On Some Key Techniques Of Temporal GIS

In recent years temporal GIS appears to be a new development of GIS. This dissertation focuses on some key techniques and algorithms of temporal GIS. Aim to build a forestry fire spreading model, a new temporal data model, data-image fusion and fast image mosaicing approaches, cellular automata model and it's rule evolution algorithms are explored. The research work can be summaried as following:1. Based on the current data models of GIS and regarding the forestry fire speading model, a new scalabile temporal data model is proposed, which gives a natural representation of the real forestry fire behavior with less data redundancy and better fits for the operation of the cellular automata.2. Data integration is one of most time costy work for building a GIS application. And data-image fusion is the important step of data integration. Aiming to promote efficiency of data integration of GIS, a new approach of multiresolution data-image fusion is introduced, which utilizes features directed auto-align. The calculation time of the same approach is further reduced by localized calculation of Laplacian pyramid to a statistically defined boundary neighbor. The time response of the image mosaicing of multiple partition images of CCD is also benefit from the approach.3. A typical cellular automata runs with a set of deterministic rules. However in case of the environment change there is a need of rule adjustment. So an immunity learning method for the rule adjustment of 2d cellular automata is proposed to improve the precision of geo-cellular automata model.4. Some principles of image process with cellular automata are also discussed with two examples. One of the examples is the edge extraction of image which shows better result than typical operators. And then an approach of image skelecton extraction is presented, which is useful for compare and aligning of RS image and DEM data when data fusion is concerned. The dissertation provides a new algorithm which employs cellular automata to search the contour of water. With multiscale iterativing the algorithm aperently improved the efficiency of image skelecton extraction.5. A forestry fire spreading expert system model with cellular automata as inference engine which is driven by the knowledge is finally presented.This research is supported by the National Natural Science Foundation of China.