Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e.g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics, soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.