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Open AccessJournal ArticleDOI

Development of a global land cover characteristics database and igbp discover from 1 km avhrr data

TLDR
The IGBP DISCover global land cover product as mentioned in this paper is an integral component of the Global Land Cover database, which provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface and presents a detailed interpretation of the extent of human development.
Abstract
Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised ...

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Citations
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Journal ArticleDOI

Status of land cover classification accuracy assessment

TL;DR: It is likely that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.
Journal ArticleDOI

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

TL;DR: The datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4, are described, with a four-fold increase in spatial resolution and changes in the input data and classification algorithm.
Journal ArticleDOI

Global land cover mapping from MODIS: algorithms and early results

TL;DR: This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP, and a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data.
References
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OtherDOI

A land use and land cover classification system for use with remote sensor data

TL;DR: The framework of a national land use and land cover classification system is presented for use with remote sensor data and uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources.
Journal ArticleDOI

Characteristics of maximum-value composite images from temporal AVHRR data

TL;DR: In this paper, satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics.
Journal ArticleDOI

A Simple Biosphere Model (SIB) for Use within General Circulation Models

TL;DR: In this paper, a simple realistic biosphere model for calculating the transfer of energy, mass and momentum between the atmosphere and the vegetated surface of the earth has been developed for use in atmospheric general circulation models.
Journal ArticleDOI

A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation

TL;DR: In this paper, a revised version of the Simple Biosphere model (SiB2) is presented, incorporating a realistic canopy photosynthesis-conductance model to describe the simultaneous transfer of CO2 and water vapor into and out of the vegetation, respectively.
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