Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)

Abstract

Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An R² value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.

Keywords

Crop damage / Disaster vegetation damage index (DVDI) / Flood inundation / Rapid assessment / Remote sensing

References

[1] Adnan, M.S.G., A.Y.M. Abdullah, A. Dewan, and J.W. Hall. 2020. The effects of changing land use and flood hazard on poverty in coastal Bangladesh. Land Use Policy 99: Article 104868.

[2] Aerts JCJH, Botzen WJW. Climate change impacts on pricing long-term flood insurance: A comprehensive study for the Netherlands. Global Environmental Change, 2011, 21(3): 1045-1060

[3] Ahmed, M.R., K.R. Rahaman, A. Kok, and Q.K. Hassan. 2017. Remote sensing-based quantification of the impact of flash flooding on the rice production: A case study over northeastern Bangladesh. Sensors 17: Article 2347.

[4] Aurbacher J, Dabbert S. Generating crop sequences in land-use models using maximum entropy and Markov chains. Agricultural Systems, 2011, 104(6): 470-479

[5] Bouwer LM. Have disaster losses increased due to anthropogenic climate change?. Bulletin of the American Meteorological Society, 2011, 92(1): 39-46

[6] Brémond, P., and A. Agenais. 2013. Flood damage assessment on agricultural areas: Review and analysis of existing methods. https://core.ac.uk/download/pdf/52629325.pdf. Accessed 20 May 2019.

[7] Capellades, M.A., S. Reigber, and M. Kunze. 2009. Storm damage assessment support service in the U.S. corn belt using RapidEye satellite imagery. In Remote sensing for agriculture, ecosystems, and hydrology XI, Proceedings of a meeting held 1–3 September 2009, Berlin, Germany, SPIE Proceedings 7472, ed. C.M.U. Neale, and A. Maltese, 747208.1–747208.14. Bellingham, WA: SPIE (International Society for Optics and Photonics).

[8] Chejarla VR, Mandla VR, Palanisamy G, Choudhary M. Estimation of damage to agriculture biomass due to Hudhud cyclone and carbon stock assessment in cyclone affected areas using Landsat-8. Geocarto International, 2016, 32(6): 1-14

[9] Chowdhury EH, Hassan QK. Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh. Natural Hazards, 2017, 88(3): 1805-1823

[10] Citeau, J.M. 2003. A new control concept in the Oise catchment area: Definition and assessment of flood compatible agricultural activities. In Proceedings of FIG Working Week Conference, 13–17 April 2003, Paris, France. Session TS 14 (14:00–15:30)—New Professional Tasks: Environmental Issues and Statutory Valuation. https://www.fig.net/resources/proceedings/fig_proceedings/fig_2003/TS_14/TS14_5_Citeau.pdf. Accessed 7 Sept 2020.

[11] Clement MA, Kilsby CG, Moore P. Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 2018, 11(2): 152-168

[12] Cressman DR, Fortin MHP, Hensel MJ, Brubacher PH, McBride RA. Estimation of cropland damages caused by overland flooding, two case studies. Canadian Water Resources Journal, 1988, 13(3): 15-25

[13] Del Ninno C, Dorosh PA, Smith LC. Public policy, markets and household coping strategies in Bangladesh: Avoiding a food security crisis following the 1998 floods. World Development, 2003, 31(7): 1221-1238

[14] Di L, Yu EG, Kang L, Shrestha R, Bai Y. RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making. Journal of Integrative Agriculture, 2017, 16(2): 408-423

[15] Di, S., L. Guo, and L. Lin. 2018. Rapid estimation of flood crop loss by using DVDI. 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics), 6–9 August 2018, Hangzhou, China. https://doi.org/10.1109/agro-geoinformatics.2018.8476083.

[16] Di, L., E. Yu, R. Shrestha, and L. Lin. 2018. DVDI: A new remotely sensed index for measuring vegetation damage caused by natural disasters. In Proceedings of IGARSS 2018—The 2018 IEEE International Geoscience and Remote Sensing Symposium, 22–27 July 2018, Valencia, Spain, 9067–9069. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE) https://ieeexplore.ieee.org/document/8518022. Accessed 11 Feb 2019.

[17] Dutta D, Herath S, Musiake K. A mathematical model for flood loss estimation. Journal of Hydrology, 2003, 277(1–2): 24-49

[18] FAO (Food and Agriculture Organization) The impact of disasters on agriculture and food security, 2015, Rome: Food and Agriculture Organization of the United Nations

[19] FEMA (Federal Emergency Management Agency). 2016. Texas severe storms and flooding (FEMA DR-4272). https://www.fema.gov/sites/default/files/2020-09/PDAReportFEMA4272DRTX.pdf. Accessed 10 Nov 2018.

[20] FEMA (Federal Emergency Management Agency). 2018a. Preliminary damage assessment report, Iowa severe storms, tornadoes, straight-line winds, and flooding (FEMA DR-4386). https://www.fema.gov/sites/default/files/2020-03/FEMA4386DRIA.pdf. Accessed 24 May 2019.

[21] FEMA (Federal Emergency Management Agency). 2018b. Preliminary damage assessment report, Nebraska severe storms, tornadoes, straight-line winds, and flooding (FEMA DR-4387). https://www.fema.gov/sites/default/files/2020-03/FEMA4387DRNE.pdf. Accessed 24 May 2019.

