INTEGRATING GIS AND REMOTE-SENSING FOR VEGETATION AND ANALYSIS AND MODELING - METHODOLOGICAL ISSUES

被引：97

作者：

GOODCHILD, MF

机构：

[1] National Center for Geographic Information and Analysis, Department of Geography, University of California, Santa Barbara, California

来源：

JOURNAL OF VEGETATION SCIENCE | 1994年 / 5卷 / 05期

关键词：

ERROR MODEL; ERROR PROPAGATION; GIS; SPATIAL DATA; UNCERTAINTY;

D O I：

10.2307/3235878

中图分类号：

Q94 [植物学];

学科分类号：

071001 ;

摘要：

GIS and remote sensing have emerged as distinct spatial data handling technologies with their own methods of data representation and analysis. Combining them as tools to support vegetation analysis and modeling thus presents a number of challenges. The paper begins by describing the major data sources, applications, and software characteristics of each technology, and then compares them within a consistent terminological framework that emphasizes the digital representation of continuously varying spatial data. Because the spatial continuum can be discretized in many different ways, and because each can only approximate the truth, both GIS and remote sensing are subject to error and uncertainty. Integration, and subsequent analysis and modeling, require that explicit attention be directed to uncertainty. The paper reviews the models of error that have been developed in recent years for spatial data and examines their use in the interface between GIS and remote sensing. The paper looks at the functional requirements of modeling, and includes discussion of error propagation.

引用

页码：615 / 626

页数：12

共 37 条

[1]

Anderson J.R., Hardy E.E., Roach J.T., Witmer R.E., A land use and land cover classification system for use with remote sensor data., (1976)

[2]

Bishop Y.M.M., Fienberg S.E., Holland P.W., Discrete Multivariate Analysis: Theory and Practice., (1975)

[3]

Blakemore M., Generalization and error in spatial data bases, Cartographica, 21, pp. 131-139, (1984)

[4]

Burrough P.A., Principles of Geographical Information Systems for Land Resources Assessment., (1986)

[5]

Campbell J.B., Spatial correlation effects upon accuracy of supervised classification of land cover, Photogramm. Eng. Remote Sens., 47, pp. 355-363, (1981)

[6]

Chrisman N.R., Modeling error in overlaid categorical maps, Accuracy of Spatial Databases, pp. 21-34, (1989)

[7]

Chrisman N.R., Lester M.K., A diagnostic test for categorical maps, pp. 330-348, (1991)

[8]

Colwell R.N., Manual of Remote Sensing., (1983)

[9]

Congalton R.G., Using spatial autocorrelation analysis to explore the errors in maps generated from remotely sensed data, Photogramm. Eng. Remote Sens., 54, pp. 587-592, (1988)

[10]

Congalton R.G., A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data, Photogramm. Eng. Remote Sens., 54, pp. 593-600, (1988)

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