https://www.nepjol.info/index.php/NJG/issue/feed Journal on Geoinformatics, Nepal 2021-08-30T16:13:56+00:00 Karuna KC info@dos.gov.np Open Journal Systems <p>Annual publication of the Survey Department, Government of Nepal. Full text articles available.</p> https://www.nepjol.info/index.php/NJG/article/view/39468 Editorial Vol.20 2021-08-30T12:39:24+00:00 Karuna K.C. karunakc9@gmail.com <p>No abstract available.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39469 Forewords Vol.20 2021-08-30T12:45:46+00:00 Prakash Joshi prakash.joshi@nepal.gov.np <p>No abstract available.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39470 Analyzing Trend and Pattern of Agricultural Drought: A Case Study of Karnali and Sudurpashchim Provinces 2021-08-30T12:51:27+00:00 Naresh Bista nareshbista9@gmail.com Dikpal Mahat abinashmahat234@gmail.com Sachin Manandhar sacheenmdr9@gmail.com Binayak Regmi regmibinay56@gmail.com Uma Shankar Panday uspanday@ku.edu.np Shashank Karki Shashank.karki480@gmail.com <p>A drought is a period of time when an area or region experiences below-normal precipitation, with characteristics and impacts that can vary from region to region. Agricultural Drought analyzes and reflects the extent of the soil moisture and morphology of crop. Deficient rainfall in the winter of 2008 resulted in a severe drop in crop production right across the country. So, there is a necessity of assessment of drought events to make informed and timely decisions. The main focus of our study is to monitor the agricultural drought in Karnali and Sudurpashchim provinces of Nepal. The condition of drought in Karnali and Sudurpashchim provinces from 2001- 2020 were analysed with the help of Drought Severity Index. MODIS NDVI (MOD13) and MODIS ET-PET (MOD16) datasets were used to monitor and analyze the trend and pattern of agricultural drought scenario. Both datasets were then normalized for DSI calculation and the DSI result was used to monitor and to analyze the trend and pattern in the agricultural drought scenario. Further, trend and pattern analyses were performed in terms of landcover, ecological zones, and the variation of DSI. After completion of this project, we can conclude that the Maximum dryness was found in March, it might be due to less NDVI and increase in evapotranspiration rate and maximum wetness in November. Agricultural area experienced more drought variation than other landcover zones</p> 2021-08-31T00:00:00+00:00 Copyright (c) 2021 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39471 Analyzing Urban Growth Pattern and Driving Factors Using Remote Sensing and GIS: A Case Study of Banepa Municipality, Nepal 2021-08-30T13:09:11+00:00 Rabina Twayana rabinatwayana123@gmail.com Sijan Bhandari sijanbhandari12@gmail.com Reshma Shrestha reshma@ku.edu.np <p>Nepal is considered one of the rapidly urbanizing countries in south Asia. Most of the urbanization is dominated in large and medium cities i.e., metropolitan, sub-metropolitan, and municipalities. Remote Sensing and Geographic Information System (GIS) technologies in the sector of urban land governance are growing day by day due to their capability of mapping, analyzing, detecting changes, etc. The main aim of this paper is to analyze the urban growth pattern in Banepa Municipality during three decades (1992-2020) using freely available Landsat imageries and explore driving factors for change in the urban landscape using the AHP model. The Banepa municipality is taken as a study area as it is one of the growing urban municipalities in the context of Nepal. The supervised image classification was applied to classify the acquired satellite image data. The generated results from this study illustrate that urbanization is gradually increasing from 1992 to 2012 while, majority of the urban expansion happened during 2012-2020, and it is still growing rapidly along the major roads in a concentric pattern. This study also demonstrates the responsible driving factors for continuous urban growth during the study period. Analytical Hierarchy Process (AHP) was adopted to analyze the impact of drivers which reveals that, Internal migration (57%) is major drivers for change in urban dynamics whereas, commercialization (25%), population density (16%), and real estate business (5%) are other respective drivers for alteration of urban land inside the municipality. To prevent rapid urbanization in this municipality, the concerned authorities must take initiative for proper land use planning and its implementation on time. Recently, Nepal Government has endorsed Land Use Act 2019 for preventing the conversion of agricultural land into haphazard urban growth.