海陆岸线多尺度矢量数据获取方法及其尺度效应评估

doi:10.12082/dqxxkx.2021.210094

摘要/Abstract

摘要：

地理空间数据的多尺度表征是制图学的基石,是支撑地理数据多尺度建模分析的前提。通过对从遥感影像获取的一定尺度的地理要素矢量数据进行选择、化简、聚合或其他处理,以获取多尺度矢量数据,但多种综合处理模型和方法会导致多尺度矢量数据存在着不同程度的信息损失。本文基于地理信息系统（ArcGIS 10.6）的矢量数据制图综合功能模块,通过整合多种内嵌的自动算法和模型,结合人机协同的辅助处理方法,构建了一套系统的海陆岸线空间尺度上推方法体系,并将其应用于南美洲大陆海陆岸线矢量数据从m级空间尺度上推到30 m、250 m和1 km。基于分形理论,首次提出线矢量数据复杂度指数概念,用以表征海陆岸线地理要素特征和对比其信息精细化程度。在此基础上,对获取的30 m、250 m和1 km海陆岸线矢量数据进行信息损失评估,结果显示制图综合引起陆地和水域空间属性的改变,不同尺度表征的地理要素信息精细度存在显著差异：相比m级基础数据,30 m、250 m和1 km海陆岸线矢量数据陆地图斑数量损失分别为32.07%、90.46%和98.61%,岛屿线矢量长度信息损失分别为6.32%、49.26%和75.47%;南美洲大陆海岸线矢量数据信息精细度分别降低1.97%、25.33%和45.39%。本文构建的计算机自动综合模型和人工处理相结合的海陆岸线矢量数据空间尺度上推方法,可以实现海陆岸线矢量数据空间尺度上推获取不同尺度的线矢量数据,并描述了不同空间尺度矢量数据的信息损失状况。

关键词: 南美洲, 海岸线, 尺度, 地理信息系统, 数据融合, 算法模型, 人机协同, 矢量数据

Abstract:

Multi-scale of geospatial data is the cornerstone of cartography, and plays a key role in supporting geographic element analysis and feature recognition. Multi-scale vector data can be generated by selecting, simplifying, aggregating, or other processing of geographic element vector data of a certain scale obtained from remote sensing images. However, a variety of comprehensive processing models and methods will also lead to various levels of information loss in multi-scale vector data. The global coastline is a geographic information element with a wide coverage area, complex curves, various island combinations, and complicated structures of land and water regions. The variation of coastline vector data attributes shows different properties at different scales. For the special coastline vector data, there are multiple influencing factors, and the relationships between them are ambiguous. Therefore, it is impossible to judge the attributes of the elements only based on the combinations of a single or a small number of characteristics of the node or line elements. Meanwhile, using a single mathematical model or algorithm for simplification, the drawing effect often has a large deviation from the actual situation, and it cannot meet the drawing needs of different regions and different scales. Thus, we used Geographic Information System (ArcGIS 10.6) technology to support the automatic comprehensive function of geospatial data mapping, integrated different embedded automatic algorithms and models, and combined human-machine collaboration to build a systematic scale-up method system to achieve different scales of coastline data. Based on fractal theory, the concept of line vector data complexity index was first proposed to characterize the coastline geographic elements and to compare the degree of declination of their information. With the m-scale coastline data interpreted by manual visual interpretation, the scale-up is used to generate coastline data on the scales of 30 m, 250 m, and 1 km, respectively. The information loss assessment was performed on the obtained 30 m, 250 m and 1 km coastline vector data, and the results showed that the mapping integration caused changes in the spatial attributes of land and water. There are significant differences in the fineness of geographic element information represented by different scales. Compared with the m-scale coastline data, the loss of the number of islands on the scales of 30 m, 250 m, and 1 km is 32.07%, 90.46%, and 98.61%, respectively, the information loss of the coastline length is 6.32%, 49.26%, and 75.47%, respectively, and the information granularity of the vector data of the coastline of South America is reduced by 1.97%, 25.33%, and 45.39%, respectively. With the processes of the up-scale of the coastline, it has an increasing trend of the median, mean of the islands area and their complexity index from the m-level to 30 m, 250 m, and 1 km scales. The scale-up method constructed in this paper to combine the computer automatic synthesis model with the artificial processing of the coastline vector data has the potential to efficiently realize the scale-up of the coastline vector data, and describe the information loss of vector data at different spatial scales.

