广州市海珠区高密度城区扩展SLEUTH模型模拟

doi:10.11821/xb201010002

地理学报 ›› 2010, Vol. 65 ›› Issue (10): 1163-1172.doi: 10.11821/xb201010002

广州市海珠区高密度城区扩展SLEUTH模型模拟

李明杰^1,2,3, 钱乐祥¹, 吴志峰⁴, 崔海山¹, 侯西勇²

1. 广州大学地理科学学院,广州510006
;
2. 中国科学院烟台海岸带所,烟台264003
;
3. 中国科学院研究生院, 北京100049
;
4. 广东省生态环境与土壤研究所,广州510650

收稿日期:2009-11-25 修回日期:2010-05-07 出版日期:2010-10-20 发布日期:2010-10-20
通讯作者: 钱乐祥, 男, 博士, 博士生导师, E-mail: qian-lx@163.com
作者简介:李明杰(1984-), 女, 在读博士, 主要从事环境资源遥感与地理信息系统, 海岸带生态环境监测与全球变化方面研究。E-mail: mjli@yic.ac.cn
基金资助:
国家自然科学基金项目(30970482); 原建设部科技项目(2007-K9-26)

The SLEUTH Model Simulation of High Density Urban Sprawl in Haizhu District of Guangzhou City

LI Mingjie^1,2,3, QIAN Lexiang¹, WU Zhifeng⁴, CUI Haishan¹, HOU Xiyong²

1. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
;
2. Yantai Institute of Coastal Zone Research, CAS, Yantai 264003, Shandong, China
;
3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
;
4. Guangdong Institute of Eco-environment and Soil Sciences, Guangzhou 510650, China

Received:2009-11-25 Revised:2010-05-07 Online:2010-10-20 Published:2010-10-20
Supported by:
National Natural Science Foundation of China, No.30970482; The Scientific and Technological Foundation of the Former Ministry of Construction, No.2007-K9-26

摘要/Abstract

摘要：

高密度城区是城市的核心区，也是城区扩展的源，对该区域的精确识别以及扩展模拟研究，具有重要的意义与价值。以广州市海珠区1979、1990、2000、2008 年4 期Landsat 影像为数据源，运用非渗透表面端元选取模型(V-I-S) 与归一化混合光谱分析模型(NSMA) 相结合的方法，辅以单窗算法反演地表温度数据(LST)，高精度提取非渗透表面丰度，进而设置合适阈值表征研究区高密度城区范围。在此基础上基于SLEUTH模型设置4 种场景模拟和预测海珠区高密度城区扩展，并用景观指数分析方法对研究区1979-2050 年长达70 年的空间扩展状况进行分析。主要结论为：① SLEUTH模型同样适用于小尺度区域的扩展模拟。② SLEUTH模型模拟中基于自然、人文以及城市扩展内在动力机制等条件参数的设置，促使模拟结果精度更高。③ SLEUTH模型模拟结果表明，自1979 至今，海珠区高密度城区以较快扩展速率扩张，尤以1990-2004 年间变动增长最快；未来的20 年其增长速率减缓，并于2030 年前后趋于稳定。这种扩张格局与变化状况与研究区产业结构、经济政策、土地规划决策等因素密不可分。

关键词: 非渗透表面丰度, 高密度城区, SLEUTH模型, 模拟, 广州市海珠区

Abstract:

High-density urban area is the core of urban region and the source of urban sprawl. Its precise identification and expansion simulation are receiving more and more attention. This study used Landsat image data of 1979, 1990, 2000 and 2008 in the Haizhu Distrit of Guangzhou city as basic data sources, in combination with Vegetation-Impervious-Soil Model and Normalized Mixing Spectral Analysis Model. It was also aided with the Land Surface Temperature data inversed by mono-window algorithm as modification to extract highly precise percent impervious surface and to represent the high-density urban area by setting an appropriate threshold for the impervious surface fraction. We ran the SLEUTH Model to simulate and predict the high-density urban sprawl of the study area from 1979 to 2050 in four scenarios, then used the landscape indices method to analyze the spatial and temporal patterns for the 70-year time series of high-density regions. The major conclusions can be drawn as follows. The SLEUTH model is suitable for simulating the sprawl of small-scale region. The excluded layer parameters of the SLEUTH model based on the natural and social dynamic mechanism of urban expansion and other conditions produced more accurate simulation results. The SLEUTH model simulation analysis results show that Haizhu District has been developed at a rapid rate of high-density urban expansion since 1979, particularly in the period from 1990 to 2004 when it witnessed a much faster growth change. In the next 20 years (from 2008-2030), its growth rate would be relatively low. In the 2030s, there would be a stabilized situation in the study area and the sprawl pattern would be closely related to the factors of industrial structure, economic policy and land planning.

