Regional dynamic monitoring is gaining rising interests in landuse/land cover study.In this article

a short-term land use/land cover change detection method was proposed

which takes periodic change of land cove into account and performs change detection between simulated image and actual image.Eight scenes of Radarsat image of Pearl River Delta was used for experiment.First

periodogram analysis was carried out on the time-series data to get the temporal pattern of the study area.Some land cover like paddy

cultivated land

orchard and forest reveal periodic variation during the research span.Thus various temporal dynamics of these land covers should be taken into account to acquire accurate short-term change detection.Then

a time-based neural networkprediction model（TNN） was built for time-series forecasting.Ten types of land cover with different temporal pattern were classified and four scenes of Radarsat images in vegetation growing seasons（April

June

August

October） in 2000 were used for network training.Land-cover type was classified based on their temporal variation.The first three scenes were used as the input and the last scene was used as the output（to be predicted）.The training result showed stable and precise simulation of TNN.In the third step

the first three scenes of Radarsat images in 2001 was taken as the input to the network and the forth scene was simulated.Finally

a distance function was defined and change threshold was set for change detection.The simulated result was used to detect the change between simulated image and actual image.The detection assessment shows that neural network simulation could well represent the short-time non-linear change of land use/land cover.The detection precision ranged from 66.67%（rural residential area） to 91.67%（water）.Other land cover type like paddy field（83.33%） and orchard（71.43%） also got relatively high precision

corresponding to their notable variation in time-series images.The average detection precision reached 81.66%

which is a satisfying result for our primary experiment on short time change detection.To sum up

this article testified the possibility of short-term change detection under dynamic cally changing environment.So far there is still few method applicable for short-term change detection.TNN network proposed in this article is a meaningful attempt for research in this field.