ElsevierCaselles, E.; Valor, E.; Abad Cerdá, FJ.; Caselles, V. (2012). Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sensing of Environment. 124:321-333. doi:10.1016Environment. 124:321-333. doi:10. /j.rse.2012 Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe
ABSTRACTThe remote sensing measurement of land surface temperature from satellites provides a monitoring of this magnitude on a continuous and regular basis, which is a critical factor in many research fields such as weather forecasting, detection of forest fires or climate change studies, for instance. The main problem of measuring temperature from space is the need to correct for the effects of the atmosphere and the surface emissivity. In this work an automatic procedure based on the Vegetation Cover Method, combined with the GLOBCOVER land surface type classification, is proposed. The algorithm combines this land cover classification with remote sensing information on the vegetation cover fraction to obtain land surface emissivity maps for AATSR split-window bands. The emissivity estimates have been compared with ground measurements in two validation cases in the area of rice fields of Valencia, Spain, and they have also been compared to the classification-based emissivity product provided by MODIS (MOD11_L2). The results show that the error in emissivity of the proposed methodology is of the order of ±0.01 for 2 most of the land surface classes considered, which will contribute to improve the operational land surface temperature measurements provided by the AATSR instrument.
This paper presents an Augmented Reality (AR) game for learning words. Thirty-two children played the AR game and the equivalent real game. We have compared the results of the two games. The results indicate that children did not found significant differences between the two games except for one question, but 81% of the children liked most the AR game.
In this paper, we present an Augmented Reality (AR) game for finding and learning about endangered animals in a fun way. It uses tangible cubes as the user interface. This game was included in the activity program of the Summer School of the Universidad Politecnica de Valencia. Forty-six children played the AR game and the equivalent real game. We have compared the results of the two games. The results indicate that children enjoyed playing the AR game more than playing the real game and that they perceived the AR game to be more fun than the real game. They also preferred the AR game to the real one. The children perceived the real game as being easier to play than the AR game. The children also seemed to learn about the subject of endangered animals.
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