Abstract
In this paper, a new logic for land cover classification at regional scale has been introduced. The critical features of this classification are that: 1) indeed distinguished from land use to avoid the confusion between land use types and land cover types; 2) based on remote sensing so that repeatable and efficient re-classifications of existing land cover will be possible; 3) based on spectrum and primary attributes of plant-canopy structure, that are important to globe change modeling and can be measured in the field for validation or/and by remote sensing; 4) based on the phonological difference among broadly defined vegetation because some typical land cover is easily distinguished by using the characteristics of seasonal dynamic; 5) based on component and function properties (e.g., influence on land surface processes) of covers to differentiate mixed land cover. Following the above ideas and using time-series MODIS 250 m data (i.e. NDVI and reflectance) which were reprocessed by a BISE algorithm to identify contaminated pixels with residual cloud, a two-level land cover classification scheme was produced for the southeast Hubei Province and middle Qinling Mountains in Shanxi Province, China. Results show there were seven primary classes and fifteen sub-classes identified and mapped.
Published Version
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