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

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.

Characteristics and risk assessment of agricultural meteorological disasters based on 30 years’disaster data from Heilongjiang Province of China

To determine the influence of agricultural meteorological disasters on agriculture in Heilongjiang Province,the disaster areas associated with different types of disasters and their variation characteristics were analyzed based on the statistical data of agricultural disasters from 1983 to 2013 in Heilongjiang Province,China.The moving average and the Mann-Kendall test were applied to identify the variation trends of drought,flooding,hailstorms and freezing(based on the disaster ratio and the disaster intensity index).Then,the Morlet wavelet analysis method was used to identify the periodicity of these four kinds of agricultural meteorological disasters.Finally,a fuzzy comprehensive evaluation method was adopted to analyze the degrees of agricultural loss induced by these disasters.The following results were obtained:1)The disaster ratio and disaster intensity index for drought exhibited increasing trends;the disaster ratio and disaster intensity index for flooding exhibited decreasing trends;for hailstorms,the disaster ratio exhibited no obvious trend of change,whereas the disaster intensity index exhibited an increasing trend;and for freezing,the disaster ratio also exhibited no obvious trend of change,whereas the disaster intensity index exhibited a decreasing trend.2)Mutation points were observed in the disaster ratio series for drought,flooding and hailstorms,whereas no mutation point was evident in the disaster ratio series for freezing.3)Multiple time-scale characteristics were observed in the disaster ratio series for all four types of agricultural meteorological disasters.Furthermore,the disaster ratio series for the different types of disasters had different main periodicities.4)From the perspective of the degree of agricultural loss induced by each type of disaster,drought was identified as the most severe type of agricultural meteorological disaster,followed by flooding,freezing,and hailstorms.The degree of agricultural loss caused by each type of disaster was different during different periods.Finally,based on the results,several strategies were identified for mitigating the effect of agricultural meteorological disasters in Heilongjiang Province.

Global Rainstorm Disaster Risk Monitoring Based on Satellite Remote Sensing

Methods of rainstorm disaster risk monitoring(RDRM)based on retrieved satellite rainfall data are studied.Due to significant regional differences,the global rainstorm disasters are not only affected by geography(such as topography and surface properties),but also by climate events.It is necessary to study rainstorm disaster-causing factors,hazard-formative environments,and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide.According to a global flood disaster dataset for the last 20 years,the top four flood disaster causes(accounting for 96.8%in total)related to rainstorms,from most to least influential,are heavy rain(accounting for 61.6%),brief torrential rain(16.7%),monsoonal rain(9.4%),and tropical cyclone/storm rain(9.1%).A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years.Taking the 7-day accumulated rainfall,3-and 12-h maximum rainfall,24-h rainfall,rainstorm threshold,and others as the main parameters,a rainstorm intensity index is constructed.Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data,population data,and economic data.Finally,a satellite remote sensing RDRM model is developed,incorporating the above three factors(rainstorm intensity index,hazard-formative environment factor,and hazard-affected body factor).The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.