The annual land cover data of Nepal (2000-2022) have been created through the National Land Cover Monitoring System (NLCMS) for Nepal. The system uses freely available remote-sensing data (Landsat) and a cloud-based machine learning architecture in the Google Earth Engine (GEE) platform to generate land cover maps on an annual basis using a harmonized and consistent classification system.

The NLCMS is developed by the Forest Research and Training Centre (FRTC), Ministry of Forests and Environment, Government of Nepal with support from the International Centre for Integrated Mountain Development (ICIMOD) through SERVIR Hindu Kush Himalaya (SERVIR-HKH), a joint initiative in partnership with the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). Collaborators include SERVIR–Mekong at the Asian Disaster Preparedness Center (ADPC), SilvaCarbon, Global Land Analysis and Discovery (GLAD) group at the University of Maryland, and the US Forest Service.

The annual land cover data of Nepal for 2000-2019 was first published in 2022 while the data for 2020-2022 was released in 2024.

Land cover change is a significant contributor to environmental change. The degradation of forests and conversion of natural areas, forests, and farmlands to other land use impact ecosystem services and biodiversity significantly. Using multiple methodologies and input data sources, national agencies in different countries of the Hindu Kush Himalayan region have conducted land cover mapping at various times. Due to the differences in classification schema, methodologies, and input data sources used, currently available land cover data is not suitable for analysis of land cover changes over time. ICIMOD collaborated with SERVIR-Mekong at Asian Disaster Preparedness Center (ADPC), Afghanistan’s Ministry of Agriculture, Irrigation and Livestock, Bangladesh’s Forest Department, Nepal’s Forest Research and Training Centre, Myanmar’s Forest Department, SilvaCarbon, the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, and the United States Forest Services to develop the Regional Land Cover Monitoring System (RLCMS) for the HKH region. The system uses state-of-the-art remote sensing science and technology on the Google Earth Engine, and a standard set of input data sources to regularly generate high-quality land cover data at the regional level for the HKH, and at national levels for Afghanistan, Bangladesh, Myanmar, and Nepal. In developing the RLCMS, ICIMOD focused on collaboration and co-development with partner organizations to define different land cover typologies/classes, collect reference data samples, and validate results. Land cover maps for the HKH region spanning 2000–2022 have been produced under its SERVIR–HKH Initiative.

Spatial coverage index compiled by East View Geospatial of set "Nepal 1:50,000 Scale Land Systems Map". Source data from NTSB (publisher). Type: Thematic - Land Use / Land Cover. Scale: 1:50,000. Region: Asia.

Digital grid data of Land cover of Hindu Kush Himalayan (HKH) Region. This dataset is basic vector layer derived from ESRI Map & Data in 2001.

The Himalaya Regional Land Cover database has been produced as part of the Global Land Cover Network - Regional Harmonization Programme. Its extent includes the Hindu Kush - Karakorum - Himalaya mountain chain, which is the widest and most recent in the world. The database was developed using 2000 Landsat satellite imagery as reference source for an automatic segmentation. The land cover change was then assessed upon other sets of historical imagery: 1970-80, 1990, and 2007. The mapping area covers about 1,500,000 km2, intersecting 6 different UTM zones. It includes portions of the following 5 countries: Afghanistan, Pakistan, India, China and Myanmar. Nepal and Bhutan are fully mapped. Aim of the programme is harmonize the regional land cover database which will allow national institutions and Governments to conduct assessment and monitoring of the land cover dynamics for environmental analysis and planning. The Himalaya land cover mapping was performed at 1:350,000 scale and derives from a combination of visual and automatic interpretation of recent Landsat ETM satellite imageries. For this purpose, the FAO’s MADCAT software was adopted, in order to perform automatic classification. FAO’s GeoVIS interpretation software was also used. The land cover legend, consisting of 35 classes, was set up using the FAO LCCS methodology, a standardized a priori land cover classifications system applied with success in a series of FAO projects. As result, 511,292 polygons were delineated. To refine the interpretation, high resolution images from Google Earth were used. The tabular attributes contains 14 fields with the following meaning: Z007CODE: GIS code of the class. Z007USLB: User label updated with the 2004/2007 images. Z007PERC: Percentage of the class. In case of a single unit the percentage is 100%; in case of mixed unit the percentage is 60/40, meaning that the first term of the mixed unit covers 60% of the polygon’s area while the second term of the mixed unit covers the 40%. HECTARES: Area of the polygon in Hectares. The calculation was made in UTM projection (WGS84), according to the UTM Zone indicated in the field “Zone”. AREA: Area of the polygon in square meters. The calculation was made in UTM projection (WGS84), according to the UTM Zone indicated in the field “Zone”. AGG: Aggregated code used for the Map layout. The aggregation criteria is shown in the attached file “Conversion table for the map_35cl”. ZONE: The UTM zone where the polygon falls. CODE1: The user label either as single unit or as first term of a mixed unit. CODE2: The user label as second term of a mixed unit, when present. BOOLEAN1: The LCCS Boolean formula of Code 1, giving its representation as land cover objects. BOOLEAN2: The LCCS Boolean formula of Code 2, giving its representation as land cover objects. LCCSMAIN1: The LCCS main group containing Code1. LCCSMAIN2: The LCCS main group containing Code2. AUTO_ID: The unique number identifying each polygon. Notice that from the fields “Boolean1”, “Boolean2”, “LCCSMain1”, “LCCSMain2” is possible to derive the objects making up the land cover class covering the polygon(s) wanted.