Key words: percentage of impervious surface, high-density urban area, SLEUTH Model, Simulation, Haizhu District of Guangzhou City

李明杰, 钱乐祥, 吴志峰, 崔海山, 侯西勇. 广州市海珠区高密度城区扩展SLEUTH模型模拟[J]. 地理学报, 2010, 65(10): 1163-1172.

LI Mingjie, QIAN Lexiang, WU Zhifeng, CUI Haishan, HOU Xiyong. The SLEUTH Model Simulation of High Density Urban Sprawl in Haizhu District of Guangzhou City[J]. Acta Geographica Sinica, 2010, 65(10): 1163-1172.

参考文献

[1] Arnold Jr C A, Gibbons C J. Impervious surface coverage: The emergence of a key urban environmental indicator. Journal of the American Planning Association, 1996, 62(2): 243-258.

[2] Clapham Jr W B. Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale. Remote Sensing of Environment, 2003, 86: 322-340.

[3] Yang L, Xian G, Klaver J M et al. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 2003, 69(9): 1003-1010.

[4] Xian G, Crane M. Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 2005, 97(2): 203-215

[5] Larke K C, Hoppen S, Gaydos L J. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environment and Planning B, 1997, 24: 247-261.

[6] Candau J. Temporal calibration sensitivity of the SLEUTH urban growth model
[D]. Santa Barbara: University of California, 2002.

[7] Clarke K C, Gaydos L J. Loose-coupling of a cellular automaton model and GIS: Long-term growth prediction for the San Francisco and Washington Baltimore. International Journal of Geographical Information Science, 1998, 12: 699-714.

[8] Arthur-Hartranft S T, Carlson T N, Clarke K C. Satellite and ground-based microclimate and hydrologic analyses coupled with a regional urban growth model. Remote Sensing of Environment, 2003, 86: 385-400.

[9] Carlson T N. Analysis and prediction of surface runoff in an urbanizing watershed using satellite imagery. Journal of the American Water Resources Association, 2004, 40(4): 1087-1098.

[10] Chen Xuegang. The simulation study of urban growth based on cellular automata
[D]. Urumqi: Xinjiang University, 2005.

[陈学刚. 基于细胞自动机的城市增长模拟研究
[D]. 乌鲁木齐: 新疆大学, 2005.]

[11] Zhang Yan, Li Jing, Chen Yunhao. Simulation of Beijing urbanization using SLEUTH. Remote Sensing Information, 2007, (2): 50-56.

[张岩, 李京, 陈云浩. 利用SLEUTH模型进行北京城市扩展模拟研究. 遥感信息, 2007, (2): 50-56.

[12] Xu Xibao. Urban land use dynamic evolution simulation and optimization based on GIS and cellular automata: A case study of Lanzhou
[D]. Lanzhou: Lanzhou University, 2007.

[徐昔保. 基于GIS 与元胞自动机的城市土地利用动态演化模拟与优化研究
[D]. 兰州: 兰州大学, 2007.]

[13] Mi Lina. The study of spatial expansion based on SLEUTH model in Yinchuan City
[D]. Lanzhou: Lanzhou University, 2008.

[米丽娜. 基于SLEUTH模型的银川市空间扩展研究
[D]. 兰州: 兰州大学, 2008.]

[14] Wu C S. Normalized spectral mixture analysis for monitoring urban composition using ETM + imagery. Remote Sensing of Environment, 2004, 93: 480-492.

[15] Qian Lexiang, Cui Haishan. Using normalized spectral mixture model analysis of urban surface composition. Land Resources Remote Sensing, 2006, (2): 64-68.

[钱乐祥, 崔海山. 运用归一化光谱混合模型分析城市地表组成. 国土资源遥感, 2006, (2): 64-68.]

[16] Qin Zhihao, Zhang Minghua, Arnon Karnieli et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data. Acta Geographica Sinica, 2001, 56(4): 456-466.

[覃志豪, 张明华, Arnon Karnieli 等. 用陆地卫星TM6数据演算地表温度的单窗算法. 地理学报, 2001, 56(4): 456-466.]

[17] Qin Zhihao, Li Wenjuan, Zhang Minghua et al. Single-window algorithm for atmospheric parameter estimation method. Land Resources Remote Sensing, 2003, (2): 37-43.

[覃志豪, 李文娟, 张明华等. 单窗算法的大气参数估计方法. 国土资源遥感, 2003, (2): 37-43.]

[18] Qian Lexiang, Ding Shengyan. Pearl River Delta land cover change on surface temperature effects. Acta Geographica Sinica, 2005, 60(5): 761-770.

[钱乐祥, 丁圣彦. 珠江三角洲土地覆盖变化对地表温度的影响. 地理学报, 2005, 60 (5): 761-770.]

[19] Lu D S, Weng Q H. Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 2006, 102(1/2): 146-160.

[20] Charles D, Clarke K C. Determination of optimal calibration metrics through the use of self-organizing maps//Institute for Environmental Studies, Amsterdam//Integrated Assessment of the Land System Workshop, 2004, 10: 28-30.

