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.
Now the fractal dimension of Land Use Types is often calculated by using raster data as the raw data, but quite a number
of spatial data is stored as vector data in fact. If these data are converted to images to calculate fractal dimension, perhaps
some pixels with inaccurate grey value will result from the "GRID" structure of raster data. And the precision of fractal
dimension calculated on raster Data is closely related to the size of pixel and Grid image.In view of this, In this paper, a
computation program of the fractal dimension for 2D vector data based on Windows platform has been designed by
using Visual Csharp. This program has been successfully applied to land-use data of the middle Qinling Mountains and
the southeast of Hubei Province in China.in the 1990s. The results show that the program is a convenient, reliable and
precise method of fractal dimension for 2D Vector Data.
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