中国建设用地扩张对景观格局演化的影响

doi:10.11821/dlxb201912011

摘要/Abstract

摘要：

探索建设用地扩张对景观格局变化的影响机理是理解人类活动对生态系统功能、格局和过程变化影响机制的关键。但中国全国尺度建设用地扩张过程对景观格局演化的影响研究明显不足。基于此,突破传统行政区划单元的分析方法,以覆盖全国的10 km格网为基本单元,基于1975-2014年建成区比率数据来表征建设用地扩张水平,运用同期土地利用数据计算相应的景观指数,定量刻画了建设用地扩张及景观格局的时空演化过程,并运用空间计量经济模型定量解析了建设用地扩张过程对景观格局演化的影响效应。同时,基于全样本、不同时间点、不同规模城市和不同区域城市的对比揭示建设用地扩张过程对景观格局演化影响的时空差异性。研究结果表明：40年间建成区比率值增长了3倍,高于同期的人口城镇化增长水平,东、中和西部间呈现出显著的梯度差异性,而且差异程度逐步增加,大、中、小城市之间的差异也较为显著。1975-1990年、1990-2000年和2000-2014年3个时间段的年均增速分别为5.87%、2.32%和2.32%。景观格局总体上也经历了显著变化,优势景观类型的主导性逐步降低,景观格局趋于破碎化,复杂度增强,邻近度趋于离散,聚集度逐步降低,结合度渐高,景观多样性和均匀度均有所提高。随着建设用地扩张水平的提高,景观越来越趋于破碎化,人类活动对景观的干扰强度和频率也不断增强。例如,建设用地扩张水平每提高1%将导致景观斑块数量提高0.45%左右。但是建设用地扩张水平与景观复杂度的变化在空间上存在错位。建设用地扩张水平的提高在一定程度上使同类型斑块间的距离变近,景观聚集度出现降低趋势,景观越来越呈现多种要素的密集格局,斑块聚合和集群性增强,相应的结合度也越来越高,景观均衡化趋势和均匀分布越来越明显。同时发现,不同规模城市和不同区域的影响差异显著,除地理区位因素差异性不显著外,其他控制要素也对景观格局演化具有差别化的影响。

关键词: 建设用地扩张, 景观格局, 影响机制, 空间计量模型, 时空异质性, 中国

Abstract:

Exploring the impact mechanisms of construction land expansion on landscape pattern changes is one of the critical tactics to understanding how human activities affect ecosystem function, pattern, and process change. However, the lack of macro-scale data and spatial analysis methods on construction land expansion and landscape has hindered China's national scale study. In response to this, here we use the recently available built-up area ratio data and the corresponding land use data to characterize the spatiotemporal process of construction land expansion and landscape metrics between 1975 and 2014 based on the 10 km grids at the national scale. And the spatial econometric model was adopted to quantitatively investigate the influence mechanism of construction land expansion process on landscape pattern evolution. At the same time, the spatial-temporal heterogeneity of the influence of construction land expansion process on the evolution of landscape pattern is examined based on the comparisons, different scale cities and different regions of China. The study found that the value of built-up area ratio has increased by threefold in the past 40 years, which is higher than the growth level of demographic urbanization in the same period. The construction land expansion has significant gradient differences between the eastern, the central and the western regions, and the gradation of difference is gradually increasing. Average annual growth rates are 5.87%, 2.32% and 2.32%, respectively, in periods 1975-1990, 1990-2000 and 2000-2014. Meanwhile, the differences between large, medium-sized and small cities are also significant. Owing to the effects of frequent human activities, the landscape pattern has also experienced significant changes. For example, the dominance of leading type has gradually decreased, the landscape pattern has become fragmented, the complexity has increased, the proximity has become more discrete, the contagion has gradually decreased, the cohesion has increased, and landscape diversity and evenness have increased. With the improvement of construction land expansion level, the landscape is becoming more and more fragmented, and the intensity and frequency of human interference to the landscape are also increasing. Every 1% increase in the expansion of construction land, for example, caused a 0.45% increase in the number of patches. However, the changes in construction land expansion level and landscape complexity are spatially mismatched. To the extent that the improvement of the level of construction land expansion has brought the distance between the same types of patches and the contagion index has decreased, and the landscape became more and more dense and aggregating, and the corresponding cohesion degree is also getting higher and higher, and the landscape equalization and even distribution are more and more obvious. At the same time, it is found that the influences of different time periods, different scale cities and different regions are significant for impact mechanisms of construction land expansion on landscape pattern changes. Besides geographical location, the other control factors have different influences on the evolution of landscape pattern.

