587 resultados para ecologically adaptive strategies
Resumo:
The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.
Resumo:
The construction industries of developed countries are faced with an aging workforce and a shortage of recruits. It is common for migrant workers/ethnic minorities (EMs) who are already part of the society to join the construction industry. With increasing involvement of EMs in the construction industry, effective strategies for improving their safety and health are urgently needed. The existing body of knowledge is mainly derived from research conducted in English-speaking countries with Western cultures. Research on safety of migrant/EM construction workers in multidialect Asian countries with Eastern cultures has been lacking. This study aimed to identify various strategies for improving the safety and health of EM construction workers from the Asian perspective. Twenty-two face-to-face semistructured interviews were performed with safety professionals in Hong Kong followed by two rounds of Delphi survey with 18 safety experts to verify the interview findings and rank the relative importance of the strategies. The study unveiled 14 strategies for improving the safety performance of EM workers. The three most important ones identified were: (1) to provide safety training in EM native languages; (2) that government and industry associations should play an active role in promoting health and safety awareness of EM workers, and; (3) to encourage EM workers to learn the local language. This study contributes to filling the research gap by evaluating the strategies for improving safety of migrant/EM construction workers in Asian countries with Eastern cultures in which English is not the first language. Research findings would assist occupational health and safety experts and relevant stakeholders in designing strategies for improving the safety and health of EM workers, which will ultimately improve overall safety performance of the construction industry.