Gong Peng

China’s wetland change (1990–2000) determined by remote sensing

Two wetland maps for the entire China have been produced based on Landsat data acquired around 1990 and 2000. Wetlands in China have been divided into 3 broad categories with 15 sub-categories except rice fields. In 1990, the total wetland area in China was 355208 km2 whereas in 2000 it dropped to 304849 km2 with a net loss of 50360 km2. During an approximate 10-year period, inland wetland reduced from 318326 to 257922 km2, coastal wetland dropped from 14335 to 12015 km2, while artificial wetland increased from 22546 to 34911 km2. The greatest natural wetland loss occurred in Heilongjiang, Inner Mongolia, and Jilin with a total loss of over 57000 km2 of wetland. In western China, over 13000 km2 of wetlands were newly formed in Xinjiang, Tibet, and Qinghai. About 12000 km2 of artificial wetlands were also added for fish farm and reservoir constructions. The newly formed wetlands in western China were caused primarily by climate warming over that region whereas the newly created artificial wetlands were caused by economic developments. China’s wetland loss is caused mainly by human activities.

An improved Landsat Image Mosaic of Antarctica

A revised Landsat Image Mosaic of Antarctica (LIMA) is presented, using the 1073 multi-band scenes of the original Land-sat-7 ETM+ LIMA image collection available at the United States Geological Survey (USGS: http://lima.usgs.gov/). Three improvements have been applied during the data processing: (1) DN saturation is adjusted by adopting a linear regression, which has a lower root mean square error than the ratio regression used by LIMA; (2) solar elevation angle is calculated using pixel-level latitude/longitude and the acquisition time and date of the central pixel of the scene, improving slightly upon the bilinear interpolation of the solar elevation angles of scene corners applied in LIMA; and (3) two additional image bands, Band 5 and Band 7, are sharpened using the panchromatic band (Band 8) and a Gram-Schmidt Spectral Sharpening algorithm to more easily distinguish snow, cloud and exposed rocks. The final planetary reflectance product is stored in 16-bit bands to preserve the full radiometric content of the scenes. A comparative statistical analysis among 12 sample regions indicates that the new mosaic has enhanced visual qualities, information entropy, and information content for land cover classification relative to LIMA.