GIS@development March 2002: Regional Land Cover Mapping

A GIS and remote sensing (RS) based methodology has been developed and tested for the land cover mapping of Bhutan and Nepal using IRS-WiFs data. The study mainly focused on generating good and reliable training samples, for the accurate classification of the image.

The Hindu Kush-Himalayan (HKH) is the mandated area of International Centre for Integrated Mountain Development (ICIMOD) covering eight regional member countries: Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan. The HKH region contains world’s highest, largest, and most populated mountain systems. To manage these scarce resources effectively it is important to study and understand the dynamics of land use and land cover change of the region and to determine which factor contributes significantly in a specific area before proper land use planning can be done. Land use/land cover change study is a diagnostic tool for determining sustainability and is therefore important that this tool can be done carefully and properly for the sustainable development of the HKH region.

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, a pilot 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 characterisation 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 resolution 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 Centre (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 Fig. 1. The conceptual model applied in the study to meet the overall objective is depicted in Fig. 2. After geo-referencing and mosaicing of the image, the image 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. The main theme of this study to generate good training samples, i.e. spectrally homogenous and spatially significant, for the image classification. Due to limitation of the human eye, we can’t distinguish the spectral homogeneity of the pixels. Therefore, an algorithm was used to extract the spectrally homogenous pixel groups (Fig. 3). To generate spectrally significant and spatially homogenous training samples, following steps were carried out:

Figure 1 : Mosaic of 12 scenes of WiFs data of the HKH region

Figure 2 : Overall methodology of the study

Figure 3 : A process for extracting spectrally homogenous and spatially defined sample areas

Derived 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. Pixels within the defined ranges were recoded to unique values. The pixels belonging to same contiguous groups were grouped and given unique identifier (Fig. 4).

Figure 4 : Neighborhood analysis to form a group of pixels and assignment of new ids to each group
The appeared salt and pepper pixels were eliminated by defining area criteria (Fig 5a & 5b). A threshold of 50 pixels, i.e. 1.62 km2, 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.

Figure 5 : Before (a) and after (b) applying area criteria
Number of Area of Interest (AOI) was created from each spatial unit in each band (Fig 6). While creating AOI, standard deviation was maintained within the limit of less than or equal to 2.0 in each class.

Figure 6 : Creation of AOIs from each sample
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 (d), which can be viewed as providing the a measure of the uncertainty. So, higher the d, there will be more uncertainty. Low d of a set of data values indicates how similar enough they are. The classes having highest frequency numbers of pixels in order and low d are selected as the final training samples for the land cover types identification.

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