Studying spatio-temporal patterns of land-use change in arid environment of China

Remotely sensed data have been the most important data source for environment change study in the past 30 years. Large collections of remote sensing imagery have provided a solid foundation for spatio-temporal analyses of the environment and the impact of human activities. This study seeks an efficient and practical methodology for integrating multi-temporal and multiscale remotely sensed data from various sources with a monitoring time frame of 30 years, including historical and state-of-the-art high-resolution satellite imagery. Based on this, spatio-temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given time frame. Multi-scale and multi-temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land use in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto-classification approach an overall accuracy of 85%–90% with a Kappa coefficient 0.66–0.78 was achieved for the classification of individual images. The temporal trajectory of land-use change was established and its spatial pattern was analysed to obtain a better understanding of the human impact on the fragile ecosystem of China’s arid environment.