文献类型：期刊文献

中文题名：广州市主城区树冠覆盖景观格局梯度

英文题名：Landscape pattern gradient on tree canopy in the central city of Guangzhou,China

作者：朱耀军[1] 王成[2] 贾宝全[2] 粟娟[3]

第一作者：朱耀军

机构：[1]中国林业科学研究院湿地研究所;[2]中国林业科学研究院林业研究所国家林业局林木培育重点实验室国家林业局城市林业研究中心;[3]广州市林业和园林局

年份：2011

卷号：31

期号：20

起止页码：5910-5917

中文期刊名：生态学报

外文期刊名：Acta Ecologica Sinica

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金："十一五"国家科技支撑计划重大项目(2006BAD03A06);中国林科院林业所所长基金资助项目

语种：中文

中文关键词：广州;树冠覆盖;城市森林;移动窗口;景观格局梯度

外文关键词：Guangzhou; tree canopy; urban forest; moving window; landscape pattern gradient

分类号：Q14

摘要：城市树冠覆盖是城市森林在小尺度上的景观表达,是衡量城市森林生态功能服务质量的重要指标。通过在ArcGIS9.2中对广州市中心城区的高分辨率航片进行目视解译,对形成的树冠覆盖专题图分析并生成栅格数据,利用Fragstats3.3软件分别选择标准方法和移动窗口方法分析研究区景观格局特征。其中基于移动窗口法形成了一系列基于所选择格局特征的连续栅格表面,每一个栅格单元代表的是设定的移动窗口半径尺度下景观类型的格局特征。分析表明,研究区景观格局呈现明显的空间异质性梯度特征。基于样带区尺度效应分析的结果表明,粒度3—5m和移动窗口半径400—600m可适合于研究区的景观格局梯度分析,选择的景观指数能够形成较为平滑的栅格表面。基于移动窗口的分析结果以连续变量图的形式对景观格局空间异质性进行可视化表达,能够为小尺度上的城市森林景观格局优化提供重要的参考。
Quantification of landscape pattern and its dynamic is essential to monitor and evaluate the consequences of urban forests.The trees in urban area provide a wide range of ecosystem services,and are beneficial to the well-being of humans as well as the environment.Previous researches have demonstrated that tree canopy coverage is closely correlated with urban heat island effect,community microclimate,air quality,biodiversity,and quality of urban life and so forth.Tree canopy coverage in urban area benefits directly the community and can be recognized as a special landscape type of urban forest at fine scale.In this paper,a vector map of tree canopy coverage was obtained by a visual interpretation of aerial image in the central city of Guangzhou,and the spatial resolution of the image was 0.4m.A raster data transformed from the map was analyzed in FRAGSTATS 3.3 software.It is well known that the observed landscape pattern and its relationship with process depend upon the scale.In this regard,these studies integrated transect analyses to discuss the change of the scale effect,including grain and extent.The methods of Standard and Moving Window were chosen respectively,and different radius were tried to quantify the landscape structure.The window moves over one cell of the landscape at a time,calculating the selected metric within the window and assigning the value to the center cell.A series of new continuous grid maps for selected metrics,including PLAND,PD,MPS,LPI,ED,PARA_AM,ENN_MN,COHESION etc.,were calculated on the class level,and spatial gradient maps were outputted in grid format.The value of each grid cell represented the selected metric within selected window,and landscape pattern was described in visualization form.Based on the moving window analysis,the result demonstrated that spatial heterogeneity of landscape pattern was significant in the study area,and the selected landscape metrics could be depicted in the smoothing images.Considering effects of the grain size and extent,3—5m and 0.4—0.6km were the optimal grain size and the moving window radius in the study area,respectively.A series of landscape pattern gradient maps in study area were obtained with the grain size of 5m and the radius of moving window of 0.5km.In comparison with quantifying metrics on the traditional holistic landscape pattern,the approach of moving window made a significant contribution to our understanding of the landscape pattern and process,which depicted a set of continuous variable maps such as vegetable density and hydrograph and relief map and others,and it linked landscape pattern and process more effectively in local scale.The analyzed result presents a continuous surface and is recorded in raster format,it is conductive to directly reveal driving forces at local level,and it can be used to compare the landscape pattern visually.Combined with other surface data such as geography information and socioeconomic data in multivariate models,the result of landscape pattern gradient analysis provided a novel way for discerning the driving force of landscape dynamic and optimizing urban forest landscape pattern at the fine scale.