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自然资源遥感  2025, Vol. 37 Issue (2): 173-184    DOI: 10.6046/zrzyyg.2023346
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
适应未来的山地城市可持续扩张绿色基础设施网络建设——以云南省临沧市为例
李健娥(), 张云()
西南林业大学园林园艺学院,昆明 650224
Construction of a green infrastructure network for sustainable expansion of mountain cities: A case study of Lincang City, Yunnan Province, China
LI Jian’e(), ZHANG Yun()
College of Horticulture and Landscape Architecture, Southwest Forestry University, Kunming 650224, China
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摘要 构建区域绿色基础设施(green infrastructure, GI)网络可缓解快速城市化进程中土地利用与生态发展的矛盾,对未来的城市规划具有重要作用。以我国西南地区典型山地城市临沧市为例,利用未来土地利用模拟(patch-generating land use simulation, PLUS)模型预测2030年临沧市在生态优先情景下的土地利用和土地覆盖(land use and land cover, LULC); 在此基础上,整合形态空间格局分析(morphological spatial pattern analysis, MSPA)、最小累积阻力(minimum cumulative resistance, MCR)模型和电路理论提取生态源地和生态廊道,构建一个经过优化的适应临沧市未来可持续城市扩张的2030年GI网络。结果表明: 预测2020—2030年间,临沧市建设用地面积增加约23%,林地、草地面积分别减少0.2%和1.3%,由于合理的治理与保护,水域增长率高达46.9%; GI景观要素核心区面积占56.12%,边缘占21.3%,支线、桥接区、孤岛、穿孔、环道占比较小,5个总占比为22.6%; 在可持续的城市扩张情景下,GI规模变化不明显,建成区的GI相对分散,优化后的GI网络将由12个生态源地和24的生态廊道组成。研究基于MSPA-PLUS模型构建临沧市2030年GI网络,为临沧市和其他山地城市规划提供新思路,对GI保护和维护区域生态安全具有重要意义。
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李健娥
张云
关键词 ： 绿色基础设施网络,  MSPA-PLUS模型,  山地城市,  可持续扩张,  临沧市    
Abstract：

Constructing a regional green infrastructure (GI) network can alleviate the contradiction between land use and ecological development in the process of rapid urbanization, playing a significant role in future urban planning. This study investigated Lincang City, a typical mountain city in Southwest China. Employing the patch-generating land use simulation (PLUS) model, this study predicted the land use and land cover (LULC) in Lincang City in 2030 under the ecological priority scenario. Furthermore, this study extracted information about the ecological source areas and corridors by integrating the morphological spatial pattern analysis (MSPA), minimum cumulative resistance (MCR) model, and circuit theory. Finally, this study constructed an optimized GI network for 2030 adapted to the sustainable expansion of Lincang City. The results show that from 2020 to 2030, the construction land area in Lincang City is projected to expand by about 23 %, while forest land and grassland will decrease by 0.2 % and 1.3 %, respectively. The water area is expected to increase by 46.9 % under reasonable management and protection. The core zone of GI landscape elements will represent 56.12% of the total area, while the edges will make up 21.3%. The spurs, bridging zones, islets, perforations, and circuits will constitute the rest 22.6%. Under the sustainable urban expansion scenario, the GI scale remains overall stable, with a relatively scattered distribution in built-up areas. The optimized GI network will involve 12 ecological source areas and 24 ecological corridors. The GI network of Lincang City in 2030 constructed based on the MSPA-PLUS model strengthens the understanding of the GI network for the sustainable development of a mountain city, adapting to future urban development. This study provides novel insights into the urban planning of mountain cities including Lincang and critical implications for GI protection and regional ecological security maintenance.
Key words： green infrastructure network    MSPA-PLUS model    mountain city    sustainable expansion    Lincang City
收稿日期: 2023-11-14      出版日期: 2025-05-09
ZTFLH:  TP79  
基金资助:云南省教育厅科学研究基金“‘荒野智慧’下的西双版纳州生物多样性保护策略研究”(2023Y0754);2021云南省专业学位研究生案例库建设项目“民族园林(传统村落保护规划)教学案例库”(编号: 云学位[2021]18号共同资助)
通讯作者: 张 云(1973-),男,副教授,主要从事景观规划与生态环境保护研究。Email: 1136482587@qq.com。
作者简介: 李健娥(1997-),女,硕士研究生,主要从事景观规划与生态环境保护研究。Email: 2522186246@qq.com。
引用本文:   
李健娥, 张云. 适应未来的山地城市可持续扩张绿色基础设施网络建设——以云南省临沧市为例[J]. 自然资源遥感, 2025, 37(2): 173-184.
LI Jian’e, ZHANG Yun. Construction of a green infrastructure network for sustainable expansion of mountain cities: A case study of Lincang City, Yunnan Province, China. Remote Sensing for Natural Resources, 2025, 37(2): 173-184.
