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At present, the methods and research documents about monitoring mining area ecological environment using remote sensing data are not much mentioned. Remote sensing has the characteristics of multi-temporal, fast dynamic monitoring, and low cost. Either the research method and theory or the results have certain ecological significance and practical value. Especially the information extraction using high-resolution images of SPOT 5 data draws more and more attention by the professionals. In the low-middle resolution images, the detail, texture, and border of mining area ecological environment elements can't be reflected, which brings difficulties for further identification and interpretation. SPOT 5 with visible and near-infrared bands at 10*10m, a shortwave infrared band at 20*20m and a panchromatic band at 2.5*2.5m. Based on the latest SPOT 5 data, this paper extracts and analyzes the information of mining area ecological environment elements by several data fusion methods, such as IHS transform, principal component analysis (PCA) and wavelet fusion. Compared to the original SPOT 5 multispectral image, the tone contrast and definition of the fused images are improved; especially some features are more easily identified, such as vegetation, subsidence area, waste dump, and power plant. Then the fused images are evaluated by four indicators: mean standard deviation, information entropy and average gradient. In conclusion, this paper uses SPOT 5 data and varieties of fusion methods to deal with the mining ecological environment elements, which achieved remarkable results and increased the information extraction precision. This not only provides basic data for evaluation and reclamation of the mining ecological environment but provides technical supports for decision-makers.