Global land cover classification at 1 km spatial resolution using a classification tree approach

TLDR: In this paper, a 1km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR) is presented. But the approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted.

Abstract: This paper on reports the production of a 1km spatial resolution land cover classie cation using data for 1992- 1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar e A ort to create a product at 8km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37294 O 1km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multi- temporal AVHRR metrics were used to predict class memberships. Minimum annual red ree ectance, peak annual Normalized Di A erence Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the e nal product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ran- ging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1km pixels consisting of greater than 90% one class within the high-resolution data sets.