[21] Dietzel C, Clarke K C. Toward optimal calibration of the SLEUTH land use change model. Transactions in GIS, 2007, 11(11): 29-45.

[22] Herold M, Goldstein N C, Clarke K C. The spatiotemporal form of urban growth: Measurement analysis and modeling. Remote Sensing Environment, 2003, 86: 286-302.

[23] Li Mingjie, Qian Lexiang, Xia Lihua et al. Effects of grain change on landscape indices of landuse in Shantou. Geography and Geographic Information Science, 2008, 24(6): 93-96.

[李明杰, 钱乐祥, 夏丽华等. 汕头市土地利用景观指数粒度效应初探. 地理与地理信息科学, 2008, 24(6): 93-96.]

[24] Zhao Wenwu, Fu Bojie, Chen Liding. Effects of grain change on landscape indices. Quaternary Research, 2003, 23(3): 326-333.

[赵文武, 傅伯杰, 陈利顶. 景观指数的粒度变化效应. 第四纪研究, 2003, 23(3): 326-333.]

[25] Lu Ling. Analysis on the landscape structure and change of the Heihe River Basin
[D]. Lanzhou: Cold and Arid Regions Environment and Engineering Research Institute, CAS, 2000.

[卢玲. 黑河流域景观结构与景观变化研究
[D]. 兰州: 中国科学院寒区旱区环境与工程研究所, 2000.]

[26] Wu Jianguo. Landscape Ecology: Pattern, Process, Scale and Hierarchy. Beijing: Higher Education Press, 2000: 185.

[邬建国. 景观生态学: 格局、过程、尺度与等级. 北京: 高等教育出版社, 2000: 185.]

广州市海珠区高密度城区扩展SLEUTH模型模拟

The SLEUTH Model Simulation of High Density Urban Sprawl in Haizhu District of Guangzhou City

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

[1]	马海涛. 知识流动空间的城市关系建构与创新网络模拟[J]. 地理学报, 2020, 75(4): 708-721.
[2]	夏军, 张永勇, 穆兴民, 左其亭, 周宇建, 赵广举. 中国生态水文学发展趋势与重点方向[J]. 地理学报, 2020, 75(3): 445-457.
[3]	孙毅中, 杨静, 宋书颖, 朱杰, 戴俊杰. 多层次矢量元胞自动机建模及土地利用变化模拟[J]. 地理学报, 2020, 75(10): 2164-2179.
[4]	张杰, 史培军, 杨静, 龚道溢. 北京地区景观城市化进程对暴雨过程的影响——以“7·21”暴雨为例[J]. 地理学报, 2020, 75(1): 113-125.
[5]	张行,梁小英,刘迪,史琴琴,陈海. 生态脆弱区社会—生态景观恢复力时空演变及情景模拟[J]. 地理学报, 2019, 74(7): 1450-1466.
[6]	崔学刚,方创琳,刘海猛,刘晓菲,李咏红. 城镇化与生态环境耦合动态模拟理论及方法的研究进展[J]. 地理学报, 2019, 74(6): 1079-1096.
[7]	朱文博, 张静静, 崔耀平, 郑辉, 朱连奇. 基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例[J]. 地理学报, 2019, 74(3): 446-459.
[8]	卓莉, 张子彦, 雷小雨, 李秋萍, 陶海燕. 基于蒙特卡洛生存分析探究东北森林物候的影响因素[J]. 地理学报, 2019, 74(3): 490-503.
[9]	蒋晓辉,夏军,黄强,龙爱华,董国涛,宋进喜. 黑河“97”分水方案适应性分析[J]. 地理学报, 2019, 74(1): 103-116.
[10]	殷杰,许世远,经雅梦,尹占娥,廖邦固. 基于洪涝情景模拟的城市公共服务灾害应急响应空间可达性评价——以医疗急救为例[J]. 地理学报, 2018, 73(9): 1737-1747.
[11]	熊鹰,陈云,李静芝,阎晓静. 基于土地集约利用的长株潭城市群建设用地供需仿真模拟[J]. 地理学报, 2018, 73(3): 562-577.
[12]	牛方曲,王芳. 城市土地利用—交通集成模型的构建与应用[J]. 地理学报, 2018, 73(2): 380-392.
[13]	马帅, 盛煜, 曹伟, 吴吉春, 胡晓莹, 王生廷. 黄河源区多年冻土空间分布变化特征数值模拟[J]. 地理学报, 2017, 72(9): 1621-1633.
[14]	葛全胜, 江东, 陆锋, 邓祥征, 王绍强. 地缘环境系统模拟研究探讨[J]. 地理学报, 2017, 72(3): 371-382.
[15]	胡胜, 曹明明, 邱海军, 宋进喜, 吴江, 高宇, 李京忠, 孙克红. CFSR气象数据在流域水文模拟中的适用性评价——以灞河流域为例[J]. 地理学报, 2016, 71(9): 1571-1587.