Data on the Distribution of Natural Forest Resources in Nepal and Gyirong

发布时间 : 2026-09-11 11:04:47 UTC      

Page Views: 10 views

Suggested Discipline : Geography 

语言 : English 

Data Type : Raster Data 

Data Format : .tif 

Creators : Wang Juanle  

Publishers : Institute of Geographic Sciences and Natural Resources Research,CAS  

Contributors : Wang Juanle  

Organizations : Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences  

Operates On : Wang Juanle  

链接 : https://api.geodata.cn/remote0/Nepal-Gyirong/Natural_Forest_Resource_Distribution_Dataset_for_Nepal_and_Gyirong_County.zip 

Start Time : 2026-09-02 

End Time : 2026-09-02 

This dataset provides the spatial extent and distribution patterns of natural forests across the region encompassing Nepal and Gyirong County, Tibet, China. Derived from multi-source remote sensing imagery obtained via the Google Earth Engine (GEE) platform in 2025, the data features a spatial resolution of 30 meters. The data presents a continuous areal/gridded distribution, exhibiting distinct vertical zonation and latitudinal extension. Large, continuous forest belts with high vegetation connectivity are observed in the central hilly regions and along the southern margins of the Siwalik Hills and the Terai Plain. In Gyirong County, the southern river valleys (such as the Gyirong Valley) show significant natural forest cover, influenced by warm, moist air currents; conversely, the high-altitude Greater Himalayas zone to the north—spanning northern Gyirong County and the northern border of Nepal—lacks extensive forest cover due to perennial snow, alpine permafrost, and exposed bedrock. Patchy gaps in forest cover also appear in certain southern valley plains and areas of intensive agriculture. The integration of data for Gyirong County and Nepal involved the following steps: utilizing a unified collection of 30-meter resolution remote sensing imagery covering the entire study area; applying consistent preprocessing (such as cloud removal and topographic correction) followed by a uniform classification algorithm to extract natural forest areas; and finally, seamlessly mosaicking and clipping the classification raster results from both regions to produce a regional natural forest dataset with consistent physical attributes and continuous spatial distribution.