Identification and optimization of hierarchical ecological nodes based on multi-target genetic algorithm: Take Jintan district of Changzhou as an example

Xiao-lin ZHANG; Xiao-bin JIN; Qing-li ZHAO; Jie REN; Bo HAN; Xin-yuan LIANG; Yin-kang ZHOU

doi:10.31497/zrzyxb.20200115

Journal of Natural Resources, Volume. 35, Issue 1, 174(2020)

Identification and optimization of hierarchical ecological nodes based on multi-target genetic algorithm: Take Jintan district of Changzhou as an example

Xiao-lin ZHANG1... Xiao-bin JIN1,2,3,*, Qing-li ZHAO4, Jie REN1, Bo HAN1, Xin-yuan LIANG1, and Yin-kang ZHOU1,23 |Show fewer author(s)

Author Affiliations

¹College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China

²Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing 210023, China

³Jiangsu Provincial Land Development and Consolidation Technology and Engineering Center, Nanjing 210023, China

⁴Land Reclamation Center of the Ministry of Natural Resources, Beijing 100035, China

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Figures & Tables(12)

Fig. 1. Analysis of ecological nodes

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Fig. 2. The construction of multi-level ecological nodes

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Fig. 3. Location of the study area

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Fig. 4. The distribution of strategic points and their impact ranges under different initial populations

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Fig. 5. Preliminary ecological network and node distribution in the study area

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Fig. 6. Ecological network and node distribution in the study area

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Table 1. Data sources and description

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Table 1. Data sources and description

数据类型	数据产品	来源	空间分辨率
土地利用数据	土地变更调查数据(2015年)	金坛区土地变更调查成果数据库	1∶10000
遥感影像数据	Landsat 8 OLI_TRIS(2015年)	地理空间数据云	30 m
基础设施数据	POI数据(2015年)	高德地图API	—
江苏省生态保护红线	江苏省国家级生态保护红线(2018年)	江苏省自然资源厅	1∶50000
DEM	ASTER GDEM V2版	地理空间数据云	30 m

Table 2. Resistance coefficient and weight assignment for different types

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Table 2. Resistance coefficient and weight assignment for different types

影响因子	类型分级	阻力	权重
土地利用类型 (土地利用阻力)	林地	1	0.3
	草地	4
	其他用地	120
	耕地	400
	水域	400
	道路用地	800
	建设用地	1000
植被覆盖度 (景观格局阻力)	>0.8	1	0.25
	0.6~0.8	20
	0.3~0.6	200
	0.1~0.3	500
	<0.1	1000
基础设施数量 (基础设施阻力)	<40	1	0.25
	40~145	200
	145~390	700
	>390	1000
距道路中心线距离/m (道路阻力)	100~200	200	0.2
	50~100	500
	<50	1000

Table 3. Indicators for ecological network performance analysis

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Table 3. Indicators for ecological network performance analysis

指标	公式	描述	意义
节点覆盖率C	$\frac{S 1 + S 2 + S 3}{S 总} \times 100 %$	表征生态节点影响范围面积与区域整体生态用地面积比率。其中 $S 1 、 S 2 、 S 3 为资源型战略点、$ 结构型战略点、结构型薄弱点影响范围 $; S 总$ 为区域总面积	节点覆盖率越高,代表区域生境斑块联系越紧密,生态网络越稳定。单个生态节点覆盖率越大,生态节点发挥效能越大,反之亦然
节点分布均匀度DE	—	—	节点分布均匀性越低,代表生态节点在研究区内的空间分布越均匀,节点发挥效能越大
平均节点连接数A	—	—	平均节点连接数越大表示通过网络的路径越多,节点发挥效能越大,网络连接越复杂
平均聚类系数CC	$\frac{\overset{N}{\sum_{i = 1}} C C_{1} (i)}{N}$ $其中 C C_{1} (i) = 2 E_{1} (i) / (k_{i} (k_{i} - 1))$	N为整个网络中节点数;CC₁(i)为节点i的聚类系数;k_i为节点i的邻居;E₁(i)为k_i节点实际存在的边数;k_i(k_i-1)/2为理论上最大边数	平均聚类系数越高,表明在生态网络格局中景观生态流的流动性越强,生态网络更稳定
网络连通性J	$[\overset{n}{\sum_{i = 1}} m_{i}] / N$	m_i为第i节点所邻接的边数	网络的连通性越高,其稳定性越好
网络密度D	$\frac{2 M}{N (N - 1)}$	M为网络中实际连接数	复杂网络中节点之间连接的紧密性

Table 4. Comparison of different resource-based strategic points
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Table 4. Comparison of different resource-based strategic points
初始种群
个数/个节点覆盖
面积/km² 平均节点
覆盖率/% 节点
连通率/%
Pop=50 625.15 1.28 96.00
Pop=30 473.95 1.62 86.67
Pop=20 231.03 1.18 70.00
Pop=10 125.06 1.28 50.00

Table 5. Comparison of preliminary ecological network evaluation results
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Table 5. Comparison of preliminary ecological network evaluation results
初级生态
网络节点覆盖率C/% 节点分布均匀度DE 平均节点连接数A 平均聚类系数CC 网络连通性J 网络密度D
N1 28.362 0.174 2.724 0.429 4.938 0.159
N2 48.574 0.099 3.154 0.441 5.125 0.165

Table 6. Comparison of ecological network evaluation results
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Table 6. Comparison of ecological network evaluation results
生态
网络节点覆盖率C/% 节点分布均匀度DE 平均节点连接数A 平均聚类系数CC 网络连通性J 网络密度D
N1 75.608 0.022 1.151 0.182 2.282 0.006
N2 93.305 0.012 1.120 0.341 2.211 0.005

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Xiao-lin ZHANG, Xiao-bin JIN, Qing-li ZHAO, Jie REN, Bo HAN, Xin-yuan LIANG, Yin-kang ZHOU. Identification and optimization of hierarchical ecological nodes based on multi-target genetic algorithm: Take Jintan district of Changzhou as an example[J]. Journal of Natural Resources, 2020, 35(1): 174

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