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https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023346      或      https://www.gtzyyg.com/CN/Y2025/V37/I2/173
Fig.1  研究区示意图
类别 数据 分辨率/m 来源
自然因素 土地利用类型 30 Global30 (http://www.globallandcover.com/)
高程 地理空间数据云(http://www.gscloud.cn)
坡度 基于DEM数据得到
坡向 基于DEM数据得到
年平均气温 1 000 资源环境科学与数据中心(https://www.resdc.cn/)
年平均降水
区位因素 省道距离 30 全国地理信息资源目录服务系统(https://www.webmap.cn/main.do?method=index)
县道距离
乡道距离
铁路距离
居民点距离
水系距离
乡镇街道距离 30 国家统计局
社会因素 GDP 1 000 资源环境科学与数据中心(https://www.resdc.cn/)
人口密度 100 全国第七次人口普查
Tab.1  数据介绍及来源
Fig.2  驱动因子分布图
LULC
类型		 耕地 林地 灌木 草地 水域 湿地 建设
用地
耕地 1 1 1 1 1 1 1
林地 0 1 0 0 0 0 0
灌木 1 1 1 1 1 1 1
草地 1 1 1 1 1 1 1
水域 0 0 0 0 1 0 0
湿地 0 0 0 0 0 1 0
建设用地 0 0 0 0 0 0 1
Tab.2  特定转换规则的成本矩阵
阻力因子 分级指标 阻力值 权重
地形起伏度/m <20 1 0.153 1
[20, 34) 3
[34, 50) 5
[50, 74) 7
[74, 666] 9
坡度/(°) <10.749 1 0.081 3
[10.749, 18.24) 3
[18.24, 25.73) 5
[25.73, 35.17) 7
[35.17, 83.06] 9
土地利用类型 林地/灌木 1 0.432 2
草地/耕地 3
水域 5
湿地 7
建设用地 9
与居民点距离/m [10 777.7, 22 163.8) 1 0.193 9
[6 866.4, 10 777.7) 3
[4 780.4, 6 866.4) 5
[2 868.26, 4 780.4) 7
<2 868.26 9
与水体距离/m [5 007.19, 10 913.11) 1 0.103
[3 338.12, 5 007.19) 3
[2 054.23, 3 338.12) 5
[941.52, 2 054.23) 7
<941.52 9
与道路距离/m [5 697.67, 12 418) 1 0.036 5
[3 603.65, 569 7.67) 3
[2 142.7, 3 603.65) 5
[925.26, 2 142.7) 7
<925.26 9
Tab.3  阻力因子分级赋值及权重
Fig.3  2020年实际和模拟LULC的比较
2020年实际
LULC		 2020年预测LULC
耕地 林地 灌木 草地 水域 湿地 建筑用地
耕地 439.468 24.206 1.373 1.201 0.002 2.054 2.267
林地 41.85 700.329 20.796 15.893 0 3.662 0.782
灌木 0.641 1.567 8.334 0.425 0 0.296 0.102
草地 1.311 7.987 0.618 13.357 0 0.037 0.057
水域 0.021 0 0 0 0.128 0.022 0
湿地 0.275 0.453 0.054 0.004 0.013 2.975 0.016
建筑用地 0.783 0.172 0.02 0.012 0 0.034 4.766
Tab.4  2020年实际LULC与2020年模拟LULC转换矩阵
驱动因子 耕地 林地 灌木 草地 水域 湿地 建筑用地
铁路距离 0.139 0.165 0.095 0.218 0.009 0.218 0.108
GDP 0.098 0.115 0.112 0.149 0.023 0.149 0.064
高程 0.091 0.120 0.162 0.060 0.761 0.060 0.018
省道距离 0.090 0.082 0.065 0.086 0.002 0.086 0.021
县道距离 0.080 0.055 0.036 0.038 0.022 0.038 0.299
年平均降水 0.073 0.053 0.026 0.073 0.019 0.073 0.078
年平均气温 0.071 0.071 0.091 0.090 0.041 0.090 0.299
人口密度 0.069 0.061 0.069 0.066 0.025 0.066 0.033
居民点距离 0.065 0.067 0.174 0.048 0.012 0.048 0.046
乡镇街道距离 0.064 0.051 0.073 0.041 0.013 0.041 0.209
乡道距离 0.060 0.048 0.030 0.035 0.008 0.035 0.037
水系距离 0.050 0.058 0.031 0.047 0.017 0.047 0.026
坡度 0.028 0.032 0.023 0.028 0.046 0.028 0.019
坡向 0.022 0.023 0.013 0.021 0.002 0.021 0.008
Tab.5  各驱动因子的贡献
Fig.4  2030年城市可持续扩张LULC模拟
LULC 2020年 2030年
耕地 8 783.21 8 708.56
林地 13 368.66 13 341.73
灌木 588.94 572.25
草地 561.07 553.63
湿地 2.48 2.29
水域 198.90 292.19
建设用地 142.32 174.94
Tab.6  2020年和2030年的LULC
Fig.5  2030年GI的MSPA类型
Fig.6  生态源地的连通性分析
生态源地 IIC dPC 面积/km2
28 59.192 67 66.281 86 2 226.609 66
29 31.441 32 35.356 68 2 135.334 02
25 24.325 80 26.007 96 151.839 31
22 19.335 24 21.656 50 87.248 11
27 16.216 41 19.930 38 25.001 09
26 15.584 89 17.100 31 20.459 06
23 13.704 60 15.128 40 439.284 98
24 12.494 35 12.984 15 39.251 32
21 9.941 41 11.331 57 310.181 37
20 11.845 72 10.377 36 33.911 14
14 2.884 15 3.066 77 138.058 89
19 2.823 52 2.772 68 291.161 51
Tab.7  生态源地的连通性分析
Fig.7  生态阻力面
Fig.8  临沧市GI网络生态廊道建设
Fig.9  2030年临沧市可持续城市扩张的GI网络图
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