Vegetation Characteristic Scale And Scale Optimization

Scale is a measure of the size of space dimension and time dimension of things or things phenomenon. Geospace surface complex, on a scale has been observed in nature, summed up the law and the establishment of the model, may be effective in another dimension, may be similar, or need to be amended, different regions of different observation scales required, with a fixed observation scale to measure the entire complex surface clearly has some limitations; Secondly, application of different scales and observation purposes, the image is also different, therefore, select optimal measurement scale is necessary. Different objects has its own optimum observation scale, not the more subtle the better, only under optimal measurement scales, only in the optimal observation scale, the most comprehensive data analysis can be done.Firstly, the paper analyzed the importance of the selection of scale in remote sensing image, and introduces several common observation scale optimal selection method, taking the vegetation canopy surface reflectance and vegetation remote sensing inversion parameter of leaf area index as the research object, using high resolution Google Earth data,1: 100,000 national land use data and Landsat TM data, analysis of the various types of landscape and vegetation canopy optimal space observation scales in different regions.The main work and conclusions are:(1) Based on the physical characteristics of the definition of canopy scale, based on the local variance and inversion exponential fitting model, build calculation model of canopy characteristic scale, and the use of high-resolution images for model validation, study area is located in the northern United States and southern Locke Macon,proposed canopy characteristic scale model quantitative validation, found Canopy characteristic scale model values and the measured spacing of the woods there is a close connection, Linear multiple correlation coefficient of 0.95.The results show, canopy characteristic scale model is rationality and universality, Canopy characteristic scale model is proposed for the flourishing vegetation canopy characteristic scale quantitative calculation provides a new method.(2) Based on the physical characteristics of the definition of canopy scale, introducing Semivariance change process parameters, calculating a different image of the range, and the use of high-resolution images for model validation, study area is located in the northern United States and southern Locke Macon, found range values and the measured spacing of the woods there is a close connection, Linear multiple correlation coefficient of 0.91. The research results show that, the variable range of semivariance function proposed value of high density vegetation canopy observation scale selection is feasible.(3) There is a very large difference in land cover and spatial variability of different regions, use fixed spatial resolution remote sensing observations of complex surface has some limitations, therefore, from the angle of quantitative remote sensing to study the optimal spatial Observation scale is important. Firstly, The paper selected 2000 National 1:10 million land use data and Landsat TM data, LAI scale effects and landscape index changes are calculated at different spatial resolution for 5 King of the study area, analyzed the relationship between landscape indices and LAI scale effects. Secondly, according to the statistical model aggregation index and LAI scale effects, calculate the optimal spatial observation scales under different conditions of China regional under the same LAI scale effects, the results show that there is a huge difference between the different regions of the space required for observation scale, thesis for Optimal Space Observation Scale of the intelligent earth observation provides a new approach, And to establish the optimal spatial scales of observation prior knowledge of Chinese Regional.