Utilization of remote sensing in post-disaster recovery for environmental damage assessment

Authors

  • Dimas Andrianto Sensing Technology Study, Faculty of Defense Science and Technology, Indonesia Defense University, Bogor, West Java 16810, Indonesia
  • Asep Adang Supriyadi Sensing Technology Study, Faculty of Defense Science and Technology, Indonesia Defense University, Bogor, West Java 16810, Indonesia

DOI:

https://doi.org/10.61511/rstde.v2i1.2025.1778

Keywords:

drones, environmental damage assessment, post-disaster recovery, radar, remote sensing, satellite imagery

Abstract

Background: Remote sensing techniques have become one of the important methods in post-disaster recovery for assessing environmental damage. They offer the ability to quickly and accurately identify and map damage at a wide scale, which is particularly useful in dynamic and often unpredictable post-disaster situations. Methods: This research aims to explore the use of various remote sensing technologies, such as satellite imagery, radar and drones, in assessing environmental damage after natural disasters. In this study, brainstorming focused on how remote sensing technologies can be optimally applied in post-disaster recovery, with an emphasis on environmental damage assessment. Findings: The results showed that remote sensing technology enables the identification of structural and environmental damage more efficiently than traditional methods. Satellite imagery provides an overview of the extent of the affected area, while radar and LiDAR technologies can be used to measure physical damage in greater detail. Drones, with their high resolution and flexibility, serve as an additional tool for detailed surveys in areas that are difficult to access. However, the application of this technology is not free from challenges, such as access to high-resolution data that is often expensive, the need for field validation to ensure accuracy, and infrastructure limitations in some disaster-prone developing countries. Conclusion: This research recommends increasing access to remote sensing data at affordable costs or for free for developing countries, integration of multi-source technologies to improve assessment accuracy. In addition, policy development based on remote sensing data for disaster risk mitigation. Thus, remote sensing is very useful for long-term disaster mitigation and adaptation planning and for post-disaster assessment. Novelty/Originality of this article: This article integrative exploration of multi-source remote sensing technologies—satellite imagery, radar, LiDAR, and drones—for comprehensive environmental damage assessment in post-disaster recovery, with a specific emphasis on challenges and policy implications in developing countries.

References

Asrar, G. R. (2019). Advances in Quantitative Earth Remote Sensing: Past, Present and Future. Sensors, 19(24), 1-4. https://doi.org/10.3390/s19245399

Atmaca, E., Aktaş, E., & Öztürk, H. N. (2023). Evaluated Post-Disaster and Emergency Assembly Areas Using Multi-Criteria Decision-Making Techniques: A Case Study of Turkey. Sustainability, 15(10). https://doi.org/10.3390/su15108350

Baccini, A., Friedl, M., Woodcock, C., & Zhu, Z. (2007). Scaling Field Data to Calibrate and Validate Moderate Spatial Resolution Remote Sensing Models. Photogrammetric Engineering & Remote Sensing, 73(8), 945-954. https://doi.org/10.14358/PERS.73.8.945

Bayarsaikhan, U., Akitsu, T. K., Tachiiri, K., Sasagawa, T., Nakano, T., Uudus, B.-S., & Nasahara, K. N. (2022). Early validation study of the photochemical reflectance index (PRI) and the normalized difference vegetation index (NDVI) derived from the GCOM-C satellite in Mongolian grasslands. International Journal of Remote Sensing, 43(14), 5145–5172. https://doi.org/10.1080/01431161.2022.2128923

Blackwell, E., Shirzaei, M., Ojha, C., & Werth, S. (2020). Tracking California’s sinking coast from space: Implications for relative sea-level rise. Science Advances, 6(31). https://doi.org/10.1126/sciadv.aba4551

Bluestein, H. B., Carr, F. H., & Goodman, S. J. (2022). Atmospheric Observations of Weather and Climate. Atmosphere-Ocean, 60(3), 149-187. https://doi.org/10.1080/07055900.2022.2082369

Brennan, L., & Reed, L. (1973). Theory of Adaptive Radar. IEEE Transactions on Aerospace and Electronic Systems, AES-9(2), 237-252. https://doi.org/10.1109/TAES.1973.309792

Brunner, D., Lemoine, G., & Bruzzone, L. (2010). Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery. IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2403-2420. https://doi.org/10.1109/TGRS.2009.2038274

Bryan, M. L. (1975). Interpretation of an urban scene using multi-channel radar imagery. Remote Sensing of Environment, 4, 49-66. https://doi.org/10.1016/0034-4257(75)90005-X

