基于空间突变特征的城市边缘区提取方法

doi:10.12082/dqxxkx.2021.200502

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (8): 1401-1421.doi: 10.12082/dqxxkx.2021.200502

• 地球信息科学理论与方法 • 上一篇下一篇

基于空间突变特征的城市边缘区提取方法

戴俊杰¹(), 董婧雯²^,³^,⁴, 杨晟²^,³^,⁴, 孙毅中²^,³^,⁴^,^*()

1.江阴市城乡规划设计院,江阴 214433
2.南京师范大学虚拟地理环境教育部重点实验室,南京 210023
3.江苏省地理环境演化国家重点实验室培育建设点,南京 210023
4.江苏省地理信息资源开发与利用协同创新中心,南京 210023

收稿日期:2020-09-01 修回日期:2020-12-10 出版日期:2021-08-25 发布日期:2021-10-25
通讯作者: * 孙毅中（1957— ）,男,江苏常州人,教授,博士生导师,主要从事时空数据分析与城市规划GIS研究。 E-mail: sunyizhong_cz@163.com
作者简介:戴俊杰（1978— ）,男,江苏盐城人,硕士,高级工程师,主要从事国土空间规划及城市规划GIS研究。E-mail: 3390232@qq.com
基金资助:
国家自然科学基金项目(41671392);国家自然科学基金项目(41871297)

Identification Method of Urban Fringe Area based on Spatial Mutation Characteristics

DAI Junjie¹(), DONG Jingwen²^,³^,⁴, YANG Shen²^,³^,⁴, SUN Yizhong²^,³^,⁴^,^*()

1. Jiangyin Urban and Rural Planning and Design Institute, Jiangyin 214433, China
2. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
3. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2020-09-01 Revised:2020-12-10 Online:2021-08-25 Published:2021-10-25
Contact: SUN Yizhong
Supported by:
National Natural Science Foundation of China(41671392);National Natural Science Foundation of China(41871297)

摘要/Abstract

摘要：

城市边缘区位于城市与乡村之间的过渡交接地带,既是城市扩张的前沿,也是城乡建设和用地置换中最具活力的地区。准确识别城市边缘区的空间范围一直是城市空间结构研究的核心问题,有助于从城乡对比的角度来衡量城市化程度。本文以城市边缘区的空间突变特征为切入点,基于格网尺度评价构建基于多源数据的城市边缘区特征识别指标,然后采用小波变换检测进行特征值突变点群的识别,并利用基于Delaunay的自动边长阈值的边界提取算法识别突变点群内外边界,从而实现了一种基于空间突变特征的城市边缘区提取方法。最后,以江阴市作为研究区进行了实证分析,并将本文方法提取结果与通过信息熵模型和土地利用程度综合指数模型提取城市边缘区的经典方法提取结果进行对比,经典方法所提取的城市边缘区结果更为混乱分散,而本文结果更为完整客观。将本文方法提取结果与使用土地利用数据与行政区划统计年鉴数据构建城市边缘区识别指标进行突变检测的不同指标方法提取结果进行对比,二者重叠度达88.03%,体现了本文方法的正确性,而从局部细节分析来看,本文结果更符合实际情况。为了更好地验证本文方法的有效性,利用景观格局指数对本文方法和不同指标方法提取的城市建成区、城市边缘区和乡村腹地的范围进行检验：从斑块类型层级指数分析, 2种方法划定的区域都具有典型的空间特征;而从景观层级指数分析,本文识别出的边缘区所计算的斑块密度、最大斑块指数、景观分离度、景观破碎化指数和香农多样性指数均高于对比方法,而蔓延度和香农均匀度均低于对比方法,说明本文识别的城市边缘区范围内景观破碎化程度和异质性更高,景观分布不均匀,社会经济条件更复杂,从而证明了本方法的有效性,尤其适合于非闭合环状的城市边缘区的提取。

关键词: 城市边缘区, 空间突变特征, 多源数据, 小波变换, Delaunay三角网, 范围识别, 格网评价, 景观格局指数

Abstract:

