基于DEM的交通线文化景观感知与功能分段研究——紫荆关长城景观的实证

doi:10.13249/j.cnki.sgs.2015.09.1086

Abstract

Abstract:

Landscape perception is the result of the interaction between human and landscape. Viewshed analysis and visibility analysis are used for Landscape perception researches as important means. In this article, based on the existing landscape perception concepts and models, we try to establish a model named traffic line landscape perception degree model to solve the research and compute problems of the large-scale landscape perception degree on specific linear spatial units or areas. The model is actually the aggregate of single-point, multi-point and group scale traffic line landscape perception degree which is divided by the size and characteristics of the landscapes. And we build three formulas to validate the method of our model about landscape perception calculation. The approach is tested using a popular traffic line through the Zijingguan section of Great Wall in Hebei Province. Firstly, for quantitative calculation of the Zijingguan landscape perception near the highway and the railway line, the DEM data are used to calculate landscape viewshed analysis with some weighting factors such as resources value and status of remains. Aspect, distance and some others can also be weighting factors of a landscape relative to the observer. Then, according to the distribution of landscape perception along traffic line, the best sensing function sections and the best viewing positions of the single-point landscapes including enemy units, beacon towers, horse face, wall landscapes, various groups landscapes and integrated landscapes are divided accurately. Finally, we know that the continuity of highways perceived position is much stronger while the ones of the railways are relatively discrete, and the highway perception effect is better than that of railway perception. It is mainly attributed to the undulating terrain. The traffic line landscape perception degree model is particularly suitable for the analysis of large-scale landscapes, magnificent buildings and other landscape perception instead of delicate landscape in a close distance, and it can quantitatively calculate the distribution of landscapes along the traffic line and determine their linear distribution pattern. The raster images enable the planners or tourists to identify areas where the peak of emotional responses may occur in order to maximize viewing opportunities of beautiful landscapes and areas, or find potential locations to view landscape along traffic line. In this way, the railways, highways, rural roads, other ground transit routes and waterways can be used as non-specialized traveling space carriers in the fast viewing process of landscapes. This research represents the traffic line landscape perception degree model as a kind of extension of the tourism landscape planning and design methods, which can expand the tourism landscape planning and design from the inside tourism area to the outside. The model is also meaningful in the study of tourism landscape guiding design, and the researches of tourism culture transmission ways and efficiency.

Key words: landscape perception degree, cultural landscape, traffic line, function division, DEM, Zijingguan Great Wall

CLC Number:

P901

Ren-jie LI, Feng GU, Feng-hua GUO, Xue-qing FU. Cultural Landscape Perception Degree Model and Perception Function Division Based on DEM of Traffic Line: A Case Study of Zijingguan Great Wall[J].SCIENTIA GEOGRAPHICA SINICA, 2015, 35(9): 1086-1094.