Key words: South America, coastline, different scales, geographic information system, data fusion, algorithm model, human-machine collaboration, vector data

张应华. 海陆岸线多尺度矢量数据获取方法及其尺度效应评估[J]. 地球信息科学学报, 2021, 23(10): 1743-1755.DOI:10.12082/dqxxkx.2021.210094

ZHANG Yinghua. Methods to Generate Different Scale Data of Coastline and Its Scale Effect Evaluation[J]. Journal of Geo-information Science, 2021, 23(10): 1743-1755.DOI:10.12082/dqxxkx.2021.210094

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参考文献 33

[1]	梁立, 刘庆生, 刘高焕, 等. 基于遥感影像的海岸线提取方法综述[J]. 地球信息科学学报, 2018, 20(12):1745-1755. doi: 10.12082/dqxxkx.2018.180152
	[ Liang L, Liu Q S, Liu G H, et al. Review of coastline extraction methods based on remote sensing images[J]. Journal of Geo-information Science, 2018, 20(12):1745-1755. ]
[2]	张华国, 黄韦艮. 基于分形的海岸线遥感信息空间尺度研究[J]. 遥感学报, 2006, 10(4):463-468.
	[ Zhang H G, Huang W G. Study on spatial scale of shoreline remote sensing information based on fractal theory[J]. Journal of Remote Sensing, 2006, 10(4):463-468. ]
[3]	闸旋, 滕惠忠, 王俊超, 等. 基于天绘一号卫星多光谱影像的海岸线自动提取方法[J]. 海洋测绘, 2018, 38(4):58-62.
	[ Zha X A, Teng H Z, Wang J C, et al. A method of automatic coastline extraction based on multi-spectral image of TH-1[J]. Hydrographic Surveying and Charting, 2018, 38(4):58-62. ]
[4]	原晓慧, 王萍, 张英, 等. 基于边缘检测的海岸线自动提取研究[J]. 北京测绘, 2019, 33(2):148-152.
	[ Yuan X H, Wang P, Zhang Y, et al. Research on edge detection and object-oriented for automatic extraction of coastline[J]. Beijing Surveying and Mapping, 2019, 33(2):148-152. ]
[5]	Ge Y, Jin Y, Stein A, et al. Principles and methods of scaling geospatial Earth science data[J]. Earth-Science Reviews, 2019, 197:102897. doi: 10.1016/j.earscirev.2019.102897
[6]	Zhou S, Jones C B. Shape-aware line generalisation with weighted effective area[J]. Developments in Spatial Data Handling, 2005:369-380.
[7]	ERsi. "Automation of Map Generalization: The Cutting-Edge Technology"[Z]. Technical Paper. Download address: http://downloads.esri.com/support/whitepapers/ao_/mapgen.pdf , 1996.
[8]	胡云锋, 徐芝英, 刘越, 等. 空间尺度上推方法的精度评价——以内蒙古锡林郭勒盟土地利用数据为例[J]. 地理研究, 2012, 31(11):1961-1972.
	[ Hu Y F, Xu Z Y, Liu Y E, et al. Accuracy analysis of up-scaling data: A case study with land use data in Xilin Gol of Inner Mongolia, China[J]. Geographical Research, 2012, 31(11):1961-1972. ]
[9]	孟斌, 王劲峰. 地理数据尺度转换方法研究进展[J]. 地理学报, 2005, 60(2):277-288.
	[ Meng B, Wang J F. A review on the methodology of scaling with geo-data[J]. Acta Geographica Sinica, 2005, 60(2):277-288. ]
[10]	Kronenfeld B J, Buttenfield B P, Stanislawski L V. Map generalization for the future: Editorial comments on the special issue[J]. ISPRS International Journal of Geo-Information, 2020, 9(8):468-471. doi: 10.3390/ijgi9080468
[11]	Atkinson P M, Tate N J. Spatial scale problems and geostatistical solutions: A review[J]. The Professional Geographer, 2000, 52(4):607-623. doi: 10.1111/0033-0124.00250
[12]	栾海军, 田庆久, 余涛, 等. 根据分形理论与五指标评价体系构建NDVI连续空间尺度转换模型[J]. 遥感学报, 2015, 19(1):116-125.
	[ Luan H J, Tian Q J, Yu T, et al. Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system[J]. Journal of Remote Sensing, 2015, 19(1):116-125. ]
[13]	Duffaut Espinosa L A, Posadas A N, Carbajal M, et al. Multifractal downscaling of rainfall using normalized difference vegetation index (NDVI) in the Andes plateau[J]. PLoS One, 2017, 12(1):e0168982. doi: 10.1371/journal.pone.0168982
[14]	Jiang J L, Liu X N, Liu C H, et al. Analyzing the spatial scaling bias of rice leaf area index from hyperspectral data using wavelet-fractal technique[J]. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):3068-3080. doi: 10.1109/JSTARS.2014.2346251
[15]	Liu Y, Li W J. A new algorithms of stroke generation considering geometric and structural properties of road network[J]. ISPRS International Journal of Geo-information, 2019, 8(7):304. doi: 10.3390/ijgi8070304
