http://www.ricescience.org/fileup/PDF/110209.pdf

Estimating Rice Yield by HJ-1A Satellite Images

This paper developed a new classifier named extension model of fuzzy matter-element to classify multi-source and multitemporal remote sensing data. Taking the National Nature Reserve of Ruoergai wetlands as study area, this paper chose the data of TM, CBERS and MODIS-NDVI of 2007 as data source and introduced the constructing process of this classifier. Results showed that, the overall accuracy (82.36%) and Kappa coefficient (0.8006) of the integrated of multi-information classification method is better than the SVM results using on TM(79.74%, 0.7704) and on CBERS (77.29%, 0.7436). Meanwhile, the paper also made an active exploration of classification on the cloudy image.

Construction of a New Classifier Integrated Multiple Sources and Multi-temporal Remote Sensing Data for wetlands

To what extent do networks of influence between market traders impact upon their individual performance and the performance of the specialists in which they operate? Such a question underpins the content of this study, as an investigation is conducted using the JCAT double auction market simulation platform, developed as a part of the CAT Market Design Tournament. Modifications to the JCAT platform allow for influential networks to be established between traders, across which they transmit information about their trading experiences to their connected peers. Receiving traders then use this information (which is the product of an n-armed bandit selection algorithm) to guide their own selection of market specialist and trading strategy. These modifications give rise to a sequence of experimental tests, the documented results of which provide an answer to the question phrased above. Analysis of the results shows the benefits of taking advice as a collective and demonstrates the properties of the communities which emerge as a result of engaging in widespread social contact.

/pdf/social-networking-and-information-diffusion-in-automated-35wk9ftqev.pdf

Social networking and information diffusion in automated markets

Organized by Sun Yat-sen University (China)  University of Cincinnati (USA)  CPGISThe International Association of Chinese Professionals in Geographic Information Sciences Co-organized by Guangzhou Institute of Geography (China)  South China Normal University (China)  Guangdong Institute of Eco-Environmental and Soil Scienc es (China)  Guangdong Association of Remote Sensing and Geographic Information Systems (China) Sponsored by NSFCNational Natural Science Foundation of China Published by SPIE

Monitoring and Assessment of Natural Resources and Environments

Spartina alterniflora is one of the most hazardous invasive plant species in China. Monitoring the changes in dominant plant species can help identify the invasion mechanisms of S. alterniflora, thereby providing scientific guidelines on managing or controlling the spreading of this invasive species at Jiuduansha Wetland in Shanghai, China. However, because of the complex terrain and the inaccessibility of tidal wetlands, it is very difficult to conduct field experiments on a large scale in this wetland. Hence, remote sensing plays an important role in monitoring the dynamics of plant species and its distribution on both spatial and temporal scales. In this study, based on multi-spectral and high resolution (<10 m) remote sensing images and field observational data, we analyzed spectral characteristics of four dominant plant species at different green-up phenophases. Based on the difference in spectral characteristics, a decision tree classification was built for identifying the distribution of these plant species. The results indicated that the overall classification accuracy for plant species was 87.17%, and the Kappa Coefficient was 0.81, implying that our classification method could effectively identify the four plant species. We found that the area of Phragmites australi showed an increasing trend from 1997 to 2004 and from 2004 to 2012, with an annual spreading rate of 33.77% and 31.92%, respectively. The area of Scirpus mariqueter displayed an increasing trend from 1997 to 2004 (12.16% per year) and a decreasing trend from 2004 to 2012 (−7.05% per year). S. alterniflora has the biggest area (3302.20 ha) as compared to other species, accounting for 51% of total vegetated area at the study region in 2012. It showed an increasing trend from 1997 to 2004 and from 2004 to 2012, with an annual spreading rate of 130.63% and 28.11%, respectively. As a result, the native species P. australi was surrounded and the habitats of S. mariqueter were occupied by S. alterniflora. The high proliferation ability and competitive advantage for S. alterniflora inhibited the growth of other plant species and we anticipate a continuous expansion of this invasive species at Jiuduansha Wetland. Effective measures should be taken to control the invasion of S. alterniflora.

