Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.

Monitoring of Pinus massoniana spatial pattern changes based on RS and GIS techniques

Our research focused on Pinus massoniana information extracted from remote sensing images based on the knowledge detection and decision tree algorithm and established a spatial pattern model, combining quantitative theoretical ecology with remote sensing （RS） and geometric information system （GIS） techniques. Applying information extraction methods and a spatial pattern model, we studied P. massoniana spatial patterns changes before and after the invasion by pine wood nematode （Bursaphelenchus xylophilus） in Fuyang and Zhoushan counties, Zhejiang Province, east China. The P. massoniana spatial patterns are clustering, whether the invasion happened or not. But the degree of clustering is different. Our results show good agreement with field data. Applying the results, we analyzed the relationship between spatial patterns and the invasion level. Then we drew the elementary conclusion that there are two kinds of patterns for pine wood nematode to spread： continuous and discontinuous diffusion. This approach can help monitor and evaluate the changes in ecological systems.