Figures/Tables 9

Fig.1

Table 1

Table 2

Fig.2

Table 3

Table 4

Fig.3

Fig.4

Table 5

References 26

[1]	Zube E H,Sell J L,Taylor J G.Landscape perception: Research, application and theory[J]. Landscape Planning, 1982, 9(1):1-33. doi: 10.1016/0304-3924(82)90009-0
[2]	Kaplan R,KaplanS.The experience of nature: A psychological perspective[M].New York:Cambridge University Press,1989.
[3]	Swanwick C.Society's attitudes to and preferences for land and landscape[J].Land Use Policy,2009,26S:S62-S75.
[4]	De Groot W T,Van den Born R J G.Visions of nature and landscape type preferences: an exploration in the Netherlands[J]. Landscape and UrbanPlanning,2003,63(3):127-138. doi: 10.1016/S0169-2046(02)00184-6
[5]	Daniel T C.Whither Scenic beauty? Visual landscape quality assessment in the 21st century[J].Landscape and Urban Planning,2001,54(1/4): 267-281. doi: 10.1016/S0169-2046(01)00141-4
[6]	张林波,王维,吴春旭.基于GIS的视觉景观影响定量评价方法理论与实践[J].生态学报,2008,28(6):2784~2791.
[7]	俞孔坚. 景观敏感度与景观阈值评价研究[J].地理研究,1991,10(2):38~51.
[8]	XU Jianhua.A study on landscape mosaic structure in urban-rural area in northwest of China with RS and GIS —A Case Study of Xigu District in Lanzhou City[J]. Chinese Geographical Science,2001,11(4):336-376. doi: 10.1007/s11769-001-0049-6
[9]	XU Yue.Application of GIS Spatial Analysis Method in Landscape Planning and Design —A Case Study of Integrated Land-Use Suitability Analysis of Nanjing Zhongshan Scenic Area[J]. Advances in Computer Science and Education Applications,2011,(202):67-73.
[10]	Gary R Clay,Terry C Daniel.Scenic landscape assessment: the effects of land management jurisdiction on public perception of scenic beauty[J].Landscape and Urban Planning,2000,(49):1-13. doi: 10.1016/S0169-2046(00)00055-4
[11]	Bishop I D,Hulse D W.Prediction of scenic beauty using mapped data and geographic information systems[J]. Landscape and Urban Planning,1994,(30):59-70. doi: 10.1016/0169-2046(94)90067-1
[12]	Vander Zanden E H. Modelling the spatial distribution of linear landscape elements in Europe[J]. Ecological Indicators,2013,(27):125-136. doi: 10.1016/j.ecolind.2012.12.002
[13]	Schirpke U.Predicting scenic beauty of mountain regions[J]. Landscape and Urban Planning,2013,(111):1-12. doi: 10.1016/j.landurbplan.2012.11.010
[14]	Sugimoto K.Quantitative measurement of visitors’ reactions to the settings in urban parks: Spatial and temporal analysis of photographs[J]. Landscape and Urban Planning,2013,(110):59-63. doi: 10.1016/j.landurbplan.2012.10.004
[15]	Sugimoto K.Analysis of Scenic Perception and Its Spatial Tendency: Using Digital Cameras, GPS loggers, and GIS[J]. Procedia - Social and Behavioral Sciences,2011,(21):43-52. doi: 10.1016/j.sbspro.2011.07.010
[16]	王守成,郭风华,傅学庆.基于自发地理信息的旅游地景观关注度研究——以九寨沟为例[J].旅游学刊,2014, 29(2):84~92. doi: 10.3969/j.issn.1002-5006.2014.02.009
[17]	Brabyn L,Mark D M.Using viewsheds, GIS, and a landscape classification to tag landscape photographs[J].Applied Geography, 2011,(31):1115-1122. doi: 10.1016/j.apgeog.2011.03.003
[18]	肖笃宁,解伏菊,魏建兵.景观价值与景观保护评价[J].地理科学,2006,26(4): 506~512. doi: 10.3969/j.issn.1000-0690.2006.04.020
[19]	Chamberlain B C,Meitner M J.A route-based visibility analysis for landscape management[J]. Landscape and Urban Planning 2013,(111):13-24. doi: 10.1016/j.landurbplan.2012.12.004
[20]	李仁杰. 虚拟地理环境中的旅游景观感知度定量计算[D].石家庄:河北师范大学,2013.
[21]	李仁杰,路紫,李继峰.山岳型风景区观光线路景观感知敏感度计算方法——以武安国家地质公园奇峡谷景区为例[J].地理学报,2011,66(2):245~256. doi: 10.11821/xb201102010
[22]	阎建忠. 高原交通干线对土地利用和景观格局的影响——以兰州至格尔木段为例[J].地理学报,2003,58(1): 34~44.
[23]	张慧. 青藏铁路沿线景观保护评价方法研究[J].生态学报,2004,24(3):574~582.
[24]	杨仲元,卢松.交通发展对区域旅游空间结构的影响研究——以皖南旅游区为例[J].地理科学,2013,33(7): 806~814.
[25]	汪德根,陈田,李立,等.国外高速铁路对旅游影响研究及启示[J].地理科学,2012,32(3): 322~328.
[26]	赵传唏. 旅游景观美的观赏研究[J].人文地理,1997,12(4):65~67.