As a transition zone between the city and the countryside, the urban fringe area is not only a geographical space affected by both of the regions, but also an area shrouded in conflicts of interest. The rapid development of urbanization witnesses tremendous changes the urban spatial structure is undergoing. Therefore, studying the spatial scope of the urban fringe area is conducive to the assessment of the current situation of urban development, and is further significant for policy formulation, population management, and resource allocation in the urban fringe area. Thanks to the development of remote sensing and geographic information technology, the types, quality, and accuracy of geospatial data that are applied to depict the characteristics of the urban fringe area have been significantly enhanced. Considering this, this paper takes the spatial mutation characteristics of the urban fringe area as a starting point, and a method for mutation point groups detection, combining multi-indices fusion and wavelet transform, is adopted to distinguish the spatial extent of urban fringe area based on the optimal results by grid-scale evaluation. And then we use the Delaunay triangulation automatic edge length threshold to extract the boundary of the mutation point groups and to obtain the spatial range of the urban fringe area. Empirical analysis is conducted taking Jiangyin City as the research area. The main experimental steps are as follow: firstly, several basic data are selected, containing land use data, road data, night light data, and service-oriented POI data according to the analysis of the characteristics of the urban fringe area. These multi-source data are then standardized based on grids and entropy weighting method for weight determination. In this way, the eigenvalues of the discriminant index of the urban fringe area are calculated. Secondly, based on the spatial mutation characteristics, the wavelet transform is employed to extract the mutation point groups on the eigenvalue sequence, which can effectively improve the discrimination accuracy of the mutation point group. It is suitable for the non-closed circular urban fringe area and for avoiding the influence of human subjective factors. Then, the algorithm based on the Delaunay triangulation automatic edge length threshold is utilized to extract the boundary of the mutation point groups and to obtain the spatial range of the urban fringe area, which can provide a reference for optimizing the urban spatial layout. Finally, the extraction results in this paper are compared with those obtained by the classical methods, like the information entropy model and the comprehensive index model of land use degree. It is apparent to see that the results from classical methods are more chaotic and scattered, while the results in this paper are more complete and objective. Comparing the extraction results of this paper with the extraction results of different index methods employing land use data and administrative division statistical yearbook data to construct urban fringe identification indicators for mutation detection, this study discovers the overlap between them is 88.03%, which testifies the factualness of this method. In terms of the analysis of local details, the results of this paper are more in line with the actual situation. To verify the effectiveness of the method proposed in this paper, the landscape pattern indices are adopted to test the range of urban built-up area, urban fringe area and rural hinterland extracted by the method of this paper and the other different index method respectively. Considering the patch class size landscape pattern indices, the areas delineated by two methods are following the spatial characteristics. Meanwhile, the value of data calculated in the urban fringe area identified in this paper is all higher than the counterpart method given the landscape size landscape pattern indices, the patch density, maximum patch index, landscape separation degree, landscape fragmentation index, and Shannon diversity index. The spread and Shannon uniformity, however, are both lower than the comparison method. It can be indicated that the fragmentation and heterogeneity of the landscape in the urban fringe area identified in this paper is higher, the landscape distribution is uneven, and the socio-economic conditions are more complex, thus proving the effectiveness of this method is especially suitable for the extraction of non-closed circular urban fringe area.

Key words: urban fringe area, spatial mutation characteristics, multi-source data, wavelet transform, Delaunay triangulation, range recognition, grid evaluation, landscape pattern index

戴俊杰, 董婧雯, 杨晟, 孙毅中. 基于空间突变特征的城市边缘区提取方法[J]. 地球信息科学学报, 2021, 23(8): 1401-1421.DOI:10.12082/dqxxkx.2021.200502

DAI Junjie, DONG Jingwen, YANG Shen, SUN Yizhong. Identification Method of Urban Fringe Area based on Spatial Mutation Characteristics[J]. Journal of Geo-information Science, 2021, 23(8): 1401-1421.DOI:10.12082/dqxxkx.2021.200502

