This paper developed an automatic identification and tracking method based on the gray feature and geometric feature of the Arctic sea ice in the satellite images. Firstly, sea ice was recognized from the image based on the different spectral characteristic of sea ice, sea water and cloud. Secondly, by considering the gray distribution feature of individual floe, the individual floe identification was performed by combining the sub-region bimodal threshold segmentation with gradient differential technique. Finally, on the basis of geometric feature of the identified floes, the matching of same individual floes in pair images was implemented. Meanwhile, the motion vectors of the matched floes in the pair images were calculated. A series image of the Fram Strait region in June 2011, which was obtained by the Medium Resolution Spectral Imager onboard FENGYUN 3A satellite (FY-3A/MERSI), was used to compute the sea ice motion. The direction of the resulting sea ice motion vectors were found to match well with the average velocity from 1978 to 2003 and the dominant wind and ocean currents in the region.
This article is based on the thermal infrared data obtained from Environmental mitigation B satellite (HJ-1B)
and Storm III satellite (FY-3), using generalized single-channel method, retrieved the surface temperature of
costal sea around the Tianwan Nuclear Power Plant in winter and summer to get the quantitative information about the diffusion and gradient of the thermal discharge from the nuclear power station. Then the retrieval
results were compared with the MODIS sea surface temperature (SST) products. It indicated that the thermal
infrared data from HJ-1B and FY-3 were able to research on the distribution of thermal discharge. Furthermore,
based on inversion of the SST distribution map, spatial and temporal distribution variations of thermal
discharge were analyzed simply. The distribution is significantly different among the different seasons. During the winter months, the thermal discharge extended to a sector area around the power station, while in summer
it limited to a rater longer and narrower area in one side of the bay. And the area where the SST has increased
more than 3 °C in winter was much larger than that in summer.
Global climate change has brought about many environmental problems. It was considered that the increase of green
house gases should be responsible for that, especially carbon dioxide (CO2). There are some in-situ observation stations
distributed in the world-wide, but it's not enough to perform the global atmospheric variation laws for the sparse
observation points with non-uniform distribution. The Greenhouse Gases Observing Satellite (GOSAT) is the world's
first spacecraft to measure the concentrations of carbon dioxide and methane from space. It has finished CO2 global
distribution maps on internet, but there is little study on the regional CO2 distribution with higher spatial resolution,
especially about the metropolitan CO2 distribution. The CO2 column amount were analyzed in the Yangtze River delta
area that indicted it varied with the seasons. In order to explain the CO2 distribution differences, the land surface
temperature (LST) and the Normalized Differential Vegetation Index (NDVI) were analyzed from the China
Huanjing-1A (HJ-1A) and Huanjing-1B (HJ-1B) satellite data. The results showed that there were some correlations
between the land surface characteristics and the CO2 column amount. It appeared that the CO2 column amount retrieved
from the SWIR band of FTS reflected the near surface atmospheric contents were affected obviously by the human
activities. More verification experiments with in-situ observation data should be conducted. The study results could be
benefit for improving the accuracy of CO2 flux estimation from the satellite data, and it's useful for the studying the
correlations between the climate change and the economic development.
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