Soil nutrient index data of Resunga Municipality, Gulmi, Nepal.

Land cover change is a critical driver for enhancing the soil erosion risk in Nepal. Losing of the topsoil has a direct and indirect effect on human life and livelihoods. The present study provides an assessment of the decadal land use and land cover (LULC) change and consequent changes in the distribution of soil erosion risk for the year of 1990, 2000 and 2010 for the entire country of Nepal. The study attempted to understand how different land cover types change over the three decades and how it has changed the distribution of soil erosion risks in Nepal that would help in development of soil conservation priority. The land cover maps were produced using Geographic object-based image analysis (GEOBIA) using Landsat images. Soil erosion patterns were assessed using the revised universal soil loss equation (RUSLE) with the land cover as input. The study shows that the forest cover is the most dominant land cover in Nepal that comprises about 62 hundred thousand ha. The estimated annual erosion was 129.30 million tons in 1990 and 110.53 million tons in 2010. The assessment of soil erosion dynamics presented at the national, provincial and district level. District wise analysis revealed that Gulmi, Parbat, Syangja and Tanahu district required priority for soil conservation

The 25 April 2015 Mw 7.8 Gorkha earthquake and its aftershocks triggered about 25,000 landslides over an area of more than 30,000 km2 in the Greater and Lesser Himalaya of Nepal and China. In order to understand the relation among landslide location, earthquake shaking, topography, tectonic geologic and climatic setting, earthquake-triggered landslides were mapped using high-resolution (<1m pixel resolution) pre- and post-event satellite imagery. Source and runout areas were differentiated and mapped separately. The data accompany an interpretive paper published in the journal Geomorphology. The published products are separate ESRI ArcMap 10.2.2 shapefiles that comprise: (i) mapped landslide source areas, (ii) mapped landslide full areas (source, transport and deposit area combined), (iii) the extent of geographic areas in which mapping was completed, (iv) obscured areas in which the mapping is incomplete because of the lack of clear, undistorted satellite data from post-earthquake dates, and (v) image quality designation for mapped regions. 24,915 landslide areas were mapped in the full20170209.shp file (full areas) compared to 24,795 landslide areas in the source20170209.shp file (source areas). This small discrepancy in total number arises because of image distortion and partial cloud cover. One hundred forty two of the full areas lack an identifiable source area. Additionally, 10 full areas have 2 corresponding source areas because the full area could not be divided into 2 separate runout areas due to image distortion; 12 other source areas do not have corresponding full areas. This work was supported by a National Science Foundation (NSF) RAPID award from the Geomorphology and Land Use Dynamics program to West (EAR-1546630) and Clark (EAR-1546631), partially supported by NSF Geomorphology and Land Use Dynamics program (EAR-1640894 to West and EAR-1640797 to Clark and Zekkos), University of Michigan internal award to Clark and Zekkos (MCubed 2.0 Project ID 917) and a Swiss Federal Institute of Technology (ETH) Research Commission research grant (ETH-15 15-2) awarded to Gallen. We thank Paul Morin from the PGC (Polar Geospatial Center) for providing imagery access and support for acquiring satellite data through a NGA (National Geospatial-Intelligence Agency) cooperative agreement with NSF. We also thank Kristen Cook, William Greenwood, Julie Bateman, Bibek Giri, Maarten Lupker and John Galetzka for their assistance during a 2015 field expedition to Nepal.

Land utilisation 1996 has been mapped from 1:20'000 air photos and field checked. Air photo interpretation has been transferred visually to base map ('eyeballed'). Contains extensive land managment practice information.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Nepal data available from WorldPop here.

Digital data of Land use of Nepal. This dataset is created using Land use Maps of 50000 scale published by Land Resource Mapping Project (LRMP), Dept. of Survey Kathmandu, Nepal in 1984.

Descriptive statistics soil physiochemical status of Resunga Municipality, Gulmi, Nepal.

High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation. Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. Part of the remote sensing (RS) and a geographic information system (GIS) based soil erosion estimation and spatial distribution of across the entire Koshi basin, a land cover map of 1990 used as a Support practice factor (PL). Land cover data for 1990 were prepared from analysis of the Landsat images using object-based image analysis.

GIS database on the Yarsha Khola watershed

    Members informations:
    Attached Vector(s):
     MemberID: 1
    Vector Name: Land use 1961
    Source Map Name: SOI toposheet
    Source Map Scale: 50000
    Source Map Date: 1905-05-14
    Projection: Nepal 87
    Feature_type: polygon
    Vector 
    digitized from topographic maps

    Members informations:
    Attached Vector(s):
     MemberID: 2
    Vector Name: Land use 1981
    Source Map Name: LRMP
    Source Map Scale: 50000
    Source Map Date: 1905-06-03
    Feature_type: polygon
    Vector 
    from Land Resources Mapping Project (LRMP)

    Members informations:
    Attached Vector(s):
     MemberID: 3
    Vector Name: Land use 1992
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-14
    Projection: Nepal 87
    Feature_type: polygon
    Vector 
    from topo sheet

    Members informations:
    Attached Vector(s):
     MemberID: 4
    Vector Name: Land use 1996
    Source Map Name: air photographs
    Source Map Scale: 20000
    Source Map Date: 1905-06-18
    Projection: Nepal 87
    Feature_type: polygon
    Vector 
    from air photographs

    Members informations:
    Attached Vector(s):
     MemberID: 5
    Vector Name: VDC
    Source Map Name: toposheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-14
    Feature_type: polygon
    Vector 
    VDC (Village Development Committee) boundaries

    Attached Image(s):
     Member ID: 6
    Image Name: Orthophoto Mosaic
    Image Source name: AIRCRAFT
    Image Resolution: 1m
    Image Number of Rows:
    Image Number of Columns:
    Image Number of Bits: 8
    Image 
    Mosaic of digitally produced orthophotos

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Nepal data available from WorldPop here.

Soil parameters were tested, and methodology was adopted.

Digital dataset of Land use of Dhading district, Nepal. This dataset is created using 1:50,000 scale Land use map of Land published by Land Resource Mapping Project (LRMP), Dept. of Survey, Nepal 1984.

Digital polygon data of ecological/vegetation zones of Nepal. This dataset is acquired from ESRI Map & Data Center in 2001.

Areas of soil categories based on soil physio-chemical properties.

This map is based on the ecological risk assessment data of the China Nepal transportation corridor, edited and produced using ArcGIS and Adobe Illustrator software, as well as spatial drawing and vector editing methods. This includes ecological risk maps of Lhasa, Shigatse, and transportation corridors at different scales, in JPG format with a temporal resolution of years (1992, 1995, 2000, 2005, 2010, 2015, 2022, and predicted years of 2030 and 2050). The spatial resolution of ecological risk assessment data is 1km, the temporal resolution is year (1992, 1995, 2000, 2005, 2010, 2015, 2022, and the predicted years are 2030 and 2050), and the data format is TIFF. Using the European Space Agency's global land use cover dataset and the SSP-RCP scenario land use dataset published by Scientific Data, a 5 * 5km grid was established. ArcGIS software and Fragstats software were used to calculate the landscape disturbance degree (landscape fragmentation, landscape separation, and landscape dominance index) and landscape vulnerability index, and a 1km grid scale ecological risk dataset was obtained through Kriging interpolation.