Affiliation:
1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
3. School of Natural and Applied Sciences, Sol Plaatje University, Kimberley 8301, South Africa
4. Qilu Aerospace Information Research Institute, Jinan 250100, China
Abstract
In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference37 articles.
1. Chen, Z., Kong, F., and Zhang, M. (2022). A Case Study of the “7–20” Extreme Rainfall and Flooding Event in Zhengzhou, Henan Province, China from the Perspective of Fragmentation. Water, 14.
2. Ministry of Emergency Management of the People’s Republic of China (2022, January 23). Top 10 National Natural Disasters in 2021, Available online: https://www.mem.gov.cn/xw/yjglbgzdt/202201/t20220123_407199.shtml.
3. A digitized global flood inventory (1998–2008): Compilation and preliminary results;Adhikari;Nat. Hazards,2010
4. Future flood losses in major coastal cities;Hallegatte;Nat. Clim. Change,2013
5. How long did crops survive from floods caused by Cyclone Idai in Mozambique detected with multi-satellite data;Bofana;Remote Sens. Environ.,2022