文献类型：期刊文献

中文题名：基于SFLA-M-L模型的景观格局优化研究

英文题名：Landscape Pattern Optimization Based on SFLA-M-L Model

作者：张启斌[1];岳德鹏[1];方敏哲[1];张耘[2];李倩[3];马欢[1]

第一作者：张启斌

通讯作者：Yue, Depeng

机构：[1]北京林业大学精准林业北京市重点实验室;[2]北京联合大学应用科技学院;[3]北京林业大学林学院

第一机构：北京林业大学精准林业北京市重点实验室,北京100083

年份：2017

卷号：48

期号：7

起止页码：159-166

中文期刊名：农业机械学报

外文期刊名：Transactions of the Chinese Society for Agricultural Machinery

收录：CSTPCD;;EI(收录号：20173904217797);Scopus(收录号：2-s2.0-85029880171);北大核心:【北大核心2014】；CSCD:【CSCD2017_2018】；

基金：国家自然科学基金项目(41371189);"十二五"国家科技支撑计划项目(2012BAD16B00)

语种：中文

中文关键词：景观格局优化;混合蛙跳算法;逻辑回归模型;马尔可夫模型

外文关键词：landscape pattern optimization; shuffled frog leaping algorithm; logistic regression model; Markov model

摘要：以内蒙古自治区巴彦淖尔市磴口县为研究区,基于混合蛙跳算法,耦合逻辑回归与马尔可夫模型构建了SFLA-M-L(Shuffled frog leaping algorithm-Markov-logistic regression)模型。利用逻辑回归,综合考虑高程、坡度、地下水埋深、干旱度指数、归一化植被指数与当前景观分布进行了景观适宜性分析;利用Markov模型,构造了县域景观转移概率矩阵。利用景观适宜性指数和景观聚集度指数构造目标函数,以景观转移概率矩阵为景观变异的控制条件,对2016年景观格局分布进行了县域景观格局优化。优化结果中,景观聚集度为96.71%,比2016年景观分布提升了6.43个百分点;景观适宜性指数为96.23%,比2016年景观分布提升了4.18个百分点;不同景观类型间相互转移超出转移概率矩阵控制仅4.66 km^2,确保了优化结果的合理性。
Landscape pattern determines the local distribution of resources and habitats, which has an important impact on a variety of ecological processes. Based on the full understand of the coupling relationship between landscape pattern and ecological processes, landscape pattern optimization is aimed at achieving the maximum ecological benefits through the adjustment of the landscape patches＇ spatial distribution and size. In order to consider more factors in landscape pattern optimization and make the optimization results more scientific and reasonable, an SFLA- M- L model was built based on shuffled frog leaping algorithm （SFLA） , logistic regression model and Markov model. The landscape pattern of Dengkou County, Bayannaoer City, Inner Mongolia was optimized to verify the model. Logistic regression model was used to analyze the landscape pattern suitability based on DEM, slope, under ground water depth, aridity index, NDVI and current landscape distribution. Markov model was used to build the landscape transition probability matrix. The objective function of SFLA - M - L was built based on the landscape suitability atlas and landscape aggregation index. Landscape pattern transition probability matrix was used to restrict the transfer of different landscape types. In the optimization results, the landscape aggregation index was 96.71% , which was 6.43 percentage points higher than the landscape pattern in 2016; landscape suitability index was 96.23% , which was 4. 18 percentage points higher than the landscape pattern in 2016; the transfer area beyond the control of landscape pattern transition probability matrix was only 4.66 km^2 , and the rationality of the optimization results was ensured.