USING MULTISOURCE DATA IN GLOBAL LAND-COVER CHARACTERIZATION: CONCEPTS, REQUIREMENTS, AND METHODS
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Abstract:
Global land-cover data are needed as baseline information for global change research. Multisource data, both coarse-resolution satellite data and ancillary data, were used to produce a land-cover characteristics database for the conterqinous United States. Ancillary data, including elevation and ecological region data sets, were critical to the development, refinement, and information content of each class in the database. They contributed essential evidence for labeling and refining land-cover classes where differing types were represented by single spectral-temporal signatures. The characterization process can be expanded to a global effort depending on (1) the availability of global satellite coverage, (2) the quality and availability of ancillary data, and (3) the evolution of more sophisticated data visualization and analysis techniques.Keywords:
Land Cover
Ancillary data
Baseline (sea)
Global Change
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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 classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.
Land Cover
Advanced very-high-resolution radiometer
Global Change
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Abstract Coarse spatial resolution, high temporal frequency satellite data from the NOAA/AVHRR system are presented to demonstrate their utility for monitoring vegetation seasonal dynamics. The techniques for processing and analysing the data are outlined and examples are given for selected applications at a range of scales. Normalized difference vegetation index images are presented for the entire globe and for the continents of Africa, South America and south-east Asia, with descriptions of the seasonal dynamics of major vegetation formations as portrayed by the transformed AVHRR data. Monitoring of forest clearance in Brazil, the productivity of African grasslands, Indian tropical forest and Chinese agriculture are selected for discussion. The paper concludes that coarse-resolution satellite data provide a valuable tool for vegetation mapping and monitoring at regional and global scales.
Advanced very-high-resolution radiometer
Tropical vegetation
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Abstract A satellite-based 1° by 1° normalized difference vegetation index (NDVI) data set has been processed to derive land surface parameters for general circulation models of the atmosphere (GCMs). Prior to calculation of the land surface parameters, corrections were applied to the source NDVI data set to account for (i) obvious anomalies in the data time-series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminates cold land surface points, and (iv) persistent cloud cover in the tropics. An outline of the procedures for calculating land surface parameters from the corrected NDVI data set is given, and a brief description is provided of source material that was used in addition to the NDVI data. The data sets summarized in this paper should represent improvements over prescriptions currently used in land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular of those related to vegetation, are obtained from direct measurements rather than indirectly inferred from survey-based land cover classifications.
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Reference data
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Data from the advanced very-high-resolution radiometer sensor on the National Oceanic and Atmospheric Administration's operational series of meteorological satellites were used to classify land cover and monitor vegetation dynamics for Africa over a 19-month period. There was a correspondence between seasonal variations in the density and extent of green-leaf vegetation and the patterns of rainfall associated with the movement of the Intertropical Convergence Zone. Regional variations, such as the 1983 drought in the Sahel of westem Africa, were observed. Integration of the weekly satellite data with respect to time for a 12-month period produced a remotely sensed estimate of primary production based upon the density and duration of green-leaf biomass. Eight of the 21-day composited data sets covering an 11-month period were used to produce a general land-cover classification that corresponded well with those of existing maps.
Land Cover
Advanced very-high-resolution radiometer
Intertropical Convergence Zone
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Land Cover
Advanced very-high-resolution radiometer
Imaging spectrometer
Radiometry
Earth observation
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Abstract Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) derived from satellite data, have been used to classify land cover at continental scales. Extending this technique to global scales raises several issues: identifying land cover types that are spectrally distinct and applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of reliable information on global land cover; and acquiring high quality global data sets of satellite sensor data for input to land cover classifications. For this study, a coarse spatial resolution (one by one degree) data set of monthly NDVI values for 1987 was used to explore these methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the feasibility of using satellite sensor data to increase the accuracy of global land cover information, although the result has not been validated systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derived from satellite sensor data.
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The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in U.S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.
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Land information system
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Information regarding the characteristics and spatial distribution of the Earth's land cover is critical to global environmental research. A prototype land-cover database for the contaminous United States designed for use in a variety of global modeling, monitoring, mapping, and analytical endeavors has been created. Database development has involved (1) a stratification of vegetated and barren land, (2) an unsupervised classificatin of multitemporal greenness data derived from Advanced Very High Resolution Radiometer (AVHRR) imagery collected from March through October 1990, and (3) post-classificatin stratification of classes into homogeneous land-cover regions using ancillary data.
Land Cover
Stratification (seeds)
Ancillary data
Advanced very-high-resolution radiometer
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