Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery

Tran Vu Van Hoa; Thien Chi Nguyen; Tung Thanh Truong; Tuan Anh Nguyen; Hoang Bao Lam; Son Thai Dang

doi:10.32628/IJSRSET2411257

Authors

Tran Vu Van Hoa The SDCT Research Group, University of Transport Ho Chi Minh City, Vietnam Author
Thien Chi Nguyen The SDCT Research Group, University of Transport Ho Chi Minh City, Vietnam Author
Tung Thanh Truong The SDCT Research Group, University of Transport Ho Chi Minh City, Vietnam Author
Tuan Anh Nguyen University of Transport Ho Chi Minh City, Vietnam Author
Hoang Bao Lam Ha Tinh Department of Construction, Ha Tinh Province, Vietnam Author
Son Thai Dang Ha Tinh Department of Construction, Ha Tinh Province, Vietnam Author

DOI:

https://doi.org/10.32628/IJSRSET2411257

Keywords:

Remote Sensing, Flood Impact Analysis, Landsat and MODIS Integration, NDWI

Abstract

This article explores the efficacy of integrating Landsat and MODIS satellite imagery for comprehensive flood impact analysis. By employing advanced remote sensing technologies and sophisticated data processing techniques, this study offers a methodological framework that enhances the precision and depth of environmental analysis. The core methodology involves the systematic processing of satellite data, including radiometric and geometric corrections, combined with the use of analytical indices such as the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI). These indices play a crucial role in accurately delineating water bodies and assessing the extent of flooding. The approach not only improves the reliability of flood mapping but also contributes to the broader understanding of environmental changes and aids in effective disaster management. Through this study, we demonstrate how strategic data integration can provide valuable insights for policymakers, enhancing responses to environmental crises.

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References

Ahmed, M.R., et al. [3] Remote Sensing-Based Quantification of the Impact of Flash Flooding on the Rice Production: A Case Study over Northeastern Bangladesh. Sensors, 2017. 17, DOI: 10.3390/s17102347. DOI: https://doi.org/10.3390/s17102347

Cianci, A., [8] Agrobiodiversity, agroecology, and private law. 2019. p. 31-36. DOI: https://doi.org/10.1201/9780429443350-2

Asare-Kyei, D., G. Forkuor, and V. Venus [6] Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches. Water, 2015. 7, 3531-3564 DOI: 10.3390/w7073531. DOI: https://doi.org/10.3390/w7073531

Dao, P., Y.-A. Liou, and C.-W. Chou, [9] Detection of Flood Inundation Regions with Landsat/MODIS Synthetic Data. 2015.

Ahamed, A., et al., [1] Near Real-Time Flood Monitoring and Impact Assessment Systems, in Remote Sensing of Hydrological Extremes, V. Lakshmi, Editor. 2017, Springer International Publishing: Cham. p. 105-118. DOI: https://doi.org/10.1007/978-3-319-43744-6_6

Irimescu, A., et al., [17] The use of remote sensing and GIS techniques in flood monitoring and damage assessment: a study case in Romania. 2015.

Dao, P.D., N.T. Mong, and H.-P. Chan, [11] Landsat-MODIS image fusion and object-based image analysis for observing flood inundation in a heterogeneous vegetated scene. GIScience & remote sensing, 2019. 56(8): p. 1148-1169. DOI: https://doi.org/10.1080/15481603.2019.1627062

Rahman, M. and L. Di, [26] [27] The state of the art of spaceborne remote sensing in flood management. Natural Hazards, 2017. 85. DOI: https://doi.org/10.1007/s11069-016-2601-9

Ahmed, K.R. and S. Akter, [2] Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data. Remote Sensing Applications: Society and Environment, 2017. 8: p. 168-181. DOI: https://doi.org/10.1016/j.rsase.2017.08.010

Sadeghian, F., et al., [28] Effects of electrokinetic phenomena on the load-bearing capacity of different steel and concrete piles: A small-scale experimental study. Canadian Geotechnical Journal, 2020. 58. DOI: https://doi.org/10.1139/cgj-2019-0650

Ghorbani, M.A., et al., [13] Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting. Journal of Hydrology, 2018. 562: p. 455-467. DOI: https://doi.org/10.1016/j.jhydrol.2018.04.054

Mateus, S. and J. Branch, [22] Intelligent Virtual Environment Using Artificial Neural Networks. 2017. 43-53. DOI: https://doi.org/10.1007/978-3-319-57987-0_4

Puspitasari, S., [25] Sampul Belakang. Jurnal Penelitian Karet, 2021: p. xviii-xxii.