Remote-Sensed Monitoring of Dominant Plant Species Distribution and Dynamics at Jiuduansha Wetland in Shanghai, China

Chinese-Brazil Earth Resources Satellite No. 2 (CBERS-02) has good spatial resolution and abundant spectral information, and a strong ability in detecting vegetation. Based on five CBERS-02 images in winter wheat growth season, the spectral distance between winter wheat and other ground targets was calculated, and then, winter wheat was classified from each individual image or their combinations by using supervised classification. The train and validation samples were derived from high resolution Aerial Images and SPOT5 images. The accuracies and analyses were evaluated for CBERS-02 images at early growth stages, and the results were compared to those of TM images acquired in the same phenological calendars. The results showed that temporal information was the main factor affecting the classification accuracy in winter wheat, but the characteristics of different sensors could affect the classification accuracy. The multi-temporal images could improve the classification accuracy, compared with the results derived from signal stage, with the producer accuracy of optimum periods combination improved 20.0% and user accuracy improved 7.83%. Compared with TM sensor, the classification accuracy from CBERS-02 was a little lower.

[Accuracy of winter wheat identification based on multi-temporal CBERS-02 images].

Naked cropland elimination is an important part of Beijing Olympic ecological project. In this paper, Multi-temporal satellite data were used to monitor and position the naked croplands. Three Landsat TM images and two Beijing-1Micro-Satellite images were selected to calculate NDVI series according to crop phenological calendars and investigated information of agricultural cropping structures in Beijing suburb. Based on the phenological spectral characteristics of main agricultural land use types, a classi fication scheme was proposed to extract the naked croplands. Considering the structural characteristic hierarchical classification and various demands of feature selection in different periods, decision tree algorithm and a stepwise masking technology were employed to extract typical crops in each season, and hence the naked croplands were left. Accuracy assessment of the naked croplands in winter and spring were performed with comparison of the monitoring areas with statistical data. The results show that the area of the naked croplands in winter and spring was 170368.1ha in Beijing. The areas of the top five districts (Yanqing, Shunyi, Daxing, Miyun and Tongxian) were 17933.3ha, taking a percent of 69.2% of that of Beijing. The areas of the naked cropland were 25719.6 ha, 4485.4 ha and 3325 ha in summer, autumn and all the year round respectively. Experimental results demonstrated that our method would fast and simply monitor agricultural land use. Keywords: Naked cropland, Multi-temporal, Monitoring, Crop phenological calendars, Vegetation index

Monitoring the naked croplands in Beijing with multi-temporal remote sensing images

Construction of network clusters and identifying hub nodes from networks has attracted more and more attentions in spatial network analysis. In this paper, we proposed clustering algorithm and outlying node detection algorithm for spatial road network analysis. Network clustering algorithm consists of constructing clusters and creating a simplified structure of the network. When performing clustering on the network, we introduced the definitions of strong cluster and weak cluster, where each node has more connections within the cluster than with the rest of the graph, for achieving reliable and reasonable clusters. For users understanding the structure of the network, we constructed a simplified graph approximation of the network, whose nodes were representative nodes in clusters of the network, and edges were the connections between those representative nodes. In outlying n ode detection algorithm, a node is identified as an outlier, not because of its distribution different from that of other nodes but for its unexpected sta tistical information. Whether a node is an outlier or not is examined with centrality index. The larger the node has centrality indexes, the more probabilistically it may be identified as an outlier. The experime ntal results on artificial data sets demonstrated that two algorithms are very efficient and effective. Keywords: Spatial network, network Clustering, Outlying node detection

Detection of clusters and outlying nodes in spatial networks

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Papers

[Accuracy of winter wheat identification based on multi-temporal CBERS-02 images].

Monitoring the naked croplands in Beijing with multi-temporal remote sensing images

Detection of clusters and outlying nodes in spatial networks