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Along with fast economic development, the Yangtze Delta region is experiencing rapid environmental changes. A high concentration of aerosols with diverse properties is emitted in this region, providing a unique opportunity for understanding the impact of environmental change on climate systems, especially in urban regions. Aerosol optical properties including aerosol optical depth (AOD), the Ångström exponent (α), and water vapour and their relationship with aerosol particle concentrations were examined using ground-based measurements at three sites in the Hangzhou district: Hangzhou (120.01° E, 30.26° N), Lin'an (119.73° E, 30.26° N) and Qiandaohu (119.05° E, 29.55° N). The results from these observations show that there exists strong temporal and spatial variation in aerosol optical properties and particle matter (PM) concentrations. Monthly averaged AODs at the Hangzhou and Lin'an sites show a similar trend. AODs in Qiandaohu have the highest values in winter and spring and the lowest values in summer and autumn. In 2008, the minimum monthly mean α for every site was lowest in December due to the weather conditions. From spring to summer, these three locations experienced a drop in AOD and an increase in α due to the humidity-swelling effect. There is a positive correlation between AOD and aerosol particle concentrations; the correlation coefficients were obtained as 0.40, 0.55 and 0.69 in Hangzhou, Lin'an and Qiandaohu, respectively. The association between daily PM10 concentration and AOD is marginally statistically significant at the three sites, which suggests that AOD is a useful tool for mapping PM distribution over large spatial domains. [ABSTRACT FROM PUBLISHER]

Since the 1950s, with national policy changes and socio-economic development, the habitat of the giant pandas has altered accordingly. This can also be inferred from the population changes of the giant pandas as reported in three national surveys. Thus, monitoring the changes in giant panda habitat and then taking appropriate action would be a valuable contribution to giant panda protection. In this paper, using existing habitats and potential habitats of the giant pandas as the study area, multitemporal remotely sensed data from the three national surveys are used as the data source. The land cover of the study area is mapped by the maximum likelihood classification (MLC) method. The overall accuracy and kappa statistics for the resulting classification are 0.8 and 90%, respectively. The results reveal that the current status of the giant panda habitat is very good. Between 1974 and 1989, because of deforestation in the area, the giant panda habitat deteriorated considerably; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs decreased from 62.03% to 57.40% in the study area. However, from 1989 to 2002, due to the conservation policies put into action, the giant panda habitat recovered to some extent; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs increased from 57.40% to 60.68% in the study area. However, conditions are different among the mountain systems. Taking into account only the total of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs, in the Minshan mountain system, the forest cover changed from 57.70% in 1974 to 56.74% in 1989 and to 56.30% in 2002, which can be regarded as stable. In the Qinling mountain system, forested areas changed from 70.05% in 1974 to 66.93% in 1989 and to 67.17% in 2002, which reveals little change in this area. In the Qionglai mountain system, forested areas changed from 72.84% in 1974 to 71.53% in 1989 and to 73.22% in 2002; therefore, an increase can be noted in this area. In the Xiangling mountain system, forested areas also increased from 50.81% in 1974 to 50.20% in 1989 and to 53.15% in 2002. In the Liangshan mountain system, forested areas changed from 68.43% in 1974 to 55.81% in 1989 and to 60.07% in 2002. These results are in accordance with the giant panda population changes in different mountain systems. Thus, the best way to improve the threatened status of the panda population is to protect the current habitat and the potential habitat. [ABSTRACT FROM AUTHOR]

One risk of planting transgenic crops is the escape of transgenes to conspecifics and sexually compatible wild relatives. Detecting transgene escape is thus a crucial biosafety issue world-wide, but most current detection methods are expensive and laborious, as well as being unfeasible for large-scale use in commercial cultivation. We undertook field spectral reflectance studies of non-transgenic oilseed rape (B. napus cv. Westar), a transgenic oilseed rape, Wild Indian mustard (B. juncea var. gracilis) and a hybrid Wild Indian mustard. Simulated reflectances for the spectral bands of several different satellite-flown hyperspectral and multi-spectral scanners were generated. The differences obtained between the simulated reflectances of the different plants leads to the possibility that these differences could be used to detect transgene escape and genomic effects among related taxa from the Moderate Resolution Imaging Spectroradiometer (MODIS) hyperspectral scanner and from the Landsat Thematic Mapper (TM), Satellite Pour l'Observation de la Terre (SPOT) High Resolution Visible (HRV) and IKONOS multi-spectral scanners. [ABSTRACT FROM AUTHOR]

Accurate and timely information describing wetland resources and their changes over time, especially in coastal urban areas, is becoming more important. In this study, we mapped and monitored land-cover change in an urban wetland using high spatial resolution IKONOS images acquired in June 2003 and January 2006. An optimal iterative unsupervised classification (OIUC) method was used to overcome the limitations of unsupervised classification. The images were categorized into six classes, and an accuracy assessment was conducted using error matrices and the Kappa coefficient. The overall accuracies were 83.2% and 86.3% for the 2003 and 2006 images, respectively. A post-classification comparison method was used to detect the wetland change by calculating a detailed land-cover type transformation matrix. The results indicated a decrease in the area of water bodies and an increase in the area of vegetation in the wetland. This paper shows that high spatial resolution remote sensing data is advanced in studying an urban wetland at a local scale. An OIUC method, combined with visual interpretation, could yield high classification accuracy. A post-classification comparison method is also efficient in wetland change detection. [ABSTRACT FROM AUTHOR]