21 November 2012 Global land cover classification using annual statistical values
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Proceedings Volume 8524, Land Surface Remote Sensing; 85241R (2012); doi: 10.1117/12.977321
Event: SPIE Asia-Pacific Remote Sensing, 2012, Kyoto, Japan
Global land cover data sets are required for the study of global environmental changes such as global biogeochemical cycles and climate change, and for the estimation of gross primary production. To determine land cover classification condition, producers examine the phenological feature of each land cover class’s sample area with vegetation indices or only reflectance. In this study, to detect the phenological feature of land surfaces, we use the universal pattern decomposition method (UPDM) three coefficients and two indices; the modified vegetation index based on the UPDM (MVIUPD) and the chlorophyll index (CIgreen). The UPDM three coefficients are corresponded to actual objects; water, vegetation and soil. To detect the phenological feature of each land cover class simply, we use annual statistical values of the UPDM coefficients and two indices. By visualizing three statistical values with combination of RGB, land areas with similar phenological feature are able to detect globally. We produced the global land cover products by applying this method with MODIS Aqua Surface Reflectance 8-Day L3 Global 500m data sets of 2007. The result was roughly similar to the MOD12Q1 of the same year.
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Noriko Soyama, Kanako Muramatsu, Motomasa Daigo, "Global land cover classification using annual statistical values", Proc. SPIE 8524, Land Surface Remote Sensing, 85241R (21 November 2012); doi: 10.1117/12.977321; https://doi.org/10.1117/12.977321




Classification systems

RGB color model


Climate change

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