Damage to vegetation caused by secondary disasters of the Wenchuan earthquake in severely damaged counties was estimated through the visual comparison of SPOT images acquired before the earthquake and ADS40 aerial images acquired after the earthquake, and a series of spatial analyses. In this paper, we (1) interpret 2-meter resolution aerial images that cover areas severely affected by the earthquake, and obtain statistical information on vegetation damage for the counties of Beichuan, Wenchuan, Maoxian, Lixian, Pingwu, Qingchuan, Anxian and Jiangyou; (2) spatially analyze the relationships between vegetation damage and slope gradient and distance from active faults using ArcGIS software to obtain information on vegetation damage under different geologic and geomorphologic settings; and (3) estimate the area of vegetation damage for the whole region using the above results for the areas covered by imagery. The results indicate that (1) farmland and grassland were less damaged than forestland was since they are mostly located on less steep slopes; (2) Wenchuan was the worst damaged county; and (3) the proportion of damage to vegetation first decreased and then increased with increasing distance from the three main faults of the Longmenshan fault zone owing to the combined effects of the three faults and the effects of regional geology and landforms.
People usually use qualitative terms to express spatial relations, while current geographic information systems (GIS) all use quantitative approaches to store spatial information. The abilities of current GIS to represent and query spatial information about geographic space are limited. In order to incorporate the concepts and methods people use to infer information about geographic space into GIS, research on the formal model of common sense geography becomes increasingly important. Previous research on the formalizations of natural-language descriptions of spatial relations are all based on crisp classification algorithms. But the human languages about spatial relations are ambiguous. There is no clear boundary between "yes" or "no" if a spatial relation predicate can express the spatial relations between objects. So the results of crisp classification algorithms can not formalize natural-language terms well. This paper uses a fuzzy decision tree method to formalize the spatial relations between two linear objects. Topologic and metric indices are used as variables, and the results of a human-subject test are used as training data. The formalization result of the fuzzy decision tree is compared with the result of a crisp decision tree.