The potential impacts of agricultural fires (agri-fires) on regional air quality over China were examined using active fire products derived from satellite remote sensing and air mass trajectory modeling from 2009 to 2010. Agri-fires were found in most administrative areas. More than 80% of the agri-fires were in the heartlands of agricultural regions such as Anhui, Jiangsu, Shandong and Henan Provinces. Agri-fires had a seasonal pattern, with two distinct peaks in summer and autumn harvest periods, especially in June (61-86%) and October (5-14%). Agri-fire smoke was transported in the atmosphere on a continental scale in three directions, moving northeasterly, northwesterly and southwesterly away from source areas. Particles from agri-fire smoke contributed more than 35% of aerosol optical depth (AOD) over regions of the Jiaodong Peninsular, the North Plain, East China and other areas, and exceeded 60% in some areas of Shandong, Henan and Jiangsu Provinces. In the boundary layer atmosphere, particles from agri-fire smoke contributed more than 29% of PM 10 in parts of Anhui, Jiangsu and Shandong Provinces. Due to agri-fires the amount of PM 10 was highly correlated (R 2 = 0.6) with the smoke air masses in the main potential sink regions, and the mean PM 10 during the summer harvest of 2010 reached 0.24 mg/m 3 , far higher than the adjacent periods without smoke.

Land-cover datasets are crucial for earth system modeling and human-nature interaction research at local, regional and global scales. They can be obtained from remotely sensed data using image classification methods. However, in processes of image classification, spectral values have received considerable attention for most classification methods, while the spectral curve shape has seldom been used because it is difficult to be quantified. This study presents a classification method based on the observation that the spectral curve is composed of segments and certain extreme values. The presented classification method quantifies the spectral curve shape and takes full use of the spectral shape differences among land covers to classify remotely sensed images. Using this method, classification maps from TM (Thematic mapper) data were obtained with an overall accuracy of 0.834 and 0.854 for two respective test areas. The approach presented in this paper, which differs from previous image classification methods that were mostly concerned with spectral "value" similarity characteristics, emphasizes the "shape" similarity characteristics of the spectral curve. Moreover, this study will be helpful for classification research on hyperspectral and multi-temporal images.

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Remote sensing image based on the complexity of the background features, has a wealth of spatial information, how to extract huge amounts of data in the region of interest is a serious problem. Image segmentation refers to certain provisions in accordance with the characteristics of the image into different regions, and it is the key of remote sensing image recognition and information extraction. Reasonably fast image segmentation algorithm is the base of image processing; traditional segmentation methods have a lot of the limitations. Traditional threshold segmentation method in essence is an ergodic process, the low efficiency impacts on its application. The ant colony algorithm is a populationbased evolutionary algorithm heuristic biomimetic, since proposed, it has been successfully applied to the TSP, job-shop scheduling problem, network routing problem, vehicle routing problem, as well as other cluster analysis. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. Improved ant colony algorithm can greatly enhance the speed of image segmentation, while reducing the noise on the image. The research background of this paper is land cover classification experiments according to the SPOT images of Qinling area. The image segmentation based on ant colony algorithm is carried out and compared with traditional methods. Experimental results show that improved the ant colony algorithm can quickly and accurately segment target, and it is an effective method of image segmentation, it also has laid a good foundation of image classification for the follow-up work.

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