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Study of Urban Sprawl and Its Impact on Vegetation, Land Surface Temperature and Air Pollution Using Remote Sensing and GIS in Kathmandu Valley from 2015 to 2020

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Journal of Geoscience and Environment Protection 2024年第3期12卷 28-53页

作者： Ankit Kandel kismat pokhrel Department of Geography Geology and Environment University of Leicester Leicester UK Golden Gate International College Battisputali Kathmandu

The Kathmandu Valley has seen substantial urbanization over the past decades while being the nation’s economic centre. Built-up areas have expanded quickly along with the population, having a significantly negative influence on the environment. Recently, Kathmandu was named as the most polluted city in Asia. Urban sprawl has had a negative influence on Kathmandu’s residents in several ways. The state of urban sprawl and the effects it has had on the Kathmandu Valley have been examined using land sat imagery. In this study, IDW was used in GIS to analyze the pollution status using data of PM 2.5 and PM 10 obtained from various monitoring sites. A supervised classification was used to create a LULC map of Kathmandu for the years 2015, 2018, and 2020. To assess the state of the vegetation and determine whether the Kathmandu Valley is being affected by urban heat, NDVI and Land sat temperature calculations were also made. The study’s results were obtained using remote sensing and GIS technology. The built-up area in Kathmandu Valley has grown by 20% over the past five years, impacting land use patterns and deteriorating vegetation cover. Due to the rise of built-up area, which is a good heat absorber, the temperature in the Kathmandu Valley is rising along with the degradation of the vegetation cover. The pollution in the Kathmandu Valley is at its worst, and residents are compelled to breathe air that is significantly more polluted than the prescribed limit.

关键词： Urban Sprawl Pollution Land Use Landsat Image Builtup Area

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The Influence of Alluvial Mining on the Morphology and Ecological Health of the Jhimruk River in Pyuthan, Nepal

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Journal of Geoscience and Environment Protection 2023年第8期11卷 33-48页

作者： Ankit Kandel kismat pokhrel Lenin Adhikari Deep Narayan Shah Goldengate International College Tribhuvan University Battisputali Kathmandu Nepal Central Department of Environment Science Tribhuvan University Kirtipur Kathmandu Nepal

Historically, alluvial rivers have been a desirable source of sediment for various industrial purposes. However, the demand for sand is rapidly increasing, leading to environmental concerns associated with its extraction. The extraction of sediment from riverbeds has both visible and invisible effects. This research aims to investigate the dynamics of the Jhimruk River’s morphology and its impacts by evaluating the river’s quality and studying its changing morphology. To assess morphological changes, satellite images from different years were compared using Geographic Information System (GIS). Physical assessments were conducted by calculating a Habitat score based on various parameters. Water quality assessments involved measuring factors such as pH, temperature, nitrate levels, and alkalinity. A water quality map of the river was generated using color coding to indicate different conditions. The Habitat score demonstrated variations in the data collected from all sites, with factors such as instream cover, bottom substrate stability, riparian vegetation, and aesthetic of the river stretch playing crucial roles in influencing the total score. The Water Quality Index value for the disturbed site indicated moderate pollution, falling under class II. Aquatic macroinvertebrates displayed natural responses to increasing levels of stressors across their life stages, as observed through the analysis of the GRS-BIOS/ASPT index. With the exception of the disturbed site, all other sites were classified under water quality class II, while the disturbed site belonged to class III, indicating a moderate to critically polluted state for most of the Jhimruk River. Furthermore, an increase of 180.87 meters in the river width at the disturbed site provided evidence of morphological changes occurring over the specified period. Mining activities were identified as a significant contributor to the alteration in river morphology.

关键词： Alluvial Mining Physical Assessment Biological Assessment Water Quality Assessment Jhimruk River Sand Mining

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Identification of Potential Landslide Susceptible Area in the Lesser Himalayan Terrain of Nepal

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Journal of Geoscience and Environment Protection 2019年第11期7卷 24-38页

作者： kismat pokhrel Bharat Prasad Bhandari Goldengate International College Tribhuvan University Kathmandu Nepal Central Department of Environment Science Tribhuvan University Kathmandu Nepal

Landslide susceptibility mapping is a very important tool to identify potential landslide-prone areas. In this work, weight of evidence method is applied to obtain landslide susceptibility assessment. Weight of evidence model is commonly applied in the landslide study as it is widely acceptable and easy to use. The objective of this paper is to prepare the landslide susceptibility map of Lung Khola catchment, Pyuthan District of Nepal. Altogether, 84 landslides were identified after landslide inventory. The thematic layers of all causative factors and existing landslides are prepared in Arc GISsoftware. Mainly, Digital Elevation Model (DEM) based causative factors and field data were used to prepare the data layers of the causative factors. In this research, 8 intrinsic factors were used for the landslide assessment. South East and East facing aspects, slope >60 degrees, elevation ranges 1300 - 1700 m, phylitic rocks and agricultural land followed by forest are the major contributors of landslide hazard in the study area. The weight of evidence model was validated by using area under curve method. The success rate curve showed the accuracy of 73.16%. It can be concluded that weight of evidence model is suitable model for landslide susceptibility analysis and the area is highly susceptible to landslide occurrence.

关键词： Weight of Evidence Landslide Modeling Bivariate Method DEM

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