. A., Kuo, C. P., & Nugroho, U. (2023). Assessment of Petobo Flowslide Induced by Soil Liquefaction during the 2018 Palu–Donggala Indonesian Earthquake. Sustainability (Switzerland), 15(6). https://doi.org/10.3390/su15065371
Wang, L., Cui, S., Li, Y., Huang, H., Manandhar, B., Nitivattananon, V., Fang, X., & Huang, W. (2022). A review of the flood management: from flood control to flood resilience. In Heliyon (Vol. 8, Issue 11). https://doi.org/10.1016/j.heliyon.2022.e11763
Wang, Q., & Taylor, J. E. (2016). Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disasters. PLoS ONE, 11(1). https://doi.org/10.1371/journal.pone.0147299
Wasis, B., Saharjo, B. H., & Putra, E. I. (2019). Impacts of peat fire on soil flora and fauna, soil properties and environmental damage in Riau province, Indonesia. Biodiversitas, 20(6). https://doi.org/10.13057/biodiv/d200639
Wigati, S. S., Sopha, B. M., Asih, A. M. S., & Sutanta, H. (2023). Geographic Information System Based Suitable Temporary Shelter Location for Mount Merapi Eruption. Sustainability (Switzerland), 15(3). https://doi.org/10.3390/su15032073
Xu, Q., & Khan, M. I. (2023). Reflections on the environmental damage compensation regime in
Chinese civil legislation. Heliyon, 9(4). https://doi.org/10.1016/j.heliyon.2023.e15154
Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021). A forest fire detection system based on ensemble
learning. Forests, 12(2). https://doi.org/10.3390/f12020217
Ye, P. (2022). Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 6). https://doi.org/10.3390/ijerph19063701
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. https://doi.org/10.1109/JSTARS.2022.3148139
Zhao, X., Li, H., Wang, P., & Jing, L. (2020). An image registration method for multisource high-resolution remote sensing images for earthquake disaster assessment. Sensors (Switzerland), 20(8). https://doi.org/10.3390/s20082286
Zuev, V. V., Zueva, N. E., & Savelieva, E. S. (2017). The role of the Mt. Merapi eruption in the 2011 Arctic ozone depletion. Atmospheric Environment, 166. https://doi.org/10.1016/j.atmosenv.2017.07.040
Agarwal, P., Sahu, D., Parida, Y., Ranjan Paltasingh, K., & Roy Chowdhury, J. (2023). Land Use changes and natural disaster fatalities: Empirical analysis for India. Ecological Indicators, 154. https://doi.org/10.1016/j.ecolind.2023.110525
The National Disaster Management Agency (BNPB) on the definition of Disaster. Retrieved September 17, 2024 from https://bnpb.go.id/definisi-bencana
Cameron, L., & Shah, M. (2015). Risk-taking behaviour in the wake of natural disasters. Journal of Human Resources, 50(2). https://doi.org/10.3368/jhr.50.2.484
Chandrasekar, V., Chen, H., & Philips, B. (2018). Principles of high-resolution radar network for hazard mitigation and disaster management in an urban environment. Journal of the Meteorological Society of Japan, 96A. https://doi.org/10.2151/JMSJ.2018-015
Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., & Bian, L. (2017). Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188. https://doi.org/10.1016/j.ijpe.2017.03.024
Duan, C., Zheng, X., Jin, L., Chen, Y., Li, R., & Yang, Y. (2022). Study on the Remote Sensing Spectral Method for Disaster Loss Inversion in Urban Flood Areas. Water (Switzerland), 14(14). https://doi.org/10.3390/w14142165
Efendi, F., Indarwati, R., Aurizki, G. E., Susanti, I. A., & Maulana, A. E. F. (2022). Policymakers' perspectives on responding to the elderly's mental health needs in post-disaster situations. Journal of Public Health Research, 11(1). https://doi.org/10.4081/jphr.2021.2386
Ghaffarian, S., Roy, D., Filatova, T., & Kerle, N. (2021). Agent-based modeling of post-disaster recovery with remote sensing data. International Journal of Disaster Risk Reduction, 60. https://doi.org/10.1016/j.ijdrr.2021.102285
Guo, G., & Duan, R. (2022). A new method to determine the scale range of environmental damage caused by water pollution accidents. Water Supply, 22(4). https://doi.org/10.2166/ws.2022.027
Hsiang, S., Oliva, P., & Walker, R. (2019). The Distribution of Environmental Damages. In Review of Environmental Economics and Policy (Vol. 13, Issue 1). https://doi.org/10.1093/reep/rey024
Huang, Y., CHEN, Z. Xin, YU, T., HUANG, X. Zhi, & GU, X. fa. (2018). Agricultural remote sensing big data: Management and applications. In Journal of Integrative Agriculture (Vol. 17, Issue 9). https://doi.org/10.1016/S2095-3119(17)61859-8
Jalil, A., Fathani, T. F., Satyarno, I., & Wilopo, W. (2021). Liquefaction in Palu: the cause of massive mudflows. Geoenvironmental Disasters, 8(1). https://doi.org/10.1186/s40677-021-00194-y
