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

Md. Shahinoor Rahman; Liping Di; Eugene Yu; Li Lin; Zhiqi Yu

doi:10.1007/s13753-020-00305-7

Volume 12 Issue 1

Dec. 2021

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Article Navigation > International Journal of Disaster Risk Science > 2021 > 12(1): 90-110

Md. Shahinoor Rahman, Liping Di, Eugene Yu, Li Lin, Zhiqi Yu. Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)[J]. International Journal of Disaster Risk Science, 2021, 12(1): 90-110. doi: 10.1007/s13753-020-00305-7

Citation:

Md. Shahinoor Rahman, Liping Di, Eugene Yu, Li Lin, Zhiqi Yu. Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)[J]. International Journal of Disaster Risk Science, 2021, 12(1): 90-110. doi: 10.1007/s13753-020-00305-7

Citation:

Md. Shahinoor Rahman, Liping Di, Eugene Yu, Li Lin, Zhiqi Yu. Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)[J]. International Journal of Disaster Risk Science, 2021, 12(1): 90-110. doi: 10.1007/s13753-020-00305-7

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

doi: 10.1007/s13753-020-00305-7

Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA

Funds:

This research was funded by grants from NASA Applied Science Program (Grant # NNX14AP91G, PI: Prof. Liping Di) and NSF INFEWS program (Grant # CNS-1739705, PI: Prof. Liping Di). Authors would like to thank seedling companies including Pioneer Hi-Bred International, Inc., Beck’s, Syngenta, Bayer Global, and Golden Harvest Seeds for their portals for crop yield information. We are grateful to Hossain Mohiuddin, Zhi Chen, and Reuben Grandon of the University of Iowa for their assistance during field data collection in Iowa. We would like to express our special thanks to United States Department of Agriculture (USDA) for the Cropland Data Layer (CDL).

Available Online: 2021-12-25
Publish Date: 2021-12-25

Abstract

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.
- Crop damage,
- Disaster vegetation damage index (DVDI),
- Flood inundation,
- Rapid assessment,
- Remote sensing

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References(51)

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