Publications (7) View all
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Article: Evaluating Land Use Dynamics and Forest Cover Change in Nepal’s Bara District (1973–2003)
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ABSTRACT: This research uses Landsat Multi-Spectral Scanner of 1973, Thematic Mapper of 1989, Advance Earth Observing Satellite of 1996, and Enhanced Thematic Mapper of 2003 imagery to examine the effects of socioeconomic and demographic factors on land use and forest cover dynamics in the Bara district of the Central Tarai Region of Nepal for 1973–2003. These satellite images are used to generate three transition matrices for 1973–1989, 1989–1996, and 1996–2003; and the outcomes of the final transition matrix are compared with the land use and cover patterns seen on the IKONOS (1 × 1m) 2003 images. This paper contributes to our understanding of how land use and cover dynamics correspond to national-level socioeconomic developments, demographic changes, and environmental awareness as envisioned by Ecological Modernization Theory. It also demonstrates how such a set of transition matrices generated from different resolution images helps assess these explanatory relationships at different temporal and spatial scales.Human Ecology 04/2012; 36(1):81-95. · 1.63 Impact Factor -
Article: Urban Vulnerabilities in the Kathmandu Valley, Nepal: Visualizations of Human/Hazard Interactions.
J. Geographic Information System. 01/2010; 2:63-84. -
Article: Determinants of deforestation in Nepal's Central Development Region.
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ABSTRACT: The process of deforestation in the Central Development Region (CDR) of Nepal is diverse in space and time, with rapid deforestation still occurring in areas outside the national parks and wildlife reserves. This paper identifies the spatial driving forces (SDFs) of deforestation in the CDR for 1975-2000 using satellite data of 1975 (MSS), 1990 (TM), and 2000 (ETM+) along with socio-demographic and socioeconomic variables. Radiometrically calibrated satellite images are individually classified into seven distinct classes and merged together to cover the entire CDR. Classification accuracies are also assessed. Areas of land use and cover within the areas of each Village Development Committee (VDC) and municipality represented by GIS polygons are calculated from the classified images by overlaying vector files of 1845 polygons representing sections of VDCs and municipalities in 30-1199 m, 1200-2399 m, 2400-4999 m and >5000 m elevation levels. These elevation levels were estimated from the DEM compiled from 24 ASTER scenes taken on different dates. Only the first three elevation levels are used in the analysis because area >5000 m is under permanent snow cover where human related forestry activities are almost negligible. A transition matrix is generated for 1975-1990 using classified images of 1975 and 1990 and then this product is used to further develop another transition matrix for 1990-2000 with the classified ETM+ 2000 images as the final stage. The GIS polygon layer is overlaid on the transition matrices to calculate deforestation areas for 1975-1990 and 1990-2000. Biophysical and socioeconomic information collected from various sources is then brought into a GIS platform for statistical analyses. Six linear regression models are estimated using SAS; in effect, two models for each elevation range representing the 1975-1990 and 1990-2000 periods of change to identify SDF influences on deforestation. These regression analyses reveal that deforestation in the CDR is related to multiple factors, such as farming population, genders of various ages, migration, elevation, road, distance from road to forest, meandering and erosion of river, and most importantly the conversion of forestland into farmland.Journal of Environmental Management 10/2009; 91(2):471-88. · 3.24 Impact Factor -
Article: Frontier Migration and Upward Mobility: The Case of Nepal.
Economic Development and Cultural Change 02/1993; 41(4):787-816. · 0.98 Impact Factor -
Article: Issues in population pressure, land resettlement, and development: The case of Nepal
Studies in Comparative International Development 01/1985; 20(1):55-82. · 0.65 Impact Factor
