Applied Research, Based On Remote Sensing Technology, Urban Greening

For protecting and developing the resource environment effectually, we must make use of accurate and real time current datum and the key elements of the resource environment in different times first. At the same time, the remote sensing image is a real time recording of the characteristic of the surface feature electromagnetism spectrum. People can according to the characteristic recording on the image, come to deduce the surface feature electromagnetism spectrum character. Different surface feature has different characteristics and different behaviour on the image, so we can distinguish different surface feature according to their change and difference, and it is a effect method to monitor resource by making use of remote sensing technique. It is also one of effect methods to drawing and detecting urban afforestation information by making use of remote sensing technique.With the development of computer, people take attention to the remote sensing technique application in computer treatment. That the computer identification classifies is the important component of remote sense application. Supervised Classification and Unsupervised Classification both are the comparatively mature computer classification methods and they both are statistical methods. For improving the classification accuracy, there are some new methods of classification technologies such as Artificial Neural Network, Fuzzy Classification and Expert System classification method.Having considered the characteristic of the remote sense afforests to the city, there is the change of the Karhunen—Loeve analyses and vegetation indices to the remote sensing, and we carry out correlation analyses of wave bands, calculating the OIF exponent and the best constitutes of bands to the image by the modern remote sensing technique. And then supervised classification and unsupervised classification are carried out of the ultimate combining image being composed of three best wave bands, and two results are compared, and the latter method is more suitable to the model. It is an accurately convenient and real time model to the urban afforestation model that satisfies the need of modern urban afforestation application.