Applying a socio-ecological green network framework to Xi’an City, China

生态网络 地理 城市化 景观生态学 栖息地 娱乐 环境资源管理 生态学 景观连通性 紧凑型城市 城市规划 环境规划 人口 环境科学 社会学 生态系统 生物 生物扩散 人口学
作者
Na Xiu,Maria Ignatieva,Cecil C. Konijnendijk,Shuoxin Zhang
出处
期刊:Landscape and Ecological Engineering [Springer Nature]
卷期号:16 (2): 135-150 被引量:11
标识
DOI:10.1007/s11355-020-00412-z
摘要

Abstract Green–blue space loss and fragmentation are particularly acute in Chinese cities due to rapid urbanization, large ring-road system and the following city compartments. Therefore, connecting urban green–blue spaces has been recently advocated by central government. This paper revised and applied the recently developed urban green network approach to the case of Xi’an city, China, a city which has been rarely studied before from this perspective. The focus was on connecting fragments of urban green–blue spaces to compact green–blue networks, integrating both social and ecological functions into a fully functioning entity. Landscape metric analysis was added to identify that the main city outside the city core should be a planning priority zone. The Eurasian tree sparrow ( Passer montanus ), Asiatic toad ( Bufo gargarizans ) and humans at leisure were selected as three focal species to meet the emerged socio-ecological benefits. Sociotope and biotope maps were drawn up to identify patches with high human recreation and wildlife shelter values and providing crucial network structures. Least-cost-path model was used for identifying potential linkages between patches. This model was based on network structures and cost surface, which measures the theoretical energy cost of travelling between landscape elements. By integrating the potential paths for the selected organisms with density analysis, the updated framework generated three improvement maps for species indicators, and 10 network corridors for establishing green–blue networks at city scale. At neighbourhood scale, one site with habitat and linkage examples illustrated specific measures that could be taken in local practice.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
逐风完成签到,获得积分10
刚刚
无奈的酒窝完成签到,获得积分10
1秒前
1秒前
2秒前
blingbling发布了新的文献求助10
2秒前
今后应助SherlockLiu采纳,获得30
4秒前
daniel发布了新的文献求助10
4秒前
Jason应助温言采纳,获得20
5秒前
逐风发布了新的文献求助30
6秒前
hhzz发布了新的文献求助10
6秒前
日月轮回完成签到,获得积分10
7秒前
8秒前
Yimim发布了新的文献求助10
8秒前
小小li完成签到 ,获得积分10
8秒前
小蘑菇应助细腻晓露采纳,获得10
8秒前
又胖了完成签到,获得积分10
9秒前
Eva完成签到,获得积分10
10秒前
10秒前
喵喵喵完成签到,获得积分20
10秒前
独摇之完成签到,获得积分10
10秒前
怡然雁凡完成签到,获得积分10
10秒前
顾jiu完成签到,获得积分10
11秒前
科研通AI5应助热依汗古丽采纳,获得10
11秒前
优秀剑愁完成签到 ,获得积分10
11秒前
敏感网络发布了新的文献求助50
12秒前
院士人启动完成签到,获得积分10
12秒前
13秒前
黄花菜完成签到 ,获得积分0
15秒前
15秒前
顾jiu发布了新的文献求助30
15秒前
Yimim完成签到,获得积分10
15秒前
16秒前
白菜完成签到,获得积分10
16秒前
17秒前
虚心山灵完成签到 ,获得积分20
17秒前
18秒前
白菜发布了新的文献求助30
19秒前
19秒前
xx发布了新的文献求助10
20秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527928
求助须知:如何正确求助?哪些是违规求助? 3108040
关于积分的说明 9287614
捐赠科研通 2805836
什么是DOI,文献DOI怎么找? 1540070
邀请新用户注册赠送积分活动 716904
科研通“疑难数据库(出版商)”最低求助积分说明 709808