基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用

首先给出ＣＯ２倍增下遥感－光合作物产量的概念模型，之后分析未受ＣＯ２倍增的遥感－光合作物产量估测模型；在考虑ＣＯ２倍增对作物产量的影响后，对影响干物质累积的作物光合速率的模型进行修正，进而修正遥感－光合作物产量估测模型。建立ＣＯ２倍增下作物产量影响模型，求取各参数，并在ＣＯ２倍增下对我国华北地区冬小麦产量影响进行填图，表明模型的估测结果有良好的可比性。

Application of Bayesian Statistics Inference Techniques Based on GIS to the Evaluation of Habitat Probabilities of Bos Gaurus Readei

At present, GIS has been widely applied to the study of wildlife habitat. However, GIS, which is a tool of spatial data analysis and processing, lacks of the capacity of heuristic reasoning. Therefore, it is an important way to solve this problem by the integration of Bayesian statistics inference with GIS. in this article, the Naban river nature reserve of Xishuangbanna was taken as an experimental area, GIS and multivariate statistical techniques were applied to the development of two logistic multiple regression models for Bos gaurus readei habitat: trend surface model and environmental model. Independent variables were locational coordinates in the first model, and a set of environmental factors in the second model. Bayesian statistics were then used to integrate the two models into a Bayesian integrated model. The results show that the Bayesian integrated model is superior to the environmental model and can be applied to wildlife habitat evaluation.