编号	类型	遗存		资源		编号	类型	遗存		资源
编号	类型	现状	权重			价值	权重	现状	权重	价值	权重
D1	敌台	差	0.4	全国重点文物	0.8	MZ2	马面	较差	0.6	全国重点文物	0.8
D2	敌台	差	0.4	全国重点文物	0.8	MZ3	马面	一般	0.8	全国重点文物	0.8
D3	敌台	差	0.4	全国重点文物	0.8	MZ4	马面	差	0.4	全国重点文物	0.8
D4	敌台	差	0.4	全国重点文物	0.8	MZ5	马面	差	0.4	全国重点文物	0.8
D5	敌台	差	0.4	全国重点文物	0.8	MZ6	马面	差	0.4	全国重点文物	0.8
D6	敌台	差	0.4	全国重点文物	0.8	MZ7	马面	差	0.4	全国重点文物	0.8
D7	敌台	一般	0.8	全国重点文物	0.8	MZ8	马面	差	0.4	全国重点文物	0.8
D8	敌台	一般	0.8	全国重点文物	0.8	MX1	马面	差	0.4	省级文物	0.6
D9	敌台	差	0.4	全国重点文物	0.8	MX2	马面	差	0.4	省级文物	0.6
F1	烽火台	差	0.4	省级文物	0.6	MX3	马面	较差	0.6	省级文物	0.6
F2	烽火台	一般	0.8	省级文物	0.6	MX4	马面	差	0.4	省级文物	0.6
MZ1	马面	一般	0.8	全国重点文物	0.8	MX5	马面	差	0.4	省级文物	0.6

编号	景观位置	长度(m)	特征点数	编号	景观位置	长度(m)	特征点数
Q1	小金城北墙体	1949.56	65	Q12	紫荆关东城东南墙	247.86	9
Q2	小金城	600.73	20	Q13	紫荆关东城西北墙	150.69	14
Q3	小金城南墙体	83.49	6	Q14	紫荆关西城东南墙	713.94	59
Q4	紫荆关关城东墙	439.26	25	Q15	紫荆关西城南墙	862.33	48
Q5	紫荆关关城南墙	473.35	42	Q16	紫荆关西城西南墙	1727.97	82
Q6	紫荆关关城西北墙	180.80	16	Q17	紫荆关西城1中段	138.04	10
Q7	紫荆关关城北墙	97.81	6	Q18	紫荆关西城2中段	609.57	42
Q8	紫荆关瓮城东墙	47.01	6	Q19	紫荆关西城西北墙	530.09	22
Q9	紫荆关瓮城南墙	462.41	37	Q20	紫荆关西城1北墙	833.47	34
Q10	紫荆关东城东北墙	1731.87	105	Q21	紫荆关西城2北墙	198.87	15
Q11	紫荆关东城中段	557.30	41	Q22	拒马河南岸墙体	243.32	17

编号	可见像元数		可见长度（m）		感知路段占路线百分比		编号	可见像元数		可见长度（m）		感知路段占路线百分比
编号	铁路	公路	铁路		公路	铁路	公路	铁路	公路	铁路	公路	铁路	公路
D1	21	99	1050	4950	15.33%	33.56%	MZ2	29	74	1450	3700	21.17%	25.08%
D2	52	177	2600	8850	37.96%	60.00%	MZ3	39	70	1950	3500	28.47%	23.73%
D3	0	53	0	2650	0.00%	17.97%	MZ4	48	78	2400	3900	35.04%	26.44%
D4	5	31	250	1550	3.65%	10.51%	MZ5	30	54	1500	2700	21.90%	18.31%
D5	26	137	1300	6850	18.98%	46.44%	MZ6	32	68	1600	3400	23.36%	23.05%
D6	39	128	1950	6400	28.47%	43.39%	MZ7	32	42	1600	2100	23.36%	14.24%
D7	18	46	900	2300	13.14%	15.59%	MZ8	14	7	700	350	10.22%	2.37%
D8	46	141	2300	7050	33.58%	47.80%	MX1	9	16	450	800	6.57%	5.42%
D9	5	34	250	1700	3.65%	11.53%	MX2	12	32	600	1600	8.76%	10.85%
F1	53	77	2650	3850	38.69%	26.10%	MX3	39	70	1950	3500	28.47%	23.73%
F2	60	73	3000	3650	43.80%	24.75%	MX4	48	78	2400	3900	35.04%	26.44%
MZ1	32	18	1600	900	23.36%	6.10%	MX5	9	28	450	1400	6.57%	9.49%