Key words: construction land expansion, landscape pattern, impact mechanism, spatial econometric model, spatial-temporal heterogeneity, China

李广东, 戚伟. 中国建设用地扩张对景观格局演化的影响[J]. 地理学报, 2019, 74(12): 2572-2591.

LI Guangdong, QI Wei. Impacts of construction land expansion on landscape pattern evolution in China[J]. Acta Geographica Sinica, 2019, 74(12): 2572-2591.

图/表 10

表1

景观指数解释表

分类	指数	公式
面积边缘指数	最大斑块所占景观面积的比例(LPI)	$LPI = max (a ij) A × 100$ a_ij为斑块ij的面积;A为景观总面积。
面积边缘指数	边缘密度(ED)	$ED = E A (10,000)$ E为景观内的斑块的边缘总长度;A为景观总面积。
密度大小及差异指数	斑块数量(NP)	NP=N N为景观内斑块数量。
密度大小及差异指数	平均斑块面积(MPS)	MPS=A/NP A等于总景观面积;NP为景观内斑块数量。
形态复杂度指数	面积加权平均形状指数(AWMSI)	$AWMSI = ∑ i = 1 m ∑ j = 1 n 0.25 P ij a ij a ij A$ P_ij为斑块周长;a_ij为斑块面积;A为斑块总面积。
形态复杂度指数	面积加权平均斑块分形指数(AWMPFD)	$AWMPFD = ∑ i = 1 m ∑ j = 1 n 2 ln (0.25 P ij) ln a ij a ij A$ P_ij为斑块周长;a_ij为斑块面积;A为斑块总面积。
邻近度指数	平均邻近度 (PROX_MN)	$PROX_MN = ∑ g = 1 n a ijg h ijg 2$ a_ijg为斑块ijg与斑块ij的相邻面积;h_ijg为斑块ijg与斑块ij间的距离。
邻近度指数	欧氏平均最邻近距离(ENN_MN)	$ENN_MN = ∑ j = 1 n h ij n i$ h_ij等于离斑块ij最近的同类型邻居斑块的距离;n_i为斑块总数量。
聚散性指数	聚集度指数(CONTAG)	$CONTAG = 1 + ∑ i = 1 m ∑ k = 1 m (p i) × (g ik / ∑ k = 1 m g ik) ln (p i) × (g ik / ∑ k = 1 m g ik) 2 ln (m) × 100$ p_i为斑块类型占景观的比例;g_ik为斑块类型i和k临接数量;m为景观内斑块数量。
聚散性指数	结合度指数(COHESION)	$COHESION = 1 - ∑ i = 1 m ∑ j = 1 n P ij * ∑ i = 1 m ∑ j = 1 n P ij * a ij * × 1 - 1 Z - 1 × 100$ P_ij^为斑块周长(元胞计数);a_ij^为斑块面积(元胞计数);Z为景观中的元胞数量。
多样性指数	香农多样性指数 (SHDI)	$SHDI = - ∑ i = 1 m P i ln P i$ P_i为类型i斑块占景观内斑块的比例。
多样性指数	香农均度指数(SHEI)	$SHEI = - ∑ i = 1 m P i ln P i / ln m$ P_i为类型i斑块占景观内斑块的比例;m为斑块数量。

