Remote Sensing Monitoring Of Agricultural Disasters In Heilongjiang Province Based On Anomaly Vegetation Index

Heilongjiang Province is not only a major agricultural province in China,but also a major commodity grain base in China.Since the beginning of the 21 st century,with the gradual deterioration of climatic conditions and the intensification of changes in the ecological environment,agricultural disasters such as forest fires,floods,low-temperature freezes,pests and diseases have frequently appeared,and the area affected by the disaster has been severe,resulting in a serious degree of damage.The huge economic losses in the area have seriously threatened the safety of agricultural grain production and slowed down the development of the agricultural economy.Therefore,it is the focus of current research to propose and find effective agricultural disaster monitoring methods.Only timely access to accurate agricultural disaster information can promote accurate forecasting and early warning of disasters,and analyze the spatial and temporal distribution characteristics of agricultural disasters and the causes of disasters.It will effectively curb the spread of agricultural disasters,ensure the safety of food production,promote the sustainable development of agriculture,and minimize economic losses as much as possible.In the past,traditional agricultural disaster monitoring methods mainly used a large amount of manpower and material resources to conduct on-the-spot investigation and sampling.The process of obtaining data is not only cumbersome but also consumes too much resources,and the obtained data is not universally representative.Failure to provide timely feedback on disaster information cannot effectively assess the extent of damage,and will seriously slow down the work process of disaster monitoring and post-disaster relief work,and will not be able to conduct long-term and large-scale dynamic monitoring of agricultural disasters.The emergence of remote sensing technology not only avoids the drawbacks of traditional agricultural disaster monitoring,but also uses its own real-time access to disaster information,low cost,wide monitoring range,high flexibility and high monitoring accuracy,and has revolutionized the monitoring methods of agricultural disasters.It has become an important part of the agricultural disaster monitoring business.Based on real-time and accurate monitoring of large-scale agricultural disasters,this paper combines the continuous multi-phase synthesis of 250 m spatial resolution MODIS reflectivity products and sunlight agriculture with each other in the critical period of crop growth from 2000 to2017(late June to late August).The data provided by the insurance company is the source for the dynamic monitoring of agricultural disasters in the insured plots in Heilongjiang Province.Accurately extract the NDVI median vegetation index of the same phase for many years as the background value,establish an effective agricultural disaster model(RNDVI_MED(i)),and extract the plots in which three consecutive phases are affected,that is,a certain phase of the year.Disaster area.Using the disaster information reported by Sunshine Agriculture Mutual Insurance Co.,Ltd.and the HJ-1A/1B of the same phase and the disaster area extracted from this paper to verify the accuracy,combined with meteorological factors and geographical environment for the time and space of agricultural disasters in Heilongjiang Province Analysis of distribution characteristics,and exploration of the causes of disasters.The main research contents and results are analyzed as follows:(1)Accurately obtain time series NDVI data sets.The vegetation index of each phase was extracted by MRT and ENVI software,and the NDVI median vegetation index of a certain time phase was extracted by ARGIS software to reflect the vegetation index of the normal growth of crops in a certain time of the year.Establish(2)Establish an agricultural disaster monitoring model.The monitoring model is RNDVI_MED(i)=NDVI(i)-NDVIMED(i).The NDVI median vegetation index extracted from a certain period of time is taken as the standard value.The difference between the NDVI and the standard value of the same time phase in the current year is extracted,and the region where the NDVI values of the three consecutive phases are negative is the Heilongjiang Province.Inflicted in the affected area.(3)Accuracy verification of disaster results.Based on the disaster information data provided by the insurance company,the accuracy of the 2017 disaster results extracted by the RNDVI_MED(i)model was verified.The verification results showed that the overall accuracy Pc was 0.93 and the Kappa coefficient PA% was 85%.The results showed that the monitoring accuracy of the model was proved.Higher and more practical.Can be applied to the monitoring of agricultural disasters in Heilongjiang Province.(4)Temporal and spatial distribution characteristics of Heilongjiang Province.According to the disaster results of the 2017 crop growth key period extracted from this model,combined with historical meteorological data such as temperature and rainfall,the spatial and temporal characteristics of disasters in Heilongjiang Province were analyzed and the genetic mechanism was studied.The study found that the northwestern and southeastern parts of Heilongjiang Province are relatively prone to drought disasters,the central region is low in terrain,and there are many water resources,which tend to accumulate water and cause floods.Agricultural disasters in Heilongjiang Province are high in the period from early July to mid-August.The results show that the 8 D synthetic 250 MHz resolution MODIS reflectivity product is based on the critical period of the crop growth period from 2000 to 2017,and the results of the RNDVI_MED(i)extraction model for a long-term agricultural disaster monitoring model arehighly accurate.It has universal applicability.It can be applied to the dynamic monitoring of agricultural remote sensing disasters,providing an effective means for forecasting and warning of agricultural disasters in Heilongjiang Province.