Study on the Delimitation of Urban Growth Boundary based on FLUS Model and Kinetic Energy Theorem

Kang HUANG; Wenyuan DAI; Wangli HUANG; Hui OU

doi:10.12082/dqxxkx.2020.190415

Journals >Journal of Geo-information Science >Volume 22 >Issue 3 >Page 557 > Article

Journal of Geo-information Science
Vol. 22, Issue 3, 557 (2020)

Study on the Delimitation of Urban Growth Boundary based on FLUS Model and Kinetic Energy Theorem

Kang HUANG^{1、1、2、2}, Wenyuan DAI^{1、1、2、2、3、3、*}, Wangli HUANG^{1、1、2、2、3、3}, and Hui OU^{1、1、2、2}

Author Affiliations

¹Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou 350007, China

¹福建师范大学湿润亚热带生态地理过程教育部重点实验室,福州 350007

²School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China

²福建师范大学地理科学学院,福州 350007

³Institute of Geography, Fujian Normal University, Fuzhou 350007, China

³福建师范大学地理研究所,福州 350007

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DOI: 10.12082/dqxxkx.2020.190415 Cite this Article

Kang HUANG, Wenyuan DAI, Wangli HUANG, Hui OU. Study on the Delimitation of Urban Growth Boundary based on FLUS Model and Kinetic Energy Theorem[J]. Journal of Geo-information Science, 2020, 22(3): 557 Copy Citation Text

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Fig. 1. Technical route for delimiting the inertial boundary of urban growth

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Fig. 2. Location map of Fuzhou central city in 2015

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Fig. 3. Factors affecting urban expansion of Fuzhou central city

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Fig. 4. Land use status from 2000 to 2015 and simulation forecast results from 2009 to 2027

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Fig. 5. Comparison of UGB delineation in different window sizes

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Fig. 6. Urban land growth inertia boundary in Fuzhou central city in 2030

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传感器	日期	轨道号	多光谱波段分辨率/m
Landsat-05	2000-06-29	119/042	30
Landsat-08	2015-09-27	119/042	30

Table 1. Remote sensing image data information of Fuzhou central city

	生产者精度（PA）										Kappa	OA
年份	耕地	园地	林地	草地	交通运输用地	水域及水利设施用地	其他土地	城镇用地	村庄	工矿用地	Kappa	OA
2009	0.8048	0.9857	0.9880	0.8706	0.2643	0.9526	0.7508	0.7656	0.7790	0.8911	0.8603	0.8981
2015	0.8725	0.9395	0.9917	0.9475	0.6379	0.9698	0.8849	0.8871	0.8695	0.9438	0.9165	0.9389

Table 2. Accuracy evaluation of FLUS model

方向	N	NNE	NEE	E	SEE	SSE	S	SSW	SWW	W	NWW	NNW
v₁（km/a）	2.99×10^-3	6.71×10^-3	2.07×10^-2	2.59×10^-3	7.26×10^-3	3.22×10^-3	2.28×10^-3	3.89×10^-3	2.32×10^-3	3.47×10^-3	3.12×10^-3	1.62×10^-3
v₂（km/a）	4.93×10^-3	3.46×10^-3	2.38×10^-3	1.95×10^-3	4.89×10^-3	5.28×10^-3	1.56×10^-3	2.15×10^-3	1.07×10^-3	1.75×10^-3	4.22×10^-3	1.06×10^-3
a（km/a²）	1.61×10^-4	2.71×10^-4	2.62×10^-5	5.29×10^-5	1.98×10^-4	1.72×10^-4	6.01×10^-5	1.45×10^-4	1.04×10^-4	1.43×10^-4	9.17×10^-5	4.69×10^-5
平均坡度/%	100.0438	118.8172	205.8168	105.0857	16.2596	96.9632	210.7418	203.8767	225.3412	317.7963	40.3332	214.0893
坡度摩擦系数	0.0539	0.0640	0.1109	0.0566	0.0088	0.0523	0.1136	0.1099	0.1215	0.1713	0.0217	0.1154
用地摩擦系数	3.7742	2.8323	30.4088	18.4963	2.8350	12.1389	20.7054	18.5137	19.1656	21.5786	6.8599	21.1438
总摩擦系数	1.9140	1.4482	15.2599	9.2765	1.4219	6.0956	10.4095	9.3118	9.6435	10.8750	3.4408	10.6296
惯性距离/m	39.3777	15.2297	7.1216	3.8815	42.4928	13.2985	1.9418	1.7098	0.5755	0.9841	28.2787	1.1188

Table 3. Parameters （speed, acceleration, slope and friction coefficient） and inertia distance in all directions

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