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Land Use/Land Cover
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Regional land cover mapping of the Hindu Kush-Himalayan using satellite image: An approach to understand the dynamics of land use and land cover change
To generate spectrally significant and spatially homogenous training samples, following steps were
carried out:
- Derived all the possible spectral groups of pixels of Red and near infrared (NIR) bands that are within
the specific range of values, e.g. 5. The pixels having 0 histograms were omitted while selecting. The
pixels within the defined ranges were recoded to unique values.
- The pixels belonging to same contiguous groups were grouped and given unique identifier(Fig 5a & 5b)
- The appeared salt and pepper pixels were eliminated by definig area criteria (Fig 6a & 6b). A threshold of 50 pizels, i.e. 1.62KM2 was defined to obtain spatially homogenous training samples. The group of pixel was again recoded to an integer identifier of particular spectral class. These samples were used as the basic units for regional reviews and legends for land use land cover mapping which indicate the basic spatial unit.
- Number of Area of Interest (AOI) was created from each spatial unit in each band (Fig 7). While
creating AOI, standard deviation was maintained within the limit of less than or equal to 2.0 in each
class.
- After delineating AOI from Red and NIR band, they were merged together as a single file.
- After merging the AOIs, statistical report was derived. The homogeneity of training samples was
measured by means of standard deviation (þ), which can be viewed as providing the a measure of the
uncertainty. So, higher the þ, there will be more uncertainty. Low þ of a set of data values indicates
how similar enough they are. The classes having highest frequency numbers of pixels in order and low
? are selected as the final training samples for the land cover types identification. The spectral range,
standard deviation (þ) and land cover are illustrated in Table 1.
Image Classification
Thus generated spectrally homogenous and spatially significant training samples are the basic spatial
units for ground truth and primary data collection for the image classification.
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