摘要
采用分形几何研究法和调研测绘研究法,选取肇庆市3家医院进行实地调查,并从中选取27个实验样地进行研究。结果表明:人工植物群落的结构层次郁闭度最高的是第一人民医院,但第一人民医院植物群落中乔木层所占比例低,其三维总绿量也低,这说明植物群落的结构层次郁闭度与乔木层所占比例无关,而绿化三维绿量主要与其相关;样地总体三维绿量呈正态分布,残差分析没有呈现明显图案,所得最优方程为y=137.713x2-231.661;根据模拟方程标准回归系数,12个因子中对小面积人工绿地三维绿量影响最大的因子为平均冠幅。结果为在小面积人工绿地康复性园林医院的绿地规划设计提供实证依据。
Fractal geometry and surveying method were used and twenty-seven plots from artificial greenbelts of three hospitals in Zhaoqing were chosen. Based on the plant community's characteristic value of an artificial greenbelts of a small area,the correlation analysis and stepwise regression analysis were adopted. The results showed that the canopy structure of artificial plant communities wasthe highest in the first people's hospital,but the proportion of tree layer in the plant community was very few and three-dimensional green biomass( TGB) was the lowest. The TGB of plant community was mainly determined by the proportion of tree layer,not the canopy structure. TGB had a normal distribution and no evident pattern was shown in the residual analysis meaning that the simulated equations could be used to predict TGB of the artificial greenbelt. The most convenient simulated equation is y = 137. 713 x2-231. 661. Thus,according to the standard regression coefficients for the simulated equations,average crown was the most important factor affecting TGB of a small area,artificial greenbelts. This study is to provide a empirical basis for the green space planning and design in a small area artificial greenbelts.
作者
陈华
刘锐兵
黎淑霞
周丽萍
郭纯爱
张洪萍
CHEN Hua;LIU Ruibing;LI Shuxia;ZHOU Liping;GUO Chun' ai;ZHANG Hongping(The Department of Landscape Architecture,College of Life Science,Zhaoqing University,Zhaoqing 526061,China;Construction Project Office of Dinghu District in Zhaoqing,Zhaoqing 526070,China)
出处
《重庆理工大学学报(自然科学)》
CAS
北大核心
2018年第8期121-129,共9页
Journal of Chongqing University of Technology:Natural Science
基金
国家自然科学基金资助项目(31600573)
国家级大学生创新创业训练计划项目(201710580016)
关键词
人工绿地
康复园林
三维绿量
回归方程
artificial greenbehs
rehabilitation garden
three-dimensional green biomass
regression equation