| At the beginning of the 20th century,the global annual output of maize exceeded that of wheat and rice,and maize became the world's largest food crop.Maize production maintained an average annual growth rate of 2.7%.Maize is a traditional Chinese field crop,and its annual output ranks first among China's three largest crops.With the global climate change,the frequency of flood disaster has become higher and higher,and the scope of spread has become wider and wider.The harm to the stable production and the quality of agricultural products has become more and more significant.Maize needs large amounts of water but it cannot tolerant much water.Flood disaster have gradually become one of the main limiting factors for the high and stable production of maize.With the rapid development of remote sensing science and technology,remote sensing acquires spectral information or image information of a target object in a non-contact manner,remote sensing technology can observe and measure the target object repeatedly.Remote sensing has gradually become an important measure for monitoring agricultural disasters in China,remote sensing has broad application prospects.This paper attempts to use Hyperspectral and LiDAR to analyze the surface information of waterlogged maize,and evaluate the disaster situation of flood.Hyperspectral remote sensing technology has outstanding performance in quantitative remote sensing monitoring of agriculture,due to its high resolution,strong continuity,and large amount of information.LiDAR as an active remote sensing technology,the advantages include the following:high resolution of angles,distances,and velocities,strong anti-jamming capability,and direct access to three-dimensional spatial information of objects.LiDAR is equipped on the UAV platform to quickly and effectively extract vegetation canopy height information.Therefore,it has great theoretical and practical significance to monitor the growth of maize in flood,physiological and biochemical parameters inversion,and yield loss estimation through the use of Hyperspectral remote sensing and LiDAR technology.This paper study a community control experiment at the Xiaotangshan Research Base of the Beijing Academy of Agriculture and Forestry Sciences in 2015-2016.By simulating the waterlogging field environment,to obtain remote sensing data,agronomic data,meteorological data,and other experimental data of summer maize with waterlogging.The changes of key growth parameters of maize after waterlogging were studied,and explained with reasonable agronomic mechanisms.Analyze the change of spectral spectrum of maize in different periods.Leaf Area Index retrieval model of waterlogged maize is constructed.Based on airborne LiDAR data,canopy height inversion was studied in the study area.LiDAR change rule for different degree of flood(slight,moderate,severe)is analyzed using statistical methods.Canopy height and disaster degree model is constructed.Realizing remote sensing assessment of flood disaster of maize.The specific research content and conclusions are as follows:(1)By selecting typical physiological and biochemical parameters of maize,and analyzing of parameters for whole growth period data,it is discovered that there is a significant difference between the LAI between the jointing stage and the tasseling stage of the waterlogged maize.By comparing the canopy hyperspectral data of waterlogging and normal maize in different periods,it is found from the spectrum curve that there is a "Green Peak" and "Red Valley"phenomena in the visible spectrum.The "Green Peak" in the green band(about 550nm),is caused by the formation of low reflection characteristics of chlorophyll.The "Red Valley" in the red band(about 650nm),is caused by absorption of red light by chlorophyll.By studying the spectral changes of waterlogged maize with the two sets of experimental data from the tasselling and filling stages,it is observed that the reflectivity in the visible spectrum of the waterlogged maize is higher than that of the normal maize,and the visible light reflectance of the waterlogged maize is greater than that of the near infrared.There is a "blue shift" phenomenon in the spectral curve of the waterlogged maize.(2)LAI is a key parameter that can characterize the plant growth and the degree of stress.This paper constructs the LAI inversion model of waterlogged maize based on the spectral index.Grey correlation analysis and correlation analysis are conducted to determine the order of relevance degree of the self-variable parameters.Akaike Information Criterion is applied to determine the spectral index involved in the model.Partial Least Squares Regression is adopted in constructing the LAI estimation model of the waterlogged maize.Model verification is performed through modeling and verification of sample separation methods and Cross-validation.The accuracy of evaluation are R2 and RMSE.The results of the study show that the estimated models have reached a significant correlation at the level of 0.01,of which the accuracy of the model R2 is 0.85,and the RMSE is verified as 0.313.(3)The internal structure of maize will change a lot after waterloges,of which the height is the most obvious and significant one.Based on LiDAR data,the height of maize plant canopy in the study area is obtained.Then,by implementing mathematical statistics methods,the canopy height range of the plants are classified into severe,moderate,and slight.Thirdly,the remote sensing monitoring model of disaster is constructed from the height thresholds.Finally,the accuracy evaluation was performed by qualitative and quantitative methods.The results show that the overall accuracy is 72.15%,and the Kappa coefficient is 0.44,The remote sensing distribution map of disaster basically matches with the actual waterlogging situation in the area being studied. |
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