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Proceedings Paper

Study on Land Use Cover Change (LUCC) based on remote sensing and GIS
Author(s): Hong Zhang; Xuanbing Zhang; Ning Shu
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Paper Abstract

As a key element for land use cover change research, change detection technique is of urgent demands and has great potential in scientific applications. Conflation is the process of combining the information from two (or more) geodata sets to make a master data set that is superior to either source data set in either spatial or attribute aspect. The objectives of conflation include increasing spatial accuracy and consistency, and updating or adding new spatial features into data sets. Based on the analysis and summarizations of researched home and aboard, the paper focused on Land Use/Cover Change detection using feature database of basic types based on vector-image data conflation, that is : Combining of Land use map and RS image, features(grey feature, texture feature and shape feature) are extracted. This methodology belongs to "Feature class" of LUCC. It should be pointed out that the researches must be focused on the land use span other then traditional methods of the pixels. Each spans of T2 will be classified according to the minimum Euclidean distance to the T2 sample span accepted, and the corresponding land use type will be assigned to the current patch, Change information are extraction automatically based on Boolean operations. The method is tested on the Quick Bird images of a district in Wuhan and the precision of the results is high as 92.6% (in urbanization).The experimental results demonstrate that the proposed method can cut down the computational costs and improve the accuracy.

Paper Details

Date Published: 13 October 2009
PDF: 9 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749202 (13 October 2009); doi: 10.1117/12.838274
Show Author Affiliations
Hong Zhang, Nanchang Univ. (China)
Xuanbing Zhang, Nanchang Univ. (China)
Ning Shu, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining

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