Flood Mapping and Impact Assessment in Agricultural Land in Hoa Vang, Da Nang Using Remote Sensing and Google Earth Engine
Main Article Content
Abstract
Hoa Vang district in Da Nang city, is frequently affected by floods. This research was conducted to generate a flood map in Hoa Vang District, Da Nang City, during the peak flooding period in 2022, using Sentinel 1 imagery on the Google Earth Engine (GEE) cloud computing platform. The research also aims to assess the impact of this flooding event on the productivity of agricultural land in the district. Based on the Sentinel 1 imagery's backscattering coefficient, the study utilizes the Otsu algorithm for thresholding to determine the flood inundated areas. The results indicated that the optimal Otsu threshold value range from -16.003 to -10.631 dB. In addition, to assess the reliability of the flood mapping results, the study utilizes the in-situ flood inventory data to calculate the overall error and Kappa index. The research findings indicate a high level of reliability with an overall accuracy of 0.89 and a Kappa index of 0.79. Based on this analysis, the study successfully established a flood map for the peak flooding period 2022 in the study area. Furthermore, the study assessed the impact of floods on agricultural land productivity in the communes of Hoa Vang District. The findings showed that the flooded agricultural land area was 1,979.8 hectares, accounting for 77.3% of the total flooded area and 2.7% of the total natural area, primarily concentrated in the southern communes of the district such as Hoa Tien, Hoa Chau, Hoa Phuoc, and Hoa Phong. These findings serve as scientific background for assessing further impacts of flooding on agricultural land in Hoa Vang District, Da Nang City.
Keywords:
Map, flooding, agricultural land production, remote sensing, radar.
References
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[2] J. Rentschler, S. D. V. Robbé, J. Braese, N. H. Dzung, M. V. Ledden, B. P. Mayo, Resilient Shores: Vietnam’s Coastal Development Between Opportunity and Disaster Risk, World Bank, Washington DC, 2020.
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E. A. Romanowicz, S. Brunzell, C. J. Richardson, Effects of Seasonal Hydrologic Patterns in South Florida Wetlands on Radar Backscatter Measured from ERS-2 SAR imagery, Remote Sens. Environ., Vol. 88, 2003, pp. 423-441,
https://doi.org/10.1016/j.rse.2003.08.016.
[15] M. S. Horritt, D. C. Mason, D. M. Cobby, I. J. Davenport, P. D. Bates, Waterline Mapping in Flooded Vegetation from Airborne SAR Imagery, Remote Sens. Environ., Vol. 85, No. 3, 2003,
pp. 271-281,
https://doi.org/10.1016/S0034-4257(03)00006-3.
[16] M. A. Clement, C. G. Kilsby, P. Moore, Multi-temporal Synthetic Aperture Radar Flood Mapping Using Change Detection, J. Flood Risk Manage, Vol. 11, No. 2, 2018, pp. 152-168,
https://doi.org/10.1111/jfr3.12303.
[17] S. Martinis, J. Kersten, A. Twele, A Fully Automated TerraSAR-X Based Flood Service, ISPRS J. Photogramm. Remote Sens., Vol. 104, 2015, pp. 203-212,
https://doi.org/10.1111/jfr3.12303.
[18] N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern, Vol. 9, No. 1, 1979, pp. 62-66,
https://doi.org/10.1109/TSMC.1979.4310076.
[19] Z. Du, W. Li, D. Zhou, L. Tian, F. Ling, H. Wang, Y. Gui, B. Sun, Analysis of Landsat-8 OLI Imagery for Land Surface Water Mapping, Remote Sens. Lett., Vol. 5, No. 7, 2014, pp. 672-681,
https://doi.org/10.1080/2150704X.2014.960606.
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pp. 103010, https://doi.org/10.1016/j.jag.2022.103010.
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[22] N. Gorelick, M. Hancher, M. Dixon,
S. Ilyushchenko, D. Thau, R. Moore, Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone, Remote Sens. Environ., Vol. 202, 2017, pp. 18-27,
https://doi.org/10.1016/j.rse.2017.06.031.
[23] T. Q. Toan, Climate Change and Sea Level Rise in the Mekong Delta: Flood, Tidal Inundation, Salinity Intrusion, and Irrigation Adaptation Methods, N. D. Thao, H. Takagi, M. Esteban, Coastal Disasters and Climate Change in Vietnam, Elsevier, Oxford, 2014, pp. 199-218,
https://doi.org/10.1016/B978-0-12-800007-6.00009-5.
