期刊
REMOTE SENSING
卷 6, 期 7, 页码 6089-6110出版社
MDPI
DOI: 10.3390/rs6076089
关键词
Object-Based Image Analysis (OBIA); land use; segmentation; urban; agriculture; dryland; arid; ASTER
类别
资金
- Environmental Remote Sensing and Geoinformatics Lab at Arizona State University
Land-use mapping is critical for global change research. In Central Arizona, U. S. A., the spatial distribution of land use is important for sustainable land management decisions. The objective of this study was to create a land-use map that serves as a model for the city of Maricopa, an expanding urban region in the Sun Corridor of Arizona. We use object-based image analysis to map six land-use types from ASTER imagery, and then compare this with two per-pixel classifications. Our results show that a single segmentation, combined with intermediary classifications and merging, morphing, and growing image-objects, can lead to an accurate land-use map that is capable of utilizing both spatial and spectral information. We also employ a moving-window diversity assessment to help with analysis and improve post-classification modifications.
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