AVHRR Global Land Cover Classification


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, we have employed the NASA/NOAA Pathfinder Land (PAL) data set with a spatial resolution of 1km. This data set has a length of record 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. We aim through this study to 1) develop methodologies for global land cover classifications that are objective, reproducible, and feasible to implement on data from additional years and 2) produce a global land cover classification at 1 km spatial resolution accessible to the global change research community.


Reliable, geographically-referenced data on global vegetative cover is an important requirement for global models of the earth system. Satellite data provide the only truly synoptic view of the earth, and may potentially increase the quality, internal consistency, and reproducibility of global land cover information.

This project initially aimed to develop a coarse resolution, global land cover data set from satellite data for use in climate models. To this end, AVHRR data were resampled to a spatial resolution of one by one degree and used to carry out a conventional, supervised classification of global land cover. Classifications have also proceeded at a finer spatial resolution of 8km at a continental scale. In addition to describing vegetative cover according to topological schemes, the project has explored methodologies to represent vegetative cover more realistically as gradients and mosaics of cover types.

Most recently we have worked with colleagues in the Geography Department at the University of Maryland to develop land cover characterizations for net primary productivity models. Supervised classifications at finer spatial resolutions are underway, drawing particularly on the Pathfinder 1-km and 8-km data sets. The current 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.

Procedure Training Data

To identify the pixels to be used for training of the 1 km AVHRR Pathfinder data, we collected a total of over 200 high resolution scenes of which we were confident of which cover type occurs. Most of the scenes used were acquired by the Landsat Multispectral Scanner System (MSS), and a few by Landsat Thematic Mapper and the LISS (Linear Imaging Self-Scanning Sensor).

Of the initial 200 scenes, we considered 156 suitable for interpretation. Scenes were considered unsuitable if haze or poor quality data obscured the scene or if the cover types in the scene could not be visually distinguished. For most scenes, we aimed to identify only one cover type within the scene. It was possible, however, to identify more than one cover type in some scenes if croplands were visually identifiable based on the spatial patterns of fields or if vegetation maps showed the presence of clearly identifiable cover types.

These training data provide the basis for carrying out a global land cover classification. They also provide data for validating other land cover classification products. The methodology and Landsat images used for deriving these training data for classification of AVHRR data at 8km resolution can also be applied to 1km AVHRR data and, in the future, MODIS data at 250m and 500m resolution.

Deriving the Classification Product

The second part of the study involved deriving a global land cover classification product. The product was derived by testing several metrics that describe the temporal dynamics of vegetation over an annual cycle. These metrics have the potential to be used as input variables to a global land cover classification. The tested metrics are based on 1) the ratio between surface temperature and NDVI, 2) seasonal metrics derived from the NDVI temporal profile such as length of growing season, 3) a rule-based approach that determines cover type through a series of hierarchical trees based on surface temperature and NDVI values, and 4) annual mean, maximum, minimum, and amplitude values for all optical and thermal channels in the AVHRR Pathfinder Land (PAL) data. These metrics were applied to 1984 PAL data at 8km resolution to derive a global land cover classification product using a decision tree classifier. The method described in this website can be applied to AVHRR data from other years and to data at higher spatial resolutions.

AVHRR Land Cover
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