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Rapid urbanization took place in the Pearl River Delta of south China since 1980. Although drastic land use change took
place in very short interval within this area, hardly any research has been done on this phenomenon for lacking of
available data. Remote sensing is presently the most favorable observation method for land use and land cover change
(LUCC) researches. While located in the south of China, the Pearl River Delta suffers from heavy cloud cover for more
than half of the year. This makes real-time LUCC monitoring and change detection almost impossible with commonly
used optical remote sensing data. In this paper, the orbital highest resolution SAR (Synthetic Aperture Radar) data - Fine Mode Radarsat data was used for trail of short-term land use change detection. Three scenes of repeat-pass
Radarsat data was collected over the study area. Although repeat-pass Radarsat enable continuous land use monitoring
under all weather condition, images acquired during different time are inevitably affected by seasonal land cover change
and variable environmental status such as air humidity and raining. Besides, some significant observation bias might be
induced because of the platform and sensor instability. All these variations and instability made short-term land use
change detection quite a perplex problem. In this paper, short-term land use change caused by human activity was
considered as abnormal phenomena in both spatial and temporal domain in time series images. And a Density-based
Anomaly Detection (DBAD) algorithm was designed to detect abnormally changed land parcels in time series Radarsat
images. Firstly, totally 3 scenes of fine mode Radarsat images were collected in the study area from January 1st to May
3rd, 2006. Simply stacked temporal images reveal apparent backscattering variation between the three scenes of images,
which mainly owes to the fast vegetable growth during the observation period. Then image segmentation was done on
the multi-temporal Radarsat images and object features including mean value of backscattering coefficient (Mean),
minimal value of backscattering (Min), homogeneity of gray level co-occurrence matrix (GLCMhomo) and dissimilarity
of gray level co-occurrence matrix (GLCMdis) were extracted basing on segmented image objects. After that change-vector
was constructed for each land objects. In the third step DBAD algorithm was applied to the change vector dataset
to detect anomaly change in the 3 scenes of images. Finally field surveying data plus manual interpretation were used
for validation. Comparing with object-based image regression method, DBAD results in better accuracy. Besides, data
validation also shows that DBAD have better accuracy in both under-constructed area and newly built up area (error
lower than 12%). While for built up area and some mixed used area, it gains relatively lower accuracy than other land
types (from 10% to 28.57%). To conclude, short-term land use change in time series images could be defined as spatial
and temporal anomaly in remote sensing images. By extending traditional anomaly detection to spatial-temporal
anomaly detection, land use change caused by human activity could be effectively detected during short time intervals.
The algorithm DBAD focus only on the density of change vectors in feature space, which is independent of the
amplitude and direction of change vectors. This enable DBAD effectively discriminate temporal image variation caused
by observation system, environment or seasonal land cover change, especially in vegetation and cultivated area which
changed remarkably during the observation period, from land use change caused by human activities. This helps to
decrease the false alarming in short-term change detection.
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