| The world today is facing the severe challenges of climate change,and related research has also received much attention.Vegetation has a vital impact on global climate and regional climate change.Under this condition,the mechanism of interaction between climate and surface vegetation has been valued by experts in the fields of ecology,climate and geophysics.Among them,the leaf area index(LAI),which plays a key role in the response model of vegetation canopy to climate change,is particularly important for its objective,reliable and rapid evaluation.As one of the main food crops in China,it is of great practical significance to monitor its growth and development process and carry out estimation of corn yield.LAI can calculate the dry matter accumulation of crops and reasonably express the canopy structure of corn.Therefore,to accurately monitor the growth of crops and estimate their yield,the first accurate LAI value is obtained.Large-area corn canopy related information can be easily obtained through optical remote sensing image technology.However,it cannot give information on the vertical structure of the corn canopy,so that the canopy cannot be reasonably reflected in the inversion of the corn LAI index.The contribution of the internal blades results in a low LAI value for the inversion.Although ground-based lidar technology can obtain high-precision three-dimensional structural information of the corn canopy,it can only be obtained in a limited sample area at a time.Combining the advantages of these two technologies,the high-resolution canopy structure information is extracted by canopy analysis using the method of voxelization of lidar data;the large-area corn canopy reflectance is obtained by remote sensing image,and the canopy is obtained.The structural information was subjected to regression analysis to invert the accurate LAI results of the large area of the corn canopy.Through the collection and analysis of corn leaf area by laser radar technology and remote sensing image,we can draw the following conclusions:(1)Leaf area index(LAI),as an important measure of crop growth characteristics,is an important physiological parameter indicating plant canopyinformation structure and growth posture.Studying the leaf area index has an extremely important influence on monitoring the growth of corn and increasing corn yield per unit area.(2)As an important crop in China,corn plays an important role in our life and production.Through field visits,a well-developed corn field in the Midong District of Urumqi was selected as a test field,and its leaf area index was monitored during the better growing season.(3)Using Rieger(RIEGL)VZ-400 and VZ-1000 3D laser scanner scanning system as support,based on the theory of geography,using LiDAR360 point cloud processing,SPSS,Arcgis,ENVI and other software,through laser radar voxelization The method of canopy analysis is used to extract the area of corn leaves in the sample area.(4)In this work,the FieldSpec Pro F R2500 back-mounted field hyperspectral radiometer(manufactured by American ASD Company)was used for hyperspectral measurement of corn canopy.The spectroscopic measurement of the canopy was carried out in a windy,windless and sunny weather,with a time period of 10:00-14:00.The ratio vegetation index RVI,the normalized vegetation index NDVI,and the optimized ratio vegetation index MSR were calculated by reflectivity.(5)Through the laser radar voxelization method,the leaf area index extracted by canopy analysis method and the calculated vegetation index were analyzed by regression analysis,and the correlation between the normalized vegetation index(NDVI)and the laser point cloud calculation was the strongest.The correlation coefficient is R2=0.8086,the root mean square error(RMSE)is 0.1230,and the ratio is the most correlated by the index(RVI).The difference is R2=0.7079 and the RMSE is 0.1520.The average relative error of the three models is less than 10%,and the reliability of the model is higher. |
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