Forests are mostly confined to higher slopes and consist of both natural mixed broadleaf forests as well as pine plantations. A single large block natural forest in the Mahabharat Mountains in the southern region represents around 50 percent of the total forest area of the watershed. The rest of the forests are generally fragmented and scattered over the agricultural landscape. Many of these lower elevation forests have been handed over to the local Forest User Groups (FUG) under the community forestry program of the government. By the end of 2000, a total of 2135 ha. public forestland in the watershed had been handed over to 63 FUGs consisting of 6808 households and many other user groups were awaiting formal registration (DFO, 2001a). The Australian Agency for International Development has been supporting the implementation of community forestry program through successive bilateral projects since the inception of the program in 1978. Leasehold forestry is another form of community-based forest management system implemented by the government since 1992 with initial supports from Food and Agriculture Organization of the United Nations and International Fund for Agricultural Development. A total of 128 households living below poverty line were managing 110 hectares of degraded forestland in the watershed by the end of 2000 under leasehold forestry program (Singh and Shrestha, 2000).
The development of the watershed is not uniform. The Punyamata River valley stretching from Nala in the north to Panauti in south is one of the most fertile and economically important areas in Kabhrepalanchok district, where most of the commercial activities are concentrated. The local economy and employment opportunities of these semi-urban areas differ from rural areas. Semi-urban centers are connected to Kathmandu valley by all-weather roads, have alternative sources of energy, and most of the households are not dependent on agriculture. Rural people in the surrounding areas are primarily dependent on arable agriculture and livestock raising for their livelihood. This high variability in the ecological and economic conditions makes the watershed an appropriate site to study land use dynamics and factors associated with it.
3. Data Sources
The main data used in the research included a Landsat Multi Spectral Scanner satellite image (hereafter MSS image) from 1976, a Landsat Thematic Mapper satellite image from 1989 (hereafter TM image) and an Indian Remote Sensing satellite image from 2000 (IRS-1C, LISS-III; hereafter IRS image). A brief description of the satellite images used is shown in Table 1. Eight black-and-white aerial photographs of 1:50,000 scale from 1978 and 1992 each, were used for “ground-truth” information required for classification and accuracy estimation of classified MSS and TM images respectively. Four photographs from each of the periods were used as training material for land use/land cover (hereafter land use) classification and the rest four were used for testing the classification results. Four topographic maps of 1:25,000 scales published by the Survey Department, His Majesty’s Government of Nepal (HMGN) and digital topographic data with contour interval of 20 m produced by the same agency were also used.
Table 1: Satellite images used in land use classification
| Satellite type | Sensor | Number of bands | Pixel spacing (m) | Observation date |
| Landsat 2 | MSS | 4 | 57 x 57 | 20 December 1976 |
| Landsat 4 | TM | 7 | 28.5 x 28.5 | 24 January 1989 |
| IRS-1C | LISS-III | 4 | 23.5 x 23.5 | 7 March 2000 |
The MSS and TM images were provided by the Center for the Study of Institutions, Population, and Environmental Change (CIPEC) at Indiana University, USA. IRS image was acquired directly from Indian Remote Sensing Agency, Hyderabad, India. Aerial photographs and digital topographic data were acquired from the Survey Department, His Majesty’s Government of Nepal and the topographic maps were purchased from a bookstore in Kathmandu.
The ground-truth information required for the classification and accuracy assessment of IRS image was collected from the field during January-April, 2001 using a training sample protocol designed by CIPEC in 1998 with some modifications. In addition, a self-designed format was used to collect forest level information on forest types, condition and history of land use provided by the local people and direct observation in the field.
4. Methods
4.1 Geometric correction
Subsets of satellite images and aerial photographs were rectified first for their inherent geometric errors using digital topographic maps in Modified Universal Transverse Mercator coordinate system obtained as above as the reference material. IRS image was first registered to the digital topographic maps using distinctive features such as road intersections and stream confluences that are also clearly visible in the image. A first-degree Rotation Scaling and Translation transformation function and the Nearest Neighbor resampling method were applied. This resampling method uses the nearest pixel without any interpolation to create the warped image (Richards, 1994). A total of 20 points were used for registration of IRS image subset with the rectification error of 0.1083 pixels.
The MSS and TM images were registered to the already registered IRS image through image-to-image registration technique with rectification errors of 0.1612 and 0.0882 pixels respectively. A very high level of accuracy in the georerencing of the images was possible because of the use of digital source as the reference data that allowed zooming to the nearest possible point location.
The eight aerial photographs used in the research were scanned, saved in tiff format and registered to the digital topographic maps in the same manner as the IRS image. This allowed direct comparison of features between the images and aerial photographs during the selection of sample plots for use in image classification and accuracy assessment of classified images.