The Application Research About Fusion Technology Of Multi-scale Data In Forestry Remote Sensing

Because of the hierarchy of geographical phenomena and complicated landcover types, the issue of spatial scale has been brought to the remote sensing research. As the basis of geographic information science theory, scale theory is also one of the basic properties of geographic data. Now the development of high spatial resolution sensors has brought the status of remote sensing information's sea quantify. Forestry remote sensing is the branch of remote sensing, and hierarchy is also its character. How to use these redundant data resources and increase the accuracy in forestry remote sensing, which has become one of the issues need to be resolved to forest scientists around the world.Remote sensing images reflect the combining and complicacy of environment in geoscience, in addition mathematical method is the powerful analysis tool which is the main method in the research. According to the different spatial resolution of remote sensing data, as the scale relevantly theory basis, using multi-source information fusion theory, mining the redundancy and complementarity of multi-scale remote sensing data, research the applications of multi-scale remote sensing data fusion technology in forest.Considering the advantages and constraints of multi-source information fusion, concerning the key issues in scale study, as the forest resource information research object, the research line consists of three rank, they are pixel, feture and decision. The paper's main contents as following:1 In data pre-processing step, different geometric correction models are used for different data types. By comparing results produced by different methods, selecting a reasonable method for radiometric correction of terrain, and this solves the high dissimilarity of information and reduce the ambiguity in contents.2 Through a series of methods to reduce the complexity of study, as selecting different bands combination, based on the corridor ideas of landscape ecology, the study area is partitioned to different regions.3 Through comparing the common resolution fusion methods by multiple evaluation indexes, the adaptive fusion method is obtained. This achieve multi-spectral data sources in six different spatial scales, the coverage of the spectral space is expanded.4 According to the evaluation criteria in object-oriented segmentation, by constructing average segmentation evaluation index, the optimal segmentation scale parameters and the best segmentation object sets are obtained, which not only enriches the segmentation evaluation method, but also change the traditional method for the pixels into object-oriented research.5 According scale key issues research, three main fields are selected in this study such as optimal scale selection, scale effects and scale dependence. Through improving the limitations to adapt to multi-spectral research of local variance and variograms, an objective basis to the choice of remote sensing images from space is provided. By fractal geometry theory and fractal dimension, the remote sensing scaling effect is researched. Based on the inheritance of objects with different scale levels, as the additive of gray linear a condition, a quantitative study about the dependency of different data sources is given, and the dependency relation results are obtained of different scales for different types in diverse landscape areas, while makes the topological relations research during different scales based on a scientific quantitative basis.6 Based on the research results of scale dependence, by constructing multi-scale objects constraints fusion model, without changing the basis of scale abstraction level, with the results of classification on basis of knowledge, the fusion of different scale datas come to true. By the results of experiment, the contribution of multi-scale fusion method to forestry remote sensing is validated, which is the innovation point of the research.