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
Objectives
The primary goal of the project is to understand the dynamics of land use and land cover change in
mountain ecosystems of the HKH region. To meet this primary goal, an initial study has been carried out,
which is the main focus of this paper, with the main objective as follow:
- Develop a standard methodology to understand and explain the pattern of land cover
characterization of the region using satellite images.
The specific objectives of the study are:
- To investigate the spectral information content of different land cover types to generate spectrally
homogenous and spatially significant training samples;
- To study to improve the land cover classification using knowledge-based rules, topographic
factors, and higher remotely sensed data;
- To study to produce detailed forest types classification; and
- To assess the accuracy to validate the proposed methodology.
Sources of Data
The study used the IRS WiFs (Wide Field Sensor) satellite data. The WiFs satellite data has two bands –
Red and Near Infrared (NIR), with spatial resolution 188.3m. The project acquired the WiFs satellite data
between the periods of 1996-1999 covering the whole HKH region. The study also used other ancillary
data, DEM (Digital Elevation Model), rainfall and temperature data to meet the main objective.
Methodology
The study acquired 12 scenes of the WiFs satellite data covering the whole HKH region. Each of these
scenes were rectified and geometrically corrected using ground control points (GCPs) from defense
mapping agency aerospace center (DMAAC), Missouri, USA. All the GCPs were verified in the
Operational Navigation Chart (ONC) of scale 1:1000,000, and the same location were identified on the
images and registered using Erdas Imagine 8.4 software. Overall root mean square error (RMS) was
limited within a pixel. Then it was resampled to pixel size 180m x 180m. Resampling was done using
nearest neighborhood method, which maintains the original DN of pixels.
The images were projected into Albers Conical Equal Area with WGS84 spheroid and datum. After geo-referencing,
all the images were combined (mosaic) into a single image as shown in Figure 2.
After geo-referencing and mosaicking of the images, the
images were subset to individual band. From each band, the training samples were generated. Remote
sensing technology sounds very interesting and is useful, but its quality highly depends on the quality of
training samples. It requires good training samples for the accurate image classification. Good training
sample means – it should be spectrally homogenous and spatially large enough. It’s very critical process
to select good training samples for the accurate image classification. So, the main theme of this study is
how can we generate good training samples, i.e. spectrally homogenous and spatially significant, for the
image classification. Because of the limitation of our human eye, we can’t distinguish the spectral
homogeneity of the pixels. Therefore, an algorithm was used to extract the spectrally homogenous pixel
groups (Figure 4).