[24] B. D. Vries, C. Huang, J. Armston, W. Huang,
J. W. Jones, M. W. Lang, Rapid and Robust Monitoring of Flood Events Using Sentinel-1 and Landsat Data on the Google Earth Engine, Remote Sens. Environ., Vol. 240, 2020, pp. 111664,
https://doi.org/10.1016/j.rse.2020.111664.
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[26] D. Geudtner, R. Torres, P. Snoeij, M. Davidson,
B. Rommen, Sentinel-1 System Capabilities and Applications, IEEE Geosci. Remote Sens. Symp., 2014, pp. 1457-1460,
https://doi.org/10.1109/IGARSS.2014.6946711.
[27] F. Aires, J. P. Venot, S. Massuel, N. Gratiot, P. D. Binh, C. Prigent, Surface Water Evolution (2001-2017) at the Cambodia/Vietnam Border in the Upper Mekong Delta Using Satellite MODIS Observations, Remote Sens., Vol. 12, No. 5, 2020, pp. 800, https://doi.org/10.3390/rs12050800.
[28] M. Chini, R. Hostache, L. Giustarini, P. Matgen, A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation As A Test Case, IEEE Trans. Geosci. Remote Sens., Vol. 55, No. 12, 2017, pp. 6975-6988,
https://doi.org/10.1109/tgrs.2017.2737664.
[29] T. Bangira, S. M. Alfieri, M. Menenti, A. V. Niekerk, Z. Vekerdy, A Spectral Unmixing Method with Ensemble Estimation of Endmembers: Application to Flood Mapping in the Caprivi Floodplain, Remote Sens., Vol. 9, No. 10, 2017, pp. 1013, https://doi.org/10.3390/rs9101013.
[30] H. T. Khuong, M. Menenti, L. Jia, Surface Water Mapping and Flood Monitoring in the Mekong Delta Using Sentinel-1 SAR Time Series and Otsu Threshold, Remote Sens., Vol. 14, No. 22, 2022, pp. 5721, https://doi.org/10.3390/rs14225721.
[31] N. Tsutsumida, A. J. Comber, Measures of Spatio-Temporal Accuracy for Time Series Land Cover Data, Int. J. Appl. Earth Obs. Geoinf., Vol. 41, 2015, pp. 46-55,
https://doi.org/10.1016/j.jag.2015.04.018.
[32] J. Cohen, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, Vol. 20, No. 1, 1960, pp. 37-46,
https://doi.org/10.1177/001316446002000104.
[33] D. G. Altman, Practical Statistics for Medical Research, Chapman and Hall/CRC Press, London, 1991, https://doi.org/10.1002/sim.4780101015.
[34] The People's Committee of Hoa Vang District, Report on Damages Caused by the Rain and Flood from 14/10/2022 in Hoa Vang District, 2022
(in Vietnamese).
[35] The People's Committee of Da Nang City, Project: Building Da Nang City Safe from Natural Disasters for the Period 2022 - 2030, Vision to 2045, 2023 (in Vietnamese).
[36] T. K. Haraldsen, Flood Damage on Agricultural Land and Methods for Restoration of Agricultural Soils after Catastrophic Floods in Cold Areas, Flood Risk in a Climate Change Context, IntechOpen, Rijeka, 2023,
http://dx.doi.org/10.5772/intechopen.109111.
[37] N. B. Ngoc, N. H. Ngu, T. T. Duc, T. T. Phuong, P. G. Tung, N. M. Tri, Assessing Damages of Agricultural Land Due to Flooding in A Lagoon Region Based on Remote Sensing and GIS: Case Study of the Quang Dien District, Thua Thien Hue Province, Central Vietnam, Journal of Vietnamese Environment, Vol. 12, No. 2, 2020, pp. 100-107,
https://doi.org/10.13141/jve.vol12.no2.pp100-107.
[38] T. T. Nga, V. H. Cong, L. Hung, Assessing The Effects of Urbanization on Flood in Vu Gia - Thu Bon River Basin, Journal of Hydraulic Engineering and Environmental Sciences, Vol. 76, 2021,
pp. 47-55 (in Vietnamese).
[39] The People's Committee of Hoa Vang District, Report on Summarizing Agricultural Production in 2022 and Implementing the Production Plan
for the Winter-Spring Crop 2022-2023, 2022
(in Vietnamese).