顾及轨道交通影响的浙中城市群土地利用多情景模拟与分析

doi:10.12082/dqxxkx.2020.190305

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (3): 605-615.doi: 10.12082/dqxxkx.2020.190305

• “土地利用模拟”专栏 • 上一篇下一篇

顾及轨道交通影响的浙中城市群土地利用多情景模拟与分析

王家丰¹, 王蓉², 冯永玖^1,^*(), 雷振坤², 高忱², 陈书睿², 金雁敏¹, 翟淑婷²

^1. 同济大学测绘与地理信息学院,上海 200092
^2. 上海海洋大学海洋科学学院,上海 201306

收稿日期:2019-06-13 修回日期:2020-02-25 出版日期:2020-03-25 发布日期:2020-05-18
通讯作者: 冯永玖 E-mail:yjfeng@tongji.edu.cn
作者简介:王家丰（1992— ）,男,安徽阜阳人,硕士生,主要从事空间数据分析与SAR应用研究。E-mail:m170300595@st.shou.edu.cn
基金资助:
国家自然科学基金项目(41771414);国家自然科学基金项目(41601414);上海市科委“扬帆计划”项目(16YF1412200)

Simulating Land Use Patterns of the Mid-Zhejiang Urban Agglomeration Considering the Effects of Urban Rail Transit

WANG Jiafeng¹, WANG Rong², FENG Yongjiu^1,^*(), LEI Zhenkun², GAO Chen², CHEN Shurui², JIN Yanmin¹, ZHAI Shuting²

^1. College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
^2. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

Received:2019-06-13 Revised:2020-02-25 Online:2020-03-25 Published:2020-05-18
Contact: FENG Yongjiu E-mail:yjfeng@tongji.edu.cn
Supported by:
National Natural Science Foundation of China(41771414);National Natural Science Foundation of China(41601414);Shanghai Sailing Program(16YF1412200)

摘要/Abstract

摘要：

土地利用变化受到地形地貌、自然环境、城市规划和经济发展等的影响,预测其未来情景对政策调整具有重要的参考意义。元胞自动机模型是模拟和预测不同规划政策下土地利用变化的常用方法。本文基于GlobeLand30数据集,利用浙中城市群2000—2010年土地利用变化校准FLUS模型,并模拟2010年土地利用格局,其总体精度、Kappa系数和图形优化（FOM）分别为89.74%、82.69%和29.86%。采用马尔可夫链预测2030年各类型土地总量,利用FLUS预测一般条件下（常规情景）和城市轨道交通规划站点影响下（轨交情景）浙中城市群未来土地格局。结果表明,在5 km范围内城市轨道交通站点对建设用地增长影响较大,在该区域轨交情景比常规情景面积增加45.25 km ²、且主要发生在城市边缘区。建设用地扩张主要通过侵占优质农田实现,轨交情景5 km范围内农田转化为建设用地比常规情景增加33.34 km ²,建设用地扩张强度高于常规情景,其中最低扩张强度以上占比高于常规情景3.70%。景观指数表明,2种情景中林地、草地和水域格局具有较高相似性。本研究表明,综合使用FLUS、遥感、GIS等技术方法,能够准确模拟和预测不同规划条件下未来土地利用格局,并为规划和政策调整提供高可信空间数据。

关键词: 土地利用, 轨道交通站点, 元胞自动机, FLUS, 轨交情景, 常规情景, 城市扩张强度, 浙中城市群

Abstract:

Urban rail transit possesses significant impacts on land use change and urban development. This study applies Future Land Use Simulation Model (FLUS) to reproduce land use changefrom 2000 to 2010 in the Mid-Zhejiang urban agglomeration based on GlobeLand30 datasets. The simulation results in 2010 show that the FLUS model can reproduced a realistic land use pattern with an overall accuracy of 89.74% and FOM 29.86%. A Markov chain is then used to predict the total land demand in 2030 for predicting future land use scenarios. We design two scenarios: the scenario of business-as-usual (BAU-scenario) and the scenario based on planned urban rail transit sites (RTS-scenario). Within 5 km from the urban rail transit, the RTS-scenario yields a significant effect on built-up areas with an increasing expansion intensity, where the newly built-up areas are allocated in the suburb sand are greater than that produced by BAU-scenario by 45.25 km ².The newly built-up cells mainly occupy high-quality farmland. The farmland transformed to built-up area is higher in RTS-scenario than in BAU-scenario by 33.34 km ².We categorize the built-up expansion intensity (BUI) into five levels: lowest, low, medium, high and highest. The BUI for RTS-scenario is higher than that for BAU-scenario because the former’s proportion of expansion intensity above the lowest level is 3.70% greater than of latter. Spatial patterns for forest, grassland and water are similar between both scenarios. This study not only indicates that FLUS can be used to capture land use change and predict future scenarios, but also helps to examine the effects of urban rail transit site plansin the Mid-Zhejiang urban agglomeration.