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39473 Assessing the Accuracy of Remotely Sensed Forest Maps for Nepal 2021-08-30T13:55:44+00:00 Menaka Hamal menakahamal@gmail.com Rajesh Bahadur Thapa rajesh.thapa@icimod.org <p>The accuracy assessment is vital to validate the remotely sensed thematic output before being front to the users. The statistical accuracy measures and modeling have been using widely for the accuracy assessment of the remote sensing product. This study uses six open-access land cover products - Land Cover of Nepal 2010, GlobeLand30, Treecover2010, Global PALSAR-2 forest/Non-Forest, Tree Canopy Cover (TCC), and ESACCI Land Cover 2010, to find out the most reliable forest product for Nepal. The forest/non-forest data were extracted from each product. The stratified random sampling was used to create test points and verified ground truth in Google Earth (GE) by visual interpretation. The overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA), Kappa statistics, and the Nash-Sutcliffe model efficiency coefficient (NSE) were measured for each forest/non-forest map. The OA and UA were found to be highest by 94%; the Kappa statistics showed an 89% level of agreement and NSE showed 77 % performance level for Nepal Land Cover 2010 which is the highest among six datasets. Whereas ESACCI land Cover 2010 was found to be the least performer - OA and UA are 53% and 66% respectively, Kappa shows a 53% level of agreement and NSE shows 4%.. The ESACCI land Cover 2010 was found to be the highest coverage whereas Tree Canopy Cover (TCC) has the least one for each province. This study gives the methodological insight to compare remotely sensed datasets and help the user in the selection of the most reliable open-source forest map for Nepal.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39474 Flood Modeling Assessment: A Case of Bishnumati River 2021-08-30T14:19:19+00:00 Noor Dangol noordgl6@gmail.com <p>Flood, a common water induced disaster of a monsoon season, is the recurring phenomenon in Nepal. To study this disaster, different flood modelling had been conducted for different river basins following different modelling tools. This study describes the technical approach of probable flood vulnerability and hazard analysis of Bishnumati river catchment and analyze the result with previous study done for the same study area. The method adopted for previous study in 2009 was adopted in this study as well in order to compare the results. One dimensional hydraulic model HEC-RAS with HEC GeoRAS interface in co-ordination with Arc GIS was applied for the analysis.. Hazard maps, landuse vulnerability maps of various return periods (10 Yrs, 20Yrs, 50Yrs and 100Yrs), were prepared in ArcGIS. The results of flood frequency analyzed by WECS/ DHM method showed discharges of 445 m³/s, 541m³/s, 648m/s, 725 m³/s for 10, 20, 50 and 100 years return period floods. The primary data of the study showed most of the flooding area had water depth greater than 3m. The assessment of the flood inundated area showed that large percentage of vulnerable area lied in built up areas followed by barren land.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39476 Fusion of Radar and Optical Data for Land Cover Classification Using Machine Learning Approach 2021-08-30T14:34:32+00:00 David Nhemaphuki davis10ge@gmail.com Kiran Thapa Chetri thapachetrikiran@gmail.com Sanjeevan Shrestha shr.sanjeevan@gmail.com <p>This study evaluates the advantages of combining traditional space borne optical data with longer wavelengths of radar for land cover mapping. Land cover classification was carried out using Optical, radar data and combination of both for the Bardiya district using Random Forest algorithm. The fusion of optical and radar shows better land cover discrimination with 96.98% overall accuracy in compared to using radar data and optical data separately with overall accuracy of 69.2% and 95.89% respectively. Additionally, the qualitative result demonstrates that the combined utilization of optical and radar imagery yields useful land cover information over those obtained using either type of image on its own.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39478 Geo-Information Modeling of Soil Erosion for Sustainable Agriculture Land Management in Sambhunath Municipality 2021-08-30T14:50:29+00:00 Bikash Karma Karna bikashkumarkarna@gmail.com Shobha Shrestha shova216@gmail.com Hriday Lal Koirala koiralahriday@gmail.com <p>Geo-information science has attempted to estimate the actual soil loss and its correlative interpretation with land use and cover types in an agricultural land, Sambhunath Municipality. Among several empirical and physically based soil erosion models, Revised Universal Soil Loss Equation (RUSLE) are widely used and employed to estimate soil loss based on rainfall, topographic contour, and soil map. The soil erosion ranges values are found from 0 to 2635 t ha-1 yr-1 in terms of soil loss per year in the municipality. Soil erosion rates are found highly correlated with the increasing exposure of land surface in Chure range mostly on forest area. Agriculture lands spatially concentrated in 51.70% of the Municipality extent, is contributing significantly as of 16293 t ha-1 yr-1 of the total potential soil loss from fertile cropland. Based on severity of soil loss, cultivation agriculture areas are priority for reducing soil loss for optimum agriculture management practices in land use planning.