表1

图1

图2

图3

图4

图5

表2

表3

不同规模城市空间面板回归模型计算结果

因变量	Expansion			DEM			Slope			Dis_city			Dis_county
因变量	大城市	中等城市	小城市	大城市	中等城市	小城市	大城市	中等城市	小城市	大城市	中等城市	小城市	大城市	中等城市	小城市
LPI	-13.6524^a	-42.4918^a	-51.0103^a	-0.0017^a	-0.0028^a	-0.0022^a	-0.0612^a	0.0465^a	0.1208^a	0.0001^c	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
ED	0.5650^a	2.8146^a	4.1404^a	0.0002^a	0.0003^a	0.0002^a	0.0027	-0.0073^a	-0.0149^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
NP	-0.6139^a	2.4466^a	6.5400^a	0.0003^a	0.0006^a	0.0003^a	-0.0125^a	-0.0272^a	-0.0406^a	0.0001^a	0.0000	0.0001^b	0.0001^a	0.0001^a	0.0001^a
MPS	97.8616^b	-535.4359^a	-682.0202^a	0.0893^a	0.1322^a	0.0764^a	3.2694^a	4.7074^a	6.9650^a	0.0007^a	-0.0001	0.0011^a	0.0056^a	0.0057^a	0.0051^a
AWMSI	-0.0492^a	-0.1763^a	-0.2235^a	0.0001^a	0.0001^a	0.0001^a	-0.0003	0.0003	-0.0003	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
AWMPFD	-0.0008	-0.0045^b	-0.0048	0.0001^a	0.0001^a	0.0001^a	0.0000	0.0000	0.0000	0.0001^b	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
PROX_MN	0.9067^a	1.4117^a	1.3282^a	0.0002^a	0.0003^a	0.0002^a	0.0126^a	0.0079^a	0.0018	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
ENN_MN	-319.6872^a	-459.1625^a	-913.3113^a	-0.1075^a	-0.0931^a	-0.1332^a	-5.6793^a	-2.1100^c	-2.9725^a	0.0012^a	0.0000	-0.0003^b	0.0017^a	0.0001	-0.0003
CONTAG	-20.4752^a	-42.6078^a	-44.6049^a	-0.003^a	-0.0053^a	-0.0026^a	-0.0660^b	0.2004^a	0.0149	0.0001^a	0.0000	0.0001^c	0.0001^a	0.0001^a	0.0001^a
COHESION	-2.1468^a	-14.5988^a	-16.5450^a	0.0003^a	-0.0003^a	-0.0003^a	-0.0386^a	0.0130^c	0.0267^a	0.0000	0.0001^c	0.0000	0.0000	0.0000	0.0001^a
SHDI	0.1704^a	0.6539^a	0.9141^a	0.0001^a	0.0001^a	0.0001^a	-0.0003	-0.0013^a	-0.0026^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
SHEI	0.1507^a	0.3921^a	0.5242^a	0.0001^a	0.0001^a	0.0001^a	0.0011^a	-0.0001	-0.0003^b	0.0000	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
因变量	大城市		中等城市		小城市
	δ或λ	选取模型	δ或λ	选取模型	δ或λ	选取模型
LPI	0.6900^a	SAR-TFE	0.7090^a	SAR-TFE	0.7000^a	SAR-TFE
ED	0.7460^a	SAR-TFE	0.7310^a	SAR-TFE	0.7550^a	SAR-TFE
NP	0.7030^a	SAR-TFE	0.6730^a	SAR-TFE	0.6620^a	SAR-TFE
PD	0.7330^a	SAR-TFE	0.7040^a	SAR-TFE	0.6960^a	SAR-TFE
MPS	0.5920^a	SEM-TFE	0.6040^a	SEM-TFE	0.6290^a	SEM-TFE
AWMSI	0.6070^a	SAR-TFE	0.5750^a	SAR-TFE	0.5980^a	SAR-TFE
AWMPFD	0.6200^a	SAR-TFE	0.5930^a	SAR-TFE	0.6330^a	SAR-TFE
PROX_MN	0.5000^a	SAR-TFE	0.5370^a	SAR-TFE	0.5270^a	SAR-TFE
ENN_MN	0.1720^a	SAR-TFE	0.2230^a	SAR-TFE	0.2290^a	SAR-TFE
CONTAG	0.6370^a	SEM-TFE	0.6340^a	SEM-TFE	0.6340^a	SAR-TFE
COHESION	0.7250^a	SAR-TFE	0.6990^a	SAR-TFE	0.6910^a	SAR-TFE
SHDI	0.7480^a	SAR-TFE	0.7350^a	SAR-TFE	0.7500^a	SAR-TFE
SHEI	0.6540^a	SAR-TFE	0.6570^a	SAR-TFE	0.6679^a	SAR-TFE

