中国建设用地扩张对景观格局演化的影响

doi:10.11821/dlxb201912011

Abstract

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

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

Figures/Tables 10

Tab. 1

Interpretation table for landscape metrics

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。

Tab. 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tab. 2

Tab. 3

Result of spatial panel regression for different city scales

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

Tab. 3

Tab. 4

Result of spatial panel regression for different regions of China

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

Tab. 4

Fig. 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

Impacts of construction land expansion on landscape pattern evolution in China

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