|Table of Contents|

Study on Landscape Pattern of Urban Green Space of Small Cities in Western China Based on GF-1 Imagery(PDF)

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2017年01
Page:
88-94
Research Field:
Publishing date:

Info

Title:
Study on Landscape Pattern of Urban Green Space of Small Cities in Western China Based on GF-1 Imagery
Author(s):
ZHU Yuguo12DU Lingtong12XIE Yingzhong123LIU Ke12HU Yue12HOU Jing12
(1.Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Ningxia University,Yinchuan,Ningxia 750021;2.Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwest China of Ministry of Education,Ningxia University,Yinchuan,Ningxia 750021;3.School of Agriculture,Ningxia University,Yinchuan,Ningxia 750021)
Keywords:
GF-1 PMSobject-oriented classificationlandscape patternwestern town
PACS:
-
DOI:
10.11937/bfyy.201701021
Abstract:
Based on the GF-1 PMS image in September,2013,the green scape information of Yanchi county,Ningxia Province,a small town of western China,was extracted by using the object-oriented classification.According to the principle and method of landscape ecology,the landscape pattern of the urban green land in Yanchi county was analyzed with the support of the Fragstats software from the aspects of landscape area,dominance,diversity,and fractal dimension.The distribution analysis results showed that GF-1 PMS image had a higher classification accuracy in extract of urban green land,which overall accuracy was 90.80% and the Kappa coefficient was 0.796 1,and this accuracy was satisfied with the demand of the urban landscape extracting.The landscape analyzing results showed that the urban green land in Yanchi county had three types of characteristics.Firstly,the structure of green system did not reasonable especially in the diversity of green land,and the area of three different kinds of green land did not matched scientifically.Secondly,green landscape was more fragmentized as a whole and the ancillary green,whose patch density reached 53.92,was more prominent.Besides,the FRAC index was low,which mean that the green land lacked green patch from nature,as well as artificial modification was more serious.

References:

 

[1]陈天,臧鑫宇,王峤.生态城绿色街区城市设计策略研究[J].城市规划,2015,39(7):63-69.

[2]ATTWELL K.Urban land resources and urban planting:case studies from Denmark[J].Landscape and Urban Planning,2000,52(2-3):145-163.

[3]刘滨谊,姜允芳.论中国城市绿地系统规划的误区与对策[J].城市规划,2002,26(2):76-80.

[4]李佳璇,伏玉玲,象伟宁,.生态智慧与当代城市绿地建设[J].北方园艺,2015(16):87-93.

[5]况平.城市园林绿地系统规划中的适宜度分析[J].土木建筑与环境工程,1996,18(3):8-14.

[6]吴浩,花向红,王佩军,.基于RSGIS的城市绿地评估系统的一种模式[J].地理空间信息,2005,3(1):18-20.

[7]杨威,陈秋晓.基于Quickbird影像的中小城市绿地景观格局分析:以乐清市为例[J].浙江大学学报(理学版),2011,38(6):716-721.

[8]孙恺,杨延征,赵鹏祥,.基于遥感技术的西安城市景观格局时空演变及分析[J].西北林学院学报,2015,30(2):180-185.

[9]谭丽,何兴元,陈玮,.基于QuickBird卫星影像的沈阳城市绿地景观格局梯度分析[J].生态学杂志,2008,27(7):1141-1148.

[10]韩玲玲,费鲜芸,田牧歌.面向对象的泰安市城市附属绿地信息提取[J].淮海工学院学报(自然科学版),2012,21(3):43-47.

[11]熊轶群,吴健平.面向对象的城市绿地信息提取方法研究[J].华东师范大学学报(自然科学版),2006(4):84-90.

[12]严海英.基于对象的城市绿地信息提取技术的研究[J].地理空间信息,2008,6(2):9-11.

[13]曾小箕.面向对象的高分一号影像信息提取技术研究[D].乌鲁木齐:新疆大学,2014.

[14]冀呈莹.新型城镇化路径选择之盐池大县城实践探究[D].北京:北京信息科技大学,2015.

[15]杜凤兰,田庆久,夏学齐,.面向对象的地物分类法分析与评价[J].遥感技术与应用,2004,19(1):20-23.

[16]常虹,詹福雷,杨国东,.面向对象的高分遥感影像信息提取技术研究[J].测绘通报,2015(1):99-101.

[17]张秀英,冯学智,丁晓东,.基于面向对象方法的IKONOS影像城市植被信息提取[J].浙江大学学报(农业与生命科学版),2007,33(5):568-573.

[18]高峻,杨名静.上海城市绿地景观格局的分析研究[J].中国园林,2000,16(1):53-56.

[19]王捍卫.基于RSGIS的武汉城市绿地景观格局分析[D].武汉:华中师范大学,2009.

[20]CJJ/T 85-2002.城市绿地分类标准[S].北京:中国建筑工业出版社,2002.

[21]姜雪娇.基于遥感与GIS的城市绿地信息提取与空间格局研究[D].成都:成都理工大学,2015.

[22]李秀珍,布仁仓,常禹,.景观格局指标对不同景观格局的反应[J].生态学报,2004,24(1):123-134.

[23]孙娟,蓝崇钰,夏汉平,.基于QuickBird卫星影像的贵港市城市景观格局分析[J].生态学杂志,200625(1):50-54.

Memo

Memo:
-
Last Update: 2017-03-16