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A.S. Belward

Publications and source records attributed to A.S. Belward.

4 recordsLinked to original sources

Landsat-8: Science and product vision for terrestrial global change research

Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.

Remote Sensing of Environment↗

The IGBP-DIS global 1 km land cover data set, DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.

International Journal of Remote Sensing↗

A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project. Location The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east. Methods The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’. Results The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics. Main conclusions The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub-tropical Asia, and it delivers reasonable thematic detail and quantitative estimates of the main land-cover proportions. The map may therefore serve for regional stratification or modelling of vegetation cover, but could also support the implementation of forest policies, watershed management or conservation strategies at regional scales.

Journal of Biogeography↗

The International Geosphere Biosphere Programme Data and Information System global land cover data set (DISCover)

The International Geosphere Biosphere Programme Data and Information System (IGBP-DIS), through the mapping expertise of the U.S. Geological Survey and the European Commission's Joint Research Centre, recently guided the completion of a 1-km resolution global land cover data set from advanced very high resolution radiometer (AVHRR) data. The 1-km resolution land cover product, “DISCover,” was based on monthly normalized difference vegetation index composites from 1992 and 1993. The development of DISCover was coordinated by the IGBP-DIS Land Cover Working Group as part of the IGBP-DIS Focus 1 activity. DISCover is a 17-class land cover data set based on the scientific requirements of IGBP elements. The mapping used unsupervised classification and postclassification refinement using ancillary data. The development of this data set was motivated by the need for global land cover data with higher spatial resolution, unproved temporal specificity, and known classification accuracy. The completed DISCover data set will soon be validated to determine the accuracy of the global classification.

Acta Astronautica↗