赞比亚塞奇铁路沿线10 km范围内植被覆盖度数据集(2017)
收藏国家对地观测科学数据中心2023-04-15 更新2024-04-21 收录
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https://noda.ac.cn/datasharing/datasetDetails/640e987ff9ef7053c413e447
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植被覆盖度(Fractional Vegetation Cover, FVC)定义为植被叶、茎、枝在地面的垂直投影面积占统计区总面积的比例(或百分数)。基于Google Earth Engine(GEE)云平台,利用长时间序列Landsat-8 OLI normalized difference vegetation index (NDVI)数据集,采用最大值合成法(Maximum Value Composite, MVC)和像元二分模型,计算得到赞比亚塞奇铁路沿线10 km范围内植被覆盖度数据集(2017)。该数据集覆盖范围:13°08′56"S ~ 14°24′19"S,30°07′34"E ~ 32°43′22"E。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。塞奇铁路沿线10 km范围内植被覆盖度主要分布在0.4~0.6之间,其面积占研究区总面积的43.34%;其次分布在0.6~0.8之间,其面积占研究区总面积的40.27%;大于0.8的面积占比为8.96%;小于0.4的面积占比为7.43%。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178 MB (压缩为1个文件,数据量34.6 MB)。基于本数据集的研究报告在《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》中。
Fractional Vegetation Cover (FVC) is defined as the ratio (or percentage) of the vertical projected area of vegetation leaves, stems and branches on the ground to the total area of the statistical region. Based on the Google Earth Engine (GEE) cloud platform, using the long-time series Landsat-8 OLI Normalized Difference Vegetation Index (NDVI) dataset and adopting the Maximum Value Composite (MVC) method and the dimidiate pixel model, this study calculated the FVC dataset (2017) within a 10 km buffer along the Saji Railway in Zambia. The dataset covers the geographic range: 13°08′56"S to 14°24′19"S, 30°07′34"E to 32°43′22"E. The FVC 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 along the Saji Railway, the FVC values are mainly distributed between 0.4 and 0.6, accounting for 43.34% of the total study area; followed by the range of 0.6 to 0.8, accounting for 40.27% of the total area; areas with FVC greater than 0.8 account for 8.96%, while areas with FVC less than 0.4 account for 7.43%. This dataset has a spatial resolution of 30 m, is stored in .tif format, consists of 3 files with a total size of 178 MB (compressed into a single file with a size of 34.6 MB). Research reports based on this dataset are included in the *2018 Annual Report on Global Eco-Environmental Remote Sensing Monitoring: Eco-Environmental Status and Trends of the Belt and Road Initiative*.
创建时间:
2023-04-15