编号	像元数		长度（m）		占路线百分比		编号	像元数		长度（m）		占路线百分比
编号	铁路	公路	铁路		公路	铁路	公路	铁路	公路	铁路	公路	铁路	公路
Q1	1	19	50	950	0.73%	6.44%	Q12	61	88	3050	4400	44.53%	29.83%
Q2	1	47	50	2350	0.73%	15.93%	Q13	14	79	700	3950	10.22%	26.78%
Q3	6	35	300	1750	4.38%	11.86%	Q14	6	3	300	150	4.38%	1.02%
Q4	26	7	1300	350	18.98%	2.37%	Q15	13	1	650	50	9.49%	0.34%
Q5	6	3	300	150	4.38%	1.02%	Q16	0	0	0	0	0.00%	0.00%
Q6	32	100	1600	5000	23.36%	33.90%	Q17	9	20	450	1000	6.57%	6.78%
Q7	21	117	1050	5850	15.33%	39.66%	Q18	19	48	950	2400	13.87%	16.27%
Q8	28	32	1400	1600	20.44%	10.85%	Q19	17	36	850	1800	12.41%	12.20%
Q9	27	0	1350	0	19.71%	0.00%	Q20	7	18	350	900	5.11%	6.10%
Q10	35	96	1750	4800	25.55%	32.54%	Q21	21	54	1050	2700	15.33%	18.31%
Q11	42	34	2100	1700	30.66%	11.53%	Q22	18	39	900	1950	13.14%	13.22%

功能段	编号	感知段长度(m)	最佳观赏点	景观感知内容
单点景观感知段	6	299	可连续观赏	敌台：D1~6,8号敌台
	7	792	可连续观赏	马面：MZ2,3号,MX3,5号马面
	11	1368	可连续观赏	烽火台：F1,2号烽火台
墙体景观感知段	10	945	可连续观赏	墙体：32%左右墙体特征点（虽无高感知点位,但感知度连续性强）
墙体景观感知段	13	847	可连续观赏	墙体：34%左右墙体特征点（虽无高感知点位,但感知度连续性强）
组景观感知段	2	337	(4)	马面、烽火台、墙体组：MZ4,5号MX2,4号马面;F1,2号烽火台;45%左右的墙体特征点
	3	424	(5)	马面、墙体组：MZ1~5号,MX3,4号马面;65%的墙体特征点
	5	1450	(8)、(9)	马面、烽火台组：MZ5~8号马面;F1,2号烽火台
	8	687	(10)	敌台、马面、墙体组：D1~5,7,8号敌台;MZ2,3号,MX3号马面;37%左右的墙体特征点
综合景观感知段	1	624	(1)、(2)、(3)	F1,2号烽火台;D1,2,5~8号敌台;MZ1~4号马面;MX 2~4马面;69%左右的墙体特征点
	4	785	(6)、(7)	F1,2号烽火台;D2,5~9号敌台;MZ1~5号,MX1,3,4号马面;64%的墙体特征点
	9	1732	(11)、(12)、(15)、(16)	F1,2号烽火台;D1~9号敌台;MZ1~6号,MX1~5号马面;66%左右的墙体特征点
	12	512	(13)、(14)	F1,2号烽火台;D2,3,5~8号敌台;MZ2~4号,MX2~4号马面;61%左右的墙体特征点

Cultural Landscape Perception Degree Model and Perception Function Division Based on DEM of Traffic Line: A Case Study of Zijingguan Great Wall