Key words: land use, rail transit sites, cellular automata, FLUS, BAU-scenario, RTS-scenario, built-up expansion intensity, Mid-Zhejiang Urban Agglomeration

王家丰, 王蓉, 冯永玖, 雷振坤, 高忱, 陈书睿, 金雁敏, 翟淑婷. 顾及轨道交通影响的浙中城市群土地利用多情景模拟与分析[J]. 地球信息科学学报, 2020, 22(3): 605-615.DOI:10.12082/dqxxkx.2020.190305

WANG Jiafeng, WANG Rong, FENG Yongjiu, LEI Zhenkun, GAO Chen, CHEN Shurui, JIN Yanmin, ZHAI Shuting. Simulating Land Use Patterns of the Mid-Zhejiang Urban Agglomeration Considering the Effects of Urban Rail Transit[J]. Journal of Geo-information Science, 2020, 22(3): 605-615.DOI:10.12082/dqxxkx.2020.190305

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类型		2010年
类型		农田	林地	草地	水域	建设用地	总计
2000年	农田	3479.41	167.68	38.01	18.75	514.06	4217.91
	林地	135.87	5506.24	130.84	12.09	9.66	5794.70
	草地	61.53	118.18	296.80	1.34	5.73	483.59
	水域	17.64	8.55	0.76	129.02	3.59	159.57
	建设用地	25.27	1.23	0.64	0.99	262.05	290.19
	总计	3719.73	5801.88	467.06	162.20	795.09	10 945.96

类别	变量	简称	分辨率/m	年份	意义	来源
地形	高程	DEM	30	2009	评估地形要素对土地利用的影响	www.gscloud.cn
地形	坡度	Slope	30	2009	评估地形要素对土地利用的影响	计算高程数据的坡度
社会经济	人口	POP	90	2015	评估社会经济要素对土地利用的影响	www.worldpop.org
社会经济	经济	GDP	900	2010	评估社会经济要素对土地利用的影响	www.ngdc.noaa.gov
区位	到市中心的距离	DisCity	30	2010	行政中心邻近度对土地利用的影响	ArcGIS计算对应矢量数据的欧氏距离。矢量数据来源： www.openstreetmap.org
区位	到区中心的距离	DisCounty	30		行政中心邻近度对土地利用的影响
交通	到主干道的距离	DisRoad	30		道路邻近度对土地利用的影响
交通	到轨道交通的距离	DisRail	30		道路邻近度对土地利用的影响

类型		2010年真实
类型		农田	林地	草地	水域	建设用地	总计
2010年模拟	农田	329.41	18.08	11.75	1.74	10.24	371.22
	林地	14.05	550.91	13.61	1.21	0.93	580.71
	草地	14.79	10.73	21.01	0.12	0.30	46.95
	水域	2.00	0.90	0.10	12.90	0.39	16.29
	建设用地	11.28	0.25	0.25	0.30	67.35	79.43
	总计	371.53	580.87	46.72	16.27	79.21	1094.60
总体精度/%		89.74
Kappa /%		82.69
FOM/%		29.86

景观指数	常规情景					轨交情景
景观指数	农田	林地	草地	水域	建设用地	农田	林地	草地	水域	建设用地
LPI	6.67	14.48	0.02	0.29	3.56	4.74	15.94	0.02	0.29	3.54
PAFRAC	1.44	1.47	1.55	1.47	1.37	1.55	1.54	1.62	1.47	1.37
DIVISION	0.99	0.95	1.00	1.00	1.00	1.00	0.94	1.00	1.00	1.00
IJI	84.85	64.52	48.42	80.24	41.18	80.59	61.37	51.45	81.58	42.81
PLADJ	89.89	94.25	52.05	77.63	93.25	87.55	93.05	45.00	77.82	93.66
AI	89.94	94.29	52.12	77.81	93.32	87.60	93.08	45.06	78.01	93.73
CLUMPY	0.86	0.88	0.50	0.77	0.92	0.83	0.85	0.43	0.78	0.93
COHESION	99.65	99.90	84.77	97.12	99.49	99.59	99.91	85.03	97.09	99.52

顾及轨道交通影响的浙中城市群土地利用多情景模拟与分析

Simulating Land Use Patterns of the Mid-Zhejiang Urban Agglomeration Considering the Effects of Urban Rail Transit

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