遗传算法支持下土地利用空间分形特征尺度域的识别  

Scale domain recognition for land use spatial fractal feature based on genetic algorithm

作　　者：吴浩[1,2] 李岩[1] 史文中[2] 陈晓玲[3] 付东杰[4] 

机构地区：[1]武汉理工大学资源与环境工程学院,武汉430070 [2]香港理工大学土地测量与地理资讯学系,香港999077 [3]武汉大学测绘遥感信息工程国家重点实验室,武汉430079 [4]中国科学院地理科学与资源研究所,北京100101

出　　处：《生态学报》2014年第7期1822-1830,共9页Acta Ecologica Sinica

基　　金：中国博士后科学基金资助项目(2013M531749);香江学者计划(XJ2012036);国家自然科学基金(40901214);武汉市青年科技晨光计划(201150431093);中央高校基本科研业务费专项资金(2013-IV-040)联合资助

摘　　要：针对土地利用空间分形特征只存在于有限尺度域的现象,采用无标度区内离散点拟合的离差平方和平均值最小作为目标函数,提出了一种基于遗传算法的土地利用空间分形特征尺度域的识别方法,用于准确计算分形维数的有效区间范围。以武汉市武昌区水域空间分形特征为例,利用Quickbird多光谱遥感影像提取土地利用空间信息,重点讨论了基于遗传算法识别土地利用空间分形特征尺度域范围的总体思路、适应度函数和遗传算子等环节;然后分别从测定系数、标准差和无标度区间3个角度,将其同人工判断法、相关系数法以及强化系数法进行对比讨论;并结合研究区域实际的水域空间分布格局,分析不同方法计算所得半径维数的合理性。结果表明,土地利用分形特征尺度域的范围对分形维数计算结果有较大影响;相对于传统计算方法来说,遗传算法在尺度无标度区间识别上具有更高的精度,可以为土地利用空间格局分形特征的研究提供客观指导意见。The spatial pattern of land use is one of the most profound human-induced alterations to the Earth＇s surface. Its change can lead to severe problems in urban ecological environments, such as heavy traffic, the heat island effect, and the spread of epidemics. An accurate examination of urban land use characteristics is helpful both in understanding quantitatively and comprehensively urban land use spatial patterns, and in discovering the potential rules of urban land use change. Because of its advantages in describing randomness and self-similarity, the fractal dimension has been used widely to analyze spatial patterns, and great achievements have been made in recent decades. However, the scale domain is largely ignored when the value of the fractal dimension is used to explain spatial patterns. To some extent, this leads inevitably to analysis uncertainty, because the spatial self-similarity characteristics of land use exist within a specific scale range rather than across a geographic scale range. Hence, the identification of the scale domain related to the fraetal dimension is more important than the computation itself. In addressing this problem, this paper presents a model for scale domain recognition, based on a genetic algorithm, to provide a meaningful range of fractal features existing in nature. Its objective function is to minimize the average from the sum of squared residuals that is derived from the result of the fitting of a scale-free region using discrete points. It can improve the computation accuracy of the fractal dimension significantly. Because of its abundant water resources, this study took the scale domain of the water fractal feature in Wuchang district as an example. A cloud-free image obtained by the Quickbird satellite, was classified and used to extract land use information by using a combination of the decision tree method and supervised classification. A general framework and three genetic operators for the scale domain recognition of the land use spatial fractal feature were desig

关 键 词：土地利用 空间结构 分形特征 尺度域 遗传算法 

分 类 号：F301.2[经济管理—产业经济]