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39479 Measurement of Height of Mt. Sagarmatha 2021-08-30T15:06:10+00:00 Susheel Dangol susheel.dangol@nepal.gov.np Prakash Joshi lightjoshiji@gmail.com Suraj KC kcsuraj21@gmail.com Mahesh Thapa mahesh100thapa@gmail.com Mahesh Thapa mahesh100thapa@gmail.com Bigyan Banjara bigyan.banjara@nepal.gov.np Shanker KC shankerkc01@gmail.com Stallin Bhandari stallin.bhandari@gmail.com <p>The height measurement of the highest peak of the world “Sagarmatha” was conducted by Nepal for the first time. The methodology for the measurement was finalized from the workshop held in Kathmandu with the constructive comments from national and international experts. Trignometrical levelling, precise levelling, GNSS survey and gravity survey was conducted. Previous air borne gravity data and present surface gravity data was used to determine the precise regional geoid for this program. Thus orthometric height was determined as 8848.86 m from the ellipsoid height observed at the top of Sagarmatha and precise geoid determined. The height was determined on the base of International Height Reference System (IHRS) and final height was announced jointly from Nepal and China on 8th of December 2020 from Kathmandu and Beijing through virtual media.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39480 Online Service Delivery in Survey Offices: Step towards e-Land Administration 2021-08-30T15:33:00+00:00 Susheel Dangol susheeldangol@nepal.gov.np Prakash Joshi lightjoshiji@gmail.com Tanka Prasad Dahal tpdahal@gmail.com <p>Survey Offices established at the districts under Survey Department are supporting with cadastral survey and the updating the parcels according to the land transactions. Almost all of the district offices among 131 of such are conducting its daily activities in digital environment. At present all the district survey offices have their own server and individual desktop applications are running to conduct the daily activities. In the recent development, Survey Department has developed three tier client-server based system architecture where application and database server are managed in central server hosted in Government Integrated Data Centre and clients access these server to provide the service. This system has enabled to integrate all the cadastral data from district survey office to single central archive. “Nepal Land Information System (NeLIS)” for daily service delivery from the survey offices and “MeroKitta” to get online service facility from survey offices has been developed and implemented in few numbers of the survey offices and planned to replicate in further offices.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal https://www.nepjol.info/index.php/NJG/article/view/39481 Preparation of High-Resolution DTM and Orthophoto Using LiDAR in Nepal 2021-08-30T15:50:09+00:00 Abhash Joshi theabhash@gmail.com Sumeer Koirala mailtosumeer@gmail.com <p>The high-resolution terrain model has varied usages including development planning, engineering works, environmental management, disaster management, urban planning, irrigation, geological study, archeological study and cadastral application. Currently, this data is not available for Nepal and it has also hindered the socio and economic development of the country. Airborne Lidar is economically cost-effective and viable means for topography related data collection. Lidar which is an acronym for Light Detection and Ranging is an active remote sensing technology in which Laser beams are used for surveying and mapping. The Survey Department of Nepal has taken initiative to prepare the High-Resolution DTM and Orthophoto of about 20,000 square kilometres of Nepal using Lidar surveying and mapping. Survey Department is conducting a LiDAR survey in the western terai regions of Nepal from Chitwan to Kanchanpur district. Airborne LiDAR survey data along with a very high resolution (0.15 m) orthophoto shall be generated. Further, processing of LiDAR points data will generate a highly precise digital terrain model of 1 m grid data having an accuracy of 0.25 m and finally 0.5 m contour interval data. This endeavor is one of the milestones in the surveying and mapping sector of Nepal and it will have far-reaching consequence in the social and economic development of Nepal.</p> 2020-12-01T00:00:00+00:00 Copyright (c) 2020 Survey Department, Government of Nepal