[22] Field CB, Barros V, Stocker TF, Qin D. Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change, 2012, Cambridge: Cambridge University Press

[23] Förster S, Kuhlmann B, Lindenschmidt KE, Bronstert A. Assessing flood risk for a rural detention area. Natural Hazards and Earth System Science, 2008, 8: 311-322

[24] Gerl, T., H. Kreibich, G. Franco, D. Marechal, and K. Schröter. 2016. A review of flood loss models as basis for harmonization and benchmarking. PloS One 11(7): Article e0159791.

[25] Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S. Global flood risk under climate change. Nature Climate Change, 2013, 3(9): 816-821

[26] Islam MM, Sado K. Flood hazard assessment in Bangladesh using NOAA AVHRR data with geographical information system. Hydrological Processes, 2000, 14(3): 605-620

[27] Kogan F, Salazar L, Roytman L. Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. International Journal of Remote Sensing, 2012, 33(9): 2798-2814

[28] Lin, L., L. Di, E.G. Yu, L. Kang, R. Shrestha, M.S. Rahman, J. Tang, M. Deng,. et al. 2016. A review of remote sensing in flood assessment. In Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics, 18–20 July 2016, Tianjin, China.

[29] Liu W, Huang J, Wei C, Wang X, Mansaray LR, Han J, Zhang D, Chen Y. Mapping water-logging damage on winter wheat at parcel level using high spatial resolution satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142: 243-256

[30] Lu, L., C. Wu, and L. Di. 2020. Exploring the spatial characteristics of typhoon-induced vegetation damages in the southeast coastal area of China from 2000 to 2018. Remote Sensing 12(10). https://doi.org/10.3390/rs12101692.

[31] Merz B, Kreibich H, Schwarze R, Thieken A. Assessment of economic flood damage. Natural Hazards and Earth System Sciences, 2010, 10(8): 1697-1724

[32] Mosleh MK, Hassan QK, Chowdhury EH. Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors, 2015, 15(1): 769-791

[33] Okamoto K, Yamakawa S, Kawashima H. Estimation of flood damage to rice production in North Korea in 1995. International Journal of Remote Sensing, 1998, 19(2): 365-371

[34] Opolot E. Application of remote sensing and geographical information systems in flood management: A review. Research Journal of Applied Sciences Engineering and Technology, 2013, 6(10): 1884-1894

[35] Osman J, Inglada J, Dejoux JF. Assessment of a Markov logic model of crop rotations for early crop mapping. Computers and Electronics in Agriculture, 2015, 113: 234-243

[36] Ostu N. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66

[37] Rahman MS, Di L. The state of the art of spaceborne remote sensing in flood management. Natural Hazards, 2017, 85(2): 1223-1248

[38] Rahman, M.S., and L. Di. 2020. A systematic review on case studies of remote-sensing-based flood crop loss assessment. Agriculture 10(4): Article 131.

[39] Rahman, M.S., L. Di, R. Shrestha, E.G. Yu, L. Lin, L. Kang, and M. Deng. 2016. Comparison of selected noise reduction techniques for MODIS daily NDVI: An empirical analysis on corn and soybean. In Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics, 18–20 July 2016, Tianjin, China. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).

[40] Rahman, M.S., L. Di, E.G. Yu, C. Zhang, and H. Mohiuddin. 2019. In-season major crop-type identification for US cropland from landsat images using crop-rotation pattern and progressive data classification. Agriculture 9(1): Article 17.

[41] Schumann GJP, Moller DK. Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts A/B/C, 2015, 83–84: 84-95

[42] Shrestha, R., L. Di, E. G. Yu, L. Kang, L. Li, M. S. Rahman, M. Deng, and Z. Yang. 2016. Regression based corn yield assessment using MODIS based daily NDVI in Iowa state. In Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics, 18–20 July 2016, Tianjin, China. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).

[43] Shrestha, R. 2017. A remote sensing-derived corn yield assessment model. Ph.D. thesis. George Mason University, Fairfax, Virginia, USA.

[44] Shrestha R, Di L, Yu EG, Kang L, Shao YZ, Bai Y. Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture, 2017, 16(2): 398-407

[45] Shrestha, R., L. Di, E.G. Yu, Y. Shao, L. Kang, and B. Zhang. 2013. Detection of flood and its impact on crops using NDVI – Corn case. In Proceedings of the 2013 Second International Conference on Agro-Geoinformatics, 12–16 August 2013, Fairfax, VA, USA. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).

[46] Tapia-Silva, F.M., S. Itzerott, S. Foerster, B. Kuhlmann, and H. Kreibich. 2011. Estimation of flood losses to agricultural crops using remote sensing. Physics and Chemistry of the Earth, Parts A/B/C 36(7–8): 253–265.

[47] USDA-ERS (United States Department of Agriculture–Economic Research Service). 2016. Crop production is concentrated in California and the Midwest. Washington, DC: United States Department of Agriculture – Economic Research Services. https://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=58320. Accessed 6 Aug 2020.

[48] Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 2006, 27(14): 3025-3033

[49] Yu, E.G., L. Di, B. Zhang, Y. Shao, R. Shrestha, and L. Kang. 2013. Remote-sensing-based flood damage estimation using crop condition profiles. In Proceedings of 2013 Second International Conference on Agro-Geoinformatics, 12–16 August 2013, Fairfax, VA, USA. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).

[50] Zhu, Q., X. Chen, H. Yang, and Z. Huang. 2007. A mathematical model for flood loss estimation based on spatial grid. In Geoinformatics 2007: Geospatial Information Technology and Applications. Proceedings of a meeting held 25–27 May 2007, Nanjing, China; Proceedings of SPIE Volume 6754, ed. P. Gong, 67541R-1–67541R-8. Bellingham, WA: SPIE (International Society for Optics and Photonics).