Land cover data are essential to most global change research objectives, including the assessment of current environmental conditions and the simulation of future environmental scenarios that ultimately lead to public policy development. Chinese Academy of Sciences generated a nationwide land cover database in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in 1990s. In order to improve the reliability of the database, we will update the database anytime. But it is difficult to obtain remote sensing data to extract land cover change information in large-scale. It is hard to acquire optical remote sensing data in Chengdu plain, so the objective of this research was to evaluate multitemporal ENVISAT advanced synthetic aperture radar (ASAR) data for extracting land cover change information. Based on the fieldwork and the nationwide 1:100000 land cover database, the paper assesses several land cover changes in Chengdu plain, for example: crop to buildings, forest to buildings, and forest to bare land. The results show that ENVISAT ASAR data have great potential for the applications of extracting land cover change information.
Synthetic Aperture Radar remote sensing has been effectively used in water compliance and enforcement, especially in
ship detection, but it is still very difficult to classify or identify vessels in inland water only using existing SAR image.
Nevertheless some experience knowledge can help, for example waterway channel is of great significance for water
traffic management and illegal activity monitoring. It can be used for judging a vessel complying with traffic rules or
not, and also can be used to indicate illicit fishing vessels which are usually far away from navigable waterway channel.
For illicit vessel identification speed and efficiency are very important, so it will be significant if we can extract
waterway channel directly from SAR images and use it to identify illicit vessels. The paper first introduces the modified
two-parameter CFAR algorithm used to detect ship targets in inland waters, and then uses principal curves and neural
networks to extract waterway channel. Through comparing the detection results and the extracted waterway channel
those vessels not complying with water traffic rules or potential illicit fishing vessels can be easily identified.