表3

表4

不同区域空间面板回归模型计算结果

因变量	Expansion			DEM			Slope			Dis_city			Dis_county
因变量	东部地区	中部地区	西部地区	东部地区	中部地区	西部地区	东部地区	中部地区	西部地区	东部地区	中部地区	西部地区	东部地区	中部地区	西部地区
LPI	-29.5331^a	-32.6791^a	-46.4523^a	0.0116^a	-0.0013^a	-0.0006^b	0.5549^a	0.1862^a	-0.0497^c	0.0001^a	0.0001^a	0.0000	0.0001^a	0.0001^a	0.0001^a
ED	1.0025^a	1.7322^a	3.8824^a	0.0000	-0.0006^a	-0.0001^a	-0.0242^a	-0.1080^a	0.0075^a	0.0001^a	0.0000	0.0001^a	0.0001^a	0.0001^a	0.0001^a
NP	-0.3719^b	1.6604^a	7.5169^a	0.0004^a	0.0004^a	-0.0005^a	-0.0539^a	-0.0551^a	-0.0368^a	0.0001^c	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
MPS	167.4843^a	-142.1613^b	-1073.6834^a	0.6892^a	0.0771^a	0.0485^a	14.8995^a	3.5402^a	-8.3365^a	-0.0027^a	0.0002^a	0.0024^a	0.0018^a	0.0041^a	0.0087^a
AWMSI	-0.0673^a	-0.3209^a	-0.0367	0.0001^a	0.0001^a	0.0001^a	-0.0060^a	-0.0008^b	0.0061^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
AWMPFD	-0.0016^c	-0.0126^a	0.0033	0.0001^a	0.0000	0.0001^a	-0.0003^a	0.0001^c	0.0003^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
PROX_MN	1.7221^a	1.6678^a	0.3876^c	0.0003^a	0.0002^a	0.0001^a	-0.0119^a	-0.0154^a	0.0323^a	0.0001^a	0.0001^a	0.0000	0.0001^a	0.0001^a	0.0001^a
ENN_MN	-247.2975^a	-622.4963^a	-242.1647^c	-0.1610^a	-0.1076^a	-0.0661^a	8.6756^a	-8.6759^a	-10.0131^a	-0.0003	0.0009^a	-0.0012^a	0.0014^a	-0.0021^a	-0.0007^a
CONTAG	-22.4641^a	-26.0176^a	-21.7274^a	0.0012^c	-0.0018^a	-0.0007^a	0.5581^a	0.0857^a	-0.2744^a	0.0001^a	0.0001^a	0.0000	0.0001^a	0.0001^a	0.0001^a
COHESION	-4.0664^a	-9.1757^a	-8.8506^a	0.0078^a	-0.0002^c	-0.0001^a	0.0561^a	0.0380^a	-0.0100^a	0.0001^a	0.0001^a	0.0000	0.0000	0.0001^a	0.0001^a
SHDI	0.2426^a	0.5348^a	0.5960^a	0.0001^a	0.0001^b	0.0001^a	-0.0020^a	-0.0032^a	0.0000	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a	0.0001^a
SHEI	0.1778^a	0.3212^a	0.2712^a	0.0001^a	0.0001^a	0.0001^a	-0.0013^a	-0.0010^a	0.0023^a	0.0001^a	0.0000	0.0001^a	0.0001^a	0.0001^a	0.0001^a
因变量	东部地区		中部地区		西部地区
	δ或λ	选取模型	δ或λ	选取模型	δ或λ	选取模型
LPI	0.7470^a	SEM-TFE	0.6660^a	SAR-TFE	0.6940^a	SEM-TFE
ED	0.7460^a	SAR-TFE	0.7780^a	SEM-TFE	0.7770^a	SEM-TFE
NP	0.7010^a	SAR-TFE	0.6280^a	SAR-TFE	0.7080^a	SEM-TFE
PD	0.7240^a	SAR-TFE	0.6060^a	SAR-TFE	0.7000^a	SAR-TFE
MPS	0.7030^a	SEM-TFE	0.5620^a	SAR-TFE	0.6390^a	SEM-TFE
AWMSI	0.6160^a	SAR-TFE	0.5830^a	SAR-TFE	0.5830^a	SAR-TFE
AWMPFD	0.6400^a	SAR-TFE	0.5940^a	SAR-TFE	0.6090^a	SAR-TFE
PROX_MN	0.5080^a	SEM-TFE	0.5410^a	SEM-TFE	0.4600^a	SEM-TFE
ENN_MN	0.1940^a	SAR-TFE	0.2340^a	SAR-TFE	0.2090^a	SEM-TFE
CONTAG	0.6679^a	SEM-TFE	0.5930^a	SAR-TFE	0.6260^a	SAR-TFE
COHESION	0.7270^a	SEM-TFE	0.6389^a	SAR-TFE	0.6860^a	SAR-TFE
SHDI	0.7510^a	SAR-TFE	0.7220^a	SAR-TFE	0.7350^a	SAR-TFE
SHEI	0.6690^a	SAR-TFE	0.6230^a	SAR-TFE	0.6640^a	SAR-TFE

