ANALYSIS OF SPECTRAL CHARACTERISTICS AMONG DIFFERENT SENSORS BY USE OF SIMULATED RS IMAGES

This research,by use of RS image_simulating method,simulated apparent reflectance images at sensor level and ground_reflectance images of SPOT_HRV,CBERS_CCD,Landsat_TM and NOAA14_AVHRR’s corresponding bands.These images were used to analyze sensor’s differences caused by spectral sensitivity and atmospheric impacts.The differences were analyzed on Normalized Difference Vegetation Index(NDVI).The results showed that the differences of sensors’ spectral characteristics cause changes of their NDVI and reflectance.When multiple sensors’ data are applied to digital analysis,the error should be taken into account.Atmospheric effect makes NDVI smaller,and atmospheric correction has the tendency of increasing NDVI values.The reflectance and their NDVIs of different sensors can be used to analyze the differences among sensor’s features.The spectral analysis method based on RS simulated images can provide a new way to design the spectral characteristics of new sensors.

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.

Peatland area change in the southern Altay Mountains over the last twenty years based on GIS and RS analysis

Analyses results of total peatland area changes in the southern AItay Mountain region over the past 20 years are discussed in this paper. These analyses were based on remote sensing （RS） and geographical information system （GIS） studies. Possible control methods are evaluated by comparing these results to other regional records and climate data. The area of the peatland zones was calculated by overlaying a peatland layer of Landsat TM （Thematic Map） image constructed by using supervised classification with a layer of slope based on a digital elevation model （DEM）. The results show that slope layer is crucial to improving the accuracy of peatland extracted from TM images. The peatland area of the Altay Mountains increased from 931.5km^2 in 1990 to 977.7 km^2 in 2010. This trend is consistent with the climate change in this region, due in part to increasing temperatures and precipitation, suggesting possible climate controls on peatland expansion. The increase in the peatland area in the Altay Mountains over the last 20 years has been influenced by the westerlies. Alternatively, changes in the largest highland peatland area of the Zoige Basin, located in the eastern Tibetan Plateau have been influenced by the intensity of the Asian summer monsoons. In addition to increased temperatures, decreased precipitation in the Zoige Basin and increased precipitation in the Altay Mountains, due to varied patterns of atmospheric circulation, are the probable causes for driving the change differences in these two peatland areas.