Ambika P. Gautam
School of Environment, Resources and Development
Asian Institute of Technology
P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand
Mail Box # 43
Tel.: (66 2) 524 5615
Fax: (66 2) 524 6431
E-mail: [email protected]
Edward L. Webb, Ganesh P. Shivakoti and Michael A. Zoebisch
School of Environment, Resources and Development
Asian Institute of Technology
P.O. Box 4 Klong Luang
Pathum Thani 12120
Watershed management has become an increasingly important issue in many countries including Nepal as government agencies and non-governmental groups struggle to find appropriate management approaches for improving productions from natural resource systems. Principles, concepts and approaches related to watershed management have experienced a vast change during the past few years but yet there is no universal methodology for achieving effective watershed management (Naiman et al., 1997; Bhatta et al., 1999). It is generally agreed that sustainable development and management of upland natural resources for the welfare of local populations should be the key objective of watershed management, which includes sustainable utilization and conservation of forest resources at community or watershed level as one of its important components (Sharma and Krosschell, 1996). To provide foundations for effective management of forest and other natural resources, an understanding of the variability in time and space of the resources and the role of human cultures and institutions in bringing those variations are some of the fundamental requirements (Naiman et al., 1997).
In addition to area coverage, the shape of land use patches is an important characteristic for evaluating the processes and effects of land use change at landscape and watershed level. The concept is related to edge effects (physical and biotic phenomena) associated with increase in patch complexity due to habitat fragmentation and is emerging as an important field in the management and conservation of fragmented ecosystems at the local as well as regional level (Laurance and Bierregaard, 1997). Patchiness in forested area is of special importance because it serves as an important indicator of natural habitat fragmentation (Kammerbauer and Ardon, 1999). This is particularly important in Nepal where forest fragmentation has been a common phenomenon in the past few decades and most of the surviving forests in the hills consist systems of small patches, which are increasingly coming under community-based forest management in recent years (Gautam and Webb, 2001).
There are various methods that can be used in the collection, analysis and presentation of resource data but the use of remote sensing and geographic information system (RS/GIS) technologies can greatly facilitate the process. Repeated satellite images and/or aerial photographs are useful for both visual assessment of natural resources dynamics occurring at a particular time and space as well as quantitative evaluation of land use/land cover changes overtime (Tekle and Hedlund, 2000). Analysis and presentation of such data, on the other hand, can be greatly facilitated through the use of GIS technology (ESCAP, 1997). A combined use of RS/GIS technology, therefore, can be invaluable to address a wide variety of resource management problems including land use and landscape changes.
This study is part of a broader research designed to assess the role of community-based forestry institutions in determining the status of forests in the study area. Within this broad framework, the objectives of this study were: i) to detect and document changes in major land use in general and forests in particular in a representative mountain watershed in central Nepal in between 1976 and 2000, and ii) to analyze patterns of changes in landscape of the study area during the period, with special focus on forest fragmentation. The study used RS/GIS with substantial input from the filed to achieve the stated objectives.
2. Study Area
The site of this study, Upper Roshi Watershed (85.39 – 85.57 E, 27.54 – 27.70 N), is situated in the western part of Kabhrepalanchok district in the Middle Hills of Nepal (Figure 1). The watershed covers an area of 15,335 hectares. The altitude varies between 1,420 m to 2,820 m above sea level. Climate is monsoonal with a dry season normally spanning from November to May and rainy season from June to October. Warm-temperate humid temperature and moisture regime prevails in most part of the watershed except at higher elevation (above 2000 m) where the climate is cool-temperate type. Microclimate varies considerably with elevation and aspect. The south-facing slopes and lower slopes are generally hotter and drier and the north-facing slopes and upper slopes are cooler and moister. Three rivers namely Punyamata, Bebar and Roshi along with their numerous tributaries drain the area, which latter converge at the southeastern corner of the watershed into Roshi River.
Figure 1: Location of the Upper Roshi Watershed in Kabhrepalanchok District, Nepal
The watershed can be divided into fertile, relatively flat valleys along the rivers and surrounding uplands with medium to steep slopes. Agricultural lands in the valleys are under intensive management with multiple cropping systems and are mostly irrigated. Paddy, potato, wheat and vegetables are major crops cultivated in the valley. Rain-fed agriculture, with or without outward facing terraces, is practiced on rest of the agricultural lands, many of which are not suitable for crop production without strong soil and water conservation measures because of their high erodability and low productivity (ICIMOD, 1994).
Land Use Dynamics and Landscape Change Pattern in a Mountain Watershed in Nepal
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.
4.1 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.
4.2 Classification of satellite images
We used supervised Maximum Likelihood classification method for the classification of all the images. Training areas corresponding to each classification item (hereafter, land use class), in case of IRS image, were chosen from among the training samples collected from the field and in case of MSS and TM images they were generated from the interpretation of aerial photographs of the study area from 1978 and 1992 respectively. Although the dates of the aerial photographs used as reference information in classification do not exactly match with the dates of the satellite images, they were used with the assumption that land use in the watershed, particularly forestry land use, was not substantially changed between the time of aerial photography and satellite observation dates. Moreover, this was the best feasible option that could be used in this research.
For producing land use maps for 1976, 1989 and 2000 and to investigate changes that occurred between these periods, the following six land use classes were considered in image classification: broadleaf forest, conifer forest, shrublands, grasslands, lowland agriculture, and upland agriculture and other. Choice of these land use classes was guided by: i) the objective of the research, ii) expected certain degree of accuracy in image classification, and iii) the easiness of identifying classes on aerial photographs. A brief description of each of the land use classes is given in Table 2.
Table 2: Land use classes considered in image classification and change detection
|Land use class||General description|
|Broadleaf forest||Forest areas with estimated 75 percent or more of the existing crown covered by broadleaf trees. The predominant species are: Castanopsis spp. and Schima wallichii in most part and Quercus spp. in higher elevations.|
|Conifer forest||Forest areas with estimated 75 percent or more of the existing crown covered by planted or naturally growing conifer trees. Pinus roxburghii, Pinus patula and Pinus wallichiana are common species.|
|Shrublands||Land covered by shrubs, bushes and young broadleaf regeneration. Degraded forest areas with estimated 3000 ha.) in each period has not been included. |
The contradictory results on the shape complexity of forest patches obtained from the above two approaches of SCI calculation and interpretation warrants for more discussion on this issue and shows the necessity of refining existing methods of SCI calculation and landscape change interpretation.
Decrease in the number of forest patches by above 50 percent in between 1976 and 2000 and substantial decrease in the watershed level SCI of forest patches indicated improved forest habitat in the watershed, while mean deviation between actual polygon SCI and optimal SCI indicated more irregular shape of forest patches in the latter periods. This difference in results from two approaches warrants more discussion on this issue for refining existing methods of SCI calculation and landscape change interpretation.
The positive changes in forest cover provides some evidences of ecological sustainability of the resource, although the reversal of the decreasing trend in shrublands during the second period has raised some questions regarding the possible continuation of the observed trends in future. These findings also signify, to some extent, the success of forest conservation efforts by local communities and external agencies involved. More location specific in-depth analysis of the relationship between governance arrangement and forest condition is necessary before drawing firm conclusion on the role of local institutions in determining forest condition. Some other important concerns related to community-based forest management, which needs to be addressed by future studies are whether and how the positive change in forest cover has benefited the local users and how sustainable are the existing community-based forestry institutions in the long run.