Camacho, A. G., Fernández, J., Samsonov, S. V., Tiampo, K. F., & Palano, M. (2020). 3D multi-source model of elastic volcanic ground deformation. Earth and Planetary Science Letters, 547. https://doi.org/10.1016/j.epsl.2020.116445

Caparros-Santiago, J. A., Rodriguez-Galiano, V., & Dash, J. (2021). Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 330-347. https://doi.org/10.1016/j.isprsjprs.2020.11.019

Chaves, M. E., Picoli, M. C., & Sanches, I. D. (2020). Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing, 12(18). https://doi.org/10.3390/rs12183062

Chien, S., & Tanpipat, V. (2012). Remote Sensingremote sensingof Natural Disastersremote sensingof natural disasters. In R. A. Meyers (Ed.), Encyclopedia of Sustainability Science and Technology (pp. 8939–8952). Springer New York. https://doi.org/10.1007/978-1-4419-0851-3_733

Cheng, C.-S., Behzadan, A. H., & Noshadravan, A. (2022). Uncertainty-aware convolutional neural network for explainable artificial intelligence-assisted disaster damage assessment. Structural Control and Health Monitoring, 29(10). https://doi.org/https://doi.org/10.1002/stc.3019

Choy, S., Handmer, J., Whittaker, J., Shinohara, Y., Hatori, T., & Kohtake, N. (2016). Application of satellite navigation system for emergency warning and alerting. Computers, Environment and Urban Systems, 58, 12-18. https://doi.org/10.1016/j.compenvurbsys.2016.03.003

Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. Isprs Journal of Photogrammetry and Remote Sensing, 92, 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013

Coro, G., Pagano, P., & Ellenbroek, A. (2020). Detecting patterns of climate change in long-term forecasts of marine environmental parameters. International Journal of Digital Earth, 13(5), 567-585. https://doi.org/10.1080/17538947.2018.1543365

Elliott, S. N., Shields, A. J., Klaehn, E. M., & Tien, I. (2022). Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks. Remote Sensing, 14(21), 1-14. https://doi.org/10.3390/rs14215331

Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Jr, R. D., Beckmann, T., . . . Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026

Franck, L., Berioli, M., Boutry, P., Harles, G., Ronga, L. S., Suffritti, R., & Thomasson, L. (2011). On the role of satellite communications for emergency situations with a focus on Europe. International Journal of Satellite Communications and Networking, 29(5), 387-399. https://doi.org/10.1002/sat.979

Ge, L., Ng, A. H.-M., Li, X., Liu, Y., Du, Z., & Liu, Q. (2015). Near real-time satellite mapping of the 2015 Gorkha earthquake, Nepal. Annals of GIS, 21(3), 175-190. https://doi.org/10.1080/19475683.2015.1068221

Hall, A., Cox, P., Huntingford, C., & Klein, S. (2019). Progressing emergent constraints on future climate change. Nature Climate Change, 9, 269-278. Retrieved from https://doi.org/10.1038/s41558-019-0436-6

Harvey, M., Rowland, J., & Luketina, K. (2016). Drone with thermal infrared camera provides high resolution georeferenced imagery of the Waikite geothermal area, New Zealand. Journal of Volcanology and Geothermal Research, 325, 61-69. https://doi.org/10.1016/j.jvolgeores.2016.06.014

Hashem, I. A., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115. https://doi.org/10.1016/j.is.2014.07.006

Hassanalian, M., & Abdelkefi, A. (2017). Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, 91, 99-131. https://doi.org/10.1016/j.paerosci.2017.04.003

Hu, D., & Minner, J. (2023). UAVs and 3D City Modeling to Aid Urban Planning and Historic Preservation: A Systematic Review. Remote Sensing, 15(23). https://doi.org/10.3390/rs15235507

Hussain, M. I., Azam, S., Rafique, M. A., Sheri, A. M., & Jeon, M. (2022). Drivable Region Estimation for Self-Driving Vehicles Using Radar. IEEE Transactions on Vehicular Technology, 71(6), 5971-5982. https://doi.org/10.1109/TVT.2022.3161378

Jordan, B. R. (2019). Collecting field data in volcanic landscapes using small UAS (sUAS)/drones. Journal of Volcanology and Geothermal Research, 385, 231-241. https://doi.org/10.1016/j.jvolgeores.2019.07.006

Kim, J.-W., Lu, Z., Lee, H., Shum, C., Swarzenski, C. M., Doyle, T. W., & Baek, S.-H. (2013). Integrated analysis of PALSAR/Radarsat-1 InSAR and ENVISAT altimeter data for mapping of absolute water level changes in Louisiana wetlands. Remote Sensing of Environment, 113(11), 2356-2365. https://doi.org/10.1016/j.rse.2009.06.014