图/表 32

图1

表1

不同尺度下的半方差函数参数

尺度/m×m	$C 0$	$C 0 + C$	$C 0 / (C 0 + C)$	$A$	$R 2$	RSS/× $10 - 5$
200×200	0.0077	0.0626	0.1230	2910	0.5860	8.0230
400×400	0.0072	0.0520	0.1380	3270	0.6470	5.2260
600×600	0.0053	0.0423	0.1260	5310	0.6360	6.2590
800×800	0.0057	0.0400	0.1430	4680	0.6240	7.7770

表1

图2

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图16

图17

图18

图19

图20

表2

图21

图22

表3

图23

图24

图25

表4

景观格局指数

景观格局指数	缩写	生态含义
斑块密度	PD	PD通常用来反映景观总体斑块的分化程度或破碎化程度,斑块密度高表示单位面积斑块的规模小,景观异质性高
平均斑块面积	AREA_MN	AREA_MN是指某一斑块类型的总面积除以该类型的斑块数,通常用来反映某种景观破碎化的程度
最大斑块指数	LPI	LPI为斑块类型中最大面积的斑块占整体景观面积的比例。该景观格局指数直接体现了景观的优势类型,直接体现了人类活动对于土地利用和景观生态变化的干扰强度和频率的变化
景观形状指数	LSI	LSI通过计算某一斑块形状与相同面积的圆或矩形间的偏离程度来测量该形状的复杂程度,反映景观斑块的变异性。LSI值增大时,斑块不规则情况增加
平均面积权重分维数	FRAC_AM	FRAC_AM反映景观边界复杂程度
景观聚集度指数	AI	AI基于栅格数据来测量景观或某种类型的聚集程度。值小时,表示景观中不同类型的斑块离散程度大;值大时,表示景观中同类型的斑块相互聚合,结构紧凑
景观结合度指数	COHESION	COHESION表示景观区域中的景观空间链接度,景观结合度指数值高时表示景观中空间链接度高
蔓延度	CONTAG	CONTAG用来反映景观中不同斑块类型的团聚程度和蔓延趋势,高蔓延值表明景观类型中的某种斑块类型形成良好的连接性;反之则表明景观中存在许多小斑块,是具有多种要素的密集格局,景观的破碎化程度较高
景观分离度	DIVISION	DIVISION表示某一景观类型中不同景观斑块分布的分离程度;分离度越大,景观在地域分布上越分散,景观分布越复杂,破碎化程度越高
景观破碎化指数	SPLIT	SPLIT表征景观被分割的破碎程度,反映景观空间结构的复杂性,在一定程度上反映了人类对景观的干扰程度,进而反映出人类对自然生态系统的影响,一般来说,破碎度越大,人类对生态系统的影响越大
香农多样性	SHDI	SHDI用来反映景观类型的丰富和复杂程度,值越大表明景观中没有优势类型且各景观类型趋于均匀分布
香农均匀度	SHEI	SHEI反映景观中各斑块类型在面积上分布的均匀程度,其值越趋于1景观分布越均匀

表4

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表7

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区域名称	第二产业产值/万元	第三产业产值/万元	非农人口/个
澄江街道	154.750	336.600	258 039
南闸街道	27.050	44.080	5456
高新区	378.340	199.470	72 876
临港经济开发区	417.450	295.340	81 538
月城镇	31.730	27.370	6305
青阳镇	32.760	33.420	8578
徐霞客镇	77.550	42.610	29 598
云亭街道	50.920	51.480	28193
周庄镇	240.930	100.630	46 749
祝塘镇	60.610	31.340	18 236
华士镇	206.140	107.580	22 819
长泾镇	29.720	32.250	20 778
新桥镇	102.020	83.090	1538
顾山镇	38.960	39.930	19 091

