Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio-temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi-temporal and multi-scale remotely sensed data have been integrated 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 timeframe. Multi-scale and multi-temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover 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 of 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 gain a better understanding of the human impact on the fragile ecosystem of China's arid environment.
Scopus Subject Areas
- Earth and Planetary Sciences(all)