表4

图6

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因变量	Expansion	DEM	Slope	Dis_city	Dis_county	δ或λ	R²	LM空间滞后	LM空间误差	稳健LM空间滞后	稳健LM空间误差	选取模型
LPI	-35.694^a	-0.0019^a	0.4248^a	0.0001^a	0.0001^a	0.7170^a	0.4370	74439.29^a	74680.49^a	124.15^a	365.35^a	SEM-TFE
ED	0.5650^a	0.0002^a	0.0026	0.0002^a	-0.0001^a	0.7460^a	0.5009	100450.35^a	99295.71^a	1155.18^a	0.54	SAR-TFE
NP	0.4538^a	0.0001^a	-0.1174^a	-0.0001^a	-0.0002^a	0.7130^a	0.4333	73644.32^a	73912.35^a	46.73^a	314.76^a	SEM-TFE
MPS	-88.3851^a	0.0267^a	1.2719^a	0.0004^a	0.0041^a	0.6260^a	0.3766	54604.60^a	54125.59^a	491.19^a	12.18^a	SAR-TFE
AWMSI	-0.1064^a	0.0001^a	0.0000	0.0001^a	-0.0001^a	0.6100^a	0.3222	49524.11^a	48513.74^a	1280.89^a	270.51^a	SAR-TFE
AWMPFD	-0.0030^a	0.0001^a	-0.0001^a	0.0002^a	0.0003^a	0.6400^a	0.3510	55127.28^a	54078.46^a	1247.60^a	198.78^a	SAR-TFE
PROX_MN	0.7655^a	0.0001^a	0.0068^a	0.0004^a	-0.0001^a	0.5280^a	0.2234	27772.44^a	27240.46^a	711.02^a	179.03^a	SAR-TFE
ENN_MN	-417.5628^a	-0.0943^a	-3.4665^a	-0.0001^c	-0.0008^a	0.2300^a	0.0451	3080.09^a	3030.71^a	115.46^a	66.08^a	SAR-TFE
CONTAG	-18.6014^a	-0.0018^a	-0.0215^a	-0.0002^a	0.0002^a	0.6380^a	0.3596	54960.28^a	54290.98^a	671.80^a	2.50	SAR-TFE
COHESION	-6.0098^a	-0.000	0.0075^a	-0.0001^a	0.0001^a	0.7010^a	0.4275	78185.94^a	77967.15^a	322.13^a	103.34^a	SAR-TFE
SHDI	0.3091^a	0.0001^a	-0.0017^a	0.0003^a	-0.0002^a	0.7440^a	0.4994	97540.60^a	95740.24^a	1827.14^a	26.78^a	SAR-TFE
SHEI	0.2104^a	0.0001^a	-0.0000	0.0001^a	0.0001^a	0.6710^a	0.3928	63597.89^a	63570.92^a	246.65^a	219.69^a	SAR-TFE