景观	区域	AREA_MN	PD	LSI	FRAC_AM	AI	COHESION	DIVISION
耕地	中心城区	23.1111	0.1362	2.7500	1.0117	22.2222	23.2475	0.9998
	边缘区	82.2472	0.3398	18.4918	1.0904	39.6834	72.6685	0.9982
	乡村腹地	1242.0000	0.0486	18.9464	1.2388	67.0384	98.2999	0.8350
林地	边缘区	57.6000	0.0382	5.0000	1.0552	46.4567	63.1566	0.9999
林地	乡村腹地	150.2222	0.0219	5.0769	1.0626	66.0256	77.3700	0.9998
草地	中心城区	20.8000	0.1513	2.8750	1.0065	16.6667	16.1449	0.9999
	边缘区	21.8537	0.0783	6.6667	1.0232	12.3711	28.5565	1.0000
	乡村腹地	24.5333	0.0364	5.6429	1.0187	16.6667	25.4607	1.0000
水域	中心城区	19.5556	0.1362	2.8571	1.0034	13.3333	11.6250	0.9999
	边缘区	25.9200	0.1909	10.4231	1.0201	17.7852	28.4724	1.0000
	乡村腹地	56.5333	0.2004	10.7755	1.0989	57.1173	84.9260	0.9946
建设用地	中心城区	2005.3333	0.0454	3.1795	1.1004	88.0785	98.3152	0.3988
	边缘区	561.0847	0.1126	16.1319	1.1734	65.9748	95.2245	0.9308
	乡村腹地	50.8235	0.4750	25.9296	1.0642	26.6473	56.5048	0.9997

景观	区域	AREA_MN	PD	LSI	FRAC_AM	AI	COHESION	DIVISION
耕地	中心城区	27.7895	0.2399	4.3333	1.0161	25.9259	31.0707	0.9996
	边缘区	105.2727	0.2983	19.1594	1.1048	44.2368	79.0000	0.9967
	乡村腹地	1391.0303	0.0444	17.8056	1.2467	67.7620	98.6820	0.7853
林地	中心城区	16.0000	0.0126	1.0000	1.0000	-	0.0000	1.0000
	边缘区	82.4000	0.0339	5.1905	1.0545	52.4324	66.2776	0.9999
	乡村腹地	141.0000	0.0215	4.6667	1.0591	65.8915	76.2608	0.9998
草地	中心城区	20.0000	0.1515	3.3750	1.0056	13.6364	14.0103	0.9999
	边缘区	21.4468	0.0797	6.9375	1.0123	13.6364	19.9432	1.0000
	乡村腹地	23.6190	0.0282	4.4167	1.0152	18.0000	24.1514	1.0000
水域	中心城区	23.1111	0.1136	2.8750	1.0201	16.6667	25.0755	0.9999
	边缘区	28.0727	0.1865	11.1786	1.0298	20.3911	37.3779	0.9999
	乡村腹地	58.7020	0.2030	10.1250	1.1001	58.6792	85.5433	0.9937
建设用地	中心城区	2309.3333	0.0379	3.3810	1.0912	87.8641	97.3064	0.6050
	边缘区	482.2222	0.1221	16.8617	1.1728	64.8846	94.7853	0.9480
	乡村腹地	45.1337	0.5027	24.6000	1.0576	24.9878	53.3212	0.9997

景观指数	本文方法			不同指标方法
景观指数	建成区	边缘区	乡村腹地	建成区	边缘区	乡村腹地
PD	0.4691	0.7598	0.7823	0.5556	0.7204	0.7997
LPI	76.7554	20.3421	39.1059	59.5960	17.7651	45.0538
LSI	3.3659	16.4304	19.6181	3.7778	16.9795	18.5328
FRAC_AM	1.0921	1.1375	1.1730	1.0816	1.1379	1.1791
AI	81.7670	54.8965	55.6802	79.4382	54.8470	56.5867
COHESION	95.1307	90.4746	95.2351	92.7071	90.1185	95.9542
CONTAG	72.2574	40.8016	40.8820	70.1049	43.3433	42.5859
DIVISION	0.3985	0.9446	0.8291	0.6044	0.9289	0.7785
SPLIT	1.6624	18.0450	5.8515	2.5279	14.0619	4.5153
SHDI	0.3180	0.8430	0.5750	0.4550	0.8360	0.5990
SHEI	0.2884	0.5894	0.6518	0.3179	0.6211	0.6383

基于空间突变特征的城市边缘区提取方法

Identification Method of Urban Fringe Area based on Spatial Mutation Characteristics

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