[16]	Han Y, Wang Z H, Lu X M, et al. Application of AHP to Road Selection[J]. ISPRS International Journal of Geo-information, 2020, 9(2):86. doi: 10.3390/ijgi9020086
[17]	Fiedukowicz A. The role of spatial context information in the generalization of geographic information: using reducts to indicate relevant attributes[J]. ISPRS International Journal of Geo-information, 2020, 9(1):37. doi: 10.3390/ijgi9010037
[18]	Karsznia I, Sielicka K. When traditional selection fails: how to improve settlement selection for small-scale maps using machine learning[J]. ISPRS International Journal of Geo-Information, 2020, 9(4):230. doi: 10.3390/ijgi9040230
[19]	段佩祥, 钱海忠, 何海威, 等. 基于支持向量机的线化简方法[J]. 武汉大学学报·信息科学版, 2020, 45(5):744-752,783.
	[ Duan P X, Qian H Z, He H W, et al. A line simplification method based on support vector machine[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5):744-752,783. ]
[20]	Courtial A, El Ayedi A, Touya G, et al. Exploring the potential of deep learning segmentation for mountain roads generalisation[J]. ISPRS International Journal of Geo-information, 2020, 9(5):338. doi: 10.3390/ijgi9050338
[21]	武芳, 邓红艳. 基于遗传算法的线要素自动化简模型[J]. 测绘学报, 2003, 32(4):349-355.
	[ Wu F, Deng H Y. Using genetic algorithms for solving problems in automated line simplification[J]. Acta Geodaetica et Cartographica Sinica, 2003, 32(4):349-355. ]
[22]	钱海忠, 何海威, 王骁, 等. 采用三元弯曲组划分的线要素化简方法[J]. 武汉大学学报·信息科学版, 2017, 42(8):1096-1103.
	[ Qian H Z, He H W, Wang X, et al. Line feature simplification method based on bend group division[J]. Geomatics and information science of wuhan university, 2017, 42(8):1096-1103. ]
[23]	应申, 李霖. 基于约束点的曲线一致性化简[J]. 武汉大学学报·信息科学版, 2003, 28(4):488-491.
	[ Ying S, Li L. Consistent line simplification based on constraint points[J]. Geomatics and Information Science of Wuhan University, 2003, 28(4):488-491. ]
[24]	Sayidov A, Aliakbarian M, Weibel R. Geological map generalization driven by size constraints[J]. ISPRS International Journal of Geo-information, 2020, 9(4):284. doi: 10.3390/ijgi9040284
[25]	Li C, Wu P, Yin Y, et al. An improved partitioning method for dissolving long and narrow patches[J]. Computers and Geosciences, 2020, 138:104358. doi: 10.1016/j.cageo.2019.104358
[26]	Kronenfeld B J, Stanislawski L V, Buttenfield B P, et al. Simplification of polylines by segment collapse: Minimizing areal displacement while preserving area[J]. International Journal of Cartography, 2020, 6(1):22-46. doi: 10.1080/23729333.2019.1631535
[27]	Shen Y, Ai T. A raster-based methodology to detect cross-scale changes in water body representations caused by map generalization[J]. Sensors, 2020, 20(14):3823. doi: 10.3390/s20143823
[28]	Lloyd C D. Scale in spatial data analysis: Key concepts[M]. John Wiley & Sons, Ltd, 2014.
[29]	Douglas D H, Peucker T K. Algorithms for the reduction of the number of points required to represent a line or its caricature[M]. John Wiley & Sons, Ltd, 1973.
[30]	Falconer K J. The hausdorff dimension of self-affine fractals[J]. Mathematical Proceedings of the Cambridge Philosophical Society, 1988, 103:339-350. doi: 10.1017/S0305004100064926
[31]	Wang Z S, Müller J C. Line generalization based on analysis of shape characteristics[J]. Cartography and Geographic Information Systems, 1998, 25(1):3-15. doi: 10.1559/152304098782441750
[32]	Mandelbrot B B. How long is the coastline of Britain[J]. Science, 1967, 156:636-638. pmid: 17837158
[33]	刘闯, 石瑞香, 张应华, 等. 基于Google Earth遥感影像全球多尺度海陆(岛)岸线数据集(2015)[J/DB/OL]. 全球变化数据仓储电子杂志(中英文), 2019.
	[ Liu C, Shi R X, Zhang Y H, et al. Global Multiple Scale Shorelines Dataset Based on Google Earth Images (2015)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2019. ]

尺度	南美洲大陆			南美洲岛屿
尺度	长度 /10³ km	面积 /10⁶ km²	CI	总数 /个	总长度 /10³ km	总面积 /10³ km²	最大周长 /km	最大面积 /10³ km²
m级	90.17	17.493	6.08	44 484	159.69	247.15	5731.48	47.54
30 m	88.41	17.495	5.96	30 218	149.59	245.16	5693.01	47.54
250 m	67.43	17.574	4.54	4242	81.02	176.81	5036.91	47.58
1 km	49.38	17.602	3.32	620	39.16	161.81	4311.98	49.55