Vegetation cover change and the driving factors over northwest China

In this paper the spatio-temporal variation of vegetation cover in northwest China during the period of 1982-2006 and its driving factors were analyzed using GIMMS/NDVI data. The annual average NDVI was increased with a rate of 0.0005/a in northwest China and there was an obvious difference between regions. The trend line slopes of NDVI were higher than 0.0005 in the Tianshan Moutains and Altay Mountains of Xinjiang, the Qilian Mountains of Gansu and the eastern part of Qinghai, which indicated the vegetation cover was significantly increased in these areas. The trend line slopes of NDVI were lower than -0.0005 in the southern region of Qinghai, the border regions of Shaanxi and Ningxia, the parts of Gansu and Tarim Basin, Turpan and Tuoli in Xinjiang, which indicated the vegetation cover was declined in these areas. The NDVI of woodland, grassland and cultivated land had an ascending tendency during the study period. The study shows that the vegetation cover change was caused by both natural factors and human activities in northwest China. The natural vegetation change, such as forests was influenced by climate change, while human activities were the main reason to the change of planting vegetation. The changes of vegetation covers for different elevations, slopes and slope aspects were quite different. When the eleva- tion is exceeded to 4,000 m, the NDVI increasing trend was very low; the NDVI at the slope of less than 25~ was increased by the ecological construction; the variation of NDVI on sunny slope was stronger than that on shady slope. The temperature rose significantly in recent 25 years in northwest China by an average rate of 0.67^-C/10a, and precipitation increased by an average rate of 8.15 mm/10a after 1986. There was positive correlation between vegetation cover and temperature and annual precipitation changes. Rising temperature increased the evaporation and drought of soils, which is not conducive to plant growth, and the irrigation in agricultural areas reduced the correlation between agricultural vegetation NDVI and precipita- tion. The improvement of agricultural production level and the projects of ecological construction are very important causes for the NDVI increase in northwest China, and the ecological effect of large-scale ecological construction projects has appeared.

Sonar Image Detection Algorithm Based on Two-Phase Manifold Partner Clustering

According to the characteristics of sonar image data with manifold feature,the sonar image detection method based on two-phase manifold partner clustering algorithm is proposed. Firstly,K-means block clustering based on euclidean distance is proposed to reduce the data set. Mean value,standard deviation,and gray minimum value are considered as three features based on the relatinship between clustering model and data structure. Then K-means clustering algorithm based on manifold distance is utilized clustering again on the reduced data set to improve the detection efficiency. In K-means clustering algorithm based on manifold distance,line segment length on the manifold is analyzed,and a new power function line segment length is proposed to decrease the computational complexity. In order to quickly calculate the manifold distance,new allsource shortest path as the pretreatment of efficient algorithm is proposed. Based on this,the spatial feature of the image block is added in the three features to get the final precise partner clustering algorithm. The comparison with the other typical clustering algorithms demonstrates that the proposed algorithm gets good detection result. And it has better adaptability by experiments of the different real sonar images.

Detection of Underwater Objects by Adaptive Threshold FCM Based on Frequency Domain and Time Domain

According to the characteristics of sonar image data with big data feature, In order to accurately detect underwater objects of sonar image, a novel adaptive threshold FCM (Fuzzy Clustering Algorithm, FCM) based on frequency domain and time domain is proposed. Based on the relationship between sonar image data and big data, Firstly, wavelet de-noising method is used to smooth noise. After de-noising, the sonar image is blocked and each sub-block region is processed by two-dimensional discrete Fourier transform, their maximum amplitude spectrum used as frequency domain character, then time domain of mean and standard deviation, frequency domain of maximum amplitude spectrum are taken for character to complete block k-means clustering, the initial clustering center is determined, after that made use of FCM on sonar image detection, based on clustered image, adaptive threshold is constructed by the distribution of sonar image sea-bottom reverberation region, and final detection results of sonar image are completed. The comparison different experiments demonstrate that the proposed algorithm get good detection precision and adaptability.