Kulawardhana, R. W. (2012). Remote sensing and GIS technologies for monitoring and prediction of disasters. International Journal of Digital Earth, 5(1), 88-90. https://doi.org/10.1080/17538947.2011.622912

Li, J., & Roy, D. P. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sensing, 9(9). https://doi.org/10.3390/rs9090902

Li, S. (2020). Summary of Agricultural Application of Radar Remote Sensing. Remote Sensing, 9(18). https://doi.org/10.18282/rs.v9i1.1097

Li, S., Sun, X., Gu, Y., Lv, Y., Zhao, M., Zhou, Z., . . . Yang, J. (2023). Recent Advances in Intelligent Processing of Satellite Video: Challenges, Methods, and Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 6776-6798. https://doi.org/10.1109/JSTARS.2023.3296451

Li, X., Chen, Y., Jiang, S., Wang, C., Weng, S., & Rao, D. (2022). The Importance oAdding Short-Wave Infrared Bands for Forest Disturbance Monitoring in the Subtropical Region. Sustainability, 14(16). https://doi.org/10.3390/su141610312

Li, X., Wang, L., Cheng, Q., Wu, P., Gan, W., & Fang, L. (2019). Cloud removal in remote sensing images using nonnegative matrix factorization and error correction. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 103-113. https://doi.org/10.1016/j.isprsjprs.2018.12.013

Li, X., Yu, L., Xu, Y., Yang, J., & Gong, P. (2016). Ten years after Hurricane Katrina: monitoring recovery in New Orleans and the surrounding areas using remote sensing. Science Bulletin, 61(18), 1460-1470. https://doi.org/10.1007/s11434-016-1167-y

Lung, T., Lübker, T., Ngochoch, J. K., & Schaab, G. (2013). Human population distribution modelling at regional level using very high resolution satellite imagery. Applied Geography, 41, 36-45. https://doi.org/10.1016/j.apgeog.2013.03.002

Martinez, A. d., & Labib, S. (2022). Demystifying Normalized Difference Vegetation Index (NDVI) for Greenness Exposure Assessments and Policy Interventions in Urban Greening. Environmental research. https://doi.org/10.2139/ssrn.4207665

Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth's surface. Reviews of Geophysics, 441-500. https://doi.org/10.1029/97RG03139

Mondini, A. C., Guzzetti, F., Chang, K.-T., Monserrat, O., Martha, T. R., & Manconi, A. (2021). Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future. Earth-Science Reviews, 216, 1-33. https://doi.org/10.1016/j.earscirev.2021.103574

Mukonza, S. S., & Chiang, J.-L. (2023). Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring. Environments, 10(10), 1-47. https://doi.org/10.3390/environments10100170

Navalgund, R., V, J., & Roy, P. S. (2007). Remote sensing applications: An overview. Current Science, 93(12), 1747-1766. https://www.jstor.org/stable/24102069

Oktaviani, J., Kumesan, C. P., & Fajar, S. (2017). Analisis Pemetaan Kerentanan Masyarakat Terhadap Bencana Gempa: Studi Kasus Gempa di Haiti Tahun 2010. Jurnal Sosial Politik, 3(1), 42. https://doi.org/10.22219/sospol.v3i1.4400

Osborn, A. (1953). Applied Imagination: Principles and Procedures of Creative Problem Solving. Charles Scribner’s Sons.

Parker, A. L., Castellazzi, P., Fuhrmann, T., Garthwaite, M. C., & Featherstone, W. E. (2021). Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia. Remote Sensing, 13(8), 1-25. https://doi.org/10.3390/rs13081422

Pepe, A., & Calò, F. (2017). A Review of Interferometric Synthetic Aperture RADAR (InSAR) Multi-Track Approaches for the Retrieval of Earth’s Surface Displacements. Applied Sciences, 7(12). https://doi.org/10.3390/app7121264

Ray, R. L., Jacobs, J. M., & Cosh, M. H. (2010). Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US. Remote Sensing of Environment, 114(11), 2624-2636. https://doi.org/10.1016/j.rse.2010.05.033

Segah, H., Tani, H., & Hirano, T. (2010). Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument. International Journal of Remote Sensing, 31(20), 5297–5314. https://doi.org/10.1080/01431160903302981

Shanmugan, K. S., Narayanan, V., Frost, V. S., Stiles, J. A., & Holtzman, J. C. (1981). Textural Features for Radar Image Analysis. IEEE Transactions on Geoscience and Remote Sensing, GE-19(3), 153-156. https://doi.org/10.1109/TGRS.1981.350344

