With the development of remote sensing technology, satellites can collect high spatial resolution images such as SPOT-5 and Quickbird. The SPOT-5 satellite simultaneously collects 5-m panchromatic and 10-m multispectral images, after interpolated in ground station 2.5-m panchromatic image can be provided (5 metres ground resolution in panchromatic mode and 2.5 metres in supermode). The Quick bird satellite simultaneously collects 0.61-m panchromatic and 2.44-m multispectral images. With Images Merged of 2.5-m panchromatic and 10-m multispectral images of SPOT-5, the approximate resolution images as Quick bird multispectral images were acquired. These images acquired with different satellites can be used to detect the change of urban. In this paper, the images of Wuhan University in China acquired with SPOT-5 and Quick bird are used to detect the change of trees in different season. The result shows it is possible to detect the change of trees and some factors that affect the change detection are listed.
Bayesian networks are used for reasoning under uncertainty. This paper examines the use of Bayesian networks for integrating multi-temporal remotely sensed data with landform data derived from digital elevation models (DEM) and groundwater data to produce maps showing areas affected by salinity in the Yellow River Delta of China. Incorporating prior knowledge about the relationships between input attributes and their relationship with salinity, a conditional probabilistic network is used to impose a known relationship between input attributes and salinity status. The results are compared with maximum likelihood classification techniques using single-date Landsat TM imagery. They show a large improvement on the maximum likelihood classifier. The network is used to produce a time-series of landcover and salinity maps for the Yellow River Delta.