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 9

References 26

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Yu Hongyan, Wang Qunyong, Zhang Bo, Liu Jisheng. Driving Mechanism and the Spatial Differentiation of Coupling Coordinated Development of Tourism Supply and Demand in China [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(11): 1889-1898.
[2]	Chai Yanwei, Xu Weilin, Zhang Wenjia, Li Chunjiang, Li Yanxi. A Research Framework of Precise Epidemic Prevention and Control from the Perspective of Space-time Behavioral Geography [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(10): 1585-1592.
[3]	Xue Bing, Xiao Xiao, Su Fang, Tang Chengrui, Cheng Yeqing, Xie Xiao, Zhao Hongbo, Wang Yanxia, Zhang Zilong, Li Jingzhong. Geographical Academic Responses and Outlook in the Novel Coronavirus Pneumonia Epidemic Prevention and Control [J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(10): 1593-1600.
[4]	Zhu Huayou,Li Han,Dai Zejuan,Jiang Ziran. Enterprises Relocalization and Its Influence on Regional Transformation and Development Based on the Spatial Externality: A Case of the Industrial Transfer Demonstration Region in Cities Along the Yangtze River of Anhui [J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(8): 1246-1255.
[5]	Zhu Xiaohua,Gao Chundong. Establishment and Application of the Comprehensive Evaluation System of Academic Value of Papers in Chinese Authoritative Core Journals: Case Study in Geography [J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(8): 1351-1360.
[6]	Cao Yongqiang, Xiao Chunliu, Yuan Liting. Bibliometric Analysis of International Impact of Chinese Physical Geography in the New Century [J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(5): 807-816.
[7]	Li Xin, Ma Xiaodong, Xue Xiaotong, Khuong Manh Ha. Spatial Supply-demand Evaluation and Layout Optimization for Urban Green Space: A Case Study of Xuzhou Central District [J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(11): 1771-1779.
[8]	Wu Lei, Jiao Huafu, Ye Lei. Spatio-temporal Characteristics of Coordinated Development Between Tourism Economy and Transportation: A Case of International Culture and Tourism Demonstration Area in South Anhui Province [J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(11): 1822-1829.
[9]	Kaishan Song, Yunxia Du, Zongming Wang, Shuwen Zhang, Bai Zhang, Zhaoli Liu, Kai Zhao, Xiaofeng Li, Dehua Mao, Baojiang Liu, Huanjun Liu, Shuqing Zhang, Ying Li. Review and Prospect of Remote Sensing Research by Northeast Institute of Geography and Agroecology (IGA), Chinese Academy of Sciences: Dedicated for the 60th Anniversary of IGA [J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(7): 1023-1031.
[10]	Yujuan Wang, Shan Yang, Lianxia Wu. Heterogeneity for Urban Human Settlements Demand from the Perspective of Multiple Subjects: A Case Study of Kunshan Economic and Technological Development Zone [J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(7): 1156-1164.
[11]	Chuanglin Fang, Chao Bao, Jinchuan Huang, Guangdong Li. Contribution, Responsibility and Mission of Geography on China's Urbanization Development [J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(3): 321-331.
[12]	Zhiwei Du, Xun Li. Characteristic and Mechanism of Urban Growth and Shrinkage from Demographic Change Perspective: A Case Study of Dongguan [J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(11): 1837-1846.
[13]	Chunhua Zhang, Xiunan Li, Mengquan Wu, Weishan Qin, Jun Zhang. Object-oriented Classification of Land Cover Based on Landsat 8 OLI Image Data in the Kunyu Mountain [J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(11): 1904-1913.
[14]	Hongyan Yu, Jisheng Liu. Coupling Coordination Development of Tourism Demand and Supply of Heilongjiang Province at the Background of Supply-side Reform [J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(9): 1374-1381.
[15]	Xiaoli Cheng, Dufang Shi. The Evaluation of Cultural Resources Abundance in International Tourism Culture Demonstration Zone of the Southern Anhui Province [J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(5): 766-772.