Sublime, J., & Kalinicheva, E. (2019). Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami. Remote Sensing, 11(9). https://doi.org/10.3390/rs11091123

Svanström, F., Alonso-Fernandez, F., & Englund, C. (2022). Drone detection and tracking in real-time by fusion of different sensing modalities. Drones, 6(11), 317. https://doi.org/10.3390/drones6110317

Syifa, M., Kadavi, P. R., & Lee, C.-W. (2019). An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia. Sensors, 19(3). https://doi.org/10.3390/s19030542

Tahu, G. J., Baker, J. C., & O’Connell, K. M. (1998). Expanding global access to civilian and commercial remote sensing data: implications and policy issues. Space Policy, 14(3), 179-188. https://doi.org/10.1016/S0265-9646(98)00011-3

Tang, X., Yao, X., Liu, D., Zhao, L., Li, L., Zhu, D., & Li, G. (2021). A Ceph-based storage strategy for big gridded remote sensing data. Big Earth Data, 6(3), 323-339. https://doi.org/10.1080/20964471.2021.1989792

Tralli, D. M., Blom, R. G., Zlotnicki, V., Donnellan, A., & Evans, D. L. (2005). Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. Isprs Journal of Photogrammetry and Remote Sensing, 59(4), 185-198. https://doi.org/10.1016/j.isprsjprs.2005.02.002

Vicente-Serrano, S. M., Cabello, D., Tomás-Burguera, M., Martín-Hernández, N., Beguería, S., Azorin-Molina, C., & Kenawy, A. E. (2015). Drought Variability and Land Degradation in Semiarid Regions: Assessment Using Remote Sensing Data and Drought Indices (1982–2011). Remote Sensing, 7(4), 4391-4423. https://doi.org/10.3390/rs70404391

Villano, M. (2015). Student research highlight staggered synthetic aperture radar. IEEE Aerospace and Electronic Systems Magazine, 30(7), 30-32. https://doi.org/10.1109/MAES.2015.150041

Voigt, S., Giulio-Tonolo, F., Lyons, J., Kučera, J., Jones, B., Schneiderhan, T., . . . Muthike, D. M. (2016). Global trends in satellite-based emergency mapping. Science, 353(6296), 247-252. https://doi.org/10.1126/science.aad8728

Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., & Mehl, H. (2007). Satellite Image Analysis for Disaster and Crisis-Management Support. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1520-1528. https://doi.org/10.1109/TGRS.2007.895830

Whitehurst, D., Joshi, K., Kochersberger, K., & Weeks, J. (2022). Post-Flood Analysis for Damage and Restoration Assessment Using Drone Imagery. Remote Sensing, 14(19). https://doi.org/10.3390/rs14194952

Wu, X., Xiao, Q., Wen, J., You, D., & Hueni, A. (2019). Advances in quantitative remote sensing product validation: Overview and current status. Earth-Science Reviews, 196. https://doi.org/10.1016/j.earscirev.2019.102875

Xing, L., & Johnson, B. W. (2023). Reliability Theory and Practice for Unmanned Aerial Vehicles. IEEE Internet of Things Journal, 10(4), 3548-3566. https://doi.org/10.1109/JIOT.2022.3218491

Yang, C., Everitt, J. H., & Murden, D. (2011). Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75(2), 347-354. https://doi.org/10.1016/j.compag.2010.12.012

Ybañez, R. L., Ybañez, A. A., Lagmay, A. M., & Aurelio, M. A. (2021). Imaging ground surface deformations in post-disaster settings via small UAVs. Geoscience Letters, 8(23), 1-14. https://doi.org/10.1186/s40562-021-00194-8

Zhao, W., Li, A., Nan, X., Zhang, Z., & Lei, G. (2017). Postearthquake Landslides Mapping From Landsat-8 Data for the 2015 Nepal Earthquake Using a Pixel-Based Change Detection Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1758-1768. https://doi.org/10.1109/JSTARS.2017.2661802

Zhu, J., Daley, D., Baise, L. G., Thompson, E. M., Wald, D. J., & Knudsen, K. L. (2015). A Geospatial Liquefaction Model for Rapid Response and Loss Estimation. Earthquake Spectra, 31, 1813-1837. https://doi.org/10.1193/121912EQS353M

Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022). Detection and Tracking Meet Drones Challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7380-7399. https://doi.org/10.1109/TPAMI.2021.3119563

Žížala, D., Minařík, R., & Zádorová, T. (2019). Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions. Remote Sensing, 11(24). https://doi.org/10.3390/rs11242947

Downloads

Published

2025-02-28

Issue

Section

Articles

Citation Check