中文摘要：

有效的景观模式特征评价及其变动预测是合理调控和管理森林景观、维持景观安全格局的基本前提.利用3期美国国家土地覆盖数据库（1992、2001和2006年）,采用景观镶嵌度指数与马尔科夫模型相结合的方法,分析了美国俄勒冈州的森林破碎化模式及森林与其他土地利用类型空间交互特征的变化.结果表明：景观镶嵌度模型中,开发主导的景观镶嵌类型（D）转变为单一的开发类型（DD）的概率最大,为0.319,说明城市化是推动区域景观格局变化的主要动力;森林安全度模型中,主要为农业和开发景观镶嵌类型（ad）的森林损失率最高,表明在城市与农业占主导的景观上森林被吞噬的可能性最大;稳态分布表明,森林破碎化趋势日益加剧,到稳定状态时森林占总区域的面积比例不到50%,空间分布趋向于混合型的景观格局.景观镶嵌度模型2006年模拟值与实际值Kappa系数达到0.82,模型精度较高;森林安全度模型Kappa系数为0.21,模型精度较差.

英文摘要：

Effectively assessing landscape pattern characteristics and predicting their dynamics have been a basic prerequisite for more reasonably regulating and managing forest landscape, and main- taining landscape security patterns. In this study, based on three U.S. National Land Cover Data- bases （1992, 2001 and 2006 ）, the landscape mosaic indicator in combination with the Markov model was adopted to analyze forest fragmentation patterns and changes in the characteristics of spa- tial interactions between forests and other land use types in Oregon State, USA. The results showed that conversion from the development-dominated type D to the single development type DD in land- scape mosaic model had the highest transition probability 0. 319, indicating that urbanization has been the major force responsible for the change of regional landscape patterns. In the forest security model, the highest rates of forest loss occurred in agriculture and the developed landscape mosaic type （ad） , showing that in the development and agriculture dominated landscapes, encroaching up- on forests was at the highest likelihood. The areal percentage of forest over the total study area was less than 50% when reaching a steady-state distribution, with an accelerating rate of forest fragmen- tation, and the landscape spatial distribution tended to be a mixed landscape pattern. The Kappa coefficient between the simulated values and the observed values from the 2006 landscape mosaic model was estimated at 0.82, indicating this model had a high precision. However, the accuracy of the forest security model was poor, with a Kappa coefficient of 0.21.