Canopy characteristic scale is a basic concept of quantitative remote sensing of vegetation

but also a very important feature

expression is of great significance for his physical definition and quantitative. The canopy characteristic scale physical c onnotation understanding and modeling mathematical expression

is the basic of research object linear and non-linear mixed

and it is the premise of optimal scale of observation objects. From the ray radiation transport point of view

there is a characteristic scale nonlinear mixed into the linear mixed transition

incident radiation between the transition characteristic scales of the object is independent of the optical properties

which can better describe the canopy group

choose the appropriate scale can get twice the result with half the effort to make remote sensing data.Base on the physical definition of optical radiation transmission proposed canopy characteristic scale

we established a mathematical calculating model for the canopy characteristic scale

and introducted the inverted geostatistics index model

put forward the calculation method of canopy characteristic scale based on analysis of local variance. Using the forest area of high resolution i mage

the canopy characteristic scale model proposed in this paper provide a quantitative validation. We chose 19 areas of higher density and vigorous growth forest as a study area

all the study areas with the same planting and regular distributed

and approximately at the same stage of the operation. Data come from the Google Earth aerial photography data

with a resolution of 0. 3 m

the data from two regions in Macon and Locke.The canopy characteristic scale model for analysis of data from the two regions

There is a certain correlation between the canopy characteristic scale model calculated and forest spacing measured

the linear correlation coefficient of 0. 95

that showed spacing and vegetation canopy characteristic scale of the two regions is the presence of certain stable relationship

canopy characteristic scale calculation of canopy characteristic scale model was about 1. 25 times the spacing.Canopy features scale models thesis

which can be in remote sensing images of forest vegetation growing season to choose their appropriate characteristic scale

canopy characteristic scale model is reasonable and feasible. Firstly

mixed pixel above the canopy characteristic scale belong to linear mixed

for its various transformations

you can use linear mixed pixel method to solve various scale difference or scale effect problems

Secondly

canopy characteristic scale is a basic object scale

when vegetation p arameter population sampling（ such as leaf area index）

canopy spectrum measurement

the sampling plots close to or greater than the canopy characteristic scale

can reflect the vegetation canopy component information; When the resolution is close to or below the canopy characteristic scale

ground surrounding pixel block or cross radiation effects to a minimum

arrive downward radiation level surface solar radiation spectrum and the corresponding pixel under the surface of the ground

ground reflectance can be calculated accurately.