The New Method for Detecting Change of the Landscape: The Differencing Image PCA Method and Its Application in the Liaohe River Delta
差分主成分分析法在辽河三角洲景观变化中的应用

This paper describes a method of the detection for the landscape change by satellite remotely sensed data. In order to improve the accuracy of detection for the landscape change, we presented the differencing image PCA method to improve the Principal Component Analysis (PCA) and the band difference of images method using the multi-temporal remotely sensed data of TM (Thematic Mapper). And the Liaohe River Delta of China was selected as a case to validate this method. The results showed that the differencing image PCA method has higher detection accuracy compared with the conventional method--post-classification change detection and the overall accuracy of the change detection reaches 0.89 and the Kappa coefficient is 0.82. The research result also showed that the landscape changed about 22% in the Liaohe River Delta area during 1984-2000. The main change is the reduction of the reed area, the increase of the paddy field and the city area.