Joakim, E. P., & Wismer, S. K. (2015). Livelihood recovery after disaster. Development in Practice, 25(3). https://doi.org/10.1080/09614524.2015.1020764
Jodhani, K. H., Patel, H., Soni, U., Patel, R., Vadodara, B., Gupta, N., Patel, A., & Omar, P. Jee. (2024). Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India. Fire Ecology, 20(1). https://doi.org/10.1186/s42408-024-00254-2
Kaku, K. (2019). Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. In International Journal of Disaster Risk Reduction (Vol. 33). https://doi.org/10.1016/j.ijdrr.2018.09.015
Kerle, N., & Oppenheimer, C. (2002). Satellite remote sensing as a tool in lahar disaster management. Disasters, 26(2). https://doi.org/10.1111/1467-7717.00197
Kharb, A., Bhandari, S., Moitinho de Almeida, M., Castro Delgado, R., Arcos González, P., & Tubeuf, S. (2022). Valuing Human Impact of Natural Disasters: A Review of Methods. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 18). https://doi.org/10.3390/ijerph191811486
Kurbanov, E., Vorobev, O., Lezhnin, S., Sha, J., Wang, J., Li, X., Cole, J., Dergunov, D., & Wang, Y. (2022). Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review. In Remote Sensing (Vol. 14, Issue 19). https://doi.org/10.3390/rs14194714
Kuswandi, Y., Erwindi, J., Dwi Hadian, Moh. S., & 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, 5(4). https://doi.org/10.25299/jgeet.2020.5.4.5653
Lassa, J., Petal, M., & Surjan, A. (2023). Understanding the impacts of floods on learning quality,
school facilities, and educational recovery in Indonesia. Disasters, 47(2). https://doi.org/10.1111/disa.12543
Li, F., Wang, Q., Hu, W., Liu, J., & Zhang, X. (2022). Rapid assessment of disaster damage and economic resilience about the flooding in Zhengzhou, China in 2021. Remote Sensing Letters, 13(7). https://doi.org/10.1080/2150704X.2022.2068987
Li, J., Jin, Y., Zhao, R., Han, Y., & Habert, G. (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, 243. https://doi.org/10.1016/j.buildenv.2023.110637
Li, X. J., Zheng, J. X., & Lu, H. (2022). A Study on the Impact of Natural Disasters on Farmers'
Relative Poverty. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.908744
Lopes, L. F., Dias, F. S., Fernandes, P. M., & Acácio, V. (2024). A remote sensing assessment of oak forest recovery after postfire restoration. European Journal of Forest Research, 143(3). https://doi.org/10.1007/s10342-024-01667-z
M, Dr. J., & Asio, F. J. M. R. (2022). The Impact of Disasters and Climate Change on Migration and Displacement. Technoarete Transactions on Climate Change and Disaster Management Research, 1(2). https://doi.org/10.36647/ttccdmr/01.02.a004
Ma, H., Chiu, Y. H., Tian, X., Zhang, J., & Guo, Q. (2020). Safety or travel: Which is more important? The impact of disaster events on Tourism. Sustainability (Switzerland), 12(7). https://doi.org/10.3390/su12073038
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, 11(5). https://doi.org/10.3390/F11050507
Marlier, M. E., Resetar, S. A., 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, 132. https://doi.org/10.1016/j.envsci.2022.02.023
McKnight, B., & Linnenluecke, M. K. (2019). Patterns of Firm Responses to Different Types of Natural Disasters. Business and Society, 58(4). https://doi.org/10.1177/0007650317698946
Monteil, C., Barclay, J., & Hicks, A. (2020). Remembering, Forgetting, and Absencing Disasters in the Post-disaster Recovery Process. International Journal of Disaster Risk Science, 11(3). 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, 6(4). https://doi.org/10.3390/drones6040096
Priyatna, M., Khomarudin, M. R., Wijaya, S. K., Yulianto, F., Nugroho, G., Afgatiani, P. M., Rarasati, A., & Hussein, M. A. (2023). Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia. Journal of Engineering and Technological Sciences, 55(1). https://doi.org/10.5614/j.eng.technol.sci.2023.55.1.10
Rahman, M. S., & Di, L. (2020). A systematic review of case studies of remote-sensing-based flood crop loss assessment. In Agriculture (Switzerland) (Vol. 10, Issue 4). https://doi.org/10.3390/agriculture10040131
Rakwatin, P., Sansena, T., Marjang, N., & Rungsipanich, A. (2013). Using multi-temporal remote-
sensing data to estimate the 2011 flood area and volume over the Chao Phraya River basin, Thailand. Remote Sensing Letters, 4(3). https://doi.org/10.1080/2150704X.2012.723833
Rokhmatuloh, Ardiansyah, Indratmoko, S., Riyanto, I., Margatama, L., & Arief, R. (2022). Burnt-
Area Quick Mapping Method with Synthetic Aperture Radar Data. Applied Sciences, 12(23). https://doi.org/10.3390/app122311922
Sakuntala, D., Majid, M. S. A., Aliasuddin, & Suriani, S. (2022). Causality between Green Stock Market with Monetary Policy, Global Uncertainty, and Environmental Damage in Indonesia. International Journal of Energy Economics and Policy, 12(6). https://doi.org/10.32479/ijeep.13348
Samuele, D. P., Filippo, S., & Enrico, B. M. (2021). Multi-temporal mapping of flood damage to crops using sentinel-1 imagery: a case study of the Sesia River (October 2020). Remote Sensing Letters, 12(5). https://doi.org/10.1080/2150704X.2021.1890262
Sato, M. (2015). Near-range radar and its application to near-surface geophysics and disaster mitigation. Journal of Earth Science, 26(6). https://doi.org/10.1007/s12583-015-0595-y
Senf, C. (2022). Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics. Ecosystems, 25(8). https://doi.org/10.1007/s10021-022-00777-2
Siemes, C., van den IJssel, J., & Visser, P. (2024). Uncertainty of thermosphere mass density observations derived from accelerometer and GNSS tracking data. Advances in Space Research, 73(10). https://doi.org/10.1016/j.asr.2024.02.057
Tafti, M. T., & Tomlinson, R. (2015). Best practice post-disaster housing and livelihood recovery
interventions: Winners and losers. International Development Planning Review, 37(2). https://doi.org/10.3828/idpr.2015.14
Towfiqul Islam, A. R. M., Talukdar, S., Mahato, S., Kundu, S., Eibek, K. U., Pham, Q. B., Kuriqi, A., & Linh, N. T. T. (2021). Flood susceptibility modeling using advanced ensemble machine learning models. Geoscience Frontiers, 12(3). https://doi.org/10.1016/j.gsf.2020.09.006
Law no. 32 of 2009 concerning Environmental Protection and Management (PPLH). Retrieved September 19, 2024, from https://jdih.esdm.go.id/peraturan/UU%2032%20Tahun%202009%20%28PPLH%29.pdf
Upomo, T. C., Chang, M., Kusumawardani, R., Prayitno, G. A., Kuo, C. P., & Nugroho, U. (2023). Assessment of Petobo Flowslide Induced by Soil Liquefaction during the 2018 Palu–Donggala Indonesian Earthquake. Sustainability (Switzerland), 15(6). https://doi.org/10.3390/su15065371
Wang, L., Cui, S., Li, Y., Huang, H., Manandhar, B., Nitivattananon, V., Fang, X., & Huang, W. (2022). A review of the flood management: from flood control to flood resilience. In Heliyon (Vol. 8, Issue 11). https://doi.org/10.1016/j.heliyon.2022.e11763
Wang, Q., & Taylor, J. E. (2016). Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disasters. PLoS ONE, 11(1). https://doi.org/10.1371/journal.pone.0147299
Wasis, B., Saharjo, B. H., & Putra, E. I. (2019). Impacts of peat fire on soil flora and fauna, soil properties and environmental damage in Riau province, Indonesia. Biodiversitas, 20(6). https://doi.org/10.13057/biodiv/d200639
Wigati, S. S., Sopha, B. M., Asih, A. M. S., & Sutanta, H. (2023). Geographic Information System Based Suitable Temporary Shelter Location for Mount Merapi Eruption. Sustainability (Switzerland), 15(3). https://doi.org/10.3390/su15032073
Xu, Q., & Khan, M. I. (2023). Reflections on the environmental damage compensation regime in
Chinese civil legislation. Heliyon, 9(4). https://doi.org/10.1016/j.heliyon.2023.e15154
Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021). A forest fire detection system based on ensemble
learning. Forests, 12(2). https://doi.org/10.3390/f12020217
Ye, P. (2022). Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges. In International Journal of Environmental Research and Public Health (Vol. 19, Issue 6). https://doi.org/10.3390/ijerph19063701
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. https://doi.org/10.1109/JSTARS.2022.3148139
Zhao, X., Li, H., Wang, P., & Jing, L. (2020). An image registration method for multisource high-resolution remote sensing images for earthquake disaster assessment. Sensors (Switzerland), 20(8). https://doi.org/10.3390/s20082286
Zuev, V. V., Zueva, N. E., & Savelieva, E. S. (2017). The role of the Mt. Merapi eruption in the 2011 Arctic ozone depletion. Atmospheric Environment, 166. https://doi.org/10.1016/j.atmosenv.2017.07.040
Published
2026-04-04
How to Cite
Rena Trisantikawaty, Sukendra Martha, & Syachrul Arief. (2026). Mapping Research Trends in Remote for Post Disaster Environmental Damage Assessment : A Bibliometric Analysis. International Journal of Education, Vocational and Social Science , 5(02), 191-204. https://doi.org/10.63922/ijevss.v5i02.2540
Contact Person +6282139474255
Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
