1 km Global Land Cover Data Set Derived from AVHRR

Over the past several years, researchers have increasingly turned to remotely sensed data to improve the accuracy of data sets that describe the geographic distribution of land cover at regional and global scales. To develop improved
methodologies for global land cover classifications as well as to provide global land cover products for immediate use in global change research, researchers employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 1 km. This data set has a record length of 14 years (1981-1994), providing the ability to test the stability of classification algorithms.

Furthermore, this data set includes red, infrared, and thermal bands in addition to the Normalized Difference Vegetation Index (NDVI). Inclusion of these additional bands improves discrimination between cover types. The project aim is to develop and validate global land cover data sets and to develop advanced methodologies for more realistically describing the vegetative land surface based on satellite data.

Forty-one (41) metrics were developed to describe global vegetation phenology and these data were used to make the 1 km land cover map. The final product contains 13 land cover classes. The data are available as a global coverage (except for the ASCII grid data) as well as regional coverages for Africa, Asia-Pacific, Eurasia, North America, and South America. The data are provided as UNIX or PKZip compressed files in a choice of three formats: 1) BSQ binary Goodes projection, 2) BSQ binary lat/long projection (Plate Carree projection), and 3) ASCII grid lat/long projection (for Arc/Info or other GIS software). Additional information and references on this data set can be found at the GLCF web site.

近数十年来，研究者愈发依赖遥感数据，以提升描述区域及全球尺度土地覆盖地理分布的数据集精度。为开发更优化的全球土地覆盖分类方法，并为全球变化研究提供可直接投入使用的全球土地覆盖产品，研究者采用了空间分辨率为1 km的NASA/NOAA陆地路径数据集（Pathfinder Land, PAL）。该数据集的时间跨度为14年（1981年至1994年），可用于测试分类算法的稳定性。

此外，该数据集除归一化差分植被指数（Normalized Difference Vegetation Index, NDVI）外，还包含红光波段、红外波段及热红外波段。新增上述波段可提升不同土地覆盖类型间的区分度。本项目的目标为开发并验证全球土地覆盖数据集，同时研发更贴合实际的基于卫星数据描述植被陆地表面的先进方法。

研究人员共开发了41项指标以描述全球植被物候，并基于这些数据制作了1 km分辨率的土地覆盖图。最终成果包含13个土地覆盖类别。该数据集提供全球覆盖数据（ASCII网格数据除外），同时也提供非洲、亚太地区、欧亚大陆、北美及南美区域的分区域覆盖数据。数据以UNIX或PKZip压缩文件格式提供，支持三种格式任选：1）波段顺序（BSQ）二进制古德（Goodes）投影格式；2）波段顺序（BSQ）二进制经纬度投影（Plate Carrée投影）格式；3）ASCII网格经纬度投影（适配Arc/Info或其他地理信息系统（Geographic Information System, GIS）软件）格式。有关该数据集的更多信息与参考文献可通过GLCF官方网站获取。