Landuyt, L., N.E. Verhoest, and F.M. Van Coillie, Flood mapping in vegetated areas using an unsupervised clustering approach on sentinel-1 and-2 imagery. Remote Sensing, 2020. 12(21): p. 3611. DOI: https://doi.org/10.3390/rs12213611

Li, K., J. Wang, and J. Yao, Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia. International Journal of Applied Earth Observation and Geoinformation, 2021. 103: p. 102497. DOI: https://doi.org/10.1016/j.jag.2021.102497

Chander, G., et al., Monitoring on-orbit calibration stability of the Terra MODIS and Landsat 7 ETM+ sensors using pseudo-invariant test sites. Remote Sensing of Environment, 2010. 114(4): p. 925-939. DOI: https://doi.org/10.1016/j.rse.2009.12.003

Robinson, N.P., et al., A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote sensing, 2017. 9(8): p. 863. DOI: https://doi.org/10.3390/rs9080863

Chander, G., D.J. Meyer, and D.L. Helder, Cross calibration of the Landsat-7 ETM+ and EO-1 ALI sensor. IEEE Transactions on Geoscience and Remote Sensing, 2004. 42(12): p. 2821-2831. DOI: https://doi.org/10.1109/TGRS.2004.836387

Islam, K.A., et al., Flood detection using multi-modal and multi-temporal images: A comparative study. Remote Sensing, 2020. 12(15): p. 2455. DOI: https://doi.org/10.3390/rs12152455

Hidayah, E., et al., Flood mapping based on open-source remote sensing data using an efficient band combination system. 2022. DOI: https://doi.org/10.3986/AGS.10598

Sivanpillai, R., et al., Rapid flood inundation mapping by differencing water indices from pre-and post-flood Landsat images. Frontiers of Earth Science, 2021. 15: p. 1-11. DOI: https://doi.org/10.1007/s11707-020-0818-0

Rahmat, A., et al. Analysis of Normalized Different Wetness Index (NDWI) Using Landsat Imagery in the Ciletuh Geopark Area as Ecosystem Monitoring. in IOP Conference Series: Earth and Environmental Science. 2022. IOP Publishing. DOI: https://doi.org/10.1088/1755-1315/1062/1/012037

Ticehurst, C., et al. Using MODIS for mapping flood events for use in hydrological and hydrodynamic models: Experiences so far. in 20th international congress on modelling and simulation, Adelaide, Australia. 2013.

Khalifeh Soltanian, F., M. Abbasi, and H. Riyahi Bakhtyari, Flood monitoring using ndwi and mndwi spectral indices: A case study of aghqala flood-2019, Golestan Province, Iran. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019. 42: p. 605-607. DOI: https://doi.org/10.5194/isprs-archives-XLII-4-W18-605-2019

Kwak, Y., B. Arifuzzanman, and Y. Iwami, Prompt proxy mapping of flood damaged rice fields using MODIS-derived indices. Remote Sensing, 2015. 7(12): p. 15969-15988. DOI: https://doi.org/10.3390/rs71215805

Thy, P.T.M. and H.D. Duan, A Study On The Potential Of Applying Moderate Resolution Imaging Spectroradiometer (Modis) For Detecting Land Cover Change In The Mekong Delta.

Son, N.-T., C.-F. Chen, and C.-R. Chen, Flood assessment using multi-temporal remotely sensed data in Cambodia. Geocarto International, 2021. 36(9): p. 1044-1059. DOI: https://doi.org/10.1080/10106049.2019.1633420

Vichet, N., et al., MODIS-Based investigation of flood areas in Southern Cambodia from 2002–2013. Environments, 2019. 6(5): p. 57. DOI: https://doi.org/10.3390/environments6050057

Ji, L., et al., On the terminology of the spectral vegetation index (NIR− SWIR)/(NIR+ SWIR). International journal of remote sensing, 2011. 32(21): p. 6901-6909. DOI: https://doi.org/10.1080/01431161.2010.510811

Dangwal, N., et al., Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM. Geocarto International, 2016. 31(6): p. 682-693. DOI: https://doi.org/10.1080/10106049.2015.1073369

Gao, W., et al., [12] Analysis of flood inundation in ungauged basins based on multi-source remote sensing data. Environmental Monitoring and Assessment, 2018. 190(3): p. 129. DOI: https://doi.org/10.1007/s10661-018-6499-4

Psychogios, A., et al., [24] Varieties of crisis and working conditions: A comparative study of Greece and Serbia. European Journal of Industrial Relations, 2019. 26: p. 095968011983710. DOI: https://doi.org/10.1177/0959680119837101

Kabenge, M., et al., [18] Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index. Natural Hazards, 2017. 89(3): p. 1369-1387. DOI: https://doi.org/10.1007/s11069-017-3024-y