亚吉（亚的斯亚贝巴-吉布提）铁路沿线10 km范围内植被覆盖度数据集（2017）

亚吉铁路指埃塞俄比亚首都亚的斯亚贝巴（Addis Ababa）与吉布提首都吉布提市（Djibouti）之间的铁路。亚吉铁路沿线10 km范围内植被覆盖度数据集（2017）覆盖区域为：8&deg27′33″N ~ 11&deg37′32″N，38&deg30′52″E ~ 43&deg10′22″E。该数据是以Landsat-8 Operational Land Imager (OLI)光学影像为基础数据，首先在Google Earth Engine云平台上计算归一化差异植被指数(Normalized difference vegetation index, NDVI)，然后合成研究区内2017年年度最大NDVI，以此数据为本底，采用像元二分模型方法计算得到的。植被覆盖度数值介于0-1之间。0表示无植被覆盖，像元数值越高表明植被覆盖程度越好。亚吉铁路沿线10 km范围内植被覆盖度主要在0~0.2之间，其面积占研究区总面积的35.08%；其次分布在0.4~0.6之间，其面积占研究区总面积的29.74%；大于0.6的面积占比为10.39%。该数据集空间分辨率为30 m，存储为.tif格式，由3个文件组成，数据量为793 MB （压缩为1个文件，数据量69.2 MB）。基于本数据集的研究报告是《全球生态环境遥感监测2018年度报告：“一带一路”生态环境状况及态势》的一部分。The Addis Ababa-Djibouti Railway refers to the railway linking Addis Ababa, the capital of Ethiopia, and Djibouti City, the capital of Djibouti. The Vegetation Coverage Dataset within 10 km along the Addis Ababa-Djibouti Railway (2017) covers the area ranging from 8°27′33″N to 11°37′32″N and 38°30′52″E to 43°10′22″E. This dataset was generated based on Landsat-8 Operational Land Imager (OLI) optical images: first, the Normalized Difference Vegetation Index (NDVI) was calculated on the Google Earth Engine cloud platform, then the annual maximum NDVI of the study area in 2017 was synthesized, and finally vegetation coverage was calculated using the dimidiate pixel model with this synthesized maximum NDVI as the baseline. The vegetation coverage values range from 0 to 1, where 0 indicates no vegetation cover, and higher pixel values correspond to better vegetation coverage. Within the 10 km buffer zone along the Addis Ababa-Djibouti Railway, vegetation coverage is mainly distributed between 0 and 0.2, accounting for 35.08% of the total study area; it is secondly distributed between 0.4 and 0.6, accounting for 29.74% of the total area; the area with coverage greater than 0.6 accounts for 10.39%. This dataset has a spatial resolution of 30 m, is stored in .tif format, consists of 3 files with a total size of 793 MB (compressed into a single file with a size of 69.2 MB). Research reports based on this dataset are part of the *2018 Annual Report on Global Eco-Environmental Remote Sensing Monitoring: Eco-Environmental Status and Trends of the Belt and Road Initiative*.