Remote Sensing Image Forestry Information Mining And Case Analysis

With the rapid development of forestry, diversification and complication of forestry information,traditional forest information obtaining method can not meet the need of modern forest production and forest science. In recent years, as the level of remote satellite image resolution have highly raised, the use of remote sensing in the forest has been widely applied. Although the forestry information mining method based on remote sensing image has been highly developed, for the high resolution remote sensing image, the application of sole and traditional pixel-based classification method will not only cause the reduce of precision,but also cause numerous redundancy of spatial data and the waste of resource.This paper chooses remote sensing images of Zhuzhou city, Hunan Province acquired by QuickBird(0.61m) as the data resource, picks a 500ha area in urban and a 160ha area in suburb as the research objects. Based on the summary of mining method based on remote sensing image of home and abroad, and research on theory and method of forestry information,the paper integrate forestry basic theories, use the technology of remote sensing and data mining to gain forest information of three aspects, that is using the integration method of vegetation index and texture feature to extract area information of forest, by the method of extracting the maximum reflectivity of crown point to acquire the stand density, and apply the multi-features analysis method of scale segment which is based on object-oriented to gain the greenland information of urban road.(1) According to the characteristics that every ground object has its own vegetation index,judging the NDVI demarcation point between forestry land(include terrace) and bare land, buildings, waters, then using the software ENVI classify forestry and un-forestry land.(2)As the remote sensing image capture time was fall, the NDVI values of terraced fields and forest were very close. Therefore, by the method of texture feature, choose the region of interest,then using GLCM method and C4.5 decision tree to make classified rules between forestry and terraced land. Finally using ERDAS the function of classification of knowledge automatically extracted the area of every land.(3) Size of crown is the key to the stand density, spectral reflectance characteristics can effectively estimate the size. Therefore, by using the method of size of crown represent the stand density in this paper to extract the stand density. Using the method of part maximum filter to make filtering processing for RS panchromatic wave band,then stack with panchromatic wave band.According to calculate differences obtain the crown number of maximal reflectivity points of crown,adopting the method of crown density represent stand density to extract stand density.(4) As the complexity of urban greenland a large number of scattered Greenland exist, conventional extraction methods are difficult to distinguish from other types of green space. According to the unique characteristics of shape and spatial location,obvious aspect ratio of road green space,with a method of multi-feature which based on object oriented scale segmentation classifying road green land to three types,set heterogeneity index threshold and spectral weight,choose different segment scale to classify the road Greenland.At present, forestry is moving toward information technology, "digital forest" is coming to us, however, the most important data source Remote Sensing Image of "digital forest" with a resolution raised to the "digital forest" for the smooth implementation of the powerful information assurance.Massive remote sensing image forestry data make data mining technology widely applied, but the remote sensing image information mining forestry is in its infancy, there are a number of imperfections, such as it's hard to measure the height and the diameter of trees, recognize tree spcies and the age of forest stand.So it can not fully replace conventional identification,only combine to conventional